ESTABLISHMENT OF OPERATIONAL GUIDELINES 
FOR TEXAS COASTAL ZONE MANAGEMENT 

Final Report on 
BIOLOGICAL USES CRITERIA 

Prepared by 
Carl H. Oppenheimer 
Thomas Isensee 
William B. Brogden 
Dinah Bowman 
The University of Texas Marine Science Institute at Port Aransas 

for 

Research Applied to National Needs Program 
Nationa1 Science Foundation 
Grant No. GI-34870X 

and 
Division of Planning Coordination 
Office of the Governor of Texas 
Interagency Cooperation Contract No. IAC (74-75)-0685 

Coordinated through 
Division of Natural Resources and Environment 
The University of Texas at Austin 

This is one in a series of eight final reports describing progress on this research project for the period June 1, 1972, to May 31, 1974. The eight 
reports are:  
Summary  Example Application I.  Implications of  
Economics & Land Use  Alternative Public Policy Decisions  
Water Needs & Residuals Management  Concerning Growth & Environment on  
Estuarine Modeling  Coastal Electric Utilities  
Resource Capability Units  Example Application II.  Evaluation of  
Biological Uses Criteria  Hypothetical Management Policies  
for the Coastal Bend Region  

ACKNOWLEDGMENTS 
We should like to acknowledge the Texas Water Development Board (TWDB), particularly the cooperation of Dr. Jack Nelson and his staff in the Coastal Data System; the Texas Water Quality Board (TWQB), especially the Galveston Bay Study Group; Henry Fleming of the Gulf Universities Research Consortium (GURC); and the Padre Island National Park Service personnel. 
Dr. William Fisher, Dr. Robert Kier, and Albert Erxleben of the Bureau of Economic Geology and Dr. Ralph Hunter of the U.S. G. S. Marine Geology Laboratory in Corpus Christi are acknowledged for use of their materials and their advice and critique in the preparation of the enclosed Biotopes map. John Wells of the Governor• s Office of Information Services assisted in ob­taining photos and information from NASA. 
The sports fish creel study was made possible by an additional grant from the Lower Nueces River Water Supply District, volunteers from the Marine Science Institute and the cooperation of the Economics Branch of the Texas Water Development Board, who provided computer support for the data analysis. Chapter IV has been taken from an interim report submitted to the Lower Nueces River Water Supply District. The study will be continued in 1974 with Don Cox of the Lower Nueces River Water Supply District, the TWDB Economic Branch and, in part, with a project being organized by the Texas Parks and Wildlife Division (TPWD). 
Some data presented in this report are from other task forces in the project, particularly the work of Michael Cullender, James Sherman, and George Murfee. 
.. 

i 
SUMMARY 


This study has produced a methodology for assessing the effects of natural and man-made changes on the estuarine environment. Examples have been presented to demonstrate its application to the Corpus Christi area. Several communication forms have been developed to describe the biotic environment. There are illustrations of the ecological assemblages through artist renditions with a written text entitled "Biotopes" and several computer data base systems. Through the identification of these discrete biotic regimes (biotopes), the coastal environment o~ the Corpus Christi area has been quantified in percentages of the total acreage and percentage of total produc­tivity. The computer Life History Data Bank (LHDB) has been established to provide information on distribution, abundance and tolerances of the living organisms in the biotopes. Information accumulated through a preliminary creel census of sportfishing has also been identified by biotope distribution. Through the use of these systems productivity and fish catch can be compared quantitatively. 
Information management systems (data banks) have been established to accommodate data concerning (1) the distribution of hydrographic features, nutrients and other materials contained in the water and sediment; (2) the life history, food preference and environmental limitations of estuarine orga­nisms; and (3) the commercial and sportfishing catch and effort. Although considerable information has been attained, the data banks will be continually increased during the subsequent phases of the study. 
Information on the carbon and nutrient cycles in Corpus Christi Bay were developed from existing data to provide information on the dynamics of pro­ductivity, the source of nutrients and the rapid turnover of carbon and nitrogen that signifies the large amount of productivity of the area. Nitrogen appears to be the limiting nutrient in Texas estuaries. The daily productivity of Corpus Christi Bay is 22 pounds per acre per day as contrasted to Galveston Bay at 5 4 pounds per day. This productivity is related to fish catch to show the efficiency of harvesting .at the present rate. These estimates are prelimi­nary, but demonstrate that the methodology developed is a reasonable approach to measure the dynamics of the bay and provide information on how man's changes of the biotopes through shoreline development and waste discharges will affect the productivity of the system. 
Water quality standards have been reviewed and recommendations made for nutrients, toxic materials and other parameters affecting the organisms of the Texas bays with emphasis on Corpus Christi Bay. 
ii 
To demonstrate the effectiveness of the methodology, two examples have been presented regarding the possible environmental impacts caused by the implementation of the following hypothetical coastal zone management policies--no change from 1970 regulations; a 1500 feet moratorium on shore­line development; and the implementation by 1990 of a no pollutant discharge regulation. In example one, a marina type housing development has been identified in terms of the biotopes affected to show the biolog.ical and physical land changes that may occur with construction under the three policies. In example two, computer model experiments have been shown for salinity dis­tributions as related to various water inputs to the bay. The model outputs in conjunction with the water quality criteria in Chapter VI and the biotope data are used for assessing the changes that may occur in the bay due to changes in salinity or other water quality parameters as they relate to the hypothetical policy constraints. 
iii 
TABLE OF CONTENTS 

ACKNOWLEDGMENTS SUMMARY TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES CHAPTER I. INTRODUCTION CHAPTER II. BIOTOPES OF CORPUS CHRISTI BAY CHAPTER III. INFORMATION MANAGEMENT ENVIR Life Hi story Data Bank Life History Descriptors Creation of the Data Bank Retrieval of Information Applications Summary Other Data Banks Chemical-Biological Data Bank Creel Census Data Bank Bird Data Bank CHAPTER IV. SPORTFISHING CREEL CENSUS PILOT STUDY, AUGUST 1973 Methods ·Results General District Breakdown Daily Breakdown Time Breakdown Biotope Distribution Fifteen Major Species and Baits Sportfishing vs. Commercial Fishing Recommendations CHAPTER V. PRODUCTIVITY AND NUTRIENT BALANCES Carbon in Texas Estuaries Carbon Sources Carbon Sinks Carbon Dynamics Summary Nutrient Balances in Texas Bays Nutrient Sources Nutrient Losses Nitrogen Budget Sum~ary Implications for Management of Bays and Estuaries 
i 
ii iv vi vi I-1 II-1 III-1 III-1 III-3 III-4 III-6 III-7 III-8 III-8 III-10 III-10 III-12 III-12 
IV-1 
IV-1 

IV-6 
IV-6 
IV-7 
IV-8 

IV-8 
IV-9 

IV-9 
IV-9 
· IV-19 
V-1 V-1 V-1 V-6 V-8 V-8 V-12 V-14 V-19 V-19 
iv 

TABLE OF CONTENTS (CONTINUED) 

CHAPTER VI. WATER QUALITY VI-1 CHAPTER VII. DEMONSTRATION OF APPROACH VII-1 Hypothetical Land Use Information VII-1 Salinity Changes VII-5 Summary VII-15 APPENDIX A. ENVIR COMMANDS, VARIABLE FORMS, ERROR MESSAGES A-1 APPENDIX B. OBTAINING LIFE HISTORY INFORMATION FROM LHBANK B-1 APPENDIX C. DESCRIPTION OF DATA FLOW FOR CHEMICAL­BIOLOGICAL DATA BANK C-1 APPENDIX D. DESCRIPTION OF DATA FLOW FOR CREEL CENSUS DATA BANK D-1 APPENDIX E. STATE & HOMETOWN OF PERSONS QUESTIONED DURING CREEL CENSUS E-1 APPENDIX F. CREEL CENSUS DATA REASONS FOR FISHING (24) & COMMENTS (25) F-1 APPENDIX G. GENERAL INSTRUCTIONS (CENSUS SHEET) G-1 APPENDIX H. REFERENCES FOR TABLE VI-5 H-1 BIBLIOGRAPHY ix OTHER REFERENCES xvii 
v 
Figure IV-1 . 
Figure VII-1. 

Figure VII-2. Figure VII-3. Figure VII-4. Figure VII-5. Figure VII-6. Figure VII-7. 
Table II-1. 
Table II-2. 
Table III-1. 
Table III-2. 
Table III-3. 
Table III-4. 

Table III-5. 
Table III-6 . 

Table III-7. Table III-8. Table III-9. Table N-1. Table N-2. Table N-3. Table IV-4. Table N-5. Table N-6. Table N-7. Table N-8. Table N-9. 
LIST OF FIGURES 

Area of Survey 
11
Effect of 1500 Ft. 11 Policy on Hypothetical Residential Development on Mustang Island Model Results: Policy I, 1970 Dry Model Results: Policy I, 1980 Dry Model Results: Policy I, 1990 Dry Model Results: Policy I, 1970 Wet Model Results: Policy II, 1990 Dry Model Results: Policy III, 1990 Dry 
LIST OF TABLES 
Biotopes of the Texas Coastal Zone Areal Extent of the Biotopes of Corpus Christi Bay Life History Descriptors & Example Items Example of Environmental Limits Information Example Query with Part of the Reply Table of Descriptors Used in the Galveston Bay Data Bank & the Corpus Christi Data Bank Creel Census Data Bank Descriptors Occurrence, Habitat, Food & Abundance of Birds Common to Corpus Christi Bay Plants & Animals Used as Food by Ducks Principal Foods of the Larger Aquatic Birds Bird Bank Sources Creel Census, August 1973 Climatological Data Rea sons for Fishing in the Area where Interviewed Fi sh Catch Data by Di strict Fi sh Catch Data by Day of Week Fish Catch Data by Time of Day Biotope Distribution Creel Data Analysis Comparison of Different Baits on Sportfish Catch 
vi 
Page 
VII-2 VII-6 VII-7 VII-8 VII-9 VII-10 VII-11 
II-2 
II-3 
III-5 
III-6 
III-9 

III-11 
III-13 

III-14 III-18 III-21 
· III-26 IV-4 IV-5 
IV-7 
N-7 
N-8 
N-8 
IV-10 IV-11 N-18 
LIST OF TABLES (CONTINUED) 

Table IV-10. 
Table N-11. TableV-1. Table V-2. 
Table V-3. Table V-4. Table V-5. 
Table V-6. 
TableV-7. Table V-8. Table V-9. TableV-10. TableV-11. 
Table V-12. Table V-13. Table V-14. Table VI-1. 
Table VI-2. Table VI-3. Table VI-4. 
Table VI-5. Table VI-6. Table VII-1. Table VII-2. 
Comparison of Sport Catch & Commercial Fish Catch by Weight IV-19 TWDB & UT Sportfishing Creel Census IV-20 Physical Factors for Selected Texas Coast Bays V-3 Comparison of Gross Planktonic Production for San Antonio, Corpus Christi & Galveston Bays V-4 Gross Production by Biotope for Corpus Christi Bay V-5 Commercial Catches in Some Texas Bays V-7 Sample Calculation of Residence Time for Organic Carbon in Corpus Christi Bay V-9 Summary of Nutrient Data, Average Concentrations in mg/l V-11 Sources of Nutrient Data V-11 Nitrogen from Precipitati.on V-13 Nitrogen Input & Freshwater Inflows V-15 Nitrogen Inflows to Texas Bays V-16 Carbon, Nitrogen & Phosphorus Equivalances of the Average Commercial Catch & Estimated Sportfishing Catch of All Organisms in Corpus Christi Bay V-17 Carbon, Nitrogen & Phosphorus Removed Per Acre Per Year from Three Texas Bays V-17 Sedimentation in Texas Bays Deduced from Historical Depth Changes V-18 Sources, Sinks & Standing Crops for Nitrogen in Three Texas Bays V-20 Comparative Values of Elements in the Environment VI-2 Application Factor & Pollution Category for Toxic Inorganic Materials VI-4 Maximum Allowable Environmental Concentrations of Selected Pesticides VI-5 Water Quality Criteria Recommendations . VI-6 Toxicity Levels for Marine & Estuarine Organisms Endemic to the Corpus Christi Bay Area VI-8 Toxicity Levels for Birds Endemic to the Corpus Christi Bay Area VI-12 Biotope Areas Affected by Three Hypothetical Management Policies VII-3 Lower Limits of Occurrence of Sports or Commer­cially Important Organisms with Respect to Salinity VII-12 
vii 
LIST OF TABLES (CONTINUED) 
Table VII-3 . 	Organisms from Initial Query with Upper Salinity Limits of Occurrence of Tolerance Less Than 35 ppt VII-14 
Table VII-4 . 	Organisms from Initial Query with Lower Salinity Limits of Occurrence Greater Than 30 ppt VII-14 
viii 

CHAPTER I 
INTRODUCTION 

The overall goal of this project is to develop a methodology and the cri­teria for assessing the economic and environmental impacts of alternative public policies for management of the Texas Coastal Zone. The objective of this task force is the development of an approach for the identification and quantification of the biological criteria that can be used to establish the base­line and natural changes that occur in the biotic environment as compared to those changes imposed by man and his many activities. The progress toward accomplishing this objective has so far included: 
1)  providing a common description (Biotopes) and identification  
of the various components of the estuarine environments of  
Texas, including a historical description of ecological change,  
the natural fluctuations, and life history information;  
2)  estimating the productivity of these various environments and  
the associated nutrient balances;  
3)  establishing water quality guidelines for these environments;  
and  
4)  demonstrating the utility of the approach by evaluating the ef­ 
fects to the environment of man-made changes associated with  
different hypothetical coastal zone management policies.  

The area of the Corpus Christi Bay system was selected for primary con­sideration and information on·all Texas estuaries was used for comparative purposes to determine baseline information and natural fluctuations. No original research except the Creel Census (described in Chapter IV) was con­ducted. The major emphasis was placed on the procurement of existing information from the literature and other sources which could be placed in computer format for analysis and retrieval. The project has been devoted to the development of data bases that can be used to evaluate the impact of various changes imposed on the environment (such as the hypothetical manage­ment policies described in Chapter VII). This task force thus concentrated on the synthesis and evaluation of existing information as needed by the state agencies that are required to implement the existing state law on Texas Coastal Public Lands as well as the 1972 Federal Coastal Zone Management Act. 
It must be realized that any coastal environment is extremely complex and natural fluctuations of all parameters of such environments are large and variable. Therefore, the information provided is only valid for the data avail­able to date and any criteria for water quality or environmental quality sug­gested should undergo constant evaluation in the coming years. 
I-1 

Two concepts should become apparent from this report. First, today's state of the art of pollution control is analagous to the start of the develop­ment of disease control in the early 1800 1 s. The regulatory agencies currently are making empirical observations of the 11 symptoms" of pollution and de­signing treatments to alleviate these "symptoms" instead of their causes. This task force has begun to establish guidelines by which such 11 symptoms" of pollution may be studied on a cause and effect basis. Difficulties are bound to arise in this analysis, as the estuarine environment is extremely variable, while at the same time constrained by strong internal interactions to maintain some type of equilibrium. 
The Corpus Christi Bay system provides an example for potential manage­ment based on cause and effect. The major sources of natural nutrients are river flow and rainfall. No appreciable nutrients enter from the Gulf. Muni­cipal, industrial and agricultural demands have resulted in decreased river flow to the estuary. If it were determined from nutrient balance information that a new source to replace the diverted nutrients were required, one such source may be found in waste discharges, after sufficient treatment to remove toxic materials and pathogenic organisms. Here again we now find treatment of symptoms. The mission is to formulate guidelines to alleviate causes of the problems rather than treatment of symptoms, and that is the concern of our report. 
Secondly, it is important to establish some method of determining the significance of fish population as this group of organisms plays such an important role in determining estuarine management concepts. Some under­standing of the interrelationship between the region of Texas and the Gulf of Mexico and its relative importance is available from the publication Fisheries of the United States, 1973 (NOAA Current Fishery Statistics No. 6400). In 1973 the total catch in the Gulf was 1.5 billion pounds or 1.35 percent of the World 1 s fish catch. The total area of the Gulf of Mexico is about 1. 3 percent of the World oceanic area. However, the major portion of the Gulf fish catch comes from the continental shelf area and as such empha­sizes the importance of the coastal fertility. The Texas portion of the Gulf catch was 100 million pounds or 6. 3 percent of the total. Texas ranked 12th out of 23 coastal states in fish and shellfish catch. Data from a NOAA sport fishing catch report of 1970 indicated that the sport fin fish catch in the Gulf was 485 million pounds or approximately 30 percent of the total Gulf catch of fin and shellfish for the same year. Hence it is important to assess the relative importance of the commercial and sportfishing catch to the State. The next step is to develop a comparative basis between the estuaries and the Gulf. 
In this project a sportfishing creel census in the Corpus Christi Bay area is being undertaken to provide information on the export of fish from the bay system and to locate those areas of the bay that are most frequently used for sportfishing. A pilot study was performed in August 1973 which provided the 
I-2 

information and procedures for a three month survey to be undertaken in the 
summer of 1974. Financial support from the Lower Nueces River Water Sup­ply District will be used for the creel census with the cooperation of the Economics Division of the Water Development Board who will provide computer 
support and assist in the field observations. Data from the two programs will be treated as a single data base. The information output will be used to ob­tain the carbon balance and the value of the estuaries to the fishermen, where­as the economic information will be used in a wide part of the public sector to show the dollar value and economic significance of sportfishing in the bay 
system. 
I-3 


CHAPTER II BIOTOPES OF CORPUS CHRISTI BAY 
The report 11 Biotopes of the Texas Coastal Zone: An Ecography11 (Oppen­heimer and Gordon, 1972) * presents the system used by this task force for defining environmental units. A biotope is defined as the biological assem­blage (biota) that occurs within an area of uniform environmental conditions (Kuchler, 196 7). Further, the biota, particularly the plants, can occur in a phase in the succession of communities that would occur naturally within the g·iven set of environmental conditions (Allee and Schmidt, 1951). 
A group of biotopes such as that provided by Oppenheimer and Gordon for the Texas Coastal Zone (see Table II-1) provides a flexible basis for evaluating or comparing the environmental setting of any location. Such a list of ap­plicable biotopes may be selected for any area. The analysis of various environmental changes may then be made in terms of the areas changed, the nature of the changes and the responses of the organisms within each biotope. 
Aerial photo interpretation combined with field checks was the method of choice for delineating the biotopes of the Corpus Christi Bay area. Success of this method is widely known (Gallagher, Reimold and Thompson, 1972; Kelly, 1969, 1971; Reimold, Gallagher and Thompson, 1972; Welch, 1972). Aerial photographs used included photos from NASA Manned Spacecraft Genter missions 84, 110 and 228, photo mosaics belonging to the Bureau of Economic Geology and aerial oblique photos taken in April, 1973. Black and white, color and color infrared photos were all used, with color infrared providing the most information. Control was achieved by overlaying the interpretations on 
U . S. G. S. topographic maps of the area. 
The enclosed map entitled "Biotopes of Corpus Christi Bay" and Table II-2 are 1972 baseline estimates of the areal extent and distribution of the biotopes found within Corpus Christi Bay. While the most extensive biotope is the bay planktonic, the salt water marsh and grassflat biotopes are probably the most important due to their high productivities (Odum and Odum, 1959; Teal, .1962). (This aspect will be considered in Chapter IV, 11 Productivity and Nutrient Balances11 .) 
The biotopes concept has been used for habitat descriptions in several applications. One is the report "Development of a Multi-purpose Deep-draft Inshore Port on Harbor Island to Accommodate VLCC Vessels" by Oppenheimer (1973). Evaluations with regard to biotopes were supplied within The University 
* 	An updated and revised edition of this report with color illustrations is currently in preparation by The University of Texas Press. 
II-1 

TABLE II-1 
BIOTOPES OF THE TEXAS COASTAL ZONE 
(Oppenheimer and Gordon, 1972) 

*Gulf shelf Open beach Dune 
*Barrier flat Spoil bank Jetty and bulkhead Oyster reef Thalassia (grass flat) Spartina (salt water marsh) Juncus (freshwater marsh) Mudflat Sandflat Bluegreen algal flat 
*River mouth Bay planktonic River flood plain fore st 
*Marina *Oil platform offshore *Bay sediment types 
* These biotopes were not illustrated in the 1972 report. 
II-2 

TABLE II-2 
AREAL EXTENT OF THE BIOTOPES OF CORPUS CHRISTI BAY 

Biotope 
Open beach Dune and barrier flat Spoil bank Jetty and bulkhead Oyster reef Thalassia (Grass flat) Spartina (Salt water marsh) Juncus (Fresh water marsh) Mudflat Sandflat Bluegreen algal flat Hypersaline Rivermouth Bay planktonic Channel 
Sum 
Acres Percentage 
1,980 1. 31 13,358 8.85 13,327 8.83 2 t 211 1.46 760 0.50 18 , 894 12.51 7,579 5.02 411 0.27 604 0.40 7,348 4.87 1,208 0.80 3,033 2.01 15,755 10.43 63,340 41. 94 1,202 0. 80 
151 ,010 100.00 
II-3 

and to the Army Corps of Engineers with reference to a proposed park in Redfish Bay, Texas. Finally, hypothetical coastal zone management policies are being evaluated in terms of the biotopes affected as described in Chapter VII and a separate final project report, "Example Application II: Evaluation of Hypothe­tical Management Policies for the Coastal Bend Region". Other uses include coupling with the Life History and Creel Census data banks where habitat information may be selectively retrieved with reference to an organism, its life stages, processes affecting it, fishing method or other information, as de scribed in the following chapter. 
II-4 

CHAPTER III 
INFORMATION MANAGEMENT 

The development of both qualitative and quantitative coastal zone manage­ment criteria for biological organisms depends on the availability of a large amount of reliable data. Such data as might be useful in evaluating biological impacts come from a wide variety of sources. The problem of sorting and organizing the data for access and retrieval has occasioned the use of an Environmental Data Management System (ENVIR) by this task force. 
The format of data storage, that is the data bank, can be de signed for a specific type of information retrieval. Three data banks, the Life History, the Chemical and Biological, and the Creel Census, are discussed in detail in this chapter. The process for setting up a data bank using bird census information as an example also is discussed. [Furthermore, a data bank is being developed for handling general fisheries information. This data bank is not treated in this chapter.] 
Bibliographic information on literature sources is accounted for by means of a key sort system. There are currently about 1500 cards with bibliographic and data entries in this system. Included among these are the references ex­tracted for compiling the data banks. For example, the approximately 140 reference sources for the Life Hi story Bank are logged on the key sort cards. 
ENViR 
The general information management program which is used to store and retrieve the various types of environmental data is called ENVIR*, for ENVironmental information .Retrieval. This program was derived from a program called TAXIR, for TAXonomic jnformation .Retrieval, during the course of a Gulf Universities Research Consortium (GURC) sponsored, NASA supported project on Environmental Data M?.nagement (NASS 26867). The unique principles by which this program creates a data bank and selects data for retrieval has been described by Estabrook and Brill (1969). 
The program has provision for storing numerical or alphanumerical data with considerable flexibility, so that normal chemical and biological nomen­clature can be used instead of codes. Commands to the program are formulated 
* 	Inquiries about the availability of the ENVIR system should be directed to Dr. James Sharp, GURC, 1611 Tremont Street, Galveston, Texas, 77550. 
III-1 

in natural language under simple syntax rules which are relatively easy to 
learn. 
The basic unit of information which is manipulated is called an item. Each item is composed of a number of descriptors. And each descriptor has a characteristic state for each item. If a descriptor is left blank in the input data, the state of the descriptor is stored as "unknown". Example items will be shown in the discussion of each data bank. 
In genera1, ENVIR stores data by reading lines (cards) of data in alpha­numeric form, and turning the data into an optimized code in binary form. The program maintains a dictionary of the codes and corresponding alpha­numeric words, and updates the dictionary as new terms are added. Before starting this process, however, the user must tell the program what to expect in the incoming data by means of a data bank definition command. This co:n·­mand causes the program to allot space in its memory for the dictionary. This dictionary and binary data file together constitute a "data bank 11 , which can be stored and queried. Because the code used is optimized, the data ban~< is usua Uy much smaller than the original input data file. 
When a query is made, a complex algorithm determines an optimum search pattern; then the program searches the binary data bank for items which match the pattern. The data requested in the query command is taken from each item which satisfies the search pattern and is stored in a temporary file, still in coded form. When the entire bank has been searched, the retrieved data is sorted completely, all duplications are eliminated, and the code is turned bank into an alphanumeric file for output. 
While ENVIR has some unique abilities, the data bases discussed in this report could have been set up using other general data base management sys­tems, such as System 2000, which operates internally on entirely different principles. We have not done comparative studies on the relative merits of these systems for these applications, but we can comment on the requirements fo:-a general data base management system for use with environmental data. Although these comments also apply to the descriptor schemes used to handle the data, it is possible to have a very flexible computer program but a-very inflexible descriptor scheme. 
1. 	
It should be possible to use any scientific terms desired rather than codes. 

2. 	
The system should be open-ended in that it should be easy to add new data, or new descriptors. 

3. 	
It should be possible to do searches using boolean logic opera­tions on the states of all or almost all of the descriptors. This requires "inverted files" in most data systems. 

4. 	
Interfacing retrieved data to programs for further analysis should be easy to accomplish. 


III-2 
Three tables are presented in Appendix A which describe the ENVIR com­mands and explain the "variable forms" and the system's error messages. 
The computer used is the CDC 6600-6400 system at The University of Texas at Austin. A teletype terminal at the Marine Science Institute, 2 0 0 miles from the main campus, is connected via a leased telephone line to the computer time sharing network, 11 TAURUS 11 All programs and data are stored
• 
at the Computation Center on magnetic tape, and are read into the system on command from the terminal (Computation Center, 1973). 
Life Hi story Data Bank* 
A recent symposium has emphasized the importance of better understanding coastal environments and the effects of stress on these environments (Ketchum, 1972). The necessity of viewing the entire system in preserving coastal zone environments has also been stated (Niering, 1973). The available information on the organisms of the coastal zone must be organized and made accesible before it can be utilized, and modern computer technology can simplify this process. 
Kohlenstein (1972) has described the usefulness of computer systems in the storage and handling of biological data and emphasizes the importance of defining the objectives of a biological data bank to maximize this usefulness. The objectives of the computer system described in the present report are to: 
1. extract desired life history information on particular organisms with regard to environmental factors, environment type, limiting physical or chemical factors, and/or trophic relationships; and 2. have the retrieved information organized to aid coastal zone management planners as well as estuarine ecologists . 
All computerized data system design requires trade-offs between complete­ness and coding/computing effort. We have designed a system which contains much more information that a bibliographic or keyword system without requiring excessive coding or computing effort. 
Wherever possible, the life history information in this system has been incorporated in quantified form, and the interpretive information has been limited to a few words. While it would be possible to record the results of individual net trawl collections with numbers of each species caught, salinity, 
* 	This section, in part, has been accepted for publication by Chesapeake Science. The authors were W. B. Brogden, J. J. Cech, Jr. , and C.H. Oppenheimer. 
III-3 
temperature, etc., we have chosen to store and retrieve a more generalized 
level of information. Kohlenstein (1972) terms this generalized information 
"soft" data; for an example of a "hard" data system, see Swartz (1972). The 
data which is coded usually represents a generalizatio:i by the author of the reference, drawing from a number of samples. The format we have chosen 
also permits recording the results of laboratory experiments such as LDSO values, as long as the organism tested can be identified. Every piece of data 
can always be trased to a specific literature reference. 
The descriptors fall into several groups: organism identification, environ­ment classification, environmental limits, trophic relationships, and reference identification. Important considerations regarding identification of the orga­nisms include consistency of nomenclature. Although common names should be recognizable to local users of the data bank, adherence to a published common name source (e.g. Bailey, 1970) extends species recognition to more distant users. Inclusion of the life stage category allows changes in environ­mental tolerance, food items, etc. with regard to life stage or reproductive readiness to be incorporated. 
Table III-1 shows the descriptors used for the Life Hi story Data Bank, with two example items. 
Environment classification, descriptors 8-24, includes the general habitat type, "biotope" (Oppenheimer and Gordon, 1972), as well as the specific area and dates, "start year" and "end year" when the work in this reference was done. The temporal distribution pattern is stored as presence, "P", absence, "A", or unknown in the separate months. The bottom type, descriptor 9, was found to be useful information to supplement biotope. The National Marine Fisheries Service coastal region codes, such as 020 for the Gulf of Mexico off Corpus Christi Bay, have been used as the "bay system" for offshore areas. 
Environmental limits of both natural and modified environments, derived from both laboratory and field investigations are entered with descriptors 25­
29. It should be noted that the units in which the parameter is expressed have to be adjusted to produce an integer value for storage; this difficulty has been overcome with other versions of ENVIR. This coding scheme has proven to be very flexible but it cannot describe nonlinear interactions between multiple environmental factors. Table III-2 shows some actual examples of both laboratory and natural environmental limits. 
The importance of the organism to man is noted with the descriptors 30-32. If the organism is important in commercial or sport fisheries, the term "direct" is used. Other terms in use include "bait", "pest", "fouling", and "indirect". 
III-4 
TABLE III-1 
LIFE HISTORY DESCRIPTORS & EXAMPLE ITEMS 

Descriptor  Example 1  Example 2  
1  Class  Chondrichthyes  0 steichthyes  
2  Family  Myliobatidae  Ariidae  
3  Genus  Rhinoptera  Bagre  
4  Species  Bonasus  Marina  
5  Common Name  Cownose Ray  Gafftopsail Catfish  
6  Life Stage  Adult  Adult  
7  Motility  Nektonic  Nektonic  
8  Biotope  ---(1)  Open Bay  
9  Bottom Type  Mud  
10  Bay System  Upper Laguna Madre  Aransas Bay  
11-22  Jan through Dec  p (2)  A  
23  Start Year  1951  1941  
24  End Year  1955  1942  
25  Parameter  Salinity  Salinity  
26  Units  0 .1 PPT  0 .1 PPT  
27  Limit Type  Occurrence  Preference  
28  Lower Limit  250  50  
29  Upper Limit  450  300  
30  Commercial  Direct  
31  Sports  Direct  
32  Other Imp  
33  Trophic Level  Carnivore  Omnivore  
34  Diet Sig  Major  
35  Food Item  Callinectes  
36  Reference  51  1  
37  Ref Remark  Commercial Catches  
38  Coded By  NDMB  BEITZ  
39  Batch  5  1  
40  Sheet  75  23  

(1) 	
This signifies that the state is unknown. 

(2) 	
Descriptors 11-22 are the months and use "P" for presence, "A" for absence, or unknown. 


III-5 

TABLE III-2 
EXAMPLE OF ENVIRONMENTAL LIMITS INFORMATION 

Parameter  Units  Limit Type  Lower Limit  Upper Limit  
SALINITY HEPTACHLOR TEMP DEPTH TEMP  0 .1 PPT PPB 0. I C FATHOMS 0 .1 c  TOLERANCE TOLERANCE ACTIVITY PREFERENCE LETHAL  0 150 118  350 700 28  

The nomenclature used in the food items descriptor is the same used in the organi smal identification categories of class, genus, or common name, where possible. The diet significance is usually a term such as major, minor, or exclusive, but the term "host" has been used to describe parasite relation­ships. This group of descriptors can be used to aid in the construction of food webs and a better understanding of trophic relationships in the various biotope communities. Unfortunately, only a few references contain trophic information. 
If a particular reference contains important information which cannot be coded, such as growth and population dynamics, this fact is noted in the "ref remarks" descriptor. The reference is recorded as a code number referring to a master reference list. This method was chosen rather than author-year, or the complete bibliographic reference to save space on output. Recording the name or initials of the person coding the form facilitates tracking down errors. Batch and sheet numbers are assigned after keypunching to aid in correct.ion of errors in the cards. 
Creation of the Data Bank 
The pre sent version of ENVIR requires a complete description of every item, even though there may be many descriptors which are the same for a long series of items. To avoid excessive keypunch costs and coding effort, a simple program was written to read cards punched directly from a coding form with provision for up to 5 biotope s, 5 environmental limits parameters, and 30 food items for one set of values of the other descriptors. As forms are coded and keypunched, they are run through a series of error checking operations, corrected, and added to the data bank in batches of 100 to 300 forms. 
III-6 
A literature survey and coding effort of 12 man-months produced 1800 forms from 13 2 references on 250 organisms. Roughly 12,000 cards were keypunched and 18 minutes of computer time were used to create the pre sent data bank of 3 900 items. Computer memory requirements are presently 32, 000 words. If more than about 50, 000 characters of stored dictionary space were required, more memory could be used. Tape storage of the data bank requires 24, 000 60 bit words. Queries in this data bank require from 2 to 30 seconds of computer. time depending on the complexity of the output and the search criteria. 
Retrieval of Information 
Retrieval of items is determined by a query command which can incorporate complex boolean operations on the states of the descriptors. The program maintains dictionaries of the terms which have been used, and these diction­aries can be obtained at any time. An example query command might be: PRINT: GENUS, SPECIES, COMMON NAME FOR SPECIES WITH LIFE STAGE, ADULT OR JUVENILE AND BIOTOPE, CHANNEL AND NOT REFERENCE, FROM 1 TO 4*. The part of the statement before "FOR SPECIES WITH" controls the output to be derived from the retrieved items, and the remainder of the state­ment selects the items retrieved. 
The output consists of a listing of the states of the requested descriptors, completely sorted and ordered in a hierarchical fashion as specified in the first part of the query command, with all duplicates eliminated. For the example query above, the first line would contain the alphabetically first genus of all of t_he retrieved items, indented on the second line would be the first species of that genus, indented on the third line would be the first common name of that species, and the remaining species and common names belonging to that genus would follow. Parentheses can be used to control which descriptors appear on the same line; for example, PRINT: (GENUS, SPECIES, COMMON NAME) etc., would have put all of the descriptors on the same line in the previous example. 
Other commands are available to modify or delete items, correct spellings, or re serve space for more descriptors. A "HOW MANY" command can be used to count the number of items which could be retrieved with specified search parameters. When operating ENVIR in the interactive mode the slow writing speed of the terminal makes it impracticalto have long output files printed, so pro­vision is made for printing long output files on a high speed printer at the Computation Center. 
III-7 

Applications 
To facilitate the use of the Life Hi story Data Bank, we have used the system itself to produce various types of indexes to the available data. Example listings include: all organisms for which data is available organized taxonomically, all geographic areas organized by biotope, and types of toler­ance information available organized by parameter. Updated versions of these indexes can easily be produced after every new batch of data is added to the bank. 
Faunal checklists can easily be produced for specific geographical areas; organization of these checklists can be by taxonomy, trophic level, seasonal occurrence, or any other descriptor. It is quite easy to produce customized checklists for specific purposes, and of course, it is easy to produce updated versions when new data becomes available. 
This system is particularly useful for retrieving and organizing the bio­logical data needed by resource managers. As an example, let us assume that a proposed powerplant development on a local bay would increase the maximum temperature in an area from 30.0° to 32.0° C. To evaluate the consequences of this proposed action, one of the first questions we might ask is shown in Table III-3, with only a small part of the full answer shown. By specifying certain bay systems, we retrieve only results from the local bays. If there are only a few observations available for the local bays, as shown by using the 11 HOW MANY" command, we might want to expand our search by asking about specific biotopes without specifying the bay. By dropping all require­ments except the parameter and upper limit, we could retrieve all temperature limits recorded in the bank with upper limit values between 30. o0 and 32. o0 , including laboratory experiments. 
Detailed information on how to obtain life history information from the data bank (with computer program and flow diagrams) is presented in Appendix 
B. 
Summary 
The general system for storing and retrieving life history and environmental limits information which we have described could be applied to any coastal environment. Biologists and resource managers can learn to use this system rapidly because standard nomenclature is used instead of codes, and because the interactive computer terminal provides an immediate response to questions. Although the effort required to prepare the entries is somewhat greater than that required to prepare a bibliography, the greatly increased usability of the data and ease of entry of new data more than compensates for the extra effort. 
III-8 

TABLE III-3 EXAMPLE QUERY WITH PART OF THE REPLY 
SHOW: (GENU sI SPECIES) I (LIMIT TYPE I UPPER LIMIT I UNITS I BIOTOPE I 
BAY SYSTEM) FOR SPECIES WITH COMMERCIAL, DIRECT OR DIRECT+ INDIRECT 
AND PARAMETER, TEMP AND UPPER LIMIT, FROM 300 TO 320 AND BAY SYSTEM, 
ARANSAS BAY OR COPANO BAY OR COPANO-ARANSAS* 

MENTICIRRHU S AMERICANUS OCCURRENCE 305 0 • 1 C SHALLOW BAY ARANSAS BAY 
PARALICHTHYS LETHOSTIGMA OCCURRENCE 305 0 .1 C OPEN BAY ARANSAS BAY OCCURRENCE 305 0 .1 C SHALLO"W BAY ARANSAS BAY OCCURRENCE 305 0 .1 C SHALLOW BAY COPANO BAY 
POGONIAS CROMIS OCCURRENCE 307 0 .1 C GRASS FLAT COPANO-ARANSAS OCCURRENCE 307 0. 1 C SHALLOW BAY COPANO-ARANSAS 
III-9 

Other Data Banks 
Because a complete description of the ENVIR program and one of the data banks, Life History, has been presented, only a synopsis of the Chemical­Biological Data Bank and the Creel Census Data Bank is presented. 
Chemical-Biological Data Bank 
This type of data bank contains the results of individual measurements taken in the field, or in the laboratory from single samples. This type of data is what Kohlenstein (1972) calls 11 hard11 data as opposed to the Life Hi story Data Bank which is " soft11 data . 
Work with this type of data bank was first started at the MSI during a Gulf Universities Research Consortium sponsored, NASA supported project on Environmental Data Management (NASS 26867). Several special programs were written during the NASA project to transfer existing computerized data files into ENVIR items. The ancestor of the RDLOG 3 program was written to take data from field measurements and produce ENVIR items. A special program ENVPROC was written to process the data retrieved by ENVIR, to calculate averages and extremes and to produce plots of the retrieved data in various ways. 
The first application was the Galveston Bay Project Toxicity Studies re­search project (Oppenheimer, Brogden, and Gordon, 1973). In the course of this project, a data bank on chemical and physical measurements taken by a variety of agencies was generated and used as a research tool. The descriptor scheme used in this bank is shown in Table III-4. As part of the NASA sponsored project, a data bank also was created for data for chemical measurements in the Gulf of Mexico, u sfng the same descriptor scheme. At the pre sent time, data from various sources for the Corpus Christi Bay area is being stored in this data bank. 
During this project the original descriptor scheme was expanded to permit recording and retrieval of biological data, such as number of fish caught in a single trawl sample. The expanded descriptor scheme also is shown in Table III-4. 
A description of the data flow in the Chemical-Biological Data Bank and a computer program used to read data and produce a file of ENVIR items are pre­sented in Appendix C . 
III-10 

TABLE III-4 
TABLE OF DESCRIPTORS USED IN THE GALVESTON BAY DATA BANK 
(INDICATED BY G) AND THE CORPUS CHRISTI DATA BANK, C 

Descriptor Name 
STATION LINE SITE LAT LONG BIOTOPE 
YR 
MO 
DY 
TIME 
DEPTH 

SHIP CRUISE AGENCY PROJECT PARAMETER UNITS VALUE 
PHASE METHOD COMMENTS GENUS SPECIES LIFE STAGE 
Bank 
G,C c c 
G,C G,C 
c 
G,C G,C G,C G,C G,C 
G G 
c c 
G,C G,C G,C 
G,C G,C G,C 
c c c 

Comments 
the station name assigned by the original investigator numbers used to provide compatability with the TWDB numbers used to provide compatability with the TWDB 
latitude in deg-min-0 .1 min 
longitude in deg-min-0. 1 min 
year 
month day time 0-2400 depth of the sample, 0 .1 meters in the G data bank, 
ft in C 
name of the parameter measured .name of the units the result is expressed in 
numerical value obtained, the units have to be adjusted so that this is an integral number such as dis solved, sediment, etc. the analytical or sampling method sueh as less than, trace , etc . standard biological nomenclature 
standard biological nomenclature 
standard biological nomenclature 

Creel Census Data Bank 
Table III-5 shows the descriptors used for the Creel Census Data Bank, with an example item (see Chapter IV for details of the project). A separate item was generated for each different species caught, as reported on the inter­view sheet; in addition, a summary item was generated for each interview, containing the total number of fish caught, the total weight, and the word "total" for the species descriptor. The climatological data will be incorporated using the additional descriptors shown in Table III-5. 
After reading the creel census items, ENVIR produced the data bank, and a vocabulary of all terms used in the data bank. The data bank was stored on magnetic tape at the Computation Center and examined for misspellings and incorrect use of the various descriptors. Any errors were corrected in the ENVIR data bank, by special "correction" commands. 
Finally, ENVIR was used with the corrected data bank, to selectively re­trieve the creel census data to produce the various types of desired information (for example, see Chapter IV). Several different forms of data retrieval are possible using ENVIR; selected data can be printed on the teletype, or at the central computer site, or data can be prepared for further processing by addi­tional programs. In order to produce the totals of weight, hook-hours, etc. (as reported in Chapter IV), a program was written to summarize and tabulate the individual interview results (see Appendix D) . 
Bird Data Bank 
The purpose of this section has been to show an early step in processing information into a data bank. 
A survey and quantitative enumeration of bird populations is necessary for calculations concerning cycling of carbon and other nutrients in the Corpus Christi Bay area. Knowledge of the population characteristics of the indigenous and migratory birds and their role in the biota is also lacking. Hopefully, from food type and waste information, feeding efficiency and also fecal influence information can be derived. Field work is being designed to fill gaps in some of the data. The handling of this data will be partitioned between two types of ongoing data bank operations. Numbers of birds and their seasonal abundances will be tabulated. Habitat requirements information may be gathered which will be useful to wildlife managers as well as estuarine managers. 
Tables III-6 through III-8 synopsize the data presently available. (Refer­ences utilized are presented in Table III-9 .) The tool for handling the data assembled by such a study will be the ENVIR system. Some of the information on hand, such as primary foods and habitats, is amenable to direct inclusion 
III-12 

TABLE III-5 
CREEL CENSUS DATA BANK DESCRIPTORS 

DescriQtor 
Number  Name  
1  Location  
2  Biotope  
3  Position  
4  Month  
5  Dy  
6  Yr  
7  Time  
8  Hours Fishing  
9  Species  
10  Number Caught  
11  Weight  
12  Hooks  
13  Bait  
14  Residence  
15  County  
16  State  
17  Fish Salt Days  
18  Fish Fresh Days  
19  Prefer To Fish  
20  Rank Facilities  
21  Rank Access  
22  Rank Fishing  
23  Rank Water  
24  Rank Other  
25  Comments  
26  Coded By  
27  Batch  
28  Sheet  
29  Wind Dir  
30  Wind Vel  
31  Cloud Cover  
32  Barometer  
33  Air Temp  
34  Water Temp  

~ 
Name Name Name Name Order Order Order Order Name Order Order Order Name Name Name Name Order Order Name Name Name Name Name Name Name Name Order Order Name Order Name Order Order 
Order 

Size 
240 120 30 40 1 to 31 1970 to 1984 0 to 2500 0 to 120 400 0 to 500 0 to 15000 0 to 250 200 400 240 100 0 to 366 0 to 366 20 20 20 20 20 240 500 120 0 to 100 0 to 5000 30 
0 to 60 20 2800 to 3200 0 to 125 0 to 125 
Exam2le 
Oso Pier Open Bay Wade Aug 8 1973 1230 2 Cynoscion Nebulosus 2 8 (ave wt in 0 . 1 lbs) 4 Cut Mullet Austin Goliad Texas 10 20 Salt 3 2 1 4 1 Free Beaches Fishing Worse This Year Litwin 1 1500 SE 10 1 2925 80 (deg. F) 70 (deg. F) 
III-13 


TABLE III-6 
OCCURRENCE, HABITAT, FOOD AND ABUNDANCE OF BIRDS 

COMMON TO CORPUS CHRISTI BAY 

Common Name 

White pelican 
Brown pelican 

Double erested cormorant Olivaceous cormorant Anhinga Great blue heron Green heron 
1-t 1-t 
1-t Little blue heron 
I I-' 
~ Cattle egret 
Reddish egret 
American egret 
Snowy egret 
Louisiana heron 

Black crowned night heron Yellow crowned night heron Least bittern American bittern Wood ibis White faced ibis Wnite ibis 
Occurrence 

Resident once Resident Apr-Nov 
Sept-May Resident Mar-Oct Resident Mar-Nov 
Resident 
Resident 
Resident Resident Resident Resident 
Resident 
Resident 
Mar-Oct 
Oct-Apr 
Jun-Nov 
Resident 
Resident 

Habitat 
Bays, Lakes Bays, Gulf 
River, Lakes, Gulf Rivers, Lakes, Gulf Lakes, Rivers Marshes, Rivers, Shores Marshes, Rivers, Ponds, Shores Marshes, Swamps, Mud flats, Shores Fields, M:irshes, Cow pastures Marsh shores, Lagoons Marshes, Ponds, Shores Marshes, Swamps, Shores Marshes, Swamps, Shores Lagoons Marshes, Swamps, Shores Marshes, Swamps, Shores 
Marshes, Swamps Marshes, Swamps Swamps, Marshes, Ponds Marshes, Swamps Marshes, Swamps 
Food 
Fish Fish 
Fish Fish Aquatic animals Aquatic animals Aquatic animals 
Aquatic animals 
Aquatic animals & insects Aquatic animals Aquatic animals Aquatic animals Aquatic animals 
Aquatic animals Aquatic animals Aquatic animals Aquatic animals Aquatic animals Aquatic animals Aquatic animals 
1
Abundance 
c 
p 
A p p 
A p 
p 
A 
c c 
A 
A 
p p p p p p p 
TABLE III-6 (CONTINUED) 

1
Common Name Occurrence Habitat Food Abundance 
Roseate spoonbill Resident Marshes, M'..idflats Aquatic animals c Mallard Sept-Apr Marshes, Swamps, Bays Aquatic plants c Ponds Mottled duck Resident Marshes, Mudflats, Ponds Aquatic plants c Bays Gadwall Sept-May Ponds, Marshes, Bays, Aquatic plants c Rivers Pintail Aug-May Marshes, Prairies, Bays Aquatic plants A Ponds Green winged teal Sept-Apr Marshes, Rivers, Bays, Aquatic plants c 
Ponds Blue winged teal Aug-May Ponds, Marshes Aquatic plants c Baldpate Oct-May Marshes, Ponds, Lakes, Aquatic plants c Bays 
H Shoveler Sept-May Marshes, Ponds, Lakes, Aquatic plants A
H H 
I
.,_, Bays 
(/1 Redhead Oct-May Lakes, Bays, Marshes Aquatic plants c Ring necked duck Oct-Apr Ponds, Rivers, Bays Aquatic plants p Canvasback Oct-May Lakes, Bays, Marshes Aquatic plants & c animals Lesser scarys Sept-May Ponds, Rivers, Bays Aquatic plants & A animals Common goldeneye Oct-Mar Lakes, Rivers, Bays, Gulf Aquatic plants & p animals Bufflehead Oct-Apr Ponds, Rivers, Bays Aquatic plants & c animals Ruddy duck Oct-May Ponds, Rivers, Bays Aquatic plants & p animals Red-breasted merganser Nov-May Lakes , Bays , Gulf Aquatic animals p & plants Whooping Crane Oct-Apr Coastal Prairie Aquatic plants p & animals 
TABLE III-6 (CONTINUED) 

1
Common Name Occurrence Habitat Food Abundance 
Sandhill crane Nov-Mar Prairies, Marshes Aquatic & terrestrial p plants & animals King rail Sept-Apr Marshes, Swamps Aquatic plants & p 
animals Clapper rail Resident Marshes A':1uatic animals p Virginia rail Oct-Feb Marshes Aquatic animals p Sora Sept-May Marshes, Swamps Aquatic animals p 
& plants Purple gallinule Mar-Oct Swamps, Marshes Aquatic plants & p animals Common gallinule Resident Marshes, Swamps Aquatic plants & p animals American coot Resident Ponds, Marshes, Bays Aquatic plants & A animals 
1-i 
1-i American oystercatcher Resident Shores Aquatic animals c
1-i 
I 
..... Semipalmated plover Apr-May Beaches, Shores, Flats Aquatic animals c 
O"l Aug-Nov Snowy plover Resident Sand flats, Beaches Aquatic animals c Wilson1 s plover Mar-Oct Beaches, Flats Aquatic animals c Killdeer Resident Fields, Flats, Shores Aquatic & terrestrial A animals Ruddy turnstone Mar-May Beaches, Flats Aquatic animals c Aug-Sept Long billed corleu Resident Flats, Beaches, Prairies Aquatic & terrestrial A animals Whimbill Apr-May Shores, Flats, Marshes, Aquatic animals p Aug-Oct Prairies Willet Resident Flats, Beaches, Marshes Aquatic animals A Greater yellowlegs Jul-May Marshes, Flats, Shores Aquatic animals c Lesser yellowlegs Jul-May Marshes, Flats, Shores Aquatic animals c Least sandpiper Jul-May Flats, Marshes, Shores Aquatic animals c 
TABLE III-6 (CONTINUED) 

1-i 1-i 1-i 
I I-' "l 
Common Name 

Dunlin Short billed dowitcher Long billed dowitcher Semipalmated Sandpiper Western Sandpiper Buff breasted sandpiper 
Marbled godwit 
Sanderling A_merican avocet Black necked stilt Herring gull Laughing gull Ring gilled gull Gu11 billed tern Forster's tern Least tern Royal tern Sandwich tern 
Caspian tern Black skimmer 
P =Present 
C= Common 
A= Abundant 


Occurrence 

Oct-May 
Resident 
Jul-May 
Jul-May 
Aug-May 
Apr-May 
Aug-Oct 
Aug-May 

Resident 
Resident 
Resident 
Oct-Apr 
Resident 
Aug-May 
Resident 
Resident 
Mar-Nov 
Resident 
Resident 

Resident 
Resident 


Habitat 
Flats, Beaches, Ponds Beaches, Flats, Ponds Flats, Ponds Beaches, Shores, Flats Shores, Flats, Ponds Fields 
Prairies, Ponds, Shores Flats Beaches, Flats Beaches, Flats, Ponds Marshes, Flats, Ponds Gulf Bays, Marshes 
Bays, Marshes Bays, Marshes Beaches, Bays, Gulf Bays, Marshes Beaches, Gulf, Coastal waters Bays, Marshes Bays, Beaches 
Food 
Aquatic animals Aquatic animals Aquatic animals Aquatic animals Aquatic animals Aquatic animals 
Aquatic animals 
Aquatic animals Aquatic animals Aquatic animals Aquatic carrion Aquatic animals Aquatic carrion Aquatic animals Aquatic animals Aquatic animals Aquatic animals Aquatic animals 
Aquatic animals Aquatic animals 
1
Abundance 
p 
p 
p p 
p p 
c 
A 
c c 
A 
c c 
A A A 
A A 
TABLE III-7 
PLANTS & ANIMALS USED AS FOOD BY DUCKS 

Food Item 

Aquatic plants Sago pondweed Wild celery Widgeon grass Eel grass Banana water lily Musk grasses Bushy pondweed Various-leafed pondweed Redhead grass Small pondweed Leafy pondweed Greater duckweed Lesser duckweed Coontail Water weed Water Cress Spiked water milfoil White water crowfoot Horned pondweed Water chinkapin Frogbit Water shield Spatter dock White water lily Water plaintain Thalia Swamp privet Water elm Samphire Saltwort Orach Docks 
Marsh plants Wild rice Wapato Chufa Wild millet Delta potato 
1

Food Value 
E E E E 
E 

G 
G 
G 
G 
G 
G 
G 
G 
G 
G 
G 
F 
F 
F 
F 
F 
F 
F 
F 
F 
F 
F 
F 
Slight 
Slight 
Slight 
Slight 

F G G G G 
III-18 

1
Habitat
Brackish Brackish Brackish Brackish Brackish Brackish Brackish Fresh Brackish Fresh Fresh Fresh Fresh Fresh Fresh Fresh Fresh Fresh Brackish Fresh Fresh Fresh Fresh Fresh Fresh Fresh Fresh Fresh Salt Salt Alkaline Alkaline 
Fresh Fresh Fresh Brackish Brackish 
% occurrence in duck stomachs 
3.99 
1.06 
8.47 
.71 
1. 71 (naiads) 
.15 (duckweed) 
.34 
.22 
.30 
4.94 
.so 
4.5 
.OS 
3.6 + .42 
4 .11 
Food Item 

American bulrush Soft stemmed bulrush Hard stemmed bulrush Bayonet grass Fall salt marsh bulrush River bulrush Olney' s bulrush Water smartweed Pale smartweed Other smartweeds Salt marsh water hemp White top Wampee Picherel weed Broad fruited burreed American burreed Bog rush Lake bank sedge Slender sedge Sawgrass Arrowhead Spike rush Beak rush Panicum Glasswort Algae Paspalum Oak Dodder Cyperus 
Wild heliotrope Penny wort Wax myrtle Pigeon grass Salt grass Button bush Cord grass Buttercup Rush Cut grass Leersia sp. 
TABLE III-7 (CONTINUED) 

Food ValueI  Habitat1  
G  Brackish  
G  Fresh  
G  Fresh  
G  Brackish  
G  Brackish  
G  Fresh  
G  Brackish  
G  Fresh  
G  Moist soil  
G  Moist soil  
G  Brackish  
G  Fresh  
F  Fresh  
F  Brackish  
F  Fresh  
F  Moist soil  
F  Fresh  
F  Fresh  
F  Fresh  

% occurrence induck stomachs 
5. 34 Bulrushes 
5. 94 smartweeds 
.06 . 0 I (Burreed) 
2.99 
2.63 
2.07 
I.96 
1.87 
1.36 
I.20 
1.02 .88 .46 .42 .36 .36 .23 .20 .17 .16 .09 .08 . 07 .05 .05 
TABLE III-7 (CONTINUED) 

Food Item 

Lippia sp. Flmbristyli s Fanwort Alligator weed Sweetgum Water star grass 
Animals Snails (gastropods) Insects Fishes Bivalves Crustaceans Misc. 
1 
More Game Birds in America (19 3 3) . E = Excellent G= Good F = Fair 
2 
Martin and Uhler (193 9) . 

% occurrence in duck stomachs 
.04 
•0 3 
.03 .02 . 01 . 01 
11. 62 
4.43 
2.86 
1.35 
1.06 
3.52 
III-20 



TABLE III-8 PRINCIPAL FOODS OF THE LARGER AQUATIC BIRDS 
The anhinga, principally a freshwater bird, feeds on aquatic insects, craw­
fish, leeches, shrimp, tadpoles, frog eggs, water lizards, young alligators, 
water snakes and small turtles. It is known to be able to swallow a fish 9t 
inches long and 2 inches wide and could devour a meal of 40 or more fishes 
3! inches long. 

The reddish egret, a common bayside inhabitant, catches small needlefish, 
pinfish, mullet, sheepshead minnows along with frogs, tadpoles, and various 
crustaceans for its food. 

Another familiar bird along the bayside, the Louisiana heron feeds on grass­
hoppers, killifish, lizards, water insects, worms, slugs, snails, tadpoles 
and crawfish. 50 nestling pellets averaged 95.4% fish, 4.2% invertebrates 
and 2% amphibians with an average pellet volume of 6 .06 ml. 
The nearly extinct whooping crane uses bulbous roots, grasses, grains, seeds, large insects, crabs, crawfish, fish, worms, molluscs, frogs, reptiles and small rodents for food. 
Plant seeds, muckleberries, killifish, tadpoles, rodents, amphibians, grass­hoppers, crickets, beetles, potatoes and grains are eaten by the Sandhill 
crane. 

One of the largest aquatic birds in the Corpus Christi area is the Great Blue heron. It has been noted to feed upon fishes, frogs, eels, salamanders, tad­poles, small birds, rodents, insects, aquatic plant seeds, and young of other birds such .as avocets, coots, or black necked stilts. While resting on a spoil island in the Laguna Madre the heron feed on sheepshead minnows, anchovies, mullet, seacatfish, halfbeaks, and shrimp. During this time, after the chicks hatched, the frequency of feeding was as follows 
Frequency  
2 days  fed 10 times in 13 hrs.  
6 days  fed 6 times in 13 hrs.  
1 week--1 st flight  fed 1-6 times during daylight  
63-69 days  fed 2 times during 15 hrs.  

Most herons have a life expectancy of 60 years. 
III-21 


TABLE III-8 (CONTINUED) 

The snowy egret feeds on shrimp, small fish, fiddler crabs, snails, aquatic insects, small amphibians, seeds of aquatic plants, grasshoppers, cutworms, and crawfish. A study of 50 nestling pellets represented an average of 87. 9% fish, 7% invertebrates and 4. 8% amphibians. On a spoil island in the Laguna Madre, 14 nestling pellets showed Gulf menhaden to be a major food item with sheepshead minnows and anchovies also eaten. 
Sometimes mistaken for a flamingo, the Roseate spoonbill feeds upon small fish such as sheepshead minnow, killifish, silversides; crabs, fiddler crab and blue crab; shrimp, grass shrimp, peneaid shrimp, and Hippolyte; molluscs Amnicola; and various insects. 
Diving from the surface of the water and swimming to secure its food, the double crested cormorant feeds upon eels, parrotfish, scianoids, crabs, flounder, capelin, tomcod, and sculpin. One study analyzed 30 stomachs. They found that the average well-filled stomach weighed 1t pounds. On the basis of 2 full meals per day, 700 cormorants would in five months consume 45 tons of fish per season. In another study, captive cormorants which maintained or increased their body weight required only a fraction over one pound of fish per day. The life expectancy of cormorants is 23 years. 
Once a resident of the Texas coast, the Brown pelican is becoming more and more rare. The chief food items of these birds are menhaden, mullet, sheeps­head, silversides, and an occasional needlefish. The pouch capacity is 3t gallons and can take a fish up to 14" in length. The life expectancy of pelicans is 41 years. 
A more common sight, the white pelican has a rookery in the Laguna Madre. These birds while inland will feed upon trout, bass, chub, carp, catfish, suckers, pickerel, and pike. A captive pelican was said to eat 6 -8 pounds of fish per meal. 
Not a commonly seen marsh bird, the American bittern feeds on small fish, mice, small amphibians, eels, crawfish, molluscs, dragonflies and grass­hoppers. 
A close relative, the least bittern feeds on insects, mice, small fish, small amphibians, snails, slugs and beetles. 
The American egret, a stately white bird, catches small fish, frogs, lizards, snakes, mice, moles, fiddler crabs, snails, grasshoppers, crawfish, and insects for its food. 
III-22 

TABLE III-8 (CONTINUED) 
Smaller, less conspicuous herons such as the green heron, black crowned 
night heron, and the yellow crowned night heron feed on small fish, crawfish, 
frogs, marine worms, shrimp, crabs, grasshoppers, crickets, squid, small 
mammals and small birds. 

The King rail, Clapper rail, Virginia rail and Sora are known to feed on small 
molluscs, fish, crawfish, insects, beetles, frogs, snails, shrimp and seeds 
from aquatic plants. 

Commonly seen walking on lily pads or thick aquatic vegetation the purple gallinule and the common gallinule feed upon seeds and roots of aquatic plants, small molluscs, insects, worms, and some domestic grains. 
Known to inhabit even the smallest ponds, the American coot favors grass sprouts, pondweed, water milfoil, wild celery, small fish, tadpoles, snails, worms and insects as food. 
The Lanus gulls such as the laughing gulls, ring-billed gull and herring gull 
are omnivorous, eating tern eggs, small crabs, shrimp, fish and just about anything else that is available. Their life expectancy is 44 years. 
The strikingly colored black skimmer feed principally on small fish and shrimp. 
The terns, which are more sensitive to changes in the environment than other sea birds, catch small fish and crustaceans for food. 
Crawfish, fiddler crabs, small molluscs, insects, small amphibians and rep­tiles are eaten by the white ibis and the white faced ibis. The wood ibis also includes wood rats and small birds to the list. 
One of the herons being displaced by cattle egrets is the little blue heron. This heron feeds on small fish, fiddler crabs, grasshoppers, small frogs, cut worms, lizards, and crawfish. A study of 50 nestling pellets averaged 54% amphibians, 32. 5% fish, 12% invertebrates, and 1 % reptiles. 
The adult cattle egret requires 74 grms or 122 individual insects per day. 50 nestling pellets averaged 63. 2% invertebrates, 32. 3% amphibians, and 
4. 4% reptiles. The average volume of the pellets were 4. 82 ml. The food requirements for other age groups of cattle egrets are as follows: 
III-23 


TABLE III-8 (CONTINUED) 
Age 1 -21 days -total of 1, 676 grms consumed 2nd week of life -average 95 grms daily 
The maximum an adult bird can carry at one time is 50 grms. 
III-24 


into the Life History bank. The compilation of this is essentially complete, 
though some gaps may remain for migrant species. Records of bird counts, 
summarized in Table III-6, are suitable for treatment similar to the Creel Cen­
sus bank, as they concern field counts. 
However the count data on hand are subject to serious drawbacks. Presently, major counts are made only during parts of May and December. These counts are not consistent with respect to effort and sampling stations from year to year. Some sets are ambiguous as to whether individuals or pairs of birds were counted. For this reason, abundance classes were used instead of counts in Table III-6 . 
This task force will propose separately to initiate field studies to update the censusing of birds in the Corpus Christi Bay area. Counts of feeding birds will be made before and after the breeding season in fixed sampling locations. Rookery counts will be made in May as usual, with adequate con­trols to insure that data are useable. 
Information concerning migrants such as ducks and cormorants will be collected separately during times of their local abundance. This information will be logged in the data banks and merged to give a year-round picture of bird influence on the area . 
The third aspect, the effects of bird wastes, is little known (Dusi, 1971). Localized effects in the shallow water areas where wading birds congregate may reflect contributions from food eaten in deeper areas. It is also hoped that ancillary data such as seine collections, bottom samples and bottom cover transect counts in the feeding areas can be gathered and used to amplify food habit and habitat requirement information. 
Design of this field enumeration will hopefully be a cooperative venture between this task force and local institutes such as Welder Wildlife Founda­tion, the Audubon Society, and the Aransas National Wildlife Refuge. 
III-2 5 


TABLE III-9 BIRD BANK SOURCES 
Anderson, D. R. 1966. A literature review on waterfowl production and habitat manipulation. Bur. Spt. Fish & Wildl., Colo. Coop. Wildl. Res. Unit. Special Rept. 14. 15 pp. 
Bent, A. C. 1922. Life histories of North American petrels and pelicans and their allies. Smithsonian Inst. U. S. Nat. Mus. Bull. 121. · 343 pp. 
Bent, A. C. 1926. Life histories of North American marsh birds. Smithsonian Inst. U.S. Nat. Mus. Bull. 135. 490 pp. 
Bent, A. C. 192 7. Life histories of North American shore birds. Smithsonian Inst. U.S. Nat. Mus. Bull. 142. 120 pp. 
Bent, A. C. 1929. Life histories of North American shore birds. Smithsonian Inst. U.S. Nat. Mus. Bull. 146. 212 pp. 
Blacklock, G. 1972. The 1972 cooperative census of fish eating birds--Texas upper coast. Unpubl. manu script, Welder Wildlife Foundation. 21 pp. 
Blacklock, G. 1974. The 1973 cooperative census of fish eating birds--Texas middle coast. Unpubl. manuscript, Welder Wildlife Foundation. 
Bowman, D. , W. Brogden, and C. Oppenheimer. 1973. An Interim Report-­Sportfishing Creel Census pilot study. Unpubl. Manu. 44 pp. 
Brogden, W. B. , J. J. Cech and C. H. Oppenheimer. 19 7 4. A computerized system for the organized retrieval of life history information. Ches. Science. In press. 
Burleigh, T. D. 1944. The birdlife of the Gulf coast region of Mississippi La. St. UN. Mus. Zool. , Occasional Pap. 20. 490 pp. 
Chapman, F. M. 1891. On birds observed near Corpus Christi, Texas during parts of March & April, 1891. Bull. Amer. Mus. Nat. Hist. III (2): 315-328. 
Cottam, C. 1939 . Food habits of North American diving ducks. U. S. Dept. Agriculture, Bur. Biological Survey. Tech. Bull. 643. 140 pp. 
Cottam, C. 1959. The whooping crane. Game Bird, Pheasant Fanciers and aviculturi sts Gazette. pp. 13-20. 
III-26 
TABLE III-9 (CONTINUED) 
Dusi, J. L. , et. al. 1971 . Ecologic impacts of wading birds on the aquatic environment. Auburn U . Water Re sources Re search Inst. Bull. 5, Project A-010-ALA. 117 pp. 
Emlen, 	J. T. n. d. Population densities of birds derived from transect counts. 
U. Wisconsin. Unpubl. manuscript, Welder Wildlife Foundation. 
Emlen, 	J. T. n .d. Size and structure of a wintering avian community in southern Texas. U. of Wisconsin. Unpubl. manuscript, Welder Wildlife Foundation. 
Gurney, J. H. 1899. On the comparative ages to which birds live. The Ibis. Jan . 18 9 9 . pp . 1 -2 4 . 
Hagar, 	C. N. and F. M. Packard. 1952. Checklist of the birds of the central coast of Texas. 15 pp. 
Hildebrand, H. and G. Blacklock. 1967. A cooperative census of large fish eating birds along the Texas coast from Pass Cavallo to Penascal point. Unpubl. manuscript, Welder Wildlife Foundation. 22 pp. 
Hildebrand. H. and G. Blacklock. 1968. The 1968 cooperative census of fish eating birds along the Texas coast from the Sabine River to the Rio Grande. Unpubl. manuscript, Welder Wildlife Foundation. 31 pp. 
Jenni, D. A. 1969. A study of the ecology of four species of herons during the breeding season at Lake Alice, Alachua Co. , Fla. Ecol. Mono. 
39: 245-270. 

Jones, 	J. C. 1940. Food habits of the American coot with notes on distribu­tion. U. S. Dept. Interior, Bur. Biol. Survey. Wildlife Research Bull. No. 2. 52 pp. 
Lohse, 	A. and J. Tyson. 1973. Environmental resources inventory and evalu­ation--Clear Creek watershed in portions of Brazoria, Fort Bend, Galveston and Harris Counties. Army Corps of Engineers. G.U .R.C. Report 12 7 . 3 8 2 pp . 
Peterson, R. T. 1960. A field guide to the birds of Texas and adjacent states. Houghton-Miflin. Boston. 303 pp. 
III-27 


TABLE III-9 (CONTINUED) 

Pratt, H. M. 19 70. Breeding Biology of Great Blue Herons and Common Egrets in Central California. The Condor. Vol. 72: 407-416. 
Sennett, G. B. 1878. Notes on the ornithology of the lower Rio Grande of Texas from observations made during the season of 1877. Part I. pp. 1-66. 
Simersky, B. L. 1971. Competition and nesting success of four species of heron on four spoil islands of the Laguna Madre. Tex. A and I Univ.: a thesis. 92 pp. 
III-28 


CHAPTER IV SPORTFISHING CREEL CENSUS PILOT STUDY, AUGUST 1973 
A sportfishing creel census project was originated to obtain information relating to the removal of fish from the Corpus Christi Bay area by sportfishing. The amount of fish caught will be related to commercial fish catch and other environmental information affecting the total productivity cycles of the bay system. The Census will be conducted during the summer months of June, July and August 1974 with input from a pilot study made during August 1973. The total catch will be used in this coastal resources management project to assess carbon, nitrogen, and phosphorous input and output to the bay system. 
A pilot sportfishing creel census of August, 1973 was conducted for two reasons. As a pilot study, the censusing methods were tested and improved upon for future programs. Also the information collected will serve to fill the void in sportfishing statistics in the Corpus Christi area. Basic informa­tion not only on fishing but also on individuals fishing and the weather was collected. 
The 1974 project will be coordinated with the Economic Survey of the Texas Water Development Board and in part with a project being organized by the Texas Parks and Wildlife Division. 
This study was made possible by an additional grant from the Lower Nueces River Water Supply District, volunteers from the Marine Science Institute, and the cooperation of the Economics Branch of the Texas Water Development Board who provided computer support for the data analysis. This chapter has been taken from an interim report submitted to the Lower Nueces River Water Supply Di strict. 
Methods 

In order to facilitate surveillance and to include as many types of environ­ments as possible, the Corpus Christi Bay study area (Figure IV-1) was divided into four survey districts. The range of the four districts were: 
(1)  Aransas Pass Causeway to Ferry landing to Ingleside  
(2)  0 so pier to Laguna Madre to Bob Hall Pier  
(3)  Port Aransas to the Fish Pass  
(4)  Indian Point Pier to Cole Park Pier  

IV-1 



[V-2 


There were 2 full time and 4 part-time census takers participating in the pilot creel census. ·The census takers randomly surveyed the fisherperson in each of these districts for approximately eight hours per day during varying hours, e.g. , I 0 AM -6 PM or 6 PM -2 AM. To supplement the personal interviews, three aerial boat counts were made, two on Saturday, August 11, and one on Wednesday, August I5. This was done to get a total count of poats fishing in the census area and to arrive at an approximate number of persons fishing from these boats to compare with ground surveys. 
The census takers were acquainted with the two forms used (Tables. IV-I and IV-2). A briefing was given on the type of information sought and the way the information should be recorded on the forms. Table IV-I is specifically concerned with information received from the individual fisherpersons. This involves not only catch information but also information on where the fisher­persons are from, how they rate fishing conditions and facilities, and any comments. Table IV-2 is concerned with climatological information observed by the census takers. The wind direction and velocity were taken from the radio weather reports until the census takers became familiar with the two. The barometer reading was also taken from the radio. To take air and water temperatures, the census takers were supplied with a thermometer. For identi­fication and naming of the fishes several references and preserved fishes were used. The final list of fish are appended as taken from the literature. The reference list included: 
Food and Game Fishes of the Texas Coast. Bulletin #33. Pub!. by 
the Texas Game & Fish Commission. I954. 68 pp.; A List of Common 
and Scientific Names of Fishes from the United States & Canada. 
I 970. American Fisheries Society, Special Pub!. #6. I50 pp.; Key 
to the Estuarine & Marine Fishes of Texas. I972. Texas A&M Sea­
grant Publication. I 78 pp.; and Moore, R. and H. Hoese. Unpublished 
manuscript (also a key on the fishes of Texas). 
After the month of surveying, a meeting was held by all participants to critique the forms and discuss any suggestions for the future survey. 
As the forms were received, they were checked for errors, and then .sent to the Texas Water Development Board, Data Processing Division, for key­punching. The punched cards were used to generate a data file on magnetic tape at the UT Computation Center, and this file was used as input to a pro­gram which read the individual interview sheets, and produced a file of data which could be read by a generalized data management program, ENvIR (Environmental Information Retrieval) . 
IV-3 


TABLE IV-1 
CREEL CENSUS 
August 1973 

(1) location of interview, (2) location where fishing done (Biotope) (3) position 



A------------------------------------------------------------------­
(4) date of interview, (5) time of interview, (6) no. of hours fishing 

B------------------------------------------------------------------­
(7) species, number, weight, no. of hooks, bait 
c 
c 

c 

c 

c 

c 

c 

c 
(8) city of residence, (9) county, (10) state 
D 
(11) 	How many days per year do you fish in salt water in this area 

E------------------------------------------------------------------­
(12) 	How many days per year do you fish in fresh water 

F------------------------------------------------------------------­
(13) 	If both good fresh water and salt water fishing are available, which do you prefer 

Rank the following characteristics of this bay that most influenced your decision to come here: 
(14) facilities, (15) accessibility, (16) good fishing H 
----------------------------------------------------------------~ 
(17) 	present water conditions, (18) other 
!___________________________________________________________________ 
(19) 	comments, (20) coded by 
J----~------------------------------------------­
IV-4 

TABLE IV-2 CLIMATOLOGICAL DATA 
Month (2)date (3)time (4) location (S)wind direction (6)wind velocity (7)cloud cover (8)barometer reading (9) air temp (lO)water temp 
<I 
en 
Results 
The data from interviews have been evaluated only for research purposes and are presented as they relate to a preliminary feasibility study as a one month pilot project. We have shown the data evaluations for several parameters to illustrate how the information can be used and to correct our interview pro­cess. Some of the results are pertinent, however, sueh as the total number of fishermen, their home base, etc. We must emphasize _th_at no generalizatioris can b§__~ag§_ {rQ.!Il the total data at this time. 

The creel census data bank consisted of 1955 total interviews. During the 28 days of the survey there were 16, 225 fish caught weighing a total of 12, 206 pounds. Instead of counting the m1mber of fisherpersons, the number of hooks used by each were counted. During the month 423 7 hooks were fished for 6218 hours. Our best estimate indicates that this represents 20% of the total fisherpersons during the survey period. Of the 1955 total interviews 940 are from residents of the Corpus Christi Bay area, which includes Corpus Christi, Aransas Pass, Flour Bluff, Portland, and Ingleside. Residents from ~)ther areas are listed in Appendix E. Of the total persons surveyed 37% pre­ferred salt water, 7% preferred fresh water, 6% had no preference, and 50% did not answer the question. 
On S:iturday morning, August 11, by an aerial survey coordinated to surface data c:::ollected, 164 boats were counted with fishing activities noted. On Saturday afternoon 184 boats were counted, and on Wednesday morning, Au9ust 15, 14 7 boats were counted. The Saturday census takers interviewed 16 boatmen, all after 1200 hrs. This represents about 10% efficiency of fishing boats surveyed by land as compared to air. Wednesday 6 boatmen were inter­viewed, all after 1200 hours. An overall estimate of fishermen per boat for the aerial survey was 2. 5. The creel interviews showed 2. 4 hooks per boat on Saturday and 1. 7 hooks per boat on Wednesday. 
To determine why the fisherpersons came to Corpus Christi Bay or fished where they did, they were asked to rank several characteristics from one to five, relative to each other. The data are summarized in Table IV-3 as taken from the computer printout presented in Appendix F . 
IV-6 

TABLE N-3 REASONS FOR FISHING IN THE AREA WHERE INTERVIEWED 
Ranking 
Characteristics Total Response 1 2 4 4 & 5 
Facilities 1443 227 403 448 365 Access 1734 642 597 302 193 Good Fishing 1702 790 484 278 150 Water 1315 40 127 298 850 Other 1571 151 113 183 1124 
Good fishing and accessibility were ranked number 1 by the majority of the persons, while the facilities were ranked 3rd in relation to the other charac­teristics by 62% of the fisherpersons. The water conditions ranked the lowest. To see the other reasons why fisherpersons came to Corpus Christi Bay see Appendix F. 
Di strict Breakdown 
The district catches were compared to one another for the weight of fish caught, number of fish caught, hours spent fishing, and number of interviews for each district. The data are shown in Table N-4. 
TABLE IV-4 FISH CATCH DATA BY DISTRICT 
District Weight Number Hours Count 
1 1608.1 1437 530 200 
2 7404 6997 3051 848 
3 4146.1 4120 1270 445 
4 1576.5 2480 1253 458 

District 2 showed the highest overall totals perhaps because it was the largest district and had the highest fishing pressure. District 1 had some of the lowest returns because it was surveyed only 5 days a week with no night censusing and was started 4 days later than the other survey districts. Dis­tricts 3 and 4 were surveyed as originally planned. 
IV-7 

Daily Breakdown 
To see if there was any particular day that received either more or less fishing pressure than other days and to test the method of daily surveys, the same comparison as above was made. These data are in Table IV-5. 
TABLE N-5 
FISH CATCH DATA BY DAY OF WEEK 

S'.lnd~ Monday Tuesday Wednesday Thursday Friday Saturday Number 3682.0 1847.0 1373 2056 2507 1956 3142 Weight 2969.2 1435.8 947.9 1353.3 1603.2 1321.3 2242.4 

997.0 605 614 724 506 687 1001 Surveys 335 217 250 302 195 251 405 
The number of fish caught and the poundage were the greatest on Sundays while the greatest number of hours spent fishing and the surveys were on Saturdays. 
Tuesdays were lowest in poundage and fish caught while Mondays were lowest in hours spent fishing and number of surveys. 
Time Breakdown The time of day is believed to have some effect on the success of fishing. The results are shown in Table IV-6. TABLE N-6 FISH CATCH DATA BY TIME OF DAY Time Weight Number Hours Count 
0600-1200 922.8 977 528 185 morning 1700-2000 4972.5 4448 1628 547 evening 0600-2000 13419.1 12681 5120 1547 day 2000-0600 1189. 7 2204 893 341 night 
The morning hours were less successful than the evening in all categories. The fact that most fishermen go to work on week day mornings and usually fish after work has some effect on these returns. The day returns were much higher 
IV-8 

than the night, even though there were a great number of people fishing at night, as was shown by the information on two nights out of the week. 
Biotope Distribution 
To compare tffi biotopes as outlined by Oppenheimer and Gordon, 1972, the same type of breakdown was conducted in order to see if any particular environ­ment was preferred by the fishermen. The data are in Table IV-7. 
The shallow bay biotope was the highest in all categories, while the hypersaline was the next most successful. The hypersaline biotope is used for the Baffin Bay area or any unrecognized fishing spot in the upper Laguna Madre. 
The jetty biotope was eliminated as such for most people fishing off the jetty were actually fishing in the channel or shallow Gulf. Very few persons were fishing directly amid the submerged rocks of the jetty. 
Fifteen Major Species and Baits 
Table IV-8 shows results concerning the success of the various baits used to catch the different species of fish. 
Dead shrimp were used by more fishermen, produced mediocre catches per effort, and produced mediocre sized fishes. Gold spoons were highly success­ful in catching King mackerel, redfish, and speckled trout. King mackerel produced the highest catch per effort of all the species. It becomes obvious from only one return for the combination of cut hard head and croker, that this combination is not used for some reason. For a comparison between live baits, dead baits, and artificial baits see Table N-9. 
Natural baits were more successful than artificial baits especially for black drum, sheepshead, and blue crabs. On a total weight basis, dead , baited hooks caught more fish than live bait except speckled trout and sheeps­head. However, live bait caught more fish per unit effort than dead bait. 
Sportfishing vs. Commercial Fishing 
To see how the creel census poundage information compared with the com­mercial poundage, the Texas Parks and Wildlife Commission fishery statistics for Corpus Christi Bay, Nueces Bay, and upper Laguna Madre during August 1973 and 1972 were used (see Table N-10). 
IV-9 

TABLE N-7 
BIOTOPE DISTRIBUTION 

Biotopes Weight Number Hours Count 
Open Bay & Oil Rigs 144.6 112 24 6 Open Bay 365.8 284 77 20 128 64
Bulkhead 80 201 Channel 1077.2 1348 475 154 189 72
Shallow Pass 409.3 707 Grass Flat 1476.4 915 368 l15 Hypersaline 2123.7 1552 669 128 
5 2
Gulf 10 1 67 12
Inshore Gulf 1346.2 125 1086 323
Shallow Gulf 2045.1 2276 Surf 947.7 1510 476 180 
Jetty 0 0 1 1 Oil Rig (off shore) 281 19 24 4 Oyster Reef 391.1 777 364 144 Pier 186.6 240 75 21 Shallow Bay 3376.1 4651 1983 695 Shallow Channel 90.2 50 10 5 
IV-10 

TABLE N-8 
CREEL DATA ANALYSIS 


Total Fish  
Number  Weight  Hook  Catch/  Average fish  
Species  Hours  Hooks  Caught  (lbs)  Hours  Effort  Bait  Size (lbs)  
Spotted Seatrout  1735  675  3017  3663.9  3358  1.091  all baits  1.214  
II  II  221  98  149  150  425  . 353  dead shrimp  1.016  
II  II  1046  258  2205  2758.7  1487  1. 855  live shrimp  1.251  
II  II  17'  6  40  37  23  1.000  a 11 color worms  .475  
II  II  54  32  21  40.8  161  .253  cut bait  1.943  
II  II  10  3  4  17  10  1.700  cut croaker  4.250  
II  II  9  3  2  8  27  .296  cut bait & eel  4.000  
II  II  68  13  109  89  116  .767  cut pinfish  .817  
II  II  12  1  45  36  12  3.000  flies  .800  
II  II  4  66  4  5.2  264  .020  dead shrimp & cut croaker  1.300  
<I .......  II II  II II  17 25  66 4  13 44  16.5 52  282 19  .059 2.737  dead shrimp & cut bait gold & silver spoons  1.269 1.182  
.......  II  II  55  11  120  198  63  3.143  gold spoon  1. 650  
II  II  9  2  12  16  9  1.778  lure  1.333  
II  II  6  3  25  25  18  1. 389  humpy lures  1.000  
II  II  3  1  1  .8  3  .267  live pinfish  .800  
II  II  5  3  14  7  15  .467  live shrimp and red worms  .500  
II  II  2  1  1  3  2  1.500  live threadfin  3.000  
II  II  1  3  1  1. 5  3  .500  orange and yellow lure  1.500  
II  II  22  35  69  90.5  69  1.312  plastic worm  1. 312  
II  II  3  2  1  1  6  .167  red and orange worms  1.000  
II  II  15  7  30  30  34  .882  red worm  1.000  
II  II  13  3  4  6.5  13  .500  silver spoon  1. 625  
II  II  4  1  2  1  4  .250  spinners  .500  
II  II  15  6  1  4  90  .044  squid  4.000  
II  II  23  6  29  29  25  1.160  white worm  1.000  
II  II  12  11  30  29.5  42  .702  yellow worm  .983  

TABLE IV-8 (CONTINUED) 

Total Fish  
Number  Weight  Hook  Catch/  Average fish  
Species  Hours  Hooks  Caught  (lbs)  Hours  Effort  Bait  __ Size (lbs)  
Spotted Seatrout  6  3  7  7  18  .389  yellow and red lure  1.000  
II  II  3  1  10  10  3  3.333  live bait  1.000  
II  II  4  3  32  30.5  117  .261  jigs/speck rig  .953  
fl  II  5  4  IO  11  10  1.100  mirror lure  1 .100  
Sand Seatrout  1018  690  2676  2030.8  2213  .918  all baits  .759  
II  II  11  31  6  3.6  98  .037  all color worms  .6  
II  II  182  119  159  162.1  565  .287  cut bait  1.019  
II  II  51  167  713  571. 3  647  .883  cut bait and dead shrimp  .801  
II  II  20  7  28  27  24  1.125  cut croaker  .964  
II  II  114  53  206  165.6  177  .936  cut croaker and pinfish  .804  
II  II  623  402  647  508.9  1435  .355  dead shrimp  .787  
<I .......  II II  II II  14 4  2 66  42 500  33 250  14 264  2.357 .947  ribbonfish dead shrimp and cut croaker  .786 . 5  
N  II  II  2  2  2  .4  4  . 1  dead shrimp and cut perch  . 2  
II  II  10  2  13  12.1  10  1. 21  spoon  .930  
II  II  38  4  69  73.5  38  1.934  jig  1.065  
II  II  17  8  39  17.9  50  .358  red worm  .459  
II  II  91  55  173  133.3  194  .687  live shrimp  .770  
II  II  5  3  1  .5  15  .033  live shrimp and red worm  . 5  
II  II  6  1  60  48  6  8  plastic worm  .8  
II  II  2  3  12  seine  4  
II  II  21  23  17  13.8  56  .246  squid  .812  
II  II  2  2  2  1  4  .25  shrimp and cut ribbonfi sh  .5  
II  II  20  12  12  9.1  70  .13  speck rig  .758  
Atlantic Croaker  1283  1011  2186  1069. 8  3107  . 344  all baits  .489  
II  II  249  205  285  110. 5  861  .128  cut bait  .389  
II  II  66  132  391  273.2  465  .588  cut bait and dead shrimp  .699  
II  II  6  2  12  3.6  12  .3  eel  . 3  
II  II  29  15  50  23.3  48  .485  cut pinfish  .466  

TABLE IV-8 (CONTINUED) 

Total Fish  
Number  Weight  Hook  Catch/  Average Fish  
Species  Hours  Hooks  Cauqht  {lbs)  Hours  Effort  Bait  Size {lbs)  
Atlantic Croaker  95  32  106  86.9  149  .583  live shrimp  .82  
II  II  1  1  1  . 2  1  . 2  cut mullet  .2  
II  II  21  5  57  28.5  388  . 073  cut croaker  .5  
II  II  972  638  1213  516  2365  .218  dead shrimp  .425  
II  II  11  7  11  4.3  42  .102  ribbonfish  .39  
II  11  36  36  51  15.9  118  .135  squid  .312  
II  11  4  1  2  .8  4  .2  dead shrimp and squid  .4  
II  11  13  32  7  7.2  99  . 073  plastic worm and all color worm  1.02  
Black Drum  77  62  46  75.4  176  .428  all baits  1.639  
II  11  5  1  1  3  5  .6  cut pinfish  3  
11  II  74  47  40  62.9  168  .374  dead shrimp  1.573  
II  II  14  6  3  8  54  .148  live shrimp  2.667  
II  II  8  46  2  1. 5  184  .008  dead shrimp and cut bait  .75  
Gafftopsail Catfish  609  503  458  637.3  2445  .261  all baits  1. 391  
1-t <I I-'  II II  II II  97 35  43 148  54 30  172.8 58.1  212 594  .815 .978  cut bait cut bait and dead shrimp  3.200 1.937  
w  II  II  13  7  5  4.3  22  .195  cut croaker  .86  
II  II  7  2  2  10  14  .714  cut pinfish  5  
II  II  356  193  321  265  1102  .240  dead shrimp  .826  
II  II  4  66  5  15  264  . 05 7  dead shrimp and cut croaker  3  
11  II  24  18  10  38.3  61  .628  eel  3.83  
II  11  4  2  1  2  8  .25  eel and squid  2  
II  11  1  1  1  1  1  1  gold spoon  1  
II  11  5  1  3  1.5  5  .3  jig  .5  
11  II  12  11  9  10.8  31  .348  live shrimp  1.200  
II  11  5  1  1  3  5  .6  plastic worm  3  
11  II  46  12  17  54.5  132  .413  squid  3.206  
II  11  3  2  1  5  6  .833  squid and cut mullet  5  
Southern Kingfish  176.  136  392  266.8  483  .552  all baits  .681  
II  11  19  15  10  3.3  57  .058  cut bait  .33  
11  II  9  3  5  2.0  27  .074  cut bait and eel  .4  
11  11  6  2  3  3.0  6  .5  cut pinfish  1  

TABLE IV-8 (CONTINUED) 

Total Fish  
Number  Weight  Hook  Catch/  Average Fi sh  
Species  Hours  Hooks  Caught  (lbs)  Hours  Effort  Bait  Size (lbs)  
S·::mthern Kingfish  153  84  147  92.9  376  .247  dead shrimp  .632  
II  II  4  66  100  100  264  .379  dead shrimp and cut croaker  1  
II  II  14  106  106  54.7  408  .134  cut croaker and cut bait  .516  
II  II  4  2  3  .9  8  .113  live shrimp  . 3  
II  II  9  4  2  1.  36  . 028  ribbonfish  .5  
II  II  2  2  2  1.  4  .25  spoon  . 5  
II  II  8  14  15  8.3  42  .198  squid  .553  
Gulf Kingfish  580  375  642  480.6  1517  .317  all baits  .749  
II  II  39  12  16  11.1  129  . 086  squid  .694  
II  II  1  2  1  .1  2  . 05  crab and squid  .1  
II  II  108  68  94  96.6  321  .301  cut bait  1.028  
II  II  6  10  15  18.9  20  .945  dead shrimp and cut bait  1.260  
II  II  354  263  469  325.6  913  .338  dead shrimp  .653  
II  II  2  1  1  .5  2  .25  dead shrimp and cut croaker  . 5  
1-1 < I  II II  II "  14 1  6 2  11 1  8.9 . 3  22 2  .405 .15  live shrimp speck rig  .809 .300  
I-' ~  II  II  47  8  18  14.5  90  .161  cut pinfish  .81  
Pinfish  1118  875  2133  557.5  3228  .173  all baits  .261  
II  2  20  1  . 1  40  .003  worms  . 1  
II  1  2  5  .5  2  .25  crab and squid  . 1  
II  148  128  259  67.5  386  .175  cut bait  .261  
II  57  116  200  48.0  459  .105  cut bait and dead shrimp  .24  
II  12  4  45  15.0  19  .789  cut pinfish  .333  
II  3  2  3  1. 2  6  .2  cut pinfish and ribbonfi sh  .4  
II  772  511  1502  400.7  1920  .209  dead shrimp  .267  
II  6  2  6  2.4  12  • 2  eel  .4  
II  2  1  1  . 2  2  . 1  dead shrimp and cut croaker  .2  
II  2  2  10  2.  4  .5  dead shrimp and cut perch  .2  
II  32  .  23  47  9.3  66  .141  live shrimp  .198  
II  1  1  1  .3  1  .3  orange worm  .3  
II  15  6  5  1.0  90  .011  ribbonfish  . 2  
II  4  2  4  .6  4  .15  silver spoon  .15  

-
TABLE IV-8 (CONTINUED) 
Total Fish  
Number  Weight  Hook  Catch/  Average Fish  
Species  Hours  Hooks  Caught  (lbs)  Hours  Effort  Bait  Size (lbs)  
Pinfish  12  6  8  1.6  24  .067  speck rig  . 2  
II  4  1  2  .2  4  .05  spinners  . 1  
II  57  58  64  12.9  218  .059  squid  .202  
Red Drum  855  367  844  2126.2  1732  1. 228  all baits  2.519  
II  II  30  58  35  83.4  173  .482  all color worms  2.383  
II  II  29  18  9  14.8  97  .153  cut bait  1.644  
II  II  24  14  10  7.8  64  .122  cut bait and dead shrimp  .78  
II  II  22  9  7  20  26  .769  cut pinfish  2.857  
II  II  315  160  331  750.3  729  1.029  dead shrimp  2.267  
II  II  1  1  1  2  1  2  gig  2  
II  II  33  5  31  112  33  3.613  spoons  3.393  
II  II  24  1  1  1  24  .041  jig  1  
II  II  25  9  45  125  43  2.907  gold spoon  2.778  
<I 1-1 (11  II II II  II II II  2 4 327  5 7 72  3 8 296  9 5.3 747.1  10 14 473  .9 .379 1. 579  live mullet squid live shrimp  3 .663 2.524  
II  II  4  3  13  32.5  12  2.708  mullet  2.5  
II  II  9  3  1  4.0  27  .148  live pinfish  4  
II  II  2  1  1  4.0  2  2  live threadfin  4  
II  II  4  1  2  8  4  2  silver spoon  4  
Sea Catfish  1208  892  1905  811. 5  2877  .282  all baits  .426  
II  II  8  1  2  1.6  8  . 2  all color worms  .8  
II  II  225  137  197  76.8  744  .103  cut bait  .39  
II  II  1  2  10  4.0  2  2  cut bait and croaker  .4  
II  II  67  85  107  38.0  322  1.18  dead shrimp and cut croaker  .355  
II  II  37  18  67  39.8  53  .751  cut pinfish  .594  
II  II  9  3  4  1.6  27  . 059  cut bait and eel  .4  
II  II  971  635  1220  521. 2  2456  .212  dead shrimp  .424  
II  II  1  2  2  1.0  2  . 5  eel  .5  
II  II  3  2  3  .6  6  . 1  cut ribbonfish  . 2  
II  II  4  66  50  25.0  264  .095  dead shrimp and cut croaker  .5  
II  II  4  1  4  1. 2  4  .3  dead shrimp and squid  . 3  
II  II  2  2  5  1.0  4  .25  dead shrimp and cut perch  . 2  

Total Fish Number Weight Hook Catch/ Average Fi sh S2ecies Hours Hooks Caught {lbs} Hours Effort Bait Size {lbs} 
TABLE N-8 (CONTINUED) 

Sea Catfish  4  2  2  . 6  8  .075  eel and squid  . 3  
II  II  5  1  15  7.5  5  1. 5  jig  . 5  
II  II  2  1  1  .3  4  .15  live fish  .3  
II  II  105  77  141  50.8  359  .142  squid  .360  
II  II  86  43  69  41.1  158  .260  live squid  .596  
II  II  2  2  1  .3  4  .075  speck rig  . 3  
II  II  4  1  1  . 2  4  .OS  yellow worm and cut bait  . 2  
Sheepshead  45  40  29  46.7  106  .441  all baits  1. 610  
II  1  1  2  2  1  2  crabs  1  
II  15  14  13  12.8  34  .376  dead shrimp  .985  
II  29  25  14  31. 9  71  .449  live shrimp  2.279  
Southern Flounder  135  58  74  102. 7  246  .417  all baits  1. 388  
II  II  14  2  5  17.0  14  1. 214  plastic worm and all color worm  3.4  
<I ...... en  II II II  II II II  12 22 10  3 6 8  8 2 2  13.4 1. 5 .6  12 68 36  1.170 .022 .017  gig cut bait cut bait and dead shrimp  1. 675 .75 .300  
II  II  58  29  22  39.3  126  .312  dead shrimp  1. 790  
II  II  51  15  22  26.7  79  .338  live shrimp  1. 213  
II  II  5  5  1  .6  25  .024  mullet  .600  
II  II  8  1  12  136  8  .450  lure  .300  
Spanish Mackerel  57  29  40  57.7  115  .502  all baits  1.443  
II  II  3  4  3  8.0  5  1. 600  cut bait  2.667  
II  II  9  3  1  1.0  27  .037  cut bait and lure  1.000  
II  II  12  10  8  31.0  29  1.069  ribbonfish  3.875  
II  II  14  5  21  13.5  23  .587  jig  .643  
II  II  9  2  1  0.4  18  .222  lure  .400  
II  II  3  1  1  1.0  27  .333  spoon  1.000  
II  II  2  1  1  0.5  2  .250  squid  .500  
II  II  5  3  4  2.3  8  .288  dead shrimp  .575  

TABLE N-8 (CONTINUED) 


Total Fish  
Number Weight  Hook  Catch/  Average Fish  
Species  Hours  Hooks  Cauqht  {lbs)  Hours  Effort  Bait  Size (lbs)  
King Mackerel  52  35  146  2150.0  224  9.513  all baits  14.73  
II  II  9  3  9  180.0  27  6.67  cut bait and lure  20.000  
II  II  7  1  4  80.0  7  11. 428  gold spoon  20.000  
II  II  6  3  8  80.0  18  4.444  red and white feather jig  10. 000  
II  II  4  2  20  260.0  8  32.5  revel lure  13.000  
II  II  59  26  105  1550.0  164  9.451  ribbonfish  14.762  
Blue Crab  306  369  1159  532.6  917  .581  all baits  .460  
II  II  98  134  539  264.1  287  .920  chicken  .490  
II  II  2  12  4.8  chicken and hardhead  .4  
II  II  62  57  118  44.2  166  .266  cut bait  .375  
II  II  9  3  6  1.8  27  .067  cut bait and eel  . 3  
II  II  3  14  20  12.8  14  .914  cut hardhead  .64  
~ I  II  II  1  5  4  .8  5  .16  cut hardhead and croaker  . 2  
....... '-J  II  II  11  19  89  41.8  25  1. 672  cut mullet  .470  
II  II  3  7  14  5.8  9  .644  cut mullet and pinfish  .414  
II  II  120  85  154  55.6  429  .130  dead shrimp  .361  
II  II  14  26  87  42.1  86  .490  fish head  .484  
II  II  8  10  7  2.9  22  .132  live shrimp  .414  
II  II  4  1  1  .4  4  . 1  pork rind  .4  
II  II  15  6  3  1.5  90  .017  ribbonfish  .5  
II  II  2  2  1  .3  4  .075  shrimp and cut ribbonfi sh  .3  
II  II  6  9  52  26  18  1.444  soupbone  . 5  
II  II  8  10  5  1.8  26  .069  squid  .36  
II  II  5  9  55  27.5  25  1.1  stew meat  . 5  

TABLE IV-9 
COMPARISON OF DIFFERENT BAITS ON SPORTFISH CATCH 

Live Bait  Dead Bait  J:Jatural B~!l  Artificial Bait  Total  Total  Total  Total  
Catch/  Average  Catch/  Average  Catch/  Average  C atch/  Average  Weight  Weight  Weight  Weight  
Effort  Size  Effort  Size  Effort  Size  Effort  Size  Live Bait  Dead Bait  Natural  Artificial  
Spotted Seatrout  1. 8SS  1. 2S 1  .240  1. 091  1. 081  1 . 231  1.240  1 . 201  2772.S  330.S9  3103  601. s  
Sand Seatrout  .687  . 770  .S43  .748  .SSl  . 749  .604  .82S  133.3  1732.1  186S.4  164.2  
Atlantic Croaker  .S83  .820  .227  .470  .239  .487  .073  1.020  86.9  976.3  1063.2  7.2  
Black Drum  .148  2.667  .189  l.S67  .183  1.639  --­ --­ 8  67.4  7S.4  0  
Gafftopsail Catfish  .348  1.200  .2S9  1 .041  . 260  1. 397  .soo  1.100  10.8  62S  63S.8  s. s  
Southern Kingfish  .113  .300  .218  .684  .217  .681  .2SO  .soo  .9  26S.2  266.1  
<I  Gulf Kingfish Pinfish  .40S .141  .809 .198  .304 .177  . 731 .263  .306 .176  .732 .261  .lSO .038  .300 .17S  8.9 9.3  449.3 SSl.4  4S8.2 S60.7  . 3 2.8  
........  
00  Red Drum  1.492  2.S39  .882  2.198  1.097  2.349  1 .189  2.889  764.1  830.7  1S94.8  329.4  
Sea Catfish  .2S9  .S91  .179  .420  .182  . 427  .4S7  .sos  41. 4  761.6  803.0  9.6  
Sheepshead  .471  2 .119  .376  .98S  .441  1. 610  --­ --­ 33.9  12.8  46.7  0  
Southern Flounder  .338  1. 213  .165  l.SS6  .206  1.402  2.409  3.118  26.7  42  68.7  S3  
Spanish Mackerel  --­ --­ .9SO  2.613  .9SO  2.613  .339  .648  --­ 41. 8  41. 8  14.9  
King Mackerel  --­ --­ 9.4Sl  14.762  9.4Sl  14.762  12. 727  13.12S  --­ lSSO.O  lSSO.O  420.0  
Blue Crab  .132  .414  .436  .4S7  . 431  .4S6  --­ --­ 2.9  S29.7  S32.6  0  

TABLE IV-10 
COMPARISON OF SPORT CATCH AND 
COMMERCIAL FISH CATCH BY WEIGHT 

Aug. 1973 Aug. 1973 Aug. 1972 Creel Census Comm. Fishing Stat.* Comm. Fishing Stat.* 
Croaker 1069.8 7846 0 Redfish 2126.2 33970 21273 Flounder 102.7 513 633 Trout 5694.7 29054 20373 Crabs 532.6 0 963 Black Drum 75.4 12833 41504 Gafftop 637.3 860 0 Sheepshead 46.7 769 2405 Pompano 16.2 3 154 
* TPWD data on commercial fish catch 
In some cases the creel census poundage is very close to the commercial poundage, so if the total estimated sport fishing population {present data x5) had been surveyed, the sport fishing poundage would have exceeded the com­mercial poundage. 
It has been suggested that the reason that no crabs were reported during August 1973 in the commercial fishery report is that the crabs are shipped to Palacios for processing and missed being counted. 
Recommendations 
The pilot creel census of August 1973 indicated that only minor changes were necessary to fulfill the two main purposes of the Summer 1974 sum·mer study to supply sportfishing statistics on the local area. Sampling will be extended to a 24 hour basis to cover night and afternoon fishing effort. 
Since August, both Tables IV-1 and IV-2 have been revised (into Table IV-11) to add more needed information and to simplify the filling out procedure. The ranking question of the original survey form was dropped be­cause of the confusion of the fishermen in answering the question. Also, a complete information guide has been written in order to assist the census takers while they are in the field. District 2 will be split in future surveys, so instead of 4 survey districts there will be 5. 
IV-19 
TABLE IV-11 TWDB & UT SPORTFISHING CREEL CENSUS 
1. Site .. ... ........ .. ... ... .......... .. .. ..... ...... ,A 

2. 
Date ... .............. ............................ . 


3. 
Time (military) ...... . . . .. . .... . . ... .... .... . . ..... . 

4. 
Interviewer .................................... ... . 


5. 
Biotope ..... ..... ...... .......... ...... .. ..... .. . . 


6. 
Position .... . . . .. . ..... . ... . .... .. .... . ..... . ..... . 

7. 
Hours fishing .............. . .... . ..... . ...... .. . . .. . 


Number Number Average Number 

8. 
Species 	Caught Kept Weight Hooks Batt 


--4--------------------.J..­'-­ -l  






I I 
! 
I 
i 
(Check here if previously interviewed this trip and stop _____ 
9. Total duration of trip in hours . ............. ........ ... · l.__c_...___________________ ·

.._I ~
1
· 
10. City of residence ......... .............. ............ . 

I 
I !
11. County (or State if out-of·State visitor) .. ...... ....... ... . 


i 
1
12. Type of outing; 1 =Family, 2 =Other ............. ... .. . 

. I I
13. Number in party .. ..................... ........ .... . 

I I 
I 
I
14. Number under 18 .. .... .... . . . .. ... .. .. ... ..... . .. . . 
i 
I I 15. Total expected cost of trip including food , lodging, and gas ... 
I 
16. 
How many days per year do you fish in salt water in this area .. 

17. 
How many days per year do you fish in fresh water . . . ... .. . 

18. 
Preference: 1 =Salt, 2 =Fresh ....... . . . .... . .... . ... . . 


19. 	
Climatological Data: Wind direction ........ . ... .. ... . . IK Wind velocity ........ . . . . .. ... .. . Cloud cover . . ... .... .... ..... .. . Barometer reading . . ..... .. . . .. . . . Air temperature .............. .. . . Water temperature .. . ... .. . .. .. .. . Total number fishing ... .... .. . .. . . Total number interviewed .. .. . .... . . Tidal flow ... ... ....... .. .. .. ... . 


B  .  '  
B  '  .  
B  .  .  .  
B  .  .  
B  .  .  
B  .  .  '  
B  ------1  

IV-20
TWDBP-WR-29 
There will be five barometer stations established, one in each district in order to achieve better climatological results. The climatological data of August 1973 has not been processed and will be discussed at a later date. 
The future creel census will be run in the same manner as previously dis­cussed except for the above changes (see Appendix G). 
At present, January -June 19 7 4, approximately fifty students from five area high schools are participating in the creel census as volunteers which will provide some continuity. The summer program will be conducted during June, July and August of 1974. 
From the effort evaluation we have concluded that the August data repre­sent 2 0 % of the total effort in the system. This indication will be used to determine the creel census efficiency during the summer of 1974. 
N-21 


CHAPTER V 
PRODUCTIVITY AND NUTRIENT BALANCES 

Productivity and nutrient balances provide a valuable baseline for discus­sion of estuarine management. Analysis of productivity and nutrient availability reveals information on the energy flow through the estuary. Nutrients are re­garded as governing agents because of their nature in limiting plant growth in the aqueous environment. 
The analysis of productivity will be accomplished by the examination of known values for production, consumption, import and export of carbon in Corpus Christi Bay. The next step will be to evaluate the impact of each of these processes and how they might be managed as estuarine gains and losses. While a large amount of information is obviously available, the treatment described here should be considered as a preliminary step in the development of an understanding of the carbon cycle in Texas estuaries. For example there is very little information on the magnitude of export of fish and crustaceans from the bay system during normal migration patterns and the loss through predation before they and the larval forms resulting from offshore spawning return to the bay system. The discussion indicates the need for new informa­tion to provide a more complete analysis of the carbon dynamics in the Bay. The results of our analyses should be considered a minimum basis for decision making; as such, they allow a few pertinent conclusions to be made. 
The discussion of nutrient balance in the estuarine system will emphasize nitrogen. The widely recognized role of nitrogen as a limiting nutrient in estuaries (Ryther and Dunstan, 1971; Smith, 1973) was the reason for its selection in this discussion. Local information is compared with recent recom­mendations concerning nitrogen for water quality management in Chesapeake Bay (Clark, et al., 1973). An analysis of nitrogen to phosphorus ratios of all available data in the Texas estuaries indicate that phosphorus is not limiting. 
Carbon in Texas Estuaries 
Carbon Sources 
Considerable information is available concerning gross productivity in the Texas coastal bay system. Some results have been published as carbon and/or oxygen produced per unit of area or volume. Others have been published as standing crop of chlorophyll a and the attendant assimilation quotient. In order to standardize and tabulate the existing data, the following assumptions were made: 1) one gram of carbon fixed by plants during photosynthesis results in the release of three grams of oxygen gas (Finenko and Zaika, 1970); 2) the 
V-1 

amount of carbon assimilated per gram of chlorophyll£ per hour is 2 grams (Odum, et al. , 195 8); 3) the ratio of moles of oxygen gas produced per mole of carbon assimilated, that is, the photosynthetic quotient, is one (Cox, 1967); and 4) productivity values reported on a volume basis were corrected to an area basis using the average bay system depths from Table V-1. 
A comparison was made of planktonic gross photosynthetic production for Corpus Christi, Galveston and San Antonio Bays. The results of this compari­son are shown in Table V-2. These three bays have been extensively studied by State agencies and others and represent the range of size, peripheral develop­ment and freshwater inflow values found in the Texas Coastal Zone. As can be seen from Table V-2, gross productivity per unit area in Galveston Bay is double that of Corpus Christi Bay and nearly six times that of San Antonio Bay. Total gross production of Galveston Bay is seven times that of Corpus Christi Bay and nearly fourteen times that of San Antonio Bay. 
Biotope-sp:; cific productivities calculated for Corpus Christi Bay are shown in Table V-3. While salt marsh and grass flat biotopes have the highest gross productivities per unit area, the bay planktonic biotope contributes the largest fraction of the total 9ross production. The gross productivity of Corpus Christi Bay totals 1. 21 x 10 pounds of carbon per year or 3. 3 x 106 pounds per day. 
The metabolic balance in Corpus Christi Bay is generally considered auto­trophic. The ratio of production (P) to respiration (R) is approximately one (Odum and Hoskin, 1958; Odum and Wilson, 1962), especially in regard to the bay planktonic biotope. A partial verification of this may be found in comparing the gross productivity with the amount of carbon introduced by man as organic wastes or entering the bay as runoff. A P/R ratio of one would generally sug­gest a low external supply of carbon. However, one must be careful to inter­pret P/R ratios to a total system. For example, a P /R value of 1. O1 would indi­cate a small amount of production or roughly 1% net gain in carbon. The 1 % net gain applied to the total or gross productivity of 3 .1 x 106 pounds of carbon fixed per day in Corpus Christi Bay would yield a net increase of 3 x 104 pounds or 1 x I 0 7 pounds per year. If the P/R ratio is 1. l, the net gain in carbon per year would be 1 x 108 pounds. 
Waste discharges account for 5. 04 x 105 pounds of carbon per year (as estimated by another project task force). This is 0. 04% of the annual gross production. For comparison, Galveston Bay, with much more peripheral development, receives an amount equivalent to 2 .1% of annual gross productivity from waste discharges (Armstrong and Hinson, 1973). A computation of organic matter received in Corpus Christi Bay from runoff can be made using average Nueces River flow information (Davis, 1973) and an organic loading factor computed by another project task force. The organic matter contributed by runoff accordin;J to these calculations amounts to 6. 55 x 106 pounds of carbon 
V-2 

TABLE V-1 
PHYSICAL FACTORS FOR SELECTED TEXAS COAST BAYS 

Bay System Area Depth Volume Freshwater "Flushing"
103-­
106 Inflows + rainfall Time (d)
2 
acres kmft. M Acre-ft 106 acre-ft/yr (c) Years 
Corpus Christi -134 540 7.6 2.3 1.02 1.03 0. 99. Nueces (a) 
Copano-Aransas 140 570 5.9 1.8 0.83 0.59 1.4 
(a) 
San Antonio 143 580 4.0 1.3 0.572 1. 9 0.30
< 
I (a)
c.v 
Lavaca-Matagorda 238 960 5.9 1.8 1.40 1.8 0.78 
(a) 
Galveston (b) 333 l,34 7 8.0 2.4 2.664 11. 6 0.23 
Notes: 
a -Davis , 197 3 . 
b -Espey et al., 1971. 
c -Texas Water Plan, TWDB, 1968. 
d -This rate is computed by dividing volume by inflow; the actual 

flushing time would be much less due to lunar and wind-driven 
tidal exchange. Use of this figure is for relative comparison 
purposes only. 

TABLE V-2 
COMPARISON OF GROSS PLANKTONIC PRODUCTION FOR 
SAN ANTONIO, CORPUS CHRISTI AND GALVESTON BAYS 

Bay grams C Qounds C ]sg_Q QOunds C m2 day acre day year year 
1 8 8
San Antonio 1.0 8.93 2.12xl0 4 . 66xl0 
2 8 8
Corpus Christi 2.52 22.20 3.89xl0 8.55xl0 
3 9 9
Galveston 5.87 52.46 2. 90 x 10 6.37 x 10 
1 
Davis, 1971; Jack Nelson, Texas Water Development Board, personal 
communication. 
2 
Davis, 1971; Odum and Odum, 1959; Odum, McConnell and Abbott, 1958; Odum, et al., 1963; Odum and Wilson, 1962; Hellier, 1962; Odum, H. T., 1967. 
3 
Armstrong and Hinson, 1973; Espey, et al., 1971; Corliss and Trent, 1971. 
V-4 

TABLE V-3 GROSS PRODUCTION BY BIOTOPE FOR CORPUS CHRISTI BAY  
Biotope  Mean Productivity amQLm lbCLA2 day day  Samples  Min. gmCLm2 day  Max. gmCLm2 day  S.D. gmCLm2 day  Acres  lbC/yr.  KgC/yr.  
Bay Planktonic1  2.51  22.3  30  0.09  8.87  2.53  105 I 456  8.55 x  108  3. 89 x  108  
Grassflat2  3.83  34.2  11  0.8  11.4  3.1  18,894  2.36xl08  1.07 x  108  
< I (Jl  Saltmarsh3 Intertidal Flats4 TOTAL  3.70 0.90  33.0 8.03  6 5  0.31 0.41  7.44 2.35  3.0 0.82  7,579 8,980  9.13xl07 2. 63 x 10 7  4. 15 x 107 1. 19 x 10 7 5.49 x 108  
1 2 3 4  Davis, 1973; Odum, E.P., 1959; Odum, McConnell & Abbott, 1958; Odum et al., 1963; Odum & Wilson, 1962; Hellier, 1962; Odum, H. T., 1967. Odum, Burkholder & Rivero, 1959; Hellier, 1962; Odum, H.T., 1967; Copeland, 1965; Waite, 1972; Pomeroy, 1960; Odum & Hoskin, 1958; Odum, H.T., 1963; McMahan, 1968; Odum, McConnell & Abbott, 1958. Odum, McConnell & Abbott, 1958; Corliss & Trent, 1971; Williams & Murdoch, 1966; Keefe, 1972; Smalley, 1959. Copeland, 1965; Burkholder, Repak & Sibert, 1965; Pamatmat, 1968; Gr¢'ntved, 1960; Pomeroy, 1959.  

per year or O. 54% of the annual gross productivity. This compares closely with the value of O. 4% contributed by runoff for Galveston Bay (Armstrong and Hinson, 1973). These data confirm that imported organic carbon is a minor contribution to the carbon budget of both Corpus Christi and Galveston Bays, although the organic waste loading is much smaller in Corpus Christi Bay. 
Carbon Sinks 
In addition to the processes that bring carbon into the bay, there are those which remove it. One of the most easily quantified forms of carbon removal from the bay system is the commercial catch of fish, shrimp, crabs, and oysters, as these data are reported in the catch statistics published by the National Marine Fisheries Service. Table V-4 shows the catch for several bays along the Texas coast for ten recent years. There are several points of interest in these data. There are large fluctuations in catch from year to year. Also, the catch of shrimp relative to fish is much larger in Galveston and San Antonio Bays. 
The rate of carbon removal for Corpus Christi Bay can be determined by using the ten year average catch. This catch represents 5. 83 x 104 pounds of carbon per year, or 0. 048% of the annual productivity. 
There are currently two estimates of harvest by sportfishing. The first is based on interpretation of a pilot creel census conducted by this task force in Ai1gust, 1973, as described in Chapter IV. An estimated 12, 206 pounds of fish were caught by sportfishermen who were interviewed during the 28-day survey. This is believed to represent 20% of the total sportfishing catch of Corpus Christi Bay during the surveyferiod. Direct extrapolation to one year gives an annual harvest of 7 .93 x 10 pounds of fish, or about 575% of the commercial harvest. However, comparison of the same sportfishing data with the August commercial fish catch gives the ratio of 61, 030 pounds of sports fin fish to 85,848 pounds of commercial fin fish, or only 71%. An earlier esti­mate of sports catch made for the Texas Parks and Wildlife Department (Belden Assoc., 1960) indicates that the sports catch of red drum, black drum, speckled trout and flounder for that year was about 700% of the commercial catch for these four species. From these data, the range of the annual sports catch varies from approximately one to seven times the commercial fish catch, exclusive of shrimp, crabs and oysters. Consequently, from 0.06% to 0.12% of the an­nual gross production of Corpus Christi Bay can be accounted for as removed by man's harvest of fish, crabs, shrimp and oysters. These figures overlook the catches by sports shrimpers. There are approximately 10,000 sports shrimpers licensed by the Texas Parks and Wildlife Department. The Belden survey (1960) estimated sports shrimping catches at 1,500,000 to 3,000,000 pounds of shrimp per year, but admitted to a very small sample of sports shrimpers upon which to base their estimates. It is understood that the Parks and Wildlife Department plans another survey of sports shrimpers in the near future. 
V-6 

TABLE V-4  
COMMERCIAL CATCHES IN SOME TEXAS BAYS  
Commercial Catch in Thousands of Pounds -Fish  
Year  Corpus Christi  Aransas-Copano  San Antonio  Galveston  
1972  316.8  683.9  205.5  493.7  
1971  216.5  626.1  259.6  212.2  
1970  113. 2  430.3  213.2  332.0  
1969  91. 7  728.2  84.7  556.7  
1968  114. 0  509.8  161. 2  519.9  
1967  250.8  327.7  287.9  767.9  
1966  81.4  468.6  241.4  592.6  
1965  59.1  551.4  79.4  875.5  
1964  56.3  552.4  154.4  498.3  
1963  78.0  866.0  189.6  219.4  

10 yr ave 137.8 574.4 187.7 506.8 
Commercial Catch in Thousands of Pounds -Shrimp, Crabs & Oysters 
Year  C orpu s Christi  Aransas-Copano  San Antonio  Galveston  
1972  521.2  2,609.6  2,650.0  9,487.4  
1971  203.9  1,044.5  1,986.2  11,199.3  
1970  345.7  2,325.3  2,060.3  12,101.3  
1969  479.8  1,503.1  2,636.7  9,438.7  
1968  634.3  1,955.1  1,839.5  7,203.8  
1967  514.7  647.5  1,813.8  6,228.5  
1966  657.2  823.9  1,159.7  7,383.4  
1965  567.2  985.3  2,376.5  10,600.1  
1964  295.3  886.8  2,250.8  9,534.1  
1963  236.5  482.1  1,436.0  6,736.8  

10 yr ave 445.6 1,326.3 2,031.0 8,991.3 
V-7 
/ 
Another parameter that may be considered in measuring the energy flow of the bay is the amount of carbon composing the standing crop of organic mater­ial. This may be measured from particulate and dissolved organic carbon analyses. Data (Maurer, 1971; Reimers, 1968; Wilson, 1962) give a standing crop for Corpus Christi Bay of 1.95 x 107 pounds of carbon. This analysis accounts for organisms up to the size of small zooplankton, but not for the larger swimming organisms. The standing crop can be evaluated by fish den­sity. One investigator (Hellier, 1962) reported a monthly value for fish bio­mass in the Laguna Madre varying between 18 and 337 pounds of fish per acre. The Laguna Madre has a higher density of fish than Corpus Christi Bay. How­ever, if the Laguna Madre values were extrapolated to Corpus Christi Bay, the fish biomass contribution would be between 1 % and 12% of the standing crop of carbon. 
The time required for inputs and productivity to replace the carbon in the standing crop is called the residence time. Table V-5 shows a sample calcu­lation of residence time of carbon in Corpus Christi Bay, omitting fish biomass. In this case, the residence time is 5. 9 days. In the case where the maximum fish biomass is considered, the residence time becomes 7 .1 days. The contri­bution of waste input carbon is so small as to have no effect on the residence time. 
Carbon Dynamics Summary 
The final step in this analysis is to relate the importance of this informa­tion to management. The amount of carbon put into the bay as waste and ex­tracted as harvest, while subject to man's control, is so small relative to the carbon balance of the bay as to produce little effect. However, other aspects of management might have more significant effects. These include control of waste discharge quality (as opposed to the amount of carbon in the discharges) and enforcement of fishing laws to prevent ecological upset by over-harvesting of some particular species. Otherwise, to reiterate, it does not appear from the data that man's influence over the carbon balance of Corpus Christi Bay is of sufficient magnitude to be used as a management tool. 
Nutrient Balances in Texas Bays 
It is widely accepted that estuaries are highly productive because of high nutrient concentrations maintained by runoff and rapid recycling within the estuary. This is especially true of the Texas estuaries where offshore upwelling does not exist and primary nutrients originate from land. One of man's impacts on the coastal zone is to alter natural nutrient inflow, sometimes to the point where sewage or industrial wastes cause undesirable effects such as algal blooms. The task force on estuarine modeling of Corpus Christi Bay has worked 
V-8 

TABLE V-5 SAMPLE CALCULATION OF RESIDENCE TIME FOR ORGANIC CARBON IN CORPUS CHRISTI BAY 
3
Daily average total organic carbon input (l) 1. 38 x 10 lb C/day 6
Daily average productivity(Z) 3 .3 x 10lb C/day 7
Standing crop (3) 1. 95 x 10 lb C 
(Dissolved and particulate organic carbon) 
Standing cropResidence time = 
Input + productivity 7
1 . 9 5 x 10 lb c
Residence time = = 5. 9 days
6
3. 3 x 10 lb C/day 
1 
J. S. Sherman, Water Needs and Residuals Management Task Force, personal communication. 
2 
Table V-3. 
3 
Maurer, 1971; Reimers, 1968, Wilson, 1962. 
V-9 

out the reactions influencing the spatial variation of nutrients within the bay 
system. The purpose of this section is to provide an overview of the sources of nutrients and the pathways by which nutrients are lost, to compare Corpus Christi Bay with other Texas bays, and to attempt to integrate this information with the discussion of productivity. 
One of the results will be to show that Corpus Christi Bay has in the past been a naturally fertile estuary. Now that man has altered the upland drainage by agriculture and water management measures such as dams, nutrient flow into the bays has changed. We would hope that an understanding of nutrient bal­ances could be used as a tool to manage the flow of nutrients to the estuaries. 
The amount of nutrients available to phytoplankton at any moment in time in the waters of Corpus Christi Bay can be evaluated from analyses for ammonia, nitrate, nitrite, phosphate and total phosphorus. In order to provide compari­sons with other Texas bays, nutrient information has been compiled from San Antonio and Galveston Bays. The average nutrient concentration averages are shown in Table V-6, while the data sources and data reduction methods are shown in Table V-7. 
In estuarine systems, the limiting nutrient is usually nitrogen. Smith (1973) reported that nitrogen was apparently limiting in Corpus Christi Bay, Aransas-Copano Bays, San Antonio Bay and Matagorda Bay as shown by algal growth potential studies. Ryther and Dunstan (1971) suggested that nitrogen is usually the limiting factor for algal growth in the coastal marine environ­ment. In the highly turbid bays of the Texas coast, light may be as much a limiting factor as inorganic nutrients (Armstrong and Hinson, 1973). However, the shallow bays are continually mixed and the phytoplankton populations are in continuous motion. The light penetration, therefore, may not be greatly significant as the plankton will be intermittently exposed to the surface. 
The average levels of inorganic nitrogen in Corpus Christi Bay are less than those in Galveston Bay proper, Trinity Bay (the portion of Galveston Bay receiving the Trinity River discharge), or in San Antonio Bay. However, the nutrient levels are about the same as the East Bay and West Bay portions of Galveston Bay, which are relatively little affected by rivers and discharges. 
The nitrogen in marine systems is recycled rather rapidly. Inorganic nitrate or ammonia is taken up by phytoplankton which in turn release organic matter containing nitrogen into the water during photosynthesis. When the phytoplankton are consumed by filter feeders and the food web, the organisms metabolize and are consumed or die; both dissolved and particulate organic matter are thus released. The dissolved and particulate organic matter is decomposed by microorganisms thereby releasing inorganic nitrogen to main­tain a cycle. The level of dissolved organic nitrogen may be used as an indicator of the nitrogen available for productivity. 
V-10 

TABLE V-6 SUMMARY OF NUTRIENT DATA, AVERAGE CONCENTRATIONS IN mg/l 
Bay System Nitrogen Phosphorus Inorganic Organic Inorganic Total 
Corpus Christi -1952 	0.04 
-1972-73  0.18  0.015  0.04  
-1972  0.16  0.62  0.031  0.18  
San Antonio  -1970-71  0.61  0.13  
-1972-73  0.28  0.16  0.20  

Galveston Bay -1968-72 Trinity Bay 0.39 .96 0.49 Main Galv. 0.44 .80 0.55 East Bay 0.16 .76 0.27 West Bay 0.13 .60 0.18 
TABLE V-7 SOURCES OF NUTRIENT DATA 
Bay System 	Nutrients Source Retrieval & Analysis 
Corpus Christi P04-P 	Hood, 1953 manual average 
II 
NH3 I N03 I N02 J. Holland, unpublished TWDB Coastal II P04 , Total P 1972-1973 surveys Data System Org. N II NH3, N03 I N02 E. M. Davis, unpublished ENVIR Data Bank II 
P04, Total P 	1972 surveys 
San Antonio No2, No3, Po4 	Cechova & Davis (1973) weighted average 1970-71 survey 
II II 
NH3 I N02 I N03 	TWDB, unpublished TWDB Coastal 1972-73 survey Data System 
II II 
P04 , Total P 
Galveston NH3, N03 	Galveston Bay Study ENVIR Data Bank 
1968-1972 surveys Total P, Organic N 
V-11 

The nutrients required to support the day to day productivity of the bay come mainly from regeneration and where the decomposition of organic matter by microorganism releases the nutrients at a relatively rapid rate. This is shown by the calculation of carbon turnover time equal to about 6 days (Table V-5). The gross productivity within the bay planktonic biotope of 22. 3 pounds of carbon per acre per day (Table V-3) implies a nitrogen requirement of about 
3 . 9 pounds of nitrogen per acre per day or 143 2 pounds per year based on the usually accepted average atomic ratio of C:N:P = 106:16:1. The average in­organic nitrogen concentration in Corpus Christi Bay is about 0 .18 ppm-N or 
3. 7 pounds per acre assuming an average water depth of 7. 6 feet. The turn­over time is therefore 3. 7/3. 9 = 0. 95 days which is substantially faster than the carbon turnover time. It should be emphasized that the above figures apply only to the planktonic productivity; production in grassflats and algal mats depends on nutrients recycled within the sediments which we cannot evaluate at this time due to a lack of data. 
The cycling of nutrients within the bay is not completely identified at this time, as there are losses to the sediment, to the Gulf of Mexico, by flushing and tide effect, and by the harvesting and migration of fish and shrimp for which no data are available. Such losses are replaced by various sources of nutrients. 
Nutrient Sources 
The following sources of nutrients are believed to be significant for Texas bays: 1) river flows; 2) direct runoff; 3) precipitation; 4) release from sedi­ments; 5) fixation by microorganisms; and 6) municipal and industrial discharges. In the following sections we will attempt to evaluate these sources. 
Fairly good measurements of river flow and nutrient concentrations are available for Texas bays, although in many cases the ungauged flow is sig­nificant. Smith (1973) summarized the gauged flows of nutrients into four Texas estuarine systems, including Corpus Christi and San Antonio Bays but excluding flows associated with hurricanes. Armstrong and Hinson (1973) have summarized gauged flows for Galveston Bay. Both of these summaries are based on U.S. G. S. streamflow monitoring stations. The year to year variation is quite substantial, because of climatic fluctuations and long range changes in the drainage areas. In proportion to its volume, Corpus Christi Bay received much less river flow than the other two bays, and correspondingly less nutrient input. 
Direct ungauged runoff is more difficult to estimate; the Texas Water Plan (TWDB, 1968) gives estimates of 0.03 million acre-feet per year for Corpus Christi mainly from Oso Creek (4. 3% of total input), 0. 2 for San Antonio (13% of total), and 2. 1 from adjacent coastal basins for Galveston Bay (20% of total 
V-12 

runoff). Apparently the effect of local runoff is small for Corpus Christi as far as nutrient and fresh water additions are concerned. 
The significance of nutrients in rainfall is well known for many environ­ments, particularly as a source of inorganic nitrogen; however, we have not found any previous evaluation of this source for Texas estuaries and must rely on extrapolation of measurements made in other environments. Table V-8 gives a summary of some of the literature on nitrogen in precipitation. 
TABLE V-8 NITROGEN FROM PRECIPITATION 
Mean Nitrogen Content in mg-N/liter rain water 
Environment  Reference  NH -N3  NO -N3  
Maritime, N. Europe  Vaccaro (1965)  0.31  0.056  
Continental USA  II  0.32  0.52  
Oceanic, Bermuda  II  0.069  
Central Florida  Brezonik (1972)  0.13  0.065  

Information from conversations with several soil chemists at Texas A&M suggested estimates of precipitation input in coastal areas ranging from 3 to 10 pounds of nitrogen per acre per year; 6 pounds per acre per year with a 45 inch rainfall corresponds to a concentration of 0. 58 mg-N/liter. Brezonik (1972) gives an average of 0. 58 gm-N/square meter per year for rainfall plus dustfall for Florida lakes; this corresponds to about 5 pounds per acre per year. In addition to the inorganic forms of nitrogen, there can be substantial input of organic nitrogen in rainfall. Edmisten (1970) reports measurements of total nitrogen, both organic and inorganic, in rainfall in Puerto Rico of 0. 46 mg-N/ liter. We have decided to evaluate the importance of precipitation on the basis of 0. 4 mg-N per liter as a value intermediate between continental and maritime averages seems appropriate for our coastal zone. For Corpus Christi Bay, this represents a significant natural source of nutrients. 
A certain fraction of the organic matter produced within the bay drops to the bottom where it is utilized by benthic organisms including microorganisms, resuspended by wave action or dredging, or lost from the system in a perma­nent sedimentary deposit. The recycling of nitrogen from this organic matter is hard to evaluate, but it is probably very substantial. It is instructive to calculate the amount of nitrogen within the top centimeter of sediment. As­suming a specific gravity of the sediment of 1. 3, a 50% water content (Shepard 
V-13 

& Moore, 1955), and an organic nitrogen content of 0 .043% (Jones, 1960), there is 2. 8 grams of nitrogen per square meter in a 1. 0 cm layer. This cor­responds to 25 pounds per acre which is roughly 6 times the amount of inorganic nitrogen in the water column. 
Nitrogen gas can be fixed by blue-green algae and by bacteria. However, the amount of information available on this source in estuaries is quite small. Partiquin and Knowles (19 7 2) have shown that nitrogen fixation in the rhizo­sphere of marine grasses is sufficient to account for the nitrogen requirements of these plants. Brooks et al (1971) detected nitrogen fixation in the upper 2 -5 cm of the sediments of the Waccasassa (Fla.) estuary amounting to the equivalent of 3. 3 pounds of nitrogen per acre per year. The fraction of this nitrogen which is released to the water column is unknown. Nitrogen fixation within the water column was detected only once out of four experiments in the Waccasas sa, and then at a low rate. 
Estimates of nitrogen input from industrial and domestic sewage discharges for Corpus Christi Bay were provided by the Water Needs and Residuals Manage­ment Task Force. San Antonio Bay has relatively little industry or domestic discharge. No estimates were found for this source, but it is believed to be small. Two estimates for Galveston Bay are available. One by Armstrong and Hinson (1973) is based on population, assuming a certain level of treatment; the other estimate is being prepared in conjunction with the Galveston Bay Project by the Texas Water Quality Board. This estimate is based on a detailed analysis of all sources and is probably the most accurate. 
Summaries of these nitrogen sources are shown in Tables V-9 and V-10 in terms of thousands of pounds of nitrogen per year, pounds per acre, and relative importance of each source. A number of interesting points are brought out in these tables. One point is that in both Galveston Bay and Corpus Christi Bay, industrial and domestic discharges supply a large fraction of total nitrogen input, but the per acre lea.ding is much higher in Galveston Bay. A..rnong the natural sources, rainfall is a greater source of nitrogen than river flow in Corpus Christi Bay, but is relatively small in San Antonio and Galveston Bays. 
Nutrient Losses 
Nutrients may be lost from the bay system by the following processes: 1) harvest of organisms; 2) sedimentation; 3) outflow to the Gulf of Mexico; 4) mixing with Gulf waters during tidal exchange; and 5) dentrification (nitro­gen only) . We have attempted to evaluate only l, 2, and 5 in the following section, and assume that 3 and 4 are responsible for the remainder of the losses required to balance the inputs. 
V-14 
TABLE V-9 NITROGEN INPUT AND FRESHWATER INFLOWS  
Bay System  Years  Ref  Average gauged flow  Average 12reci2itation  Domestic & Industrial Discharges  

acre-ft/yr acre-ft/yr 103lbs N/yr(b) acre-ft{yr x 106 103lbs N/yr x 106 @ 0 .4 ppm-N x 10 103lbs N/yr 
Corpus Christi 	62-71 ccw 0.303 339. 
53-71 ccw 0.312 349. 
62-71 EMD 0.574 228. 
53-71 EMD 0.581 567. 
41-57 TWP 0.7 

0. 90 (e)
model "wet year" II 70" MOD 11. 6 12 I 675 (a) 2,097 model "dry year" II 70" MOD 0.096 150.8 0.90 2,097 ave 3 wet years (c) EMD 1.586 2,129 0.415 451. 
<
I ave 3 dry years (d) EMD 0.095 112. 0 .194 210. 
...... Ul San Antonio 41-57 TWP 1. 5 0.40 440. 61-71 EMD 2,170. 
Galveston 41-57 TWP 10.2 1.4 1,520 ?-73 GBP 7.6 0.482 21,260 ? A&H 15,103 32,493 
note 	a -this flow rate never occurs for more than a few weeks at a time 
b -based on areas, CC= 134,000 acre, SA= 143 ,000, GB= 341,000, ref TWP 
c -1957, 58, 67 
d -1955, 62, 63 
e -includes cooling water 

References 
CCW -Corpus Christi Int. Airport Weather Station A&H -Armstrong & Hinson (1973) 
T·wp -Texas Water Plan, Texas Water Development EMD -Davis (1973) 
Board, 1968 MOD -Other task forces of this project 
GBP -Galveston Bay Project, Texas Water Quality 
Board, 1974 unpublished 

TABLE V-10 NITROGEN INFLOWS TO TEXAS BAYS 
Relative Importance of Bay System Total N InQUt Each Source -% 3
10 lbs/yr River Rain Ind. +Dom. 
Corpus Christi  ave 62-71  2,664  19.9  8. 5%  12. 7%  78. 7%  
ave 3 wet years (a)  4,677  34.9  45.5  9.6  44.8  
ave 3 dry years  (b)  2,419  18.0  4.6  8.6  86.7  

San Antonio ave 61-71 2, 610 18.3 83 .1% 16. 9% 0 (c) 
Galveston recent ? (d) 49,116 144. 30. 7% 3 .1% 66 .1% ?-74 (e) 37,883 111. 39.8 4.0 56.1 
a -using 1957, 1958, 1967 with "70" model Industrial and Domestic b -using 1955, 1962, 1963 with "70" model Industrial and Domestic c -no specific information available, assumed to be very low d -Armstrong and Hinson (1973) e -Galveston Bay Project, unpublished 
V-16 

TABLE V-11 
CARBON, NITROGEN & PHOSPHORUS EQUIVALANCES OF THE 
AVERAGE COMMERCIAL CATCH & ESTIMATED SPORTFISHING 
CATCH OF ALL ORGANISMS IN CORPUS CHRISTI BAY 

Units Carbon Nitrogen Phosphorus Total Weight 
pounds/day 197.4 59.2 4.0 1,974 
pounds/year 72,120 21,600 1,442 721,200(2) pounds/acre-year(1) 0.63 0.19 0.013 6.27 
(l) based on an area of 115, 000 acres 
2
() assumes sports catch equals commercial catch of fin fish, Table V-4 
TABLE V-12 CARBON NITROGEN AND PHOSPHORUS REMOVED PER ACRE PER YEAR FROM THREE TEXAS BAYS 
Area Total Bay System {acres} Catch Pounds Per Acre Per Year 
Carbon  Nitrogen  Phosphorus  
Corpus Christi San Antonio Galveston  115 I 000 143,000 333,000  6.27 15.4 28.5  0.63 1.54 2.85  0.19 0.46 0.86  0.013 0.031 0.057  

V-17 

The losses of nutrients from the system due to commercial and sportfishing catch can be evaluated on the basis of the average composition of fish and shrimp; 0. 10 lb carbon, 0. 03 lbs nitrogen, and 0. 00 2 lbs phosphorus per pound of organism (Vinogradov, 1953; Stansby and Hall, 1967). Table V-11 gives the estimated rate of loss of carbon, nitrogen and phosphorus from the Corpus Christi Bay system due to commercial fishing and sportfishing. Table V-12 gives the same information in terms of pounds per acre per year for Galveston Bay and San Antonio Bay in comparison with Corpus Christi Bay; however, no estimate for sportfishing has been included in the catch for San Antonio or Galveston Bay. 
Sedimentation rates in Texas estuaries have been evaluated by Shepard (19 53) by comparison of surveys taken in the late 1800' s with recent surveys. Table V-13 gives the sedimentation rates determined, with and without allow­ance for subsidence at 1. 8 feet per century. The nitrogen loss rates shown were calculated using the sediment specific gravity of 1 . 3 with a water content of 50% given by Shepard & Moore (1955) and an organic nitrogen content of 0 .043% (Jones, 1960). These should be regarded as rough approximations since we do not have detailed sediment analysis for each bay. 
TABLE V-13 
SEDIMENTATION RATES IN TEXAS BAYS DEDUCED FROM 
HISTORICAL DEPTH CHANGES 
(Shepard & Moore, 1953) 

Rate 	Units Corpus San Antonio Galveston 
Depth change Ft/100 yr 	1. 56 1.23 1.44 
Depth change + 
subsidence Ft/100 yr 3.36 3.03 3.24 

Nitrogen loss 	-min lbs-N/acre-yr 11. 9 9.25 11.0 -max 25.5 23.0 25.0 
Dentrification, the reduction of nitrate to N2 gas, is known to be an important nitrogen sink in lakes, but its importance in the marine environment is less well defined (Brezonik, 1972). Dentrification can occur only under anaerobic or very low oxygen conditions, and these conditions occur only in the sediments and in the inner harbor waters in Corpus Christi Bay. We have been unable to find information on dentrification in estuarine sediments, but the rates given by Brezonik for lake sediments and anoxic waters range from 8 to 330 micrograms 
V-18 

N per liter per day. Dentrification in sediments can only occur near the inter­
face between oxic and anoxic conditions where nitrate can diffuse into the 
sediment. Postulating a 0. 5 cm layer for these conditions, 100 micrograms 
N per liter per day corre spends to 1 . 6 pounds of nitrogen per acre per year. 
This is the same order of magnitude as nitrogen fixation, and since both pro­
cesses are equally hard to determine, we have left them out of the final nutrient 
budget summary. 

One important cycle of water interaction between the bay and gulf have 
' 	generally been neglected in the literature relating to chemical cycles. Flush­ing rates h3. ve normally been estimated by lunar tide and rainfall values. However, superimposed on these phenomena is a periodic seasonal variation in tide due to accelerations by wind and general oceanographic features in the Gulf. Thus, approximately two to six times a year we have in Corpus Christi abnormally high er low combination tides. The water level in the bay may have a rather short duration (one to two weeks) amplitude of two to three feet above or below normal. During these short periods of time from 10 to 20% of the water in the bays may be exchanged. This water is well-mixed as the level increases in the bays and thus may affect flushing rate values. 
Nitrogen Budget Summary 
Table V-14 summarizes the sources, sinks and standing crops of nitrogen in Corpus Christi, San Antonio and Galveston Bays, as they have been dis­cussed in this chapter. It is apparent that the higher fertiliza.tion rate in Galveston Bay is associated with a higher productivity and higher standing crop of inorganic nitrogen nutrients. The loss of nitrogen to the sediments could balance nitrogen inflows in Corpus Christi and San Antonio Bays, but not Galveston Bay. 
If industrial and domestic sewage treatment plant discharges to Corpus 
Christi Bay were stopped, or their nitrogen content eliminated, the nitrogen 
inflow would drop to an average of 4. 3 pounds per acre per year. The low 
inflow rate would soon cause a reduction in productivity, but the magnitude 
of this reduction cannot be determined at this time. 

Implications for Management of Bays and Estuaries 
Man's activities have a strong influence on the nutrient balance in Corpus Christi Bay, but a change in this influence could result in a mean reduction of productivity. At the present loading rates, Corpus Christi Bay does not appear to be exhibiting any symptoms of eutrophication except in localized areas such as the back half of the inner harbor. There appears to be no need for reduction of nutrients in present effluents, and in fact, a reduction might 
V-19 

TABLE V-14 SOURCES, SINKS AND STANDING CROPS FOR NITROGEN IN THREE TEXAS BAYS 
Corous Christi San Antonio 
Inflows lbs-N/acre-yr 19.9 18.3 Harvest lbs-N/acre-yr 0.19 0.46 as a fraction of source 0.95% 2. 5% Gross production and lbs-N 
1054 424
respiration (a) acre-yr Loss in sediments -min lbs-N 11. 9 9.25 -max acre-yr 25.5 23.0 Standing Crop 
lbs-N
Inorganic Nitrogen 3.72 3.05 
acre-yr 
(a) planktonic biotope only Galveston 
111. 
0.86 
0. 77% 2490 
11.0 
25.0 
9.57 
V-20 

be detrimental. River flow does not contribute a significant amount of nitrogen to the bay in average years; therefore, the main justification for maintaining river flows to the bay must be related to maintenance of a desirable salinity regime, or the addition of trace nutrients. However, river flow changes could be balanced by return flows, such as domestic wastewater, provided disburse­ment occurred at proper areas. 
The first symptoms of excessive nutrient levels are the appearance of nuisance algal blooms which would be unsightly, alter the food chain, and possibly lead to low oxygen levels in the bay during unusually calm weather. Galveston Bay, which exhibits nitrogen nutrient levels about twice those of Corpus Christi Bay, has definitely higher productivity, but does not (yet) exhibit nuisance algal blooms in the main portions of the bay. Therefore, we tentatively propose that the average inorganic nitrogen levels in Galveston Bay, 0.44 ppm-N, should be adopted as maximum nitrogen standards for the planktonic biotope of Corpus Christi Bay. The standard for inorganic nitrogen must be supplemented with one for total nitrogen because at the peak of an algal bloom, there is very little nutrient left in the inorganic form. Again using the Galveston Bay data, we suggest that a maximum total nitrogen level of 1 . 4 ppm-N should be adopted as a tentative standard. As more information about the phytoplankton and nutrient dynamics become available, these standards can be re-evaluated. 
As an index of nuisance algal blooms, a standard for chlorophyll a has been suggested for Chesapeake Bay by Clark, Donnelly and Villa (1973). The limit they propose, which is based on local algal populations, is 0. 040 mg chlorophyll a per liter. To maintain this level, they propose an inorganic nitrogen limit of 0. 80 ppm-Nanda total phosphorus limit of 0 .12 ppm-P04 
(0. 04 ppm-P). The Chesapeake nutrient data, however, show much less organic nitrogen than is pre sent in Texas bays, indicating that the dynamics of the nutrient cycles are different. At the pre sent time, there is not enough chlorophyll~ data available to suggest similar standards for Corpus Christi Bay. 

CHAPTER VI WATER QUALITY 
The intent of this chapter is to identify summarily specific water quality 
criteria for Corpus Christi Bay. The task of assigning water quality criteria 
in all estuarine waters rests with the Federal Environmental Protection Agency 
and Texas Water Quality Board. It was decided that this task force should 
examine the present proposed general criteria for their applicability to Corpus 
Christi Bay in light of known water quality conditions. 

In doing so, we have made use of two concepts which we feel have been overlooked lately. First, trace elements at low concentrations are essential to all life forms. Examples are dietary requirements of zinc and manganese in chickens, copper in pigs, and cobalt, magnesium, iron and selenium for nearly all organisms. Second, nearly all trace materials are present in sea water, thus exposing marine organisms to higher concentrations than are normally encountered by freshwater organisms. The behavior of these trace materials in sea water differs from their behavior in freshwater due to chelation, adsorp­tion and other effects caused by the presence of so many ionic forms as well as dissolved organic matter and sedimentary particles. The concentration ranges of the various elements in natural environments are shown in Table VI-1. 
Our approach is to evaluate the existence and responses of estuarine popu­lations in the face of natural levels of trace elements. This yields in situ information concerning adverse effects. Where no adverse effects can be determined, trace element concentrations could be interpreted as being within natural tolerance levels. 
The FWPCA water quality criteria published in 1°968 (FWPCA, 1968) led quickly to the recognition that in some areas naturally occurring background concentrations of some of the materials were in excess of the maximum allow­able levels. The National Academy of Sciences and National Academy of Engineering (NAS-NAE) were then given the task of recommending criteria which could cope with such environmental variations. These recommendations have been delivered to the Environmental Protection Agency (NAS-NAE, 19 7 4, in press). 
The proposed standards consist of three kinds of criteria. They are the application factors for various materials, maximum aqueous concentration levels, and maximum concentration levels in prey organisms for the top level predatory birds and mammals. 
The application factor is designed to control the concentration of waste discharges by reducing their concentration to some designated fraction below 
VI-1 


TABLE VI-1 
Comparative Values of Elements in the Environment* 

Element  Seawater Range (mg/l)  Earth Crust Average (ppm)  Marine Organisms Range (ppm)  Land Organisms Range (ppm)  
H  Hydrogen  108,000  1,400  41,000  -52,000  55,000  -70,000  
He  Helium  .0000069 -.000005  .008  
Li  Lithium  .18  -.1  20  1  -5  . 02  -.1  
Be B  BerylliumBoron  .0000005 -.0000006 4.7-4.6  2.8 10  .001 20 -120  .1 Sb  -.0003 -• 5  
c N 0 F Ne  Carbon Nitrogen Oxygen Fluorine Neon  28 o.s 857,000 1.4 -·i.3 .00014  200 510 464, 000 625 .005  345 lS,000 400,000 2  -100, 000 -7S,OOO -470,000 -4.S  280,000 30,000 186,000 .5  -465,000 -100,000 -410,000 1,500  
Na Mg Al Si P S Cl Ar K Ca Sc  Sodium Magnesium Aluminum Silicon Phosphorus Sulphur Chlorine Argon Potassium Calcium Scandium  10, 769 -10,293 1 · l, 350 1, 262 1.9 -.01 4 -.02 .1 -. 07 901 -884 19, 353 -18, 550 .6 387 -376 408 -389 .00004 -.000004  23,600 23,300 82,000 281,500 1,050 260 130 3.5 20,900 41,500 22  4,ooo 5,000 10 70 3,500 s,ooo 47,000 s,ooo 1,500  -------48,000 s,200 60 20,000 18,000 19,000 90,000 -52,000 -300,000  4,000 -1,200 1,000 -3,200 .5 4,000 120 -6,000 2,300 -44,000 3,400 -s,ooo 2,000 -2,800 . .7S (Mammalian Blood) 7,400 -1,400 200 -260,000 .008 -.00006  
Ti V  Titanium Vanadium  • 00002 .002  -• 001 -. . 0003  s, 700 135  .2 .14  --80 2  .2 1.6  -1 -.15  
Cr Mn  Chromium Manganese  .00025 .01  -.00005 -.002  100 950  1 1  --• 2 ( 108) 60  .23 · -.075 .2 -630  
Fe Co  Iron Cobalt  .1s .0001  -.001 cio-9) -.0007  56,300 25  400 .5  --700 5  140 • 5  -160 -•03  
Ni  Nickel  • 006  -• 0001  75  .4  -25  .8  -3  
Cu  Copper  .01  -.0005  SS  4  -50  2.4  - 14  
Zn Ga Ge  Zinc Gallium Germanium  .021 .000007 .00007  -.005 -.0005 -.00006  70 lS 5.4  6 • 5 .3  -1500  100 • 006  -160 -. 06  
As  Arsenic  .03  -.0003  1.8  .3  - 150  .2  
Se  Selenium  .006  -.00009  .OS  .8  .2  -1.7  
Br Kr  Bromine Krypton  66 • 0025  -65 -• 0003  2.5 .0001  60  -1,000  6  -15  
Rb Sr Y Zr Nb Mo Tc  Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium  • 2 -.12 13 -8.1 .0003 2.2 x lQ-5 .00002 -.00001 .01 -• 0003  90 375 33 16S 20 1. 5  20 20 .1 .1 .001 2.S  ------7.4 1400 .2 20 300 .4S  17 14 • 04 .3 .3 • 9  -20 -26 -•6 -.64 -• 2  
Ru Rh  Ruthenium Rhodium  • 001 .o  -• 01 -.005  .002  -.005  
Pd Ag Cd In Sn Sb Te I Xe  Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon  .0003 .00001 .003 .00033 •06 .000052  -.00004 -.00011 <.02 -.0008 -.0005 -•05 -.0001  .OS 2 • 001 .5 .00003  .Ol .07 .2 -1 .2 -• 01  11 3 • 2 .2 l  -•25 -.15 -20 -lSOO  .002 .8 -.006 .6 -• 5 .016 .15 -•3 .006 -.06 .02 -25 .42 -.43  
Cs Ba La Ce Pr Nd Pm  Cesium Barium Lanthanum Cerium Praseodymium Neodymium Promethium  • 002 •06 .0003 .0004  -• 00005 -• 01 2.2 x lo-5 -s.2 x lo-6 2.6 x 10-6 9.2 x 10-6  2 425 30 60 8.2 28  .07 30 .1 . s • 5  -.2 -10 -5 -5  • 2 (4000) 14 .0001 320 46 460  -• 064 -.75 -.08S -• 03  
Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta  Samarium Europium Gadolinium Terbium Dysprosium Holmium Erbium Thulium Ytterbium Lutetium Hafnium Tantalum  i.1 x lo-6 4.6 x 10-7 2.4 x lo-6 2.9 x 10-6 8 .8 x 10-7 2.4 x l0-6 5.2 x l0-7 2.0 x lQ-6 4.8 x 10-7 8 x l0-6 2.S x lQ-6  6 1.2 5.4 .9 3 1.2 2.8 .s 3.4 .s 3.2 2  • 04 •06 .06 • 006 .oos • 04 .02 .003 (.4 410  -• 08 -• 01 -• 01 -.01 -• 02  .01 .021 70 •OOlS .02 .s 2 .0015 .00012 4.5 • 04  -.0055 -.00012 -• 0004 -.01 -16 -46 -.00004 -.0015 -.00012 -• 01  
W  Tungsten  .00012  l.S  .0005  -.OS  .005  - .07  
Re  Rhenium  • 005  -• 001  .014  -.0005  
Os  Osmium  .0015  -.oos  
Ir Pt Au Hg Tl Pb Bi Po At  Iridium Platinum Gold Mercury Thallium Lead Bisnuth Polonium Astatine  .000015 .0003 .006 . 000017  -.000004 -.00003 < .00001 -.00003 . 0002  .001 . 005 -• 01 .004 .08 .5 13 .17 2 x 10-10  .012 .5 .3 15  -.0003 .03 -8.4 0.4 -17**  • 00002 ·-. 02 .002 .04 -.00023 • 046 -• 015 .4 2 -2. 7 •06 • 004 .1 -600**  
Rn  Radon  6 x 1016  -o.6  x lo-15  4  x 10-13  
Fr  Francium  
Ra Ac  Radium Actinium  3 X 1010  - 2 x 10-ll  9 x lo-7 s.s x 20-10  • 7  -15 x lQ-8  lo-9  -1  x 10-9  
Th Pa  Thorium Protactinium  <.ooos -.oooos 2.4 x 10-11_ 2.0 x lo-9  8.3 1.4 x 10-6  .003  -.03  .003  - .1  
U Np  Uranium Neptunium  • 015  -• 00015  2.7  • 004  -3. 2  .038  -.013  
Pu Am  Plutonium Americium  .07  -6.8**  
Cm  Curium  .0001  
Bk  Berkelium  
Cf  Californium  
Es  Einsteinium  
Fm  Fermium  
Md  Mendelevium  

* Taken from various authors listed in bibliography **Disintegrations sec.-1 kg-1 
a 96 hour lethal level. The instructions for ascertaining the 96 hour lethal levels include use of sensitive indigenous species and use of the local re­ceiving waters as the test medium. This, it is hoped, will allow for adjustment of water quality criteria with regard to high background levels of various materials by integrating these background effects into the toxicity experiments. Finally, seasonal variations must be accounted for. The application factors for toxic inorganic materials are shown together with relative pollution menace in Table VI-2. The general application factor of 0 .01 for toxic organics has been applied to 96 hour TLM50 data for local organisms; the maximum levels permissible for this area are shown in Table VI-3. 
The aqueous concentration levels specifically recommended by NAS-NAE are limited to the toxic inorganics. Table VI-4 shows the hazard and minimum risk levels designated by NAS-NAE. The next column shows levels of some of th13se materials four1d ). n the open bay environment of Corpus Christi Bay. Of these only boron and iron sometimes exceed hazardous levels. In the re­stricted waters of the Corpus Christi and La Quinta Ship Channels levels of zinc and cadmium may exceed the hazard levels by four to seven-fold (Holmes, et al. , 1974; Texas Water Quality Board, unpublished data). The fourth column shows the range of these materials in sea water. The final column constitutes this task force's recommendations concerning local levels of these materials. High recommended levels of sulfides take into account the prevalence of wind driven resuspension of anaerobic sediments in shallow areas of the bay thus releasing sulfides. 
Since no limits for aqueous levels were described for toxic organic com­pounds, no recommendations are made here. However, Tables VI-5 and VI-6 are presented as reference materials concerning toxicities of various materials in marine environments. The values shown in Table VI-5 were the lowest lethal concentrations found regardless of time of exposure or percent kill for any of the materials. The values were selected in this way due to the wide diversity of methods employed in these determinations. Table VI-6 shows the acute dose lethal to 50 percent of the birds, mallards and quail, which were dosed with materials which they might encounter in the local environment. The rationale for ingested doses was that birds were likely to ingest such materials with their food or during preening. No firm water quality criteria can be developed at this time for such toxic organic materials. 
A synopsis of this information, then, reveals a lack of data in several areas. More information is needed concerning the sensitivity of organisms indigenous to Corpus Christi Bay to both organic and inorganic materials. Use of a standard method for determining such sensitivities is required. Such a method must take into account dose, time of exposure and percent kill at that exposure. The 96 hour TLM50 method is recommended by this task force because of its present use in defining the proposed standards. The aqueous medium for these experiments should in all practical cases be non-filtered water 
VI-3 

TABLE VI-2 
APPLICATION FACTOR AND POLLUTION CATEGORY FOR 

TOXIC INORGANIC MATERIALS(!) 

(3)
2
Application Factor() Pollution Category
Substance 
Aluminum 0.01 	4c 4c
Amonia 0.1 Antimony 0.01 4c Arsenic 0.01 2c Barium 0.05 4c Beryllium 0.01 4c Boron 0.1 4c 
4c
Bromine Cadmium 0.01 2c Chlorine (Gas) 0.1 4c Copper 0.01 4c Cyanides 0.1 3c 
4c
Fluoride 0.1 4c
Iron la
Lead 0.01 Manganese 0.02 4c lb
Mercury Molybdenum 0.02 4c Phosphorus (elemental) 0.01 
3c
Selenium 0.01 3c
Silver 0.05 Sulfides (pH 6.5-8.5) 0. 1 2c Thallium 0.05 3c Uranium 0.01 3c Vanaduim 0.05 4a 
3c
Zinc 	0.01 
(l) 
NAS-NAE, 1974. 

(2) 
Fraction of TLM50 for most sensitive indigenous organism, tested in local waters. 

(3) 
1-4 in order of decreasing menace. a -worldwide, b -regional, c -local. 


VI-4 

TABLE VI-3 
MAXIMUM ALLOWABLE ENVIRONMENTAL CONCENTRATIONS 

Substance Heptachlor Aldrin Dieldrin Lindane 
Methoxychlor Endrin 
p, p'-DDT Delnav Malathion Phosdrin DDVP Methyl parathion 
(l) Eisler, 1969. 
OF SELECTED PESTICIDES 
Maximum Concentration Allowable (2) 
.08 ppb . 08 ppb .07 ppb . 05 ppb 
. 04 ppb . 02 ppb 
.006 ppb .38 ppb .33 ppb .11 ppb .04 ppb .02 ppb 
Table VI-5, reference no. 4. 
Test Organism(l) 
Crangon septemspinosa Crangon septemspinosa Crangon septemspinosa Crangon septemspinosa 
Pagurus longicarpus Crangon septemspinosa Crangon septemspinosa 
Pagurus longicarpus Crangon septemspinosa Crangon septemspinosa Crangon septemspinosa Crangon septemspinosa Crangon septemspinosa Crangon septemspinosa 
() Application factor = 0. 01 of 96 hr. TLM 50 for test organism shown. 
VI-5 

TABLE VI-4 
WATER QUALITY CRITERIA RECOMMENDATIONS 
(all concentrations in ppm unless otherwise noted) 

Substance  
Aluminum  
Ammonia  
Antimony  
/trsenic  
Barium  
Beryllium  
Boron  
<t-1 I  Bromine: Br2  
en  Br03  
Cadmium  
Chlorine (Gas)  
Copper  
Cyanides  
Fluoride  
Iron  
Lead  
Manganese  
Mercury  

NAS-NAE Recommendations 
Hazard 
1.5 
0.4 
0.2 
0.05 
1.0 
1.5 
> 5 .0 

< 0 .1 <100 
0.1 
0.01 0.05 10 ppb 
1.5 
0.3 
0.05 
0.1 
0 .1 ppb 
Minimum Risk 
0.2 
0.01 
0.01 
0.5 
0.1 
<5 .o 
0. 2 ppb 
0.01 5 ppb 
0.5 
0.05 
0.01 
0.02 
Local Water 
Concentrations(1) 

+
0 .9-6 .0 ppm NH
4 
0.06-8.1 
-
65 ppm Br 
0.8-21 ppb 
0 .1-0. 9 ppb 0-0. 8 0-7 ppb 
5-15 ppb 
Mean Sea Wat(:r Concentrations 2) 
0.01-1.9 
0. 5-0. 3 ppb 0.3-30 ppb 0.01-0.06 
-4
5 x 10 ppb 
4.7 
0 . 0 1-0 . 1 ppb 
0. 
5-10 ppb 

1.3 

0.1-0.7 


0. 3-6 ppb 2-10 ppb 
0. 03-0. 3 ppb 
Task Force Recommendations 
Coq~us Christi Ba~ 

1.5 No build up of unionized NH
3 
0.1 
0.03 
1.0 
0.5 
< 5 .0 
<0 .01 < 100 
0.001 
0.01 
0.02 
0.005 
1.0 
2.0 
0.01 
0.05 
0.0003 
TABLE VI-4 (CONTINUED) 

Substance NAS-NAE Recommendations Local Water Mean Sea Wat{r Task Force Recommendations Hazard Mini.mum Risk Conce_ntration_si!l Concentrations 2) Corpus Christi Bay Molybdenum 0.1 2 ppb --0.3-10 ppb 0.01 Phosphorus 
1 ppb --	----0.001
(elemental) 
Selenium 10 ppb 5 ppb --0 .09-6 ppb 0.01 
Silver 5 ppb 1 ppb --0. 04-0. 3 ppb 0.001 

7
1
Sulfides 10 ppb 5 ppb ----.01 open water> depth . 1 water~ 7' & anearobic sed. Thallium 0.1 0.05 --< 0 .01 ppb 0.00001 Uranium 0.5 0 .1 --0 .15-15 ppb 0.1
<
1-i 
I 
'.J 	Vanadium ------0. 3-2 ppb 1.0 Zinc 0. 1 0.02 6-60 ppb 5-21 ppb 0.6 
(1) 
Parker, et al., 1963; Parker, 1962; Hahl, et al., 1972; Hahl, et al., 1970; Blakely & Kunze, 1971; Holmes, et al., 1974. 
(2) Bowen, 1966; Comar & Bronner, 1962; Florin, 1960; Frieden, 1972; Goldberg, 1972; Jones, 1964; Miller, 1969; Nicol, 1967; Stansby & Hall, 1967; Vinogradov, 1953. 
TABLE VI-5 
TOXICITY LEVELS FOR MARINE & ESTUARINE ORGANISMS 

ENDEMIC TO THE CORPUS CHRISTI BAY AREA 
Crustace(! Plants Molluscs Birds* 	References**
Substance 	Fish 
. 8 ppb 8 ppb 	10 ppm ~2000 ppm 48,37,39,4,8
Heptachlor 
.42 ppm 27

Niacinamide 
.5 ppm .03 ppm .025 	ppm 36,29,26,25,10,23
Cu 
.14 ppm 28

CuC03 
.5 ppm 28

Cu sulfate 
. 01 ppb 10 ppb > 2400 ppm 9,46

Mirex 
.1 ppm 1,32

LAS detergent 
.5 ppm 1,17

ABS detergent 	. 7 ppm 
1 ppm .5 ppb .04 ppm .025 	ppm 30 ppm 43,48,42,40,36,34,30,
DDT 
6,22,1,2,14 < 7 -9 1
1--i 
pH
I 	5 ppb .01 ppm . 25 ppm 30.8 ppm 48,43,42,30,2
(X) Toxaphene 
.125 ppm/day 48,39,38,37,6,8,4
Endrin . 05 ppb 1. 7 ppb 	. 01 ppm .1 ppb 5 ppm/day 48,43,37,39,4,6
Aldrin . 5 ppb . 8 ppb 
6

DEF 
1.0 ppm . 5 ppm/day 6,48
Baytex .1 ppm 52. 2 ppm 6,48
Dibrom 
1 ppm 7

Fungicide w/tin 
1 ppt 19

Pure streptomycin 
1 ppt 19

Commercial streptomycin 
3.2/10,000 19

Pure aureomycin 
1 ppb 19

Commercial terramycin 
5 ppb >> 2000 ppm 48,21,11,15,20
Arochlor 125 4 5 ppb 	1 ppb 10 ppm 12
DDE 
.1 ppm 2. 5 ppm/day 48,43,39,37,4,13
Dieldrin . . 9 ppb 	1 ppb 
52 ppm 32 ppm 18

Fluoride 
Organic mercury 
1 ppb 16

compounds 
TABLE VI-5 (CONTINUED) 
Substance Fish Crustacea Plants Molluscs ~~rd.§.* References** 
Pb .1 -. 2 ppm 5 Radiation 5 I 83 3 R 24 Zn .1 ppm 29 Cd -.1 ppm 29 Cr .1 ppm 29 Lindane .9 ppb 5 ppb 2. 5 ppm .5 ppm 30 .1 ppm/day 48,43,42,39,37,4 Endrin 1. 7 ppb 1 ppm 43,39,37,4 p,p'-DDT .4 ppb . 6 ppb 10 ppm 4,39,37 Delnav 38 ppb 4 Malathion .027 ppm 33 ppb 25 ppm 1485 ppm 48,4,37,39 Phosdrin .065 ppm 11 ppb -25 ppm 4. 63 ppm 48,37,4,39 Methyl parathion 5. 2 ppm 2 ppb 25 ppm 10 ppm 48,4,39,37 
<
1--t 
Methoxychlor .12 ppb 4 ppb 10 ppm 4,39,37
I 
c..o 
Sodium acid pyrophosphate 500 ppm 500 ppm 500 ppm 3 
Quadrafos 3500 ppm 3 
Impermex 5000 ppm 1000 ppm 3 
Sodium polyphosphate 500 ppm 500 ppm 3 
Stabilite #9 500 ppm 500 ppm 3 
Caustic Soda 70 ppm 70 ppm 3 
Oil we11 cement 100 ppm 100 ppm 3 
Tannex 100 ppm 90 ppm 3,33 
White lime 125 ppm 125 ppm 125 ppm 3 
Parathion -1 ppm .01 ppm/day 43,30,48 
Silt .1 ppt 31 
Kaolin .1 ppt 31 
Caco3 .1 ppt 31 
Biodegradable detergent • 25 ppm 32 
Aquagel 110 ppm 33,3 
Turbidity 200 ppm 33 

TABLE VI-5 {CONTINUED) 

Substance  Fish ·  Crustacea  Plants  Molluscs  Birds*  References**  
Dioxathion  .6 ppb  .038 ppm  25 ppm  37,39  
Hg Dipterex TEPP  50 ppm 300 ppm  12 ppm 1 ppm 1 ppm  3. 56 ppm  41 42,43 48,42,43  
Phenol  10 ppm  10 ppm  42,43  
Dowacide A  50 ppm  1 ppm  42,43  
Orthodichlorobenzene  7. 6 ppm  10 ppm  42,43  
Chloronitropropane PVP-iodine  8 ppm 20 ppm  42 42  
Sevin Nabam  .1 ppm .1 ppm  1 ppm -. 5 ppm  125 ppm > 2560 ppm  48,42,43 48,42,43  
<1-l I I-' 0  Lignasan Fenuron Neburon Monuron Diuron  .6 ppb . 29 ppm . 04 ppm 1 ppb •02 ppb  5 ppm 2 .4 ppm 5 ppm 1 ppm  >2000 ppm  42 42,43 42,43 42 48,42,43  
Dowacide G  • 25 ppm  43  
Roccal  .2 ppm  43  
Nemagon Choramphenicol Delrad  • 25 ppm 10 ppm . 05 ppm  66 .8 ppm  43,48 43 43  
Sulmet  10 ppm  43  
Trichlorobenzene  10 ppm  43  
Acetone  100 ppm  43  
Allyl Alcohol Niagara compound N-3452 Niagara compound N-3514 Dicapthon  . 25 ppm 1 ppm -1 ppm 1 ppm  43 43 43 43  

TABLE VI-5 (CONTINUED) 
Substance 	Fish Crustacea Plants Molluscs Birds* References** 
Guthion 	.5 ppm 8. 75 ppm/day 43,48 
Zineb 	> 2000 ppm 48 
Cationic surfactants 	.3 ppm 44 
Anionic surfactants 	1 .15 ppm 44 
Nonionic surfactants 	2.33 ppm 
* 	These values are dosages either ingested, injected or assimilated from topical application. Units are derived from milligrams of dose per kilogram of weight. The symbol >>equals 11 much greater than11 
• 
**For references see Appendix H. 
<
1-'i 
I 
....... 

....... 

TABLE VI-6 
TOXICITY LEVELS FOR BIRDS ENDEMIC TO THE 
CORPUS CHRISTI BAY AREA(l) 

Substance LD 50 Dose (2) Substance LD 50 Dose (2) 
Abate 2. 5 ppm/day GS 13005 23. 6 ppm Accothion 10 ppm/day Imidan 96 ppm Actidione 50 ppm IPC-400 > 2000 ppm Agrox >2000 ppm Landrin 16. 8 ppm Al<ton >2000 ppm Lannate 7 .5 ppm/day Allethrin >>2000 ppm Matacil 22. 5 ppm Aminotriazole > 2000 ppm Merna RM 1059 ppm Atrazine >2000 ppm Mestranol ..» 1000 ppm Azodrin .25 ppm/day Meta-systox-R 53.9ppm Balan >2000 ppm Mobarn 40 ppm/day Bay 37289 5.66 ppm Nicotine sulphate 6 ppm Baygon 6 ppm/day Norbormide > 3000 ppm Bidrin . 250 ppm/day Nucleopolyhedral virus >361 ppm Bordeaux mixture >2000 ppm OMPA 36. 3 ppm Botran >2000 ppm Panogen 56 .1 ppm Casoron >2000 ppm Phosphamidon 3 .OS ppm Ceresan L 30 ppm/day Phygon >2000 ppm Ceresan M >2262 ppm Pyrethrum > 10 ,000 ppm Chlordane 1200 ppm Rotenone > 2000 ppm Ciodrin 790 ppm SD 7727 >2000 ppm CIPC >2000 ppm SD 11831 100 ppm/day Co-Ral 29. 8 ppm SD 15418 150 ppm Cotoran >2000 ppm Silvex 500 ppm 2, 4-D >>1000 ppm Sodium arsenite 323 ppm Dasanit . 749 ppm Sodium monofluoroacetate . 5 ppm/day Diazinon 3 .4 ppm Strychnine 2. 9 ppm Diesel oil #1 20 ppm Sulfoxide >2000 ppm Dimethoate 6 ppm/day Supona 85. 5 ppm Diquat 564 ppm Systox 2. 5 ppm/day Disyston 4 .24 ppm Telodrin 4 .15 ppm 
D.M. 7537 <2. 5 ppm Tenoran 2000 ppm Dursban 25 ppm TEPA 8. 54 ppm Dyrene > 2000 ppm Thimet .09 ppm Elgetol 22.7 ppm Thiodan 33 ppm EPN 3.08 ppm Thiram > 2800 ppm Famophos 9. 87 ppm Thuricide >>2000 ppm Folpet > 2000 ppm Tordon > 2000 ppm Furadan 1 ppm Treflan > 2000 ppm Gardona »2200 ppm Trithion 121 ppm GC 6506 1.12 ppm Zectran 1. 25 ppm/day Gophacide 24 ppm Zectran (acylated) >2000 ppm 
(l)  Tucker and Crabtree, 1970.  
(2)  These values are dosages either ingested, injected or assimilated from  
topical application.  Units are derived from milligrams of dose per  
kilogram body weight.  
VI-12  

from the bay system. A final information need is the magnitude of the con­centrations of the various toxic organic materials in the local waters. This procedure is required since in the natural environment the effects on organisms of most toxic materials are mitigated by adsorption and other interactions with the suspended materials. 
VI-13 


CHAPTER VII DEMONSTRATION OF APPROACH 
In this chapter preliminary information is provided to demonstrate how the biological criteria developed can be utilized in conjunction with data from other project task forces to evaluate the environmental impact of dif­ferent coastal zone ~anagement policies. The results of the evaluation of three hypothetical policies by the entire project staff are presented in a separate report of the project entitled "Final Report--Example Application 
'II: Evaluation of Hypothetical Management Policies for the Coastal Bend Region". 
Briefly, there are three hypothetical policy situations under considera­tion for the years 1980 and 1990 for the Corpus Christi area. Policy I is a no change hypothesis; laws, regulations, technology and economic interre­lationships will remain the same as in 1970, although economic and demo­graphic changes will occur. Policy II precludes development within 1500 feet landward or seaward of the mean low tide line of any bay, estuary or natural beach waterfronts or along major public channels and canals after 1980. Policy III assumes that wastewater treatment will progress by means of the be st available technology to a goal of wastewater discharges having quality approximately equal to intake waters by 1990. 
The purpose of this chapter is. to show by examples how evaluations of the policy effects can be performed. The examples will be limited to housing and marina complexes located in the Corpus Christi Bay vicinity--i. e., on Padre and Mustang Islands and near Rockport--and to predicted salinity conditions in the bays due to policy effects in 1970, 1980, and 1990. There are two primary forms of data input to this task force. The first is in the form of maps of land use patterns and tabulations of attendant human activities and demographic changes provided by other task forces. The second consists of tabular displays of the concentration distributions of various water quality parameters, provided by the work of two additional task forces. 
Hypothetical Land Use Information 
Figure VII-1 is a sample of the data format for land use information. This example depicts a hypothetical housing project on Mustang Island. Table VII-1 shows data from this and two other hypothetical community developments in the form of the acres of the various biotopes to be changed into residential lots and bulkheaded channels. In the hypothetical Mustang Island development, 126 acres of dune biotope would be exempted from devel­opment under Policy II (the 1500 feet policy) . Altogether, Policy II applied 

ft. WATER LINE CORPUS CHRISTI BAY f (BAY} 

~­
•USTAM' 
LSLA MD 

BOUNDARY OF 
< 
DEVEWPMENT
1--1 
1--1 
I 
N 
PORTION TO BE IE 
BETWEEN 1980 81990.UNDER 

11 11
/500FT POLICY ·
/500FT LINE 1500 Fr 
/lWATERUNE 
--~-------.--------~--1:~------t-:-~-------~~------~~~--~----~--~~------~~J 

~n • I " 'PORTION TO BE EXCLUDED FROM Vl SEA WA LL DEVELOPMENT BETWEEN /9£lJ 8 1990 GULF WATERLINE UNDER 11 /500 FT II POLICY ME xI co
{BU LF) 
EFFECT OF "!500 FT.11 POLICY ON HYPOTHETICAL RESIDENTIAL 
DEVELOPMENT ON MUSTANG ISLAND SCALE / 11 =2000' 
FIGURE VII-1 
__~f 
TABLE VII-1 
BIOTOPE AREAS* AFFECTED BY 
THREE HYPOTHETICAL MANAGEMENT POLICIES 

Policies I and III -Full Development 

Biotope Changed Mustang Padre Rockport Total Percent 
Dune and Barrier Flat 1519 2716 4235 31. 7 Sandflat 39 585 624 8.4 Spoilbank 20 258 70 348 2.6 Spartina 42 72 114 1. 5 Channel 93 93 7.7 Beach 145 145 7.3 Grassflat 168 168 0.9 
TOTAL 1578 4007 142 5727 
Policy II -1500 1 Setback Biotope Changed Mustang Padre Rock26rt Total Percent 
Dune and Barrier Flat 1393 1229 2622 19.6 Sandflat 39 585 624 8.4 Spoilbank 20 258 54 332 2.5 Spartina 42 24 66 . 9 Channel 93 93 7.7 Beach 145 145 7.3 Grassflat 168 168 0.9 
TOTAL 1452 2520 78 4050 
* Biotope areas are from the enclosed map. 
VII-3 

to all hypothetical housing and marinas, would result in the exemption of approximately 1600 acres of dune, 50 acres of saltmarsh, and 15 acres of 
spoil bank biotopes. Policy III will not affect the sizes of the projected developments. 
Table VII-1 illustrates how the hypothetical developments result in major changes in some biotopes. This is especially true of the dunes where ap­proximately 30 percent of this biotope in the Corpus Christi Bay area will be removed as a result of Policies I and III. As the dune areas are valuable for storm protection such change is highly significant. Application of Policy II reduces the amount of dune change, but the change is still considered significant to the total. 
The construction of marinas under pre sent practices has, in some cases (Corliss and Trent, 1971 and Nixon, et al., 1973), created habitats in which such ecological measures as plankton primary productivity, nutrient concen­tration, dissolved organics and dissolved oxygen were comparable to adjacent marsh areas. Surfaces such as bulkheads, floats and pilings support fouling communities which contribute particulate matter analagous to that contributed by marshes. In the case reported by Nixon, et al. (1973), some natural marsh remained interspersed in the developed marina area arrl tidal flushing resulted in 50 percent water renewal per tidal exchange. In the case described by Corliss and Trent (1971), conditions of West Galveston Bay are much more similar to those of Corpus Christi Bay. Here very poor tidal exchange allowed high phytoplankton densities where primary production was essentially equiva­lent to the phytoplankton production in the neighboring marsh. No natural marsh remained contiguous with the channelized marina area. Restricted flushing also allowed nutrient concentrations which were too high to support a balanced ecological situation. Unfortunately, no determination was made to compare the values of total production in the unaltered marsh with the production of phytoplankton in the channelized area. 
Copeland (1965) described the equivalent productivity between a grassflat and a blue-green algal mat. While the amount of carbon assimilated in each environment was the same, the products available to consumers as food dif­fered so as to produce entirely different consumer communities. Nixon, et al. (1973) indicate changes in the fish distributions between marsh and marina which may reflect such a change in consumer populations. The aspect of this which is applicable to management is that equivalent carbon assimila­tion between natural and created habitats does not necessarily lead to identical production in terms of the consumer communities. The Life History Data Bank should provide information on such consumer changes as more complete informa­tion is tabulated during the next two years of research. 
VII-4 

Salinity Changes 
Figures VII-2 through VII-7 represent isopleths of salinity for the three hypothetical management policies. Figures VII-2 through VII-4 illustrate salinity changes in 1970, 1980, and 1990 respectively when nearly all Nueces River flow is diverted to municipal and industrial use. Since large rainstorms do sometimes occur in late summer over the Nueces River watershed, Figure VII-5 represents salinity changes for a high river inflow during this period. Figures VII-6 and VII-7 represent salinity concentrations for Policy II and Policy III, respectively, under the low river inflow conditions. There is a problem of calibration which affects the evaluation of absolute values for these model runs. In order for the data package check values to work out, continuous supply of 26 ppt water was assumed at the boundaries on the north and south. This will be changed to reflect natural fluctuations in con­tinuing studies. The boundary to the south is the Laguna Madre, where, during dry conditions, salinities may reach 45 ppt. However, for compara­tive purposes, this discrepancy is presently not considered. 
Figures VII-2, VII-3, and VII-4 show the projected effects of Policy I through time. There are very minor variations of the salinity structure for these dry conditions, with salinities similar to those of the Gulf expanding across the bay with time. The gradients are around 0. 2-0. 3 ppt per nautical mile, such that large displacements of the isopleths may not necessarily re­flect large changes in salinity. The largest variations in salinities are be­tween dry and wet conditions where Nueces River discharge rises from around 85 to 10, 000 million gallons per day (MGD). (See Figure VII-5.) 
Table VII-2 is a summary from the Life History Data Bank of the minimum levels of occurrence with regard to salinity for organisms with commercial or sports importance in the local area. The only fish which appears to have limits which might exclude it from part of the bay during the low salinity of the wet conditions of Figure VII-5 is Menticirrhus americanus. However, the habitat of this species is primarily in the lower bay and along the open Gulf where the lower salinities are not as likely to occur. Wet conditions are transient for this area, usually lasting 30 days or less. The organisms shown in Table VII-2 are all sufficiently motile to avoid salinity stress situations. Therefore, it appears that the lowest salinity conditions predicted will not adversely affect the comercially important species populations of the bay system. 
There may be some benefits for organisms dependent on lowered salinities for reproduction or predator elimination such as oysters. However, suitable depths for oyster habitats are generally lacking in Corpus Christi Bay. A prediction of substantially increased oyster production in Corpus Christi Bay is not warranted in this case, but some may occur in the boundary of Corpus 
VII-5 


VII-6 


VII-7 

VII--8 


. . 


VII-9 

VII-10 


TABLE VII-2  
LOWER LIMITS OF OCCURRENCE OF SPORTS OR  
COMMERCIALLY IMPORTANT ORGANISMS  
WITH RESPECT TO SALINITY  
(From Data Presently in Life History Data Bank)  Lower  
Limit  
Organism  Common Name  Life Stage  (%)  
Chondrichthyes  
Carcharhinu s leucas  Bull Shark  0.0  
Crustacea  
Callinectes sapidus  Blue Crab  Adult Female  0.1  
Penaeus Aztecus  Brown Shrimp  Juvenile  0.2  
Duorarum  Pink Shrimp  Juvenile & Adult  10. 6  
Setiferus  White Shrimp  Adult  0.1  
0 steichthyes  
Archosargus  probatocephalus  Sheepshead  Adult  2.5  
Bagre marina  Gafftopsail Catfish  Adult  3.7  
Brevoortia patrmas  Gulf Menhaden  Adult  0.5  
Caranx hippos  Crevalle Jack  Juvenile & Adult  4.8  
Cynoscion arenarius  Sand Seatrout  Adult  4.8  
nebulosus  Spotted Seatrout  Juvenile & Adult  1.0  
Elops saurus  Ladyfish  2.5  
Megalops atlantica  Tarpon  Adult  2.2  
Menticirrhu s americanus  Southern Kingfish  Adult  14.4  
Micropogon undulatu s  Atlantic Croaker  1. 5  
Paralichthys lethostigma  Southern Flounder  Juvenile & Adult  2.0  
Pogonias cromis  Black Drum  Adult  0.0  
Sciaenops ocellata  Red Drum  Adult  0.0  
Mollusca  
Crassostrea virginica  American Eastern  Adult  3.0  
Oyster  

VII-12 

Christi-Nueces Bay. 
Figures VII-4, VII-6, and VII-7 show the only series available comparing Policies I, II, and III. There is essentially no change in estuarine salinity between Policies I and II. However, there is a significant change due to Policy III, primarily due to the cessation of brine discharges. Salinities in some parts of the estuary as a result of Policy I are greater than 35 ppt, while for Policy III the entire bay is less than 3 0 ppt. 
The Life History Data Bank (which is not complete) was queried for all organisms with upper salinity limits of occurrence or tolerance between 30 ppt and 35 ppt. The data indicated ten species found at salinities no higher than 35 ppt (Table VII-3). The highest recorded salinity of occurrence for these species was 32 .0 ppt. From the data presented, it could be inferred that these ten species could be adversely affected by the salinity increase resulting from hypothetical Policy I because the organisms would be at the extreme of their salinity range. In actuality, the authors realize that many of these organisms have upper limits of salinity tolerance well above 35 ppt. However, the data are not in the literature that had been entered into the Life History Data Bank at the time of the query. One notable exception is the blue crab whose data shows the tolerance of the eggs to be a measured maxi­mum of 3 2 ppt. This would indicate that this species could not multiply at 35 ppt salinity. With more extensive data in the Life History Data Bank, a query such as this would produce a list of organisms whose known upper tolerance limit for salinity was below any given number. This will allow the researcher to state that these species will be adversely affected. 
All of the effects may not be adverse, however. Table VII-4 lists 18 species of organism whose lower salinity limits, as reflected by data presently contained in the Life History Data Bank, fall between 30 ppt and 35 ppt. It can be inferred that these organisms may be favored by the increase in salinity brought about by Policy I as it either brings it within their lower limit or very near it. In actuality, all of the organisms listed occur at salinities below 3 5 ppt, but some of the data are from one recorded occurrence only. However, it can be said that the increase will make the bays more inviting to these species, all other factors being equal. Also, the data concerning the turtle grass notes that its preferred salinity range is from 33 ppt to 38 ppt. This would allow it to spread from the lower bay areas into other parts of the bay where bottom conditions are appropriate. 
The tables have been presented to show the methodology and data pre­sently available to assess the impacts of salinity change in the Corpus Christi Bay area. From the present data only, it could be inferred that the increase in salinity to 3 5 ppt may favor the species in Table VII-4 and may to a slight degree adversely affect those in Table VII-3. These effects would not necessarily be significant for any one species; however, the 
VII-13 

TABLE VII-3 ORGANISMS FROM INITIAL QUERY WITH UPPER SALINITY LIMITS OF OCCURRENCE OR TOLERANCE LESS THAN 35 ppt 
(Adult Unless Noted) 
Scientific Name 
Adenia xenica Callinectes sapidus Evorthodus lyricus Gobiesox strumosus Gobionellu s belosoma Gobionellus hastatus Hemicaranx amblyrhynchu s Leptophrys trigonus Lutjanus griseus Sygnathu s elucens 
Common Name 
Diamond Killifish Blue Crab (eggs) (tol.) Lyre Goby Skilletfish Darter Goby Sharptail Goby Bluntnosed Jack (juvenile) Trunkfish Gray Snapper Shortfin Pipefish 
TABLE VII-4 
Salinity ppt 
30.5 
32.0 
31. 7 
20.7 
30.0 
24.6 
24.3 
18.6 
16.1 
29.4 
ORGANISMS FROM INITIAL QUERY WITH LOWER 

SALINITY LIMITS OF OCCURRENCE GREATER THAN 30 ppt 
Scientific Name 
Coranx ruber Centropristis philadelphicus Cithorichthys macrops C onodon nobili s Cyclopsetta chittendeni Dasyatis sayi Echertei s noncrate s Gerre s cinereus Gobiosoma robustum Hippocampus erectus Lactophrys quadricornis Lagocephalus laevigatus Peprilus alepidotus Syacium gunteri Symphurus plagiuso Trachinotus goodei Plants Cymodocea filiforme Thalassia testudinum 
Common Name 
Bar Jack Rock Seabas s Spotted Whift Barred Grunt Mexican Flounder Bluntnosed Stingray Shark Sucker Yellowfin Mojarra Code Goby Lined Seahorse Scrawled Cowfish Smooth Puffer Harvestfish Shoal Flounder Blackcheek Tonguefish Palometa 
Manateegras s Turtlegrass 
Salinity 
ppt 
35.6 
33.6 
30.6 
36.7 
32.9 
35.6 
35.6 
30.6 
35.6 
35. 6' 
35.6 
35.6 
33.0 
30.7 
33.0 
35.7 
30.0 
30.0 
VII-14 

community structure would probably be changed. ·This may or may not change the total productivity of the area. When adequate data is entered into the Life History Data Bank, it should be possible to assess this change quantita­tively. 
Copeland (1966) has advanced theories on the freshwater inputs required by the Texas bays in regard to fisheries. Cooper and Copeland (1973) in microecosystem work found the freshwater input to be extremely important to maintain autotrophic metabolism in the less saline components of their systems. Their conclusion was that freshwater inputs were necessary for balanced community structure. The elimination of brine discharges, or at least the modeled results of eliminating them, decreases salinities as Copeland felt was required; however, there is no increase in nutrient material from upstream which was paramount in his evaluation of freshwater additions and their beneficial effects on fisheries. 
Summary 
At this point in our development of causes and effects using hypothetical situations, we cannot come to specific conclusions. However, the purpose of the discussion was to present our approach to the problem of defining ef­fects of change in estuarine environments. During the next two year phase of the project we will continue to develop the techniques to the point of decision, using quantitative effects of change. 

APPENDIX A ENVIR COMMANDS, VARIABLE FORMS, ERROR MESSAGES 
TABLE A-1 ENVIR COMMAND DIRECTORY 
Short Form Directory of ENVIR commands: the underlines capitals are the exact form required, lower case letters are used to indicate where an optional or highly _variable form is used. These variable forms are explained below, in Table A-1. 
ID 	noise this statement was used to label pages in a batch version of ENVIR in this version it can be used as a sort of memo 
used to terminate ENVIR 
TTY 	noise~ this command instructs the program to modify its normal file handling procedure so that teletype input/output can be used 
TIME noise~ 
11TM 11
causes the printout of the used since the la st TIME command 
MEMO noise~ all of the card is simply reprinted on the output file to serve as a memo 
SHOW: descriptor list FOR noise WITH boolean expression ~ for use only with teletype operation, prints out on the teletype the descriptors as specified for the items which satisfy the boolean expression 
PRINT: descriptor list FOR noise WITH boolean expression * similar to SHOW: except that output will be on TAPE3 
HOW MANY noise HAVE boolean expression ~ ENVIR will reply with the number of items which meet the requirements 
CORRECTION J...descriptor, descriptor statel noise WITH boolean expression * all of the items satisfying the boolean expression will have their descriptor specified changed to the specified state 
DELETE ITEMS noise WITH boolean expression~ items satisfying the expression will be deleted from the bank 
DELETE STATE descriptor, descriptor state ~ the specified state will be removed from the dictionary only if there are no items in the bank having this state-therefore, use of this statement usually is restricted to eliminating misspellings, etc. after a correction statement 
A-1 
TABLE A-1 (CONTINUED) 
READ DATA BANK* the binary data bank is read in; the numbers which characterize the bank, such as number of items, are printed out 
WRITE DATA BANK* if corrections or additions have been made to the bank, the new version can be written out using this command, the local file containing the old bank is written over with the new bank 
DEFINE ITEMS FROM TAPE* new items will be read from fn3 and the program will compute the binary representation of each item and put it in the data bank 
DEFINE DESCRIPTORS n descriptor def. AL descriptor def. BL etc. 2 n is the number of fields which appear in each item, item structure is discussed below. Note, up to 46 descriptor definitions can be accommodated in the pre sent program, thus this command will take up more than one line, it must be terminated with *. 
DEFINE MORE DESCRIPTORS n descriptor def. ~descriptor def. BL etc. 2 similar to the above where n is now the number of fields to be expected in the future. This command is used to alter the structure of an existing data bank. 
DEFINE AND PRINT ITEMS FROM TAPE* this command acts like the DEFINE ITEMS FROM TAPE* command except that each item is printed out on fn2 as it is read. The amount of output generated by this command is too much for the teletype. 
TABLE A-2 EXPLANATION OF "VARIABLE FORMS" 
descriptor def. -The general form of a descriptor definition is: 
descriptor name J.. de sc. numberL typeL range l where­
descriptor name can be any alphanumeric word or words desc. number is the number of the field in the item where this descriptor occurs 
type -there are at present four types of descriptor permitted, the name and general form of the range is given below-NAME the range is the expected maximum number of states 
for which space must be reserved, as an integer. 
A-2 
TABLE A-2 (CONTINUED) 

ORDER  the~range is expressed FROM nl TO n2 where nl  
and n2  are integers, n2 greater than nl.  
ORDER  the range is expressed as a  list of integers,  
separated by commas,  only numbers in the list will  
be recognized,  so the list must include all numbers  
which could appear in the items.  
CODE  the range is expressed as a  list of coded names  
separated by commas.  Again, the list must include  
all possible states.  
descriptor list  -this is a list of the descriptor names which controls the output  

of the search results. In this list, the descriptor names must be separated by commas, and they must be spelled exactly as they appear in the original "DEFINE DESCRIPTORS" command. Parentheses can be used to control the way in which the descriptors are grouped in the output, and the order in the list determines the order in the output. The output is sorted by descriptors, alphabetically for "NAME" type descriptors, low to high for "ORDER" descriptors and in the order of the original list for "CODE" type. 
noise -this represents any alphanumerics which may be used to make the copy read nicely. Names ENVIR recognizes cannot be used. 
boolean expression -this expression controls the search of the data bank, it consists of descriptors and descriptor states connected by the operators OR, AND, and NOT and may contain parentheses to control the order of evaluation. 11 NAME" type descriptors are specified as follows: 
descriptor name.L. descriptor state where the descriptor state must be spelled exactly the same as it appears 
11
in the input items. ORDER" type descriptors are specified as follows: descriptor name.L. nl or descriptor name, FROM nl TO n2 
where nl and n2 are within the range specified for this descriptor and n2 is greater than nl. Note that a comma must appear after the descriptor name, and before the descriptor state. 
The use of tre operators can best be seen in the following example boolean expressions: 
GENUS, MU GIL AND BIOTOPE, GRASSFLAT OR SHALLOW BAY GENUS, MUGIL AND SPECIES, CUREMA AND LIFE STAGE, ADULT AND BOTTOM TYPE I MUD OR SAND PARAMETER, TEMP AND LIMIT TYPE, OCCURRENCE AND LOWER LIMIT, FROM 100 TO 120 AND NOT (REFERENCE, 1 OR 4 ) 
A-3 
TABLE A-2 (CONTINUED) 

A descriptor cannot have more than one state at the same time, therefore, an expression such as GENUS, MUGIL AND GENUS, GOBIOSOMA will result in an error message. The operator "NOT" cannot be used with a descriptor state, but mu st refer to a descriptor name, descriptor state pair, or more complex clause. 
TABLE A-3 
ENVIR SYSTEM ERROR MESSAGES 

The following is a list of error conditions which ENVIR may encounter while processing. Only the error number will be printed. 
ERROR NO. 	ERROR CONDITION 
1 	Unrecognizable statement name. 
2 	Statement name not complete on first card of statement. 
3 	This is not a user error, but is an indicator of trouble in the ENVIR system. Save all printout and input cards and contact the author. 
4 	Too many descriptors in a PRINT or GENERATE descriptor list or too many (descriptor, descriptor state) pairs in a CORRECTION statement. Reduce their number or recom­pile ENVIR, enlarging tables DLIST first dimension, ITEM (first dimension) and STAT, as well as constant DLXMAX. Set their values equal to each other and equal or greater than number of descriptors in the data bank. 
5 	Descriptor name mu st not begin with a left parenthesis. 
Too many descriptors being defined. Reduce their 
number or recompile ENVIR, enlarging tables DD, DDl, 
and DD2, as well as the constant DDMAX. Set their 
values equal each other and equal or greater than the 
number of descriptors desired. 
6 
A-4 

TABLE A-3 (CONTINUED) 

ERROR NO. 
7 
8 
9 10 11 12 13 14 15 16 17 18 19 20 
ERROR CONDITION 
Too many descriptor or descriptor state names containing greater than 6 characters. Reduce their number or recom­pile ENVIR, enlarging table OVRFLO and constant OVMAX. Set their values equal each other and equal or greater than the expected number of descriptor and descriptor state names that contain between 7 and 12 characters, plus twice the expected names between 13 and 18 char­acters, plus 3 times the expected names between 19 and 24 characters, etc. 
Not enough space dimensioned in the ENVIR system for 
descriptor states. Either reduce the estimates on 
descriptors under the NAME option or recompile ENVIR, 
enlarging tables DSS, DSC ODE and the constant 
D SSMAX. Set their values equal each other and equal 
or greater than the sum of the number of states in · 
descriptors defined under ORDER OPTION. 
Coded descriptor state out of the range defined. 
Not used. 
Neither NAME, FROMTO, or ORDER option is specified. 
No number found where expected. 
90 character limit on name length exceeded. 
All blank name is not permitted. 
Trying to define the same descriptor name twice. 
Using a descriptor name never defined to ENVIR. 
Trying to define the same descriptor state name twice. 
Using a descriptor state name never defined to ENVIR. 
No right parenthesis following descriptor parameters. 
Not enough space reserved for base characteristic func­

tions in the data matrix. Reduce the size of some of the descriptors or recompile ENVIR enlarging table !FILES 
(1st dimension) and the constant DMAX. 
A-5 

TABLE A-3 (CONTINUED) 

ERROR NO. 
21 
22 
23 
24 
25 26 27 
28 
29 
30 
31 
32 
33 
34 
ERROR CONDITION 
Too many printout lines per item specified in a descriptor list. Reduce their number by combining descriptors into line combos or recompile ENVIR, enlarging table COMBO and the constant COMMAX. 
No closing parenthesis on a line combo in a descriptor list. 
No comma or asterisk after coded descriptor state. 
No. of states in this descriptor has exceeded the esti­mate given in the DEFINE DESCRIPTORS statement. At the present time there is no remedy for this other than to redefine that data bank from scratch. 
Same as #3. 
Same as #18. 
Discrepancy between number of descriptors stated in DEFINE DESCRIPTORS statement and the number found. 
Estimate of number of states in a descriptor defined under NAME option is not a positive integer. 
Statement name not followed by asterisk or FOR. 
Boolean expression too long for processing space reserved. Break up query into series of smaller queries or recompile ENVIR, enlarging table STRYNG and constant STRGMX. 
Boolean expression too long for processing space reserved. Break up query into a series of smaller queries or recom­pile ENVIR, enlarging table STACK and constant STKMAX. 
Boolean expression does not have balanced parentheses, 
i . e. , does not have same number of left and right paren­theses. 
Trying to operate on a data bank that is not present in the machine, i.e. , which has either not been defined earlier in the run or has not been read into the machine from an external device. 
Not used. 
A-6 

TABLE A-3 (CONTINUED) 

ERROR NO. 
35 
36 
37 38 39 
40 
41 42 43 
44 
45 
46 47 
48 
49 50 
51 
52 
53 
ERROR CONDITION 
A descriptor with option NAME has been included in the 
descriptor list of a GENERATE statement. ENVIR is not properly dimensioned to hold this data bank. One or more of the critical values printed for the stored data bank exceeds the maximum value permitted by the ENVIR in use. Use the ENVIR which generated the stored bank or redimension the one in use to accommodate the excess. 
Neither CARDS nor TAPE nor DISK specified. 
Not used. 
Illegal FROM-TO range in a boolean expression. 
FROM value greater than TO value in a boolean expression. 
Boolean expression must not begin with AND or OR. 
Illegal use of parentheses in a boolean expression. 
TO missing from FROM-TO range in a boolean expression. 

Illegal operator in a boolean expression. 

NOT or FROM expected but not found in a boolean expres­
sion. 
NOT expected but not found in a boolean expression. 

Same as #3. 
Same as #46. 
Same as #42. 
Expecting a numeric ordered descriptor state, it turns 

out to be neither numeric nor UNKNOWN. 
Numeric ordered descriptor state not in the defined set 
of numbers. 

In a descriptor list the left parenthesis denoting the be­ginning of a line combo has occurred before the right parenthe sis denoting the end of the previous line combo. 
Right parenthesis missing or TO expected but not found in a boolean expression. 
A-7 

ERROR NO. 
54 
55 

56 57 58 59 
60 61 
62 63 
64 
65 
66 67 
68 69 
70 71 
72 
TABLE A-3 (CONTINUED) 
ERROR CONDITION 
Illegal termination of a boolean expression. 
AND, OR, NOT, or TO expected but not found in a boolean 
expression . 
Same as #42. 
Operator expected but not found in a boolean expression. 
AND or OR expected but not found in a boolean expression. 
The right parenthesis denoting the end of a line combo 

has occurred with no previous left parenthesis. 
Same as #3. 
No left parenthesis before descriptor, descriptor state 

pair. 
No descriptor name or descriptor state name where expected. 
Asterisk encountered too soon or left parenthesis out of 

place. 

A new descriptor state has been introduced in a descriptor 
whose option is CODE or ORDER, FROMTO. 
Too many numeric descriptors. Reduce their number or 

recompile ENVIR, enlarging tables FROM, TO, BY, IN 
and constant FTBIMX. Set their values equal to each 
other and equal to or greater than the number of numeric 
descriptors de sired. 

Ordered numeric descriptor without a TO parameter. 

Ordered numeric descriptor with a non-positive BY_para­
meter. 
Equals reference to prior descriptor not valid. 
Ordered numeric descriptor with decreasing FROM-TO 

range. 
Not used. 
Not used. 
Not used. 

A-8 
TABLE A-3  (CONTINUED)  
ERROR NO.  ERROR CONDITION  
73  Not used.  
74  Not used.  
75  Not used.  
76  Cannot delete a  state from a descriptor whose option  
is other than NAME.  
77  Cannot delete the UNKNOWN state from a descriptor.  

A-9 


APPENDIX B OBTAINING LIFE HISTORY INFORMATION FROM LHBANK 
The following discussion assumes a working knowledge of the University of Texas "TAURUS" timesharing system, and access to an interactive terminal. 
1) 	A copy of the program and the data bank can be obtained as follows: 
execute the system command READPF (iiii, BENVIR, LHBANK) , where iiii 
is the number of the permanent file currently in use. 

2) 	Execution of the program: 
for interactive operation execute the following system commands-­
REWIND (LHBANK) 
BENVIR(TTY I TTY, SC I LHBANK) 

the system should reply with a system loader message, after this is received, or after the TAURUS command 11 (bell) STATUS" gives the status as "waiting for input", the following "ENVIR" commands should be input--TTY WILL BE USED 
READ DATA BANK* ENVIR will reply with a long message about the status of the bank. For batch operation, the general form of operation is--
BENVIR(fnl, fn2, fn3, fn4) where fnl, etc. are names of the files requires--fnl =input ENVIR commands, fn2 =output of ENVIR messages, fn3 =new ENVIR items to be added to the data bank, and fn4 =the name of the data bank file, usually LHBANK. 
3) 	Querying the data bank: after the data bank has been read by the program, query commands can be issued, see the command directory for correct forms of these commands. To terminate a session, enter ENVIR command "END*", or TAURUS command "(bell)ABORT". 
Flow of Data From Life History Coding Forms Into the Life Hi story Data Bank 
1) 	Creation of a LHn file where n is an integer corresponding to the batch number. -Flow Chart 1 
The person doing the coding uses the present vocabulary as much as pos­sible to code information from a reference onto a sheet. The reference must be assigned a unique number. When a sizable batch of sheets has been col­lected, from 100 to 300 is best, they are sent to the Comp. Center for key­punching, together with a coding form filled out with the control cards needed 
B-1 


FLOW CHART 1 
Creation of a LR!!, file (n is an integer corresponding to batch) 
~ ~·
. 
---... ... 
original I .--~ . "­
reference _ ---~ 
...... ...... 
--------·
­
card file
L ___ I l 



!
previous read and 
vocabulary i 
code 

J 
orm 

keypunch 
cards 

storage 
SAVEPF on permanent file 
B-2 

to read in the cards, save them as a 11 LHn11 file on a permanent file, and copy 
them to output. Cards are then mailed along with the output to the MS! for 
back-up storage. 
2) Finding and correcting errors in LHn -Flow Chart 2 
In order to find the mistakes in vocabulary, syntax, etc., which invariably creep into the life history information, we create an ENVIR data bank containing only the life hi story data in this batch, 11 BANKn" . This requires first the use of the program "PERMUTE" to turn the LHn file into ENVIR items, followed by the use of the ENVIR program with the "DEFINE" file, to create the data bank, and a control vocabulary. Since the LHn file is typically rather large, rather than search for errors directly, we get the 11 SHEET" numbers associated with each error and then use the sheet number as an indicator of relative position of the error within the LHn file. The interactive program "EDITOR" is used to correct all of the errors in the LHn file and the corrected file is 11 SAVEPF"ed over the old file. Great care should be exercised at this point to avoid use of the wrong file name. If there is any doubt about the accuracy of the correc­tions, go through the entire step again with the "corrected" file LHn. 
3) Use of corrected LHn file to add data to LHBANK. -Flow Chart 3 
Using the corrected version of the LHn file from step 2, the PERMUTE program is run to create a file of ENVIR items. The old life history bank, LHBANK, is read from a permanent file, and a check for copy errors can be run at this point, using the program BANKCHK. This program was written after copy errors were found in the data bank which caused nonsense results to be retrieved. Subsequent changes to the University of Texas permanent file system have supposedly eliminated the possibility of this type of error. The BANKCHK program produces a checksum by adding all of the words in the binary data bank dictionaries and binary data file. This pair of checksums should remain constant until the bank is next modified. The program also checks the internal consistancy of the dictionaries. 
Assuming that the new ENVIR items are on file, "ENVDATA", the system command to execute the program, would be: 
BENVIR(fn.L fn1, ENVDATA, LHBANK) 
where fn.1 would be TTY for interactive operation, or a separate file of ENVIR commands for batch operation, and fn1 would be TTY for interactive operation or a separate file of ENVIR output relies for batch operation. Batch operation is best if a control vocabulary is desired because the fn1 file will be rather long. The sequence of ENVIR commands should be: 
READ DATA BANK* 
DEFINE ITEMS FROM TAPE* 
WRITE DATA BANK* 
CONTROL VOCABULARY* 
END* 

B-3 

FLOW CHART 2 
Finding and correcting errors in LHn file 

permanent file (~____.._~.r ppERMrogrUTaEml--+;ai comments l0 L !!~: cou:t__J 

local file ENVIR items  /  
per~ile  
\ DEFINE)­ ENVIR program  

bank~~ition 
command file 
SAVEPF 
BANKn 
I~ -
-·-··---_.,.___.._____ 
_ 

permanent file 
-:\ 
LHn i 
-i 
"­
~ 
I 
I 
j 
~---------. I.. 
SAVEPF 
LHn 

local file 
LHn corrected 
batch mode 
local BANK,!!. 

EDITOR program 
interactive identify errors spelling, syntax etc. if any 
control 

formulate ENVIR queries to locate errors b sheet number etc. 
"'""·-· ·~--~. 
ENVIR program interactive
:=:t=­

l locat ions--~-rl 
I l 
l errors 
! 
B-4 


FLOW CHART 3 Use of corrected LR!!, file to add data to LHBANK 

permanent file 
(~~__, PERM_UT_E.J-..... comments
\ /: program 
etc. I 
·-·-·--J 
'·,,~ __....... 

--~ 
local file j 
ENVIR i temsl 
\ 

·\ 
\.~-------­
! 
1 

local file 1 local file f 
LHBANK t LHBANK 
old version\ new version \ 

I. ----------·--· 
~-·· . 
! 

SAVEPF 
LHBANK new version 
no 

~.,___.___~ 

l 
... _ __ 

~· 
1 
permanent file do not use bank seek help 
l---~~e
orrect for 
this bank ? 
any errors ? 

larger than before now contains items with BATCH, !! 
no 

B-5 

The newly written LHBANK is run through the BANKCHK program to obtain the 
new checksums, if all is well, the new LHBANK is saved over the old LHBANK 
on the permanent file. 
Program Instructions--Program PERMUTE 
PERMUTE is designed to read information for the Life History Data Bank in the form in which it appears on the coding sheet and produce ENVIR items corresponding to all of the possible permutations of those descriptor groups which repeat on the coding form ( B, L, AND F lines). This permutation fea­ture eliminates as much repititious coding as possible, but you should be sure that all of the permutations are valid. 
Input: Input to this program consists of a single file made up of groups of lines from each coding sheet. No special separator is required between groups but the R line must be last in each group. There are no order requirements for lines other than the R line, but the order of descriptors within each line must be followed. Commas must be used to separate each descriptor within a line, and if a descriptor state is not known, at least one blank space must be left and the separating commas still must be put in. Example: suppose the common name is not known, the S line could read--S AMERICANUS, , ADULT, NECKTONIC. Note that a comma is not required to indicate unknown de scriptor states is that if the states for the re st of a line are unknown, only the first part of the line must be put in. Example: suppose that only the 
species name is known, the S line will then look like this--S AMERICANUS. PERMUTE will automatically put in the proper number of blanks and commas for the remainder of this line . 
If the entire line is blank, it should be left out, for instance, if PERMUTE does not find any L lines in a group (terminated by a R line as always), the ENVIR items will show blanks for all descriptors Parameter, Units, etc. The only line that is absolutely required is the R line, however, it does not make much sense to fill out a sheet with only a R line on it. If on the other hand, there are too many commas in a line, PERMUTE will reject the group and write a error message. 
The F or Food line is peculiar in that up to 6 FOOD ITEM s can follow the Diet Significance descriptor, in other words, major food items could be written on an F line as follows: F MAJOR, ITEMA, ITEMS, ITEMC, ITEMD, ITEME, ITEMF. Note that separation by commas is required. ENVIR items will be written with diet significance, major and food item, ITEMA-ITEMF all permuted with the various B and L lines also. 
B-6 
11
PERMUTE writes a "BATCH" and SHEET" number in each ENVIR item. The batch number is determined by the first card of each LHn. file, this card must start with X, and have the batch number in columns 2 -10. The re­maining columns 11 -80 are available for comments or memos. The sheet number is determined by a counter within the program which is reset whenever 
11 X11
a card is encountered. 
Two output files are generated, a file of ENVIR items suitable for direct reading by ENVIR and a report file containing the following information. The 
111 11
number of 11 8 11 cards, cards, and "FOOD ITEMS". The number of cards read, and the number of ENVIR items written. The number of groups not written due to errors, andthe "C", "S", and "R" linesfromthegroupswith 
errors. 
The following commands would be used to run PERMUTE on the TAURUS timesharing system, where LHn is a file of card images derived directly from the coding sheets, and iiii is the number of the permanent file currently in use to store the binary version of the PERMUTE program, SPERM: 
READPF (iiii I SPERM) 
REWIND (LHn) 
SPERM (LHn, ENVDATA, LIST) 

The "LIST" file contains the comments, error messages, card counts, etc. , while "ENVDATA" contains ENVIR items ready to be used to create a new data bank or add to an old one. 
The program PERMUTE could be modified to write items suitable for pro­cessing by other general data base management programs besides ENVIR, however, it is not possible to describe a general method for this modification. 
B-7 

CODING SHEET FOR LIFE HISTORY DATA BANK 
1) Class , 2) Family , 3) Genus 
c~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~­
4) Species , 5) Common Name , 6) Life Stage , 7) Motility 
s~~~~~~~~~~~~~~~~~~~~~~~~~~·~~~~~ 
8) Biotope , 9) Bottom Type , 10) Bay System 
B 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
B~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ B~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ B~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ B~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
JAN, FEB, MAR, APR, MAY,JUN, JUL,AUG, SEP, OCT, NOV, DEC, Start Year, End Year 
M 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
25) Parameter , 26) Units , 27) Limit Type , 28) Lower Limit , 29) Upper Limit 
L 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
L 
~~~·~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
L 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
L~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ L~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
3 0) Commercial Importance , 31) Sport Importance , 32) Other Importance 
I.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
33) Trophic Level 
T 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
3 4) Diet Significance , 35) Food Item 
F 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
F~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ F~~~~~~~~~~~~~~~~~~~~~~~~~~~~~­
F 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
F 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
36) Reference No . , 3 7) Ref. Remark , 3 8) Coded by 
R 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
Date Coded: Sent to Keypunch: Remarks: 
B-8 


c 
c 	I 1\1 =>I J T 0 /!\ TA • f-f\tVl R TTt ~ ~ S 0 UT PU T t 0 UTPUT REM aR K S 
c 
TNIE~ER BLA~K,COMMA,FARRAY(8t10)tCt80) ,5~~0)tR(8Q,5) 
tNir6r:R M(~n) .LC~O,c;) tJ (~n) ,T(A0) .F"Cl3n,5> .~(80) tLNCHO) 
r,oMMUN/ERROH/NER 
nAIA ALANK,t"'OMMA/lM t1H,/ 
DA!A 1\ISHt:E:"T/il/ 
f1AIA N8ATCH/Q9/

c 
NH~1CM :.>HOULD Hf:. CHANGrD BY !NPUT 
c 
N3·~TCH SHOULll HF CHl~N~FO Ft"R EAt"'~ BATC~ 
I) A_I A I NC 1 t I Fl'J V • lR Au , NE RR I • 1 t 0 , 0 t 0 I 

c 
T~1:.r \,;OUNTS If\!PllT L1Nt.~, Jf~!V =E~VIR rTEM~tTD/ID= r,ROUPS SKTPPF"O c NE~R = f OTAL FRHORS NER : ERRORS yN THIS GROUP 
c 
c 
!NI~ 	I AL ILE LOOP b .-,cK HfRE AFTER FACH GRn11P 
2 ou l l) J= l • ~ () 
C(.J>=HLANK 
<;(.J>=ALANK 
I~ ( .J) =BLANK 

T<'-">=RLANK 
T (.J) •tiLANK 
R(.J>=ALANK 
no o K=l,s 

8 ( '-" t K ) : Bl HIK 
l (..Jtl\) :dLANK 
f> CON T11-J UE 

l () 	r::ol\I rl NUE 
f)O 11 K=l•1n 
no 11 J=l•A 

11 	FAk~AY(JtK):J0H 
R(bO):lH*

c 
RF-SET COUNTERS FO~ REP~ATTNr, LINF~ 
15 	~tA=O 
NL=U 
Nr=O 
Nf AR=o 

NE~~o 
c 
c 
F' 1J R~ T I< EA D L J NE S UN TT L AN R I S ~EAC1-iE O 
20 JNLT=tNCT+l 
REAO<lti!l> l}J 
21 FOkM~4T(80Al> 
IF <EOF, 1 > 300 tc4 

24 	IF(LN(l).EQ.lHC> GO TO 30 
t F (LI~ ( 1 ) •EC~. l HS) GO Tu 4 O 
If <LN <1) .EQ. lHB) GO TO Sn 
IF<LN(l>.EQ.lHM> GO TO 60 
tF<LN(l>•EQ.lHL> GO TO 70 
Jf(LN(l).EQ.lHI) Go TO Bo 
TF(LN(1>.EQ.lHT> GO TO qj, 

T F ( L '~ ( l ) • EQ • 1Hf ) G 0 T0 l O0 tF(LN<1>.Ea.lHR) GO TO lio JF(LN(l)eEQ.lHX> GO TO 400 c IF HE~E· LN IS NOT RECnr,NrZFD
c 
26 WR!T~(8t27> LN 
21 FOk~~T(*NOT RECOGNlzFD*t/80Al) 
GO Tl) 20 

3  C  PL. "CE  r t·r,~ RAC T1:­R S  l 1 ~  PR nPE r~  /\ PQ AYc:;  
4  c  
5  c  HF~F"  \"fflH  C  TYPF.  
6  30  PO  J~ J=l .•79  
8  ii?  f'(..il•LN(J+l \ rm Td 20  
9  C HFRE  W!Th .5  TYP~  
~ f'l  D0  .4-~  J.: l  '  7 <l  
1 1  4?  S(..J>=Lf\l(J+1 l  
GO  l 1J  20  
1:3  c  ~F~F wilH  t3  
14  5 ()  "J8=:\j 0 + 1  
I F' l :\j rj • LE • 5 l  r, n  T0  S~  
16  f\.t Et-< :: !'J l R+ l  
17  r,o  ft>  20  
18  56  r'\O  Co)(j  J=l•7ll  
19  Sh  r-H~• ' "t3>=Ll~L1+l>  
'30  10  2J  
21  c  HF ~F  ~I 111  M  
22  
~'3  rio  no  6t!.  J=1,10  
24  h?  M (~) ;LN (J+) \  
25  r,()  lU  20  
26  c  rl F. ~E  wJ I 1-1  L  TYp F~  
27  70  ~'.1L•NL+l  
28 29  I F l r-..1 L • L [ • ':> ) •'tEH=·-..ER+ 1  <~ 0  T<)  7 ...,  
GO  T!J  20  
3 1 32 33  7t:-i 7 8  i)() /r:1 J=l•7q L ( ..J • '"L ) =L l'J ( •J + 1 ) GO T1J 20  
34  c  ~ff ~F  ~ I I Ii  T  TYr> F  
35  
36  c  Hf~F wI~14l  l  TYPF  
37 38 39  c  J.iO DO Ht! J=lt7q 82 f(..J>=LN(J+J) r;o Tu 20 11E~F' wI I H T TYPE  
41 42 43  qo q2  oo qz J= i.7Q T(..J>=LN(J+l) GO TIJ 20  
44  c  
45 46  c  Hf. ~E v~ I 1H F lOO \JF·~~+l  TYp f  
47 48 49  I F l N.f • LE. • 5 > NEf"{=M:R+ l GO fl) 20  G0  T0  l , 
1-.  
51 52  106 DO ljH J=l,79 1n~ F' ( .J t i\I F ) =L N ( .I+ l ) GO Tv 20  
53 54 55 56  c  HE~f Wl IH R -I AST 1 l 0 f)() ll2 J=1' 'q112 ~(~)•LN(J+J l NSl'lc.LT:i"SHE FT +  Ll~i F' l  OF  <:;FT  
57  c  
58 59  c  CH~CK COMMAS, ~TC I~ AL.L RUT 120 CALL CHKCOMCJ,C(l))  F  CARDS,  COL  Bn  TS  ,  
61 62 63 64 65  CALL rALL CALL CALL CALL  CHKCOMC4t5(])) CH~C0Mfl4•M(l)' l.HKCOM(3t1(1)) CHKCOM(},T(l)) CHKCOMc?,k(l))  B-10  
66  

2 
3 c 00 ONL ~LANK H JF NH=U 4 I F <Nb • EQ • 0 l ~,1 R=l 5 l25 ro 1~6 K=l.~H G 126 CALL CHKC0 M(3trl(ltK)) 7 l ?. A I F < 1-.J L • EQ • 0 ) NL =1 
8 
l 2 Q i) 0 J J 0 K=1 • .r-1 L. 9 110 CALL CHKCOMC~el<l•K') c SA"1F. t-\)~ NL 11 c 
12 l 3 4 I F ( I~ t·_ ) 2 0 0 • 2 (l () • I 4 1) 13 c 
H~~E ~l IH F TYPFS 14 i40 l\iF~~=o 15 1)0 l 8v r(: 1 • r-tF 
16 c 
F I \I !) l. 0 1"1 M A 17 no l~'+ J=1.1q18 I F ( F <J , K ) • Ln • C 0 i'-1 M A ) r, 0 Tn l 4 8 10 144 coNTlNUE 
c 
SH :>UL I.I 1-..I E\It:: R G F l t--i t.: ~E 
21 
r'Et-=1~ER+ l 
22 GO Tu 330 
23 (., 
HE~F WlfH LOC nF FikST CO~MA, S~T ~AQ~S 
24 l 4 8 :>A AK~ l ; l 25 c 
TRl\NSl-t.R DIET ~JG Tf) L'-1 26 iso ~o 1~2 JJ=1.J 27 1 5 2 I_ N ( J J ) : F ( J J • K ) 28 MAl"<l\!::MARKl+J 
29 c 
M~~Kl lS POS n~ F!R~T ~PACF PAST CO~~A 
MAt\l\c.:MA .RK l 

31 c 
FI~Q NfAT COMMa. MA~~2 IS SPnCE REYONn LA~T ~nMMA 
32 
153 DO 1~4 J=~A~K2•7q33 rF<F<J,K>.~n.ro~MA> no rn l6h 
34 
154 	COl\IT lNUt: 
35 c 
~E ~f i F i'JO MORr::-COf>'IMAS TN TH IS L P-..!E 
36 c 
LOJK ~o~ LAST NON-BLANK AFTER P~fVlOU~ co~~A 
37 
\ !Lt.F T:AO -Ml\RK? 
38 
DO 1~6 JJ=l.~L~FT 39 ,J:CjO-JJ 
Jflf-(J,K).NF.ALANK) ~o To 164 41 156 ("Ol"T l NUE 42 c HE~E ALL BLANK, END OF LINE 43 oo ru iao 
44 
164 J:J+l 45 F' ( .J, r\) :COMM A 
46 c 
47 c 
H=:RE ' MA R K 2 Ts 8 E G 1 N"' T "'~ HI0 ~J Ts E"'0 0 r: ~nn 0 TTE M 
48 c 
49 
166 	~At-<KJ: MARK1~ 
DO 110 JJ=MA~K2•J 

51 
LN(M~RK3)=FtJJtK)52 170 ~Ak~~=MARK3+1 53 MAHK~= J+l 
54 
~At"<r<J=MARK3-l ~ c MAR(~ NOW INDirdfES POSJTION nF LA!i;T r.n~MA 
56 
C 11>1 A ~~ 2 I ~ N 0 w 8 F. G I N1'J I NG 0 F NF X T F ooD ·ITEM 
57 c 
JJ•O 
61 
"'J• l
62 
"JFAR=NFAR+ l 
63 
172 DO 178 J=ltMARKJ 64 173 JJ=JJ+l 
65 
B-11 
66 

IF (JJ.Lt::.10 l G() TO I 76 
lj 
..JJ=O 
5 r.,J_J=N..J + 1 
6 

GO Tv 11 J 
l ·7f:-i CALL MOVE ( L ~ LJ) • l t FAQ RAY (NJ t l\!F' AR) , , I J, l l 8 ifA corn l 1-.Jut. ~) G0 ru 1s3 
l il 
: 1 l 8 0 r. Ol'IJ T l N U E:: 
l.' GO TU 2~0 
13 c 
14 c 
rl ~-~ ~~ '' I rt ! N U F L l 1\J t:: S • ~F T Bt A N K S I~ 2 0 0 ''l FAR :; l 
1(_) FA~~~Y(ltl) =101 • , 11 ;.> ? () r. 0 I'd l NIIE 
18 c 
POSS!blt OlAGl\•nSTlC~ S H1ULD r,o rlfPE 19 J F < i\l ::., Hf t T • l . F • l ) " R I l r:-( 8 , 1 ;> ? l ) 
'.20 
l ~2 J F Or< '"'~ T ( *BA Tr H , 5r1 EE T , r-,r ~ , t-.L , t.,1r-A R *l 
71 
~., R i rt. ( s , 2 2 1 l "'t·M r cH • r-1cs HF. F r , N~ 11 ~L , ~' F' A~ 22 2 2 l F 0 K '": ~ T ( 5 l 4 ) Jf'"(Nt.R.GT.nl r,o TO 'l~() 
24 c 
HE ~F l F NO E.Rt:< :WS 2S C 
WR I T E Pt. R M lJ T A T TO"'' S fJF I . T1\J F c;26 DO ?61) J8=1 .~J~ 27 nO ?. a 0 J L =l • 1\1 L 28 1)0 200 ,Jf =1 , r1 FA~ 
29 c 
WRITE f NVJR ITF" M 
30 
·r EI\! V=1EN V + 1 
31 WRl'ft.. (6t22c;\ ~ 32 
wRJ. n.:(6•2?5) s 
33 
1-m ! rt: ( 6 • 2 2 S l ( R ( J • J A \ • J =1 , ~0 ) 
34 
wFH r~ (b • 2 2 f, l i·1 
35 
1-' P J. Tt. ( b • 2 2 5 l ( L ( J t JI ) t J =1 , A 0 ) 36 ·...iRJ.TE (6,225) t 37 i~p J. rt.: <6 • 2 2 5 l T 38 \vR! rt:. (6•231) (F~RKAY (J,JF) tJl:} tB) 39 2?5 fOt'<1"1AT (BOA 1 ) 40 c3 1 f" 0 t-< fl" I~ T ( 8 A l () )
41 

2 n0 \'1 R 1 rt ( F-t ' 2 b ] ' ( r.I ( J ) •. I =l • 7 (\ ) • r-J Cj ~ T cH • ~, sH E E' T 
42 
261 FOHM~f(f0A],}Ht•lJt1HttI4tlH*) 43 c 
44 c 
GO ~A~K AND RERLANK THFM ALL THf~ ~EAD NEW ~FT 
45 c 46 r,o rv 2 
47 c 
48 c 
Ht~E AFft:R EOr 
49 
j 0 0 'Mn ! Tt (8 93 0 l ) r. NcT ' l F-'f\! v • r p A D ' N f rJ R 50 301 FQHM~T(~EOF REAU AFTF~ *.rs.~ rARD~*/
51 
1 2At!6,* ENVIR lTEM~ ~RITTEN*/
52 
2 e.X •I 6 • -c> GPOUPS NOT WRITTEN f'\I IE T~ * ., I 6, .. ERRORS~)53 llJ~J.n:.(~t.30ll NSdEE.T.l\l~ATrH 54 303 FQKM~T(* NSHfFT=*•I~ ,~ NRATCH:~tl~> 55 306 WRJ.ft.(6t307l
56 
307 FOl'(1'1~T ( l4H[t-.1f) OF l Tr:MS* ) · 57 STUP 
58 c 
E~HnR ~UUTINE 59 330 r.oNr lNUE . 60 331 NEHR=N~~H+NEP 61 p.1AD-= I BAD+ l 62 WR!T~(8•333) c.s,R 
63 
3 ; 3 F' 0 KM A l ( * Enp 0 R ( s ) ~ 1\1 TH T!=\ r,Rn up* • ( I ' ~" A1 ·, ) 
64 3]5 F'O"MAT(*NU~:-c>,f3, .. \lfRR=•t-, I~J ) 65 
66 8-12 
1 2 
3 Tf l l'J t . RR • G T • ,, P l 5 T lJ Pi 4 GO TV 2 
c Hf~E wifH BATC~ NU~RER CHANAF 6 ~00 CALL -NUMH(2dOd.Ut'tERRORtVALeLN)7 TF<NERRUR> 4n4,4\0t404 
8 4()4 wR.lrl:.(8t40C.,) f\IHAlCH,NSt-fEfTtLN 9 
405 ro~1V1lq ( .. EPrH)R Ft~OM ~IUMR, NRATCH=*·I4tlt 
l * 1'1..) Ht£.. T =~ , I 5 t "" L1'4 =.. •I , 8 nA U 

11 
STl)P~ 
12 c 1-1 ER E. w! rr1 Goo D "'u,.,, r.H:. Rs 
410 wqlTt(8t303) NB~TCH,NSM~FT 14 ~111AlLH:lF!X(VAL + 0.1) 
~.15HEt:T:O 16 wR.lTt(8tJ03) N8Al.CH,~5HEFT 17 GO TU ?. 
18 
ENl.J 
19 
SUbf.<OUTl NE rHKr.O~ ( l'lr • L> 
CU1v1 1~1 UN / E R R 0 ~~ I Nt: ~ 21 
rNf~~ER L(H~).RLANK.cO~M~
22 nA I A C0 MM AI 1 H , I • HLJ\ \ 1KI l H I 23 c 
F I ~ST CU UN l C('H.1 M f\ S 24 Jc=o no iu J=l•7~ 
26 
1F ' L ( J ) • E (~ • r () ~MA ) "J r: =Jc + J
27 
io ror-..rINUE 
28 
~1rt.o=NC -JC 29 tFlNtFn> sn,J?114
c 
H ~p E CU f~ REC T f\1 l I"'1 Hf_R ' 1EE D= 0 
31 
12 PEIUhN 32 c TQJ . F~W FIND t AST NJN-RLANK 33 14 L'O !ti J=l•7~ 34 ,Jj•8U-J 
IF(L(JJ).NF.~L,ANt<) ~OTO ?0 36 18 r::o•\J ri NUt:: 37 ?.O .J..J•..JJ+ 1 38 J pt.M:·7 9 -J J 39 IF (Jk~M·LT.rNFED*2)) GO rn An 
D 0 ~b I\ =l t t-.1 f F D 41 LC..JJ):BLAN~ 
42 jJ•JJ+i
43 1. ( ..JJ ~:COMMA 
44 
26 ,JJ=JJ+ 1 
c 
COJNT NUW CORR~CT 46 GO TO l~ 47 c HE~E IF TOO MANY co~MAq48 50 INR1rt.(8t5l) L,NC
49 
51 FQHM~TC* Ton MANY C0MMAS TN •1aOAt/ * FXPFrTEn~•l4) 
NEl-<=NER+l 51 c;o TO 12 52 80 wR1TE(8t81> LtNC 53 81 FQHMAT(* Ton FEW COMMAS TN*/~nAl/
54 
1 .. E~PECTEn*tI4t* Nn1 ENOUGH SPACE TO FIX~) 
NEk=NER+l 

56 r,o ro 12 
57 
F.:NU 58 SU ti R0 lJ TI NE t,llJMH ( Ni'li F , N NL t 5 CA LE F , ~ERR, VAL , L r-.1 > 59 TNl~(,ER LN(l_40) 
C 0 MMUN IS YMR/ND I G ( l 0 > , NPER , LT t NG T , NAST t NLB , NC 0 M~A t NBLN K t t.q NlJ St NPl Uc; 61 c 
T-iiT S SUB R 0 UT I NE TUR NS TH F ~VMB0 L S I N L N ( J > ~ ~J •NF , NL 62 c 
INTU VALt A FP NUMB~~ 
63 
DAfA(NOlG(,J)tJ=ltl0,/1Hn,lHltlH2tlH3tlH4~1HStlH6tlH7tlH~,iHq I 64 QAIA NPERtLTtNGT,~A~TtNLRtNCOMMAtNRLNK,MINUStNPLUS/ 
B-13
66 
1 
2 
.) 
4 	c 
5 
6 
c 
8 
9 
JU 	c 
11 
13 14 
IG 
1 7 
18 
C 
:!O 	C 
21 
24 
27 
28 
29 
30 
31 
32 
33 
34 
35 
36 
37 
38 
39 
40 	c 
41 
42 
43 
44 
45 
46 47 48 49 50 
51 
52 	c 
53 54 55 56 
57 	c 
58 	c 
59 
60 
61 
62 
63 
64 
65 
66 
l }t1,t}H<•ll-l'>•lH*•l.h~,J.~-1,,]H •l'°"'••ll-l+/ T'H Tl Al.{ ZE 
~1r=r-1NF 
•,1L .:t.J!·Jt_ T 0 JN~uL ~\TE Ac; " I f\l '5 T CH f. r,J G F S TM NF=' • \J l 
N[Hk:n VAL=IJ.O rl~ST H~~ovE ANY RLANKS 
r-,1r= ,.,r -1 
") 	~If =1·Jr + 1 rF ( i,,... r, T. NL) GO l 0 T F-l l I~ ( 1\j F ) • F-() • r-.18 l. f\I K ) l\Jl =NL+ 1 
10 	"'L=t\IL-1 
FROM FRONT ANO ~ACK 
.. n r; 0 TO 5 
tFU,J ... GleNLl r;n TO,,,., 
J.\ L T :: ;;-Fu \I U~S I 0 i\.I n F i JI .J 1"1 H T 0 G TVE ~W P R= -1 F 0 Q Al.l 

H 1 .. AN I< 
rFtll'll(NL) .r".t-18LNK) 
1\j O ,/ ( n t: CI~ F 0 ~ c; J(~ 1'1 t N TF ( L '" C NF ) • i:-r~. t.1. INl JS> IF l 1. 1-. <Nf-) • F n • r 1Pl. lJ S ) 
5r,=::,LALEF  
~n  T(J  ~6  
lA  c;c=-SCALEF  
r-,1f::; i\Jr + 1  
~o  ro  2t:>  
20  sc=SCAL[f  
"JF=NF + 1  
2 fi  t F l t\i f • G T , NL )  tW  
no  ]U  J=NF. r--tL  
I F ( L I~ ( J ) • E(.J• 1H • )  
nor.~  K=l•l"  

1 0 
no Tn lO 
r. RUN T 
r; 0 T0 l 8 r, u T() ?. o 
t; h 
~0 T0 3 c; 
Jf(L~(J),EQ.~DIG(K)) GO TO 27 ?.5 coi-.ir l NUE GO Tu Sb 
?1 	~AL= VAL*1~.n + FL~~TCK-1) 
30 	coi"TI NUE r..o TU 50 
H : nf: l U C 0 N V f P T Dt. C l M A, L FR Ar. Tl 0 hi 
3S 	t\iP•J+ 1 I F <r,1 t-• G T • N L ) <10 T0 c; n N[)=U r)O 4H J=NF, t,!L ~l[)=Nt) + 1 I) O .J f K=l • 1 1) JF<LN(J).EQ.NOIG<K>, GO TO 45 
17 	co1H lNUt 
~O TO 56 45 Vfl.L =VAL+ (K-1>*(0.l**Nf")) 48 r,oNTlNUE HE~F wITH VAL, NO ERHt MULTIPLY RV SCALEF 
50 VAL=VAL .q, S(" PEIUHN 56 ~·Et'<R= l 
of.:1. U~N HE~E lf ALL RLA~K MO)S MA~CH 14, 7~ 
60 	"![HR=-1 
RE:. IUi-<N f NU B-14 
Operation of Program BANKCHK 
The purpose of this program is to check ENVIR data banks for two types of error: 1) Dictionary errors which result in incorrect response to queries-­these errors are believed to arise from using banks with copy errors; 2) Copy errors--believed to be caused by tape drive failures during permanent file tape reading. 
Operation: This program should be before using a copy of a previously created bank as follows; REWIND(bank) where bank is the name of the bank file BANKCHK(bank, TTY) for output on file TTY if any errors of type 1 exist, you will see output of this type 
ERROR 1138 1271 1167 
(the numbers refer to dictionary locations)
ERROR 1138 1271 1170 
DESC 4 ERRORS= 2 DELETE = 5 

to determine if errors of type 2 exist, you must compare the 11 CSUM 11 values obtained with this copy of the bank with those obtained when the bank was last modified. 
After creating a new or modified bank, you must run BANKCHK so that you will later be able to check for type 2 errors. The output of the CSUM values looks like: 
CSUM FOR DICTIONARIES= 0705 
CSUM FOR !FILES= 3541 

Other output consists of the CONTROL WORDS for the bank and the date the bank was created or last modified. 
If an error is detected, the bank is unusable. If the error is in a bank created during the present job, RETURN(bank) and repeat the job. If the error is in a bank Just copied from permanent file, execute the following commands to cause the permanent files to be dropped from the system and recopies--hopefully without error this time; 
DROPPF I iiiiI PPPP. (where iiii and pppp are tape no. &. password) READPF, iiii, bank . (if the pf has been modified--savepf etc., you will have to use DROPPF, iiii, pppp, 
M .) 
B-15 


APPENDIX C DESCRIPTION OF DATA FLOW FOR CHEMICAL-BIOLOGICAL DATA BANK 
The usual flow of data into the data bank starts with published or un­published data reports. This data is coded and keypunched, then stored on a magnetic tape permanent file. Using the "TAURUS" timesharing system, the program RDLOG3 is used to read the data and produce a report file and a file of ENVIR items. The report file contains the names of the parameters, methods, units, etc., which appear in the data file, together with the number of items generated and the number of errors detected. This file should be inspected before proceeding to use the file of ENVIR items. 
If a new data bank is to be created, the file of ENVIR commands, 
DEFINE, which defines the data bank structure, is read from a permanent 
file. The ENV:m. program is executed as follows, where "ENVDATA" is the 
file of items: 

BENVIR(DEFINE I LIST I ENVDATA, CBANK) 
11 LIST11 will contain the vocabulary and the program responses, including error messages, and 11 CBANK11 will contain the binary data bank which can then be queryed just like the Life Hi story Data Bank. 
If the new data is to be added to an old bank, read from a permanent file, the following file of commands should be generated: 
READ DATA BANK* 
DEFINE ITEMS FROM TAPE* 
WRITE DATA BANK* 
CONTROL VOCABULARY* 
END* 

This file of ENVIR commands would then be used in place of the 11 DEFINE" file in the above example. 
In some cases, data will be available in a computerized form such·as magnetic tape. If the volume of data is large, it may be more efficient to write a program to convert from the existing format in the format required for ENVIR items, than to recode the data by hand. Because of the wide variety of data formats in use, it does not appear possible to write a single general program to do all conversions. 
At the present time, specialized programs exist to convert from the Galveston Bay Study format and the National Oceanographic Data Center station data format. 
C-1 

FLOW CHART 4 Data Flow for Chemical-Biological Data 
Data Report i------~ 
...-------~ ,_. 
.."'-.... _..,__...-// .
_ 


: i
~··--~ 
.: 	Coded \ 
Data 

·-·--·-____/
\ 

----JI-.----·-··---, 
Program 
;•-____...J.,. ENVIR items( Permanent File 
RDLOG3 
\ Local file \ 
\..__--~---~ 

Addition to
Re::Jort
File 
Old Bank 
.._ 
Creation of New 
Bank 


Program 

Program 
ElJVIR 
ENVIR 

Vocabulary
Vocabulary 
Listing
Listing 




Permanent File 	Permanent File 
2 
3 
4 PRUC;r< J~M ~FADL OG ( r APF5. TAPE 6' TA PE8. oUTPtJT= TAPE,.. ·, 
5 c If\JOIJT• ;.,lJMMARY OIJTt->llTt FNVIQ ITEM~ 

6 
c 7 c I c T =CUlJI'-' T OF ft,tPI iT LI Ni:-c;. I srT=Cf'.'t INT nF STA TT MIS 8 c to::·T = L'ATA Ltr,1fS• !PCT= PARAMS, lDPcT =DUMMY P1,.St ICCT s r.OMMENTc; 
9 c Nf'JVII-< =COUNT Or ENVIL' ITEMS, ~FRR ~ ·CONV. FPRnpS 
10 c 
NS:l = ~NVlR JTfMS TH!~ STATION. ~SERrT = fRPORS THIS STAT1r1N 
11 
c 
12 (OMM0N/L!Nf/LN(l4UJ eJC(30)tNJC•NLN 
13 COMMUN/PARA /NAPHME ( ., • 2. 2rn 'sea 1.EF ','20) • 
14 ] NU i'4 I l S ( 3 , ? 0 ) • Gt: !> PL I ( 3 t ~ , ?. 0 ) , 15 
l "'!:> 1/P • NsAv N • Ll\j p () s ( 2 • ? 0 ) • ~Ip AR MA 16 tNlt.blp GESPLI 
1 7 
COM~UN/STAT /f\IST AT t L" T t LONG, NAGF.NCY ~ NPR.JCT ~ 
18 
l l\IL l i'llf:.t r~s I rF. NA 1()TOP.19 1 NY'l-<•NMO,Nt'IVeNTlME•"·!r'lEPTH . 
20 
COMMUN/CMNTS/CMNT(~,~O)tNCMNT
21 
t NI tl.JE::R CMt-.rT 
22 
coMr·1VN/EXEC /tJFIJV I"'' :,1FRR 'NSCT. J' !SERCT 23 tNIE'-'H< HLANK • tPCT,lSCT24 nAIA BLANK/\OH I 25 PA 11\ NLN·Nn1vp<.NE.RP, !CT. T5CT. yDcT. lPCTt terr. TnPCTtNSWP 26 1 /dUt0•0,0,0.0,0,u,n,0127 n. A ~ ,, NS CT •~'~ERCT I " , 0 I 
28 
C HJITIALlZl 29 10 il/RJ.Tt(6tll)
30 
l l F 0 t-< "1A T ( l H1 • * LOc; RF A0 t Nr; PR 0 GP AM u l 31 C 1-?F. AD ~ LI NE 32 20 REAl){~.21) (LN(J) •J=i ,NLM> 33 21 FOt'<M~T ( ll+OA 1) 34 II-(LOF,S> lOOt22 35 C GO:l() L[l·~r 
36 
2 2 T C I :: l CT+1 37 C CHECK tO~ TYPE nF CARD 
38 
C ~*n tt * t-< f_ vI S I 0 N l 0 I 3 l / / J 
39 
3 0 I F <LI'" ( l ) • En • l HP ) ~O T0 15 4° C I F ~ pc 1 S F 0 L V> wEL) tj Y I\ M S , D , 0 ~ C , r1 ENERA T F A 41 C 011~~y PJ. 42 I F (I"' Sl~ P • Nf • 2 > G 0 T 0 4 O 43 ~JSVH' = 4 44 IOt'CI = IOPrT+l 
45 r,o r u 3q 46 C CAR :>, I ::, A RE A L P • 47 35 Tpl. r = 1PCT + l 48 CALL NUMA ( ? , 2 ' 1 • 0 t "IF: R , R , L N ) ~ NE~µ = INT(P + O.l) 50 I F ( i~ SW P • NF.: • 2 ) G0 T0 3 6 51 IF CNEWP eNF'. 1> GO TO 3,.. 52 C GENERA!~ OUMMY p~, TH£N A REAL Pl. 53 NSWP = 5 
54 
TOt-'C.:I = IDPr:T+l 55 r:;o TU 38 56 c 
C1E 'JER~ H. ORO I NARY P ONt, y • 
57 36 
NSWP = NEWP 58 3R 
CALL C011'1F r"'n 
59 39 
CALL PLlST 60 T F <'"SW P • Nf • '+-) G 0 T0 2 n 61 c ********~**************** 
62 40 tF<Lf"(l} eNf.lHS) GO TO Sr. 
63 
rs~T=IsCT+l 64 c wRr ff: ~~t:. vI 0us sT A rr 0 N ~l JMMAR v I F' T HERE wAs ~~ C-3 
1 
2 

3 
r F ( .( ~cr • I_ E • 1 ) G ( ) l u 1.. 4 4 4 l wR ! T t ( 6 • 4 3 > t" SC T • N 5 F" RC T 
5 
43 FO~MAT(~ ENVTR IT~M~ THI~ ~TATTONo,14,0 cnNV. F~RORS*tl4' 6 c PE. SET CUUNTER.c; 
NS~l=-o 
8 
l\lSt.HC T=0 9 4 4 CA LI_ C0 MF I "W' 1lJ CALL STN 11 (;(I f (J ?. 0 
12 c 
co "1 i'1 l,,~t-\l) ~ 13 51') TFlLf~(l).NF'.lHC) Gt) TO f-.n 14 rc1...l=IcCT+ I 
r. ALL C 0 Mi-: J ''' '' 
1G 
CALL COMMErn i 7 GO TU 20 18 c Hr. ~F. wl rrl I.) A TA u 1\i t:.. c HF c K F ()R E:. XJ s TA "'c E () r c; Af\J f') p uA r A 1~ 60 Tf)l...T;...InCT+l20 { F ( J ~cT ) 6 l • h 1 • L"I 2 21 hl STUP 
h?. rr ~ i r-i cn o1 • f, L • 6 J 23 63 r()N r11\JLJl:. 24 c F T \J r~ '" ( K • E l c rr.1 L 1 hJ E:: 
2!) c 
NOT~ lHAT TrlE~f n4E lN a10 FOR~AT 
26 
r,r y k =t' L A N K 
27 
no h~ J= lt '• 28 6 5 CAL L f-1 () Vi:. ( I. ',1 ( J ) • l t f..i 'V ~ • J , 1 ) 29 
l\JMU~t;LAl\IK 
30 f)0 t,D J=ltf! 
31 
1J=J+4 e. 
32 b 6 CALL Pv10 VE ( l. i,1 ( .J J ) • 1 t ',, M0 , J • l ) 33 
!·JD Y~blAi"K 34 
no ~d J=l•2 
35 ,J,J=J + t) 
36 68 (:ALL MOVE.(i_r.t(JJ>•lt"'!nY,J,ll 37 
~nJ.Mt.:BLANI\. 
38 
DO TU J=l•~ 39 .,JJ=.l+fi 40 7 () CA L L M 0 V E ( l.'\t CJ . J ) • l ' .,l T I ~~ E • J t l ) 41 N{)t.PIH:~LANI< 
42 
no re. J=l•S 
43 
,Jj=,J + 12 44 7?. <:ALL MOVE <L N (J,J), 1, ~..111EPTH,J.l l 45 c 
46 c 
G 0 TH t-< 0 ul] H TH F PA.HI\ 1"1 ET ~P L l ~ T 47 1)0 t1U N=l tl'IPJ\RAM 48 AO r:ALL PM~AM ( N) 
49 c 
GET Nt.~r LINE 
50 GO 'fl) ~O 51 c 
WHITE END OF ITF..MS AND SUMMA.PY 
52 .i. 0 fl ~·JR J. Tt. ( A ' l O1 ) 
53 101 FQHMAT(l4H fNn UF ITfMSO 
54 c 

~MITE LAST STATlUN S0M~ARY 55 wRJ.Tt(~t43) ~~cT.NS~RCT 56 WRJ. Tt. (6• 101) 57 \JIRJ.'ff. (6t 1(1'1) lCTt l~cT. TnCTt !PCT. teer, TDPr-T 
58 
lo~; FOkM~T(~ H1PUl LINt~=0tlf\tl* STATtnt~s:4., 
59 l Ibt/O DATA L.TNES=... ro,/4.PARAM LPJFS:o, 
60 l 109/iio COMMr"1T LINl:t.;=*•If.i,/O nu._,~y PARAM L.TtJE5=*•16) 
61 wRJ.Tt(6tl07l NENVlR, NERR 

62 
1 O 7 FOr< MAT (.,_ T<JTI\ L Ehi V I 1~ L I NE S=o • yr., ' I 
63 
1 ° CONVERSTON ER~OR~ = ~,T6) 
64 
~TUP 
65 C-4 
66 

1 
2 
3 
Ff\IU 
4 ~Ut:H<UUT l Nt:.: NUMti (NNF • Nl~L' SCALEF, NERQ t VAL. t LN l TN!f':(;t-:R LN<i40)6 C0Mt"iu1\1 I SY MA I N 0 ( G ( l 0 > , NPF: R , L T ' NG T , NA ST t ML R , NC 0 ~MAt NbL N K t :..q "'J lJ S ' NP L lJ ~ 
7 8  c c  T-HS SUBROUTH1f· TURNS THE lNTV VALt A .FP N~MdE~  ~YMBOL S  IN  U.1 (,J), ,J:NF ,NL  
9 11 12  c  f') A I A t Nnl G C J ) , , J: 1 • l 0 ' I l Y 0 • 1 H l , l H 2 t l.~ 3 , l H4 , i H5 • 1 H 6 • l H 1 t r) A i A N PER t L T , hl GT , NA ST t NLR , NC qMM A • N~L N K , ~ i N11 S ' NP LU SI l } l"i • ' l H< ' l H> • l t1 ~ • l H'*, l !·h • 1H • 1 H • t l H+I l\ITTJ.ALILE  1H~ • i Hq  I  
13  Nf=1~NF  
14 16 17  c  TO  \IL ;1\lf\IL IN~ULATE AGt-.J~JST l\i[K~=O VAL=u.o  CH~~tGES  TN  NF',NL  
18  C  f T~ ST  HE. ,,,, 0 VE  At;Y  f~ LA '" i<. S  F R 0 M  F ~0 NT  AND  8 A CK  
19  l\Jf=l\J~ -1  
5  r-.1f =1Jf + l  
21  rF l l\J f' • G T • NL l  G 0  T0  1. 0  
22 23 24 26 27 28 29  C c  tFlLN(Nf).EQ.NHLNK) GO TO S NL. ::1\JL + l 10 f\JL=NL-1 JF(N F .GT.NL> GO TO ~~ AL TE:PEU Vt.RS!Ot-~ OF l\lUM8 TO GIVE Nf~R=-1 FOQ If(Lf\l(NL>.Fn.r--1BLNK) r,o Tn 10 NON CHtCK FOR STGN JN ~QO~T tF(LN(NF>.~o.~INUS) GO Tn lA  All.  8L4Ni<  
31  IF<L~(Nf),fQ.NPL0S) sc=StALEF  GU  Tn  20·  
32  GO  ro  26  
33  1~  sc=·SCALfF  
34  ~1f =Nf + 1  
GO  TU  26  
36 37  20  sc=S(..ALEF NF=Nf'+l  
38  26  IFlNF.GTeNL>  GO  TO  5~  
39  00  3 U  J=NF • ~'L  
41 42 43 44  25  I F < L1\J ( J ) • EQ • l H • > G0 T 0 3 ~ 00 2!:> K=l,]f' JF<LN(J).EQ.NOIGCK)) GO TO r;oNTlNUt: GO TO 56  27  
46  27 30  VAL= VAL*l~.o cor~TlNU£  + FL~AT(K•l)  
47 48 49  c  GO TO SO HERE TU CONVE~l ]i:; NF:J+1  DECIMAL  FRACTION  
51 52 53 54  IF<Nt.GTeNL> GO Mo=o DO '+d J=NF11\JL ND~ND+} no 31 K=l•lfl  TO  ~O  
56 57  37  IF(LN(J),EQ.NhIG(K), CONTINUE GO ·ro 56  GO  TO  45  
58 59  45 48  VAL : VAL+ coNTlNUE  <K-1)*(0.l**NO)  
61 62  c  HE~E WITH VAL, NO 50 VAL.:sVAL. * Sr, RE!U~f\J  E~R,  ~ULTtPLY AV  SC4LEF  
63  56  NEt-<H=l  
64  RE~ URN  
66  C-5  

2 
3 c 
HF.~F.· H ALL BL"r !fo\ 
4 c 12
MO'S ~A~C~ 14, 
5 
60 "iEi-<~::-1 
6 
REIUHN 
~NU 
3 S U b i Hj UTll'°' E r 01-.1 F l '.\I 0 9 
c FT \J!")S CUM MAS p .1 L '" 
!O (QMMUN/LINt/1..i\1(14\J) ,JCC3n) tNJCt~LN 11 
,,J{_ =() 12 nQ 5 J: 1 , NL ''J 13 t F l L '~ ( J ) • NF • 1 H • ) G 0 T 0 r; 
14 ~j(.;::l'JJ( + l '~ ,..J c ( 1\1 Jc ) =J 
lG 
5 C O 1\i r l ~~ llE I l QE !lJt-<N 
18 
fl'-JU 19 SUtit~\J LJ 1 I r"E PL t ST 
20 c 
P'JT S t-' AI-< A~E T E ~ I !\IF 0 Ft< '' M L"' PH 0 VAR I r;US ARR Av S :n r,()M\1U~JILINFll. '\dl40> .JC(3n> tl\IJr".NLN 
22 
CO 1"1•"i0 '-.JI PAR AFlM' r·H·~ E. ( ., , 2 , 2 <' ) , SC Al E F ( 1 t 2 0 ) , 
1
23 1 '" u ~ I T s<3 • ;> n > • GE sP1 t < ' • ~ • 2 o> • "' swP , N c; A \' "' , 24 1 LN~0S(2t20) tNµ4R4M 
25 
TN! E\>t::R GESP L I 
26 
I '.\I I t. l~ ER 8 LA"-!~ 27 ('lf\JA ALANKIJOH I 
2
8 C «> *o e> * H F v I ~ l 0 N l U I j l / 7 J 
29 
C DE : ·r0t. l F 0UMMY P-~ 
30 I F ( 1\1 S~, P • r, F • i_. ) GU TL1 2 t'1 4 31 c 
GtT NWEA FROM ~IRST ~L~T 32 2 
CALL. NUMd(J~<1>•l•Jrc2>-1.1.o.NER,q,LN)33 TF l•~ER eNF. fl) SlOri lnl 34 f\!: 11,1 I (R + 0 • 1 ) 
35 c 
SA\IF lNUEX FOR A l'hJ"-1MY RETlJRN TO THic; suqQTN 
36 
~15AV i'J : N 
3 7 l\JM :: N-1 38 tF<N•GleNPARAM) NPA~AM:N 
39 c 
A P tAHD ~ITH I~nE~ 1 RFSFTS NPARA~ 40 I F Ci" • E1~ • l ) l'J PAK A·.1 : 1 41 c sI"' I T cH Ac c0 RD l "' r, T 0 p l • p ~ • ('\ Q ~, 
42 c 
43 5 IF Ci-J S ~ P -~ l ~ , 4 0 t .,, l 0 
44 C HE ~E wl I I "i T Y P F: D 1 45 
b IF ( f\1 JC • NF • 7 ) S T 0 P ~I') 1 46 
c GET NAMt.. PHA Sf • ME l MOL). EAc''. H CAN BE ttP TO n10 BLOCKS OF 1 n 
47 
c CHARACT~R5 PER ALOCK• ~l INntCATES ~ft~E· PH~~Et AND METHOn ~y48 c l • ? t 1\ I~[) 3 t RF'. sp EcT l v..._. L y • K2 rNnI cArEs BL 0cK • 49 6 lo no ,.,..;o K1=t,3 
50 DO f:».12 Kc=l.2 51 6lc f\JAt-JHME(KltK~tN> = 81 ANK 
52 
c KC '.)Ml Jd'-41) KCUM? ARE co~~MA NUMREI~ s 
53 
KCLl1"1 l =Kl+ l 
54 
l<CL1Mt'=Kl+2 55 NF=JC ( KC 0M1 ) + 1 56 C IF REt-'~ATEU PARAMElER ~O TO DITTO 
57 
IF (LN(NF) .E~. P1= .AND. N .r;r. u GO Tn 1.i2a 
58 
NLt:. ·T = JC(Kr,fJM2l-Nf 
59 
~,1cs=1-.JL ET 
60 
'<2=1 61 622 IF (NS .GT. 10) NS=11') 62 D 0 6 ~4 J =l t \I S 63 CALL M 0 VE C I . N ( NF ) t 1 , "'AP H~F ( K l , ~ 2 , ~ ' t J , 1 ) 
64 
624 NF=1~r + l 65 C-6 
66 
1 
2 

3 T F <1'' L E T • I.. F • 1 0 • 0 ~ • K2 • EQ • ~ ) G0 T0 ,.. 3 tJ 
4 
K2=2 
NF: JC(KCOMl)+ll 
6 
r\15 = JC {KCOMi?) -NF 
7 r;o ru '122 · 
8 C DITTO P~HAMETER FRUM PQFVIOUS Pl 
9 6 2 i3 1\1 A~H,.it E ( K l • l , N ) = N A f:) MM F ( K l t l • MM ) 

NA~HMEC~lt2,N) = NAPHME(K)t2t~M)
11 63 ·1 C'O''H 11\JUE 12 c ***~****~**~***********~ 13 C GET L.lr~t:. ~05IlION5 
14 
r"F=JC (~> +1 
HL=JCC6) -1 

16 
CALL i\l UMB C Nr , 1\1 L. t l • 0 • 1\1 ER , ~ , L N)
17 
J F (NI:. H •NE• r, ) c; Tt)p 2 
18 
LN~O~(ltNl:rNTCR+0,1)
19 "' F =JC ( t"1 > + l l\lt_=,JC (7)-)
21 C A L I_ 1\1 UM B ( N ~ , "' L t l • 0 , f\1 E R t P , l N ) 
22 
TFlNt.R.NE.Ol STUP2 
23 
24 Ll'-!t-' U:, ( 2 t N) : fl\! T ( tH U • 1 ) C OtJ TPU I CH F. CK ,lHIf) Rf:: TU~~. 1 F Pf'\ M P l T Y Pf: CA Lt. 
26 
C ***it* t-< E vI S I UN 1 0I .3 1I 7:.;
2 7 
l b "1 p i rt. (6 t l 7 ) ~I , ( ( "' AP I ~ ME ( Ki , K2 t f\I ) t K?. ~ 1 t ? ) • K1 : 1 t 1 ) • 
28 
l LN ~0 S ( l t f': ) t L"·H.> 1.1 S ( ;;? t N ) 29 l 7 F Ot-< M AT C* P ti R A 11-1 *t I ~ , 3 C 2 X , 2 A l n l • 2 X , 2 I 1 l RE! UKN 
31 c 32 c 
H~~E Wl!H fYPF P? 
33 c 
H0 ~ M"4 N T CH 0 I CF ~ 0 F S Cfq_ E: A r·.:O UN t T S 34 40 
IF CNJC .NE. 4) GU TO 42 
I< = l 
36 GO TU 46 37 42 tF <NJC .Nf. ~, GO Tn 44 
38 
K = t!. 39 GO Tu 46 44 If (l'i JC •NE • A ) S 1 uiJ 3 0 2 41 I( = j 
42 '+b DO ~O t= ltt< 
43 c **************••·~·······
44 NUNllS(l,N):BLANK 
JNt"=~•I 
46 NF=JC(JNF)+l 
47 c 
CHECK 1~ REPEAT SCALE 48 JF (~N(Nf) .FQ. lrl= .AND. N .GT• ll 
49 
1 u<J ro 41 
t\il =JC ( JNF + 1 ) -1 

51 
CALL NUM8(N~tNL•l•OeNERt~CALEF'fl1Nl tLNl 52 JFl1'4t.R.NE.0) STOP '+ 53 GO TU 48 
54 '+ 7 
SCAL~f (ItN) : SCALEF(ltNM) 
~8 

NF~JC.:CJNF+})+\ 
56 c 
CH~CK lF REPEAT UNITS 
57 
J F <LN ( NF ) • f Q • l H= • AND • N • <; Tt 1 ·,58 l l.:10 TU 80 59 c 
GET UNI!S AJn 
NL=JC(JNF+?\

61 
~1s=NL•NF 
62 JFlNS.GT.10) NS=lO 
63 no 50 J=l·N~ 
64 CALL MOVE(LNCNF> ele,..iuNtT~<ltN) ,J,l> 
66 C-7 

3 	so :\!F=~·ir" + 1 
4 (;0 I IJ 9 U 
5 

80 l\1UNl Is ( l tN) = l\llJNl Tc; ( l .l\IM) 
G c ~v p I r E :, ui..1~I AR y . 
7 C *~{jot>•> t-1·rvt~lON . 1fl/31/73 

8 	90 JF t~-~> lon.110.~1 
C f)tJ;:>LI'-'A .I[ UNIT~ TF f\iECJ:SSARY. 

10 
100 NLJl\IJl<;:(2tN) = t-Jltf\llS(ltN) 
11 ~C~L t. F ( 2 t N ) : S C AL [ r:-( 1 , ~' ) 12 
l l l~ t\i LJ I~ I l S ( J t N ) .• f\!l JN l T ~ ( 2. , N ) 13 
~CALlF(3tN) = SCALl~C2tN) 
14 	5 l 
C·O '=''~ 1=1,~. 5? wR J. T l ( f. t 5 3 ) 1,1 , t • SC A I F: F ( I , f\I ) , t\11.. J~ J T c:; ( I , N ) 1G ~:~ F" oH ,., 1~ r ( 2 I 4 • x • F I 1i • J • '< • A l o , 60 coNll Nut:. 18 ;:.1 E I ut~N 
19 c Hf~E" WJ IH OUMMy p]. Ut;F INnFX FPn~ PQE'Vtnll~ f"2e 
20 2 0 '+ \j : NS~\ V \! 21 
C 11f~F. wI I H ~t.AL P~, GET GF="f\.111c;, SPFCJF~• At--lfl t. TF'f c;TAGE. E:'l'"'.H 
22 
c c fJ. \I 5 t. u ~, T 0 T\,f () b I_('\ ( "'~ 0 F 1n cHt..~ Ac TF. R5 pf~ ~L 0 r: t< • Kl I hJ nTr.~ Tt:. ~ 
23 
c ~F \I l1s ' ~.., Fc1E: s • A1\11) L l ~ F sTf. G F ~v 1 ' ~ ' At-.J[) , • RF sp Ec r I \IE. Lv • I< e 24 C I t\J ) f CA f L 5 t.1 L 0 C K 25 du r-iO ?1?. Kl= 1 •3 
26 
no t:.12 K2=1.?. 2 7 2 1 2 GE ~~L I ( K l ' I<' ? • ~.I ) = RI " N K 28 c cH ~ r. K F-·1 t< uuMM v P 3 /\ NP 1~ An r MW 29 JF ( t~SwP .Gr. 4) ~o TO ?!=i4 30 I F l ·"l JC • i\I ~ • ~ ) .:...; 1 CW ~ 0 3 
31 
no ?. Jo K 1 = 1 , ) 
32 C t<C:lMl Al-l() l\COM? Akl Cv~1MA f\l tWRt:k5 33 
f'CU"11 =1< l + l 34 
!<'CU Hc.:Kl+2 35 ~IF.:: JC ( K C0 M J l + 1 36 c I F p En~ ~TE(J p /\ p ,, MF T [ K 17, n TO nt TT 0 
37 
TF ( L N ( NF ) • E<~ • , H= •Af\1 n • "' •r, T • l ' G(\ Tn ~2 8 
38 
,..., l t_ l = JC ( KC 0 M~ ) -NF 39 
"15=NLE T 
40 
K2=1 
41 
2c2 JF ( 1'1S •GT • 1 0 ) "IS:: i n 42 no 2~4 J=l, ~' s 
43 

("AL I_ 1-10 VE (I_f\! ( "'F ) • J • ~f. ~PI T ( t< l • I( 2, ~ l 'J, 1 > 
~-' F : l\I t + I 

45 
JF (1-.JLET ·L F. 10 .or. . K? .EGl.it) GO To ~3n 
46 
1<2=~ 
47 NF= JC<KC0Ml)+ll48 NS : JC(~COM~)-NF
49 
GO TU 222 5° C DJ TT O fJ ~RA ME Tf n FR0 "'1 P~ E V I 0 t 1c; P 3 51 22b ~E~PLJ (Kl t l •"') : GE~PLJ (1<1 t l t~iM)
52 
GE~Pl..l (Klti?."Jl = Gt:>;PL! (Kl t2tNM)
53 
2 3 0 CO"" T l NUE. 54 C wR IT E. SUMMAH Y 
55 
2 5 0 WR J. Tt ( 6 ' 2 5? l N , ( C Gr E. ~PLT ( Kl • I< 2 , ~!) , K?s 1 , ? > t t< 1 =1 ~ 1 ) 
56 
2 5 ?. F Ot-< M ;\ T < l 4 , (1 ( ? ~ t 21~ i n ) > 
57 
GO TU 2ss 
~ 	c Dfcrot lf DUMMY P3 FUL LOWED RY RFAL Pl 
59 
2 5 4 yF CNSWP • m: • ':\ > G 0 T0 2c; 5 
60 
NSWJ.J : l 
61 
~o n; 2 62 2~~ 
COIH lNUE 
63 
RE!lJHN 
M 	C ***~****~oo*****~****~*~~ 
65 
66 
1 2 
3 
f Nl.J 4 SUU.-<UUTlNE STN 
5 
c0 M,.., 0"' I L I NF. IL N ( 14 0 ) • Jc (3 n , • NJr t NL N 
G 
COMMUN;STAT/NSTATtLATtLONGtNAGfNCV~NPRJCT,
7 
l NL!NE,NSIT~tNRlOTO~. 
8 
l NYktNMOtNOYt~TlMEt NOEPTH 
9 
Tt\I I E. Hf. R BL Af\I I< 10 DAI~ BLANK/10H I 
11 
[)A~ A I ILAT' TtLONG/l-.noo ,qqqq91 12 c 
13 c TH I 5 ~UHRO UTI NF' GE TS S Tl\ T I 0 ~' 0 AT A 0 llT 0 F <; CAPD S 
14 c 15 
C *** .. .,. t-<FVISION 1 n13l/7316 NL .1 N~-=BLANK 
17 
t-.15.l ft = BLAl'ltc' 
18 
1\16 J. 0 .!0 P = RL Af\J K 19 JF (NJC .E:Q. f-.) GO Tt"'\ 3 20 J F ( i'J JC • Nf. • 9 ) S l LID 5 21 C GET Li r..Jt. 22 IJS = JC ( 7) -JC (6) -1 
23 
24 yF C'" ~ • c; T • J O) NS • l 0 
DO 11 0 J = 1 , t--1 S 25 t\I = JC ( b ) + •.J 26 l J. 0 CALL M\) Vf:: CLN < N) • l • "IL 1NE • ,J t 1 )
27 
C Gf T S.1 1t. 28 NS : J C ( 8 ) -.JC ( 7 ) .. l 29 TF <i\J S • GT • J 0 ) l'J S : 10 
30 
00 1~0 J=ltl\JS 
31 ,, 
= .Jc (7) + J 
32 l 2 •) CAL L MU'V E ( t N ( N ) • l t 1\1 S 1 TE , J t 1 )
33 C Gf T 8 J. 0 I 0 Pf~ 

34 NS =JC(9) -JCC8) • l 35 IF Cl'JS .GT. JO) NS s 10 36 DO PO J: 1. NS 37 ~I : JC ( 8) + ,..J 38 lJO CAL..l MOVE CL NCN) 'l t N810TOPtJ• 1 l 39 c **************.,.**********4° C GE T S I A l I 0 N NA ME 
41 
3 NS! Ar· = BLA"JK 
42 "' s = Jc (2 ) -Jc (1 ) -! 43 J F ( N ~ • GT • l n) NS= 1 0 44 DO S J =1 ' NS
45 N=.JC t l ) +J 
46 5 CALL MOVE(LNCN) tltNSTATtJtl> 

47 
C GET f\bENr.y NAMF 
48 
49 NS=JCCS)-JCc4>-l50 IF (NS.GTelO> NS=lO 51 NAbF.NCY = Rt.ANK 52 DO 11J J=ltN<;53 N:.JC(4)+J 
54 l 0 MOVE
CALL (LNCN)tlt~AGENCVtJt\) 55 c GE'T pt-<OJECT NAME 
56 NS=J~(b)•JCCS>-1
57 !FCNS.GT.ln) NS•lO 58 NPt-<JCT = BLAf\JK 59 00 l!:> J=ltNS 
60 N=~C(S)+J 
61 15 CALL M0 VE ( LN CN) • l t "'PR ,JCT , J , l ) 62 c 
GET L~flTUDE 63 MF•J(.;(2)•164 NL:JC ( 3 ) -·1 65 C-9 
66 
? 
3 r i) ('ALL. t JlJ tv1 H ( t-.i ~ , ~ t L • l • C: • ~1 f Pt P , LN) 
4 rr < 1J 1: F< > 2 3 1 • 2 c' •-c 1 

21 <;TUf.J t., 
?. ?. I. I.Ii I : l f'1 l ( R + fl • l ) 
e~ IFlL~\l.GT.1rirH)n)c,J .,.('I 24 

8 
LAl:!LAT*lO 
c;o r u 2 ;i 
c30 L.Al=lJLAT 

11 C GFT LUhlbJTLJOE 12 
?. 4 ~ .i F=J ~~ ( ~ ) + l 13 NL=JC(4)-l 14 C. ~OIJ C'"'Li-NUM~ 
I ~ ! 
CALL 1,1UM8 ( "'I=" , 1\1 L • 1 • 0 • "'ER , P • l N ) IC T F ( I>It k ) ~,... (1 • '2 h • 2 :> 1 l 25 STU!--(, 18 ? "> LO i'JC;:; 1 r.... T < k + ~, • l ) 19 TFll.Uf'JG.GT.1nooO) Gn TO ~l'l L Ol"u=-t_ ONG* l ··; 
~>:k\1-l0.0 
hO fu cb :iJ 260 
l (') '" (1 = I I L 0. "-I(; 
24 
-=.o co11J 1lrwt:. 25 c 
Wi-i TTE SU~AMARY r. ~·!II Kf TU.~"' 2b 31 
~' ~ J. TL c6 • 3 3 ) r. ·c; T/\. 1 • L 1\ T ' L o1\1 G , "' '1 f'; F N cY • NP R .1 cT , 
27 
1 NL! tJftNSIT~t~!RJOlOo 28 3J F 0 H fvi f.I. T ( * S r .AT I \.1r\1 * . fl.. l n, ? I A , ~ ( 2 X , A 1 U ) ) 
29 
~EI IH~f\J 
F IJl..i 31 
SUt:HWlJT I Nf f".OM~it N l 32 c T~~ CUMM~NT CAhn ASSUC1ATES ~N Aln wo~u ~ITH A l nR ~ SYMijnL 
3J c 
wH~"' lt;t. SYMA()l IS ~ou~!I" IN ti [)t\T~ FTF'Lr:>. T~F' r.OMMENT !5 Uc;s:n yl\i 34 c 
I N Hit. L NV J. R I TF. ~. 3b COMM Uf\I / l I NE / L f\1 < J4 0 > , ,JC C 3 f't ) • NJC • 'I l ~ 36 INrEulM HLA~K,CNNT 
37 COMMUN/ CMNT S/ (-MtH ( 2 • ?0) • l'ICMNT 
38 OAIA bLANK/lOH I 
39 c 
FI\ln ~YMnoL 
IF <t"0c.NEe?.) STOP7 

41 
~l F =J L ( 1 ) + 1 
42 11.1 5=-JL ( 2) -NF 43 
!\IS Y M=HLANK 
44 DO S J:ltNS 45 r. AL L M 0 vE ( l. f, I ( t\I F ) • l ' "·' Ci yM • J • 1 ) 46 S f,1F=r.JF + l 
47 c 
SEARCH ~REVIOU~ l !ST I~ ANY 
48 !FlNCMNT.EQ.O) \,() ln 11 49 
DO l 'J N= 1 t f'lr ~· 4 t--ll 
J F <C1111 N T ( 1 , N > • f. (J • 'l S'f , , ) G 0 T 0 l c;

1
51 10 roNTlNUE 52 c 
AD~ tJt. w C 0 MME ~H 0 l\i T 0 P 
53 1 1 f\I C1"i i\J f ;;NCMN T + 1 54 
l F l i-..J C.. M~T • GT • ? n ) t--J L ": ~" T=? 0 
55 
r-..1:NCMNT 
56 c 
8L~"JI< OLD 57 l~ CM~T<2,N) : HLANK 58 DO 20 ,_i=ltln 59 l\1F=,J~ ( 2) +J 
2 0 r. ALL M0 VE ( U·! < f\J F ) d t r. ~~ 1\1 T C ;> • N ) , J , U 61 c RE::>L A<..E SYMBOL 62 CMl·d l l ,N) =Nc::Y~ 
63 
WRJ. f t:. ( h t 2 5 ) C: MN T ( l t ~ ; ) • CMN T ( 2 • ~1 ) 64 2 5 F O t< MP 1 ( ~ C(J ~ H 1F I\! l •• ' ?. A 1 0 ) 65 C-10 
66 2 3 4 5 6 7 8 9 
11 12 13 14 15 16 1 7 18 19 
21 
22 
23 24 25 
26 
27 28 29 
31 32 33 34 35 36 37 38 39 
41 42 43 44 45 46 47 48 49 
51 52 53 54 55 56 57 58 59 
61 62 63 64 65 
66 
~f ~dhN 
f f~U 
!:> lJ Uk 0 UT I ~I~ P A R A M ( ~ 1 \ CO ~1 MUN IL IN~ IL. N. ( 1 4 0 ) , . ..IC C3 0 ) • NJCt NL N COl"'ll111Uj\,UPA~A /~iAPHME. (.,. 2. 2r) • SCAL EF (,' ~o l. MtJ~'l T~ ( 3' 20)' 
l bf~PLl<3t?•2U),NS~P,NSAVNt l LNµU~(2•20l•NPARAM 
INlt.l1ER GESPLJ 
co~11V!UN1STAT /l\i$ TAT' L "T 'L o~:G' NAnENCV ~ NPR JCT. 1 NL I I~ t , NS I T F , f\J h l U T 0 ~ • l Ny ...... J\J M0 • N [' y • ~I TTMEt ~ I r" Ep T H 
.I N!l:. Li t. R C M t-!1 
('OfV1111iUN1CMNT~/l.MNT (2.~0) eMCMNT 
roMMUN/ t:Xt.c / NE i'-1v1 r<. r •. FHR' fll5C T t ~-JSERCT 

JN~E::'-'f.R t:1LAt-tK,8LANK'.~ 
QAIA dLANK,~L~N~S/l~ •lOH 
FIRST 	M[MOVE RL~N~~ 
l'l F =Ll'J I' () S < l , ~: ) ~IL=L i\I p 0 s ( ?. • ~I ) 
5 	JF ( l 1'1 ( NF ) • l\i r • TF (t-.1F ,Gt..NL. l ~w::Nr + 1 oo ru s 
~L Ai\J K ) ~U T() l 0 RETltRf\1 
C. 	lH~: Rt. llJRN IS FOR ALL l~LAN~ 10 JF(LN(~L).NF.RLANK) ~o Tn 15 
J. F \ 1·!~-• GE • NL ) RE I U k N 
r-JL :;\ll. • l 
GO 1 U 10 

C.: 	ASSllML lHAT A t\lll~RtH R~MAIMC:, CALL 
15 	t<CUM=BLANK CALL NUMB ( "' F , l'I l • c; CA 1 • F. F C 2 , N ) , r,; r: ~ JF tr"t.R) l8t40t?O 
1~ PEl 1 Ji-<N C HE~E tok pOSSI~LE COMMfNT 
?O 	l'CU~=LNCNF> 
NF:""~+ l 
DO 2~ K= 1'Nr"~lf\tT 

C 	LOJK . tOK MATCH Tn Sl~GLE LETTER JFlKCOM.EQ.r.~NT(ltK,, GO rn 3n 2s co"n l Nut: 
C ADD 	?NU LETTER ~NO TRY AGAIN CALL MOVE<LNCNf),ltKC0Mt?.tl> no ? 1 t< =i, l\1rM~J'T y~<KCOMeEQ.c~NT(l•K)) GO TO 2~ 
?1 coNTlNUE. 
C NO MAJCH, CALL IT AN EPROR 

2A 	NS~RCT:NSERrT+l 
t-.1Et-<~=NERR+ l 
~t'"I u..:<N 

C 	2N). Lt. rt ER GAVF A ~1A TCi.-4 
29 MFl:Nf· + 1 

C !ST LtTlER MATrH 
30 ~CUM=CMNT(2,K) 
C CHECK ~0" cO~MENT ONLY 

c 
JF<~F.GTeNL) GO TO an 
C 
PE\1~ iNDtR SHOt.11 fl ~E NU..•RER 
I 
NUMA ' VA 1.: t l N l 
CALL NUMt3(NFtNL•SCA1 Ef <2el\I) ,NERtVAL•LN) 
JF<N~R,NEeOl  GO  TO  
C  HE~E  Wl~H GOOD  VAL  AND  
C  CHECK  ~lZE OF  VAL  
40  Jf l~AL.GEel,OE+05)  

~8 CO~MENT 
~O 	TO 50 
C-11 
2 _,
'") ~· f f"'1 : " AL -F L o."i T C 1 f\11 4 
T F < i( t. !"" • Gt: • n • fl 0 1 ) 5 
c Vtq,,. 1 O" 6 K UN J l ; NUN I T c:; ( 2 ' :·J) (.;O ru '70 8 c PE)uCt. VAL 
( v ·~ L + ~l • ~ ) l G0 T0 6 o 
~1 S 0 	V A L =\I A l ~S C fl LF F ( l • ~ 1< Uf\I l l =NuNI T ~ ( 1 • 1\J ) 
11 
GO 10 7U 
12 
(.; 
J'\l:PE:A~t. VAL 
) I ~C A l f F' ( ?. t ~' ) 
38 d::S FoH 1¥:AT <6 H , 39 
~O TO 78 f NU 
41 42 43 44 45 46 47 48 49 
51 52 53 54 55 56 57 58 59 
61 62 63 64 65 
66 
13 6 0 VAL.=" Al~5 CA I FF C J • N) / c:; CALF F' ( 2 • t-!) 14 KUI~ I~ =l\JU•\Jl TS ( J' ~~)
Ei 
c wRI T E E!'~ V I H I Tr: H 
1() 70 tdH rt. (8t71) f\ISl 4TttAT.L0NGt''iLINt.1!\ISITFt~l~TOTnPt I } l f\iYl",NMO,l\lnY I(: ll r 0 k I-''/\ T ( Al 0 • ? ( 1H • ' l ,.., ) ' .3 ( 1H ' ' t.i. l n ) ' 3 ( l H ' • Ac:; ) ) 
19 
NVAI_ = ll\IT(\1f.1L + eb' 
\\1 R! TI". ( b ' 7 ? ) t'-. 1 r 1·"I ~ ' \ I nEp TH ' "J A(-; f. Ncy • t\I p ~J cT ' 21 l ( ( '" 1\ PH ME. (K 1. • K r •f\J ) • I< 2 : l • ?. ) t K 1: 1 , ~ )22 7' For~M~T (2(1Ht•M~) •c: (1H,,A\O) t3(1H.,2Alfl))23 wRlft. (8t71l KllNJT,r1vALtt<C'OMt24 1 t (\.:IESPLI (~l ,t<.~.~) .K2:1,?) ,~. 1:1,1)
25 
13 F0 r< M A I ( l H , , A 1 0 t 1rt • , T 5 • l H • • .ft t n • 3 ( l 1-1 ' , ? /\ ] 0 ·, • 1H'"' ) 26 c COJ,..;T 27 7R ~E~Vlh:NE~VTR+l 
28 
r-!S~ r=l\1sCT+ 1 
29 
~E:. I l111f--1 
c 
31 c WR I Tl: CUMMENT ()f\JL Y E-NV Tq L. I ~'F 32 c V~L MlJ~l tiE Pl_/\NK 
33 80 
WR ! l l ( 8 ' 7 1 l f\I S l A T , I A T • Lrn,1G , ~J I Tt\J F , ~t S I TF: t f\IS:q 0 T nP • 34 ] N Y k • ~·JMU t N0 Y 35 ~1R i ft ( 8, 7~) ~' l I ME• ~1nEPTH, NA<1FNCy, NP~JCT, 
36 
1 ( ( ''I µ, t-> H M t: (I< 1 , K 2 • ~ ) t i< 2 =1 t ? ) t Kl : l t 3 ) _ 
37 
\\I FH I t. ( 8 ' 8 3 ) Kc0 M • ( ( Gf sp L r oq •K? • ~I ) • I( ? = , • ? ) • I( pt 1 ' 3 ) 
, l H , , A l o , 3 ( l H , , 2 A 1n ) , 1H*> 
C-12 

APPENDIX D DESCRIPTION OF DATA FLOW FOR CREEL CENSUS DATA BANK 
The data from the interview field sheets and climatological data is checked for correct use of vocabulary and sent to keypunch. A permanent file copy of the data is created on magnetic tape, to serve as a .source of input to the program "CREEL". This program performs a number of error check­ing functions, mainly to insure that non-numeric data does not appear in numeric fields, and that the card images appear in the correct order. CREEL also totals the weight and number of fish caught and generates one ENVIR item for each interview with this total information. One additional item is generated for each species of fish caught. 
If an error is detected in the set of card images representing one inter­view, CREEL writes this set on a file of "bad" data. In many cases, this data can be corrected, using the interactive "EDITOR" program available through the TAURUS timesharing service. Corrected data can then be pro­cessed by CREEL and added to the data bank later. 
The process of forming a data bank from the ENVIR items is similar to that for the other data banks. 
D-1 



FLOW CHART 5 Data Flow for Creel Census Data Bank 



~RUG~AM CREfl (TAPE5,TAPEh:\OO~tTAPF8tTAPE4tOUT?UT=TAPE6\ C$ DATA l~· COMMENTS our, ~NVDATA, ~AO AROUPS 
coMt-llJN/L I NF" I LN ( 8 0) 
cor-1MUN A <a o, , R(Ho> , r can.~> , D ( P n> , F.' t 8 o > 

IN!fGfR AtB 9 CtUtt IN If:. l:>F R BL.Hit< oAiA HLANK/]H /tNLlNEStNfNVtNERR10.o.01 DA I A ""A•NB.,..,c,Nu.NE/l'\tOtn.0,01 
~Al~ NFLG,NFISHtNd~~.N~T.TFISHNtTrtSHW/O,n.0.0,0.0,0.01 
nAIA JAtJ"•,JC.JD/O,".a,01 
c N~TE ~SKJP IS Th£ NJM~ER OF LINES TO SKIP 

· 
c ALTFR TU FIT RATCH 
nAIA JCSWtNRATCrltNS~rP,NfMAil-l•ltn•lO/ 
c JCSW lS NFG Foe SU~MAMv ITE~S ONLY 

c WR!TtC6tll) NRATr.M l 1 F0 ~ ,.,, ~T ( 0 1 CRE E. L CEN 5lJS c AU~~tt< ALL ARRAv~ 14 rio L::> J=l•8n ACJ>=HLANK " fj(J)=ULANK 
l''H ..n = BLANK DO L:> K=ltJr. C(..Jtt\):BLANK 
15 CONT 1NUE 
l~ Jc=n 

c 
c ~EAU f\J E W L I "'F 
20 REA0(5,21) 1.N 
21 F'QHM~T(80Al\ 

JFU:':OF,5) 1A(h25
c 
c 

CHECK SKIP 25 NLlNfS:NLINFS+l 
B A TC Hatt , I 4 ) 
If lNLI~ES-NSKIP) 20,20t30 
ARA~1CH 
3 0 	I F H.'\J ( 1 ) • EQ • l HA ) G 0 T 0 tF<LN(}).EQ.lHR> GO TO JFlLN(l).£Q.lHC) (:;Q TU tF<Li~(l>.EQ.lHD> GO TO I F l U>J ( l > • EQ • 1HE > G 0 T 0 
C ERRJR !F NOT REr.OGNlZEO 34 WR1fE(6t35) NLINESt~N 
4 O Sn 60 tin 
1 t? 0 
35 	FOHMAT(* LJNE *•ISt~ NOT NFL.G=9 
NEH~=NERR+l 
c 
HERE !F A 
40 CAL.L CHKC<3.NER> 

IF<NER) 41,4},90 
41 1)0 42 J=ltArt 
42 A(JJ=LN(J) 


NA•NA+l 
JAaJ~+l 
GO 	TU 20
c 
HE~E WlfH 8 
50 CALL CHKCCS,NER> 

tF<Nt..R) 51,c;J ,90 
51 00 Si J= l t EM 
52 B(J>=LN(J) 

N81i:NB+l 
JB=Jti+l 
. 
~ECOGNIZE~••tAOAil 
1 2 
3 
GO Tl-' 20 
4 
c 
5 C HE ~F wl rH C LT"' E 6 ~0 CA~L CriKC(~,NF~) JF ll\Jl:.R) 61,~J ,90 
61 JC;Jl..+J 
"'c=f\il•.~ + ] 10 JF <JC.GT .8> GO T1) 64 
11 
no t>i J=lt~n 
1~ f,? CC-.JtvC)=LN(.J ) 13 r.;o r'J r:, 7 14 C Hf ~r:· tc l l h TOO MAl\IY C' Lr ~,flS 15 6'4 t\IF L(.;:9 rn \a1Rlri:..C6•0S) t-if 
17 
615 F'Ot-<IV!A I, .. Ton r-AA'~Y c 1. INE'c:; ... , p;,18 
C "J 0 ,J Gt. r T 0 TA L f'.J 0 A1-.J n \·;1 
19 c FI \JQ .. t HST cow,~A 20 
67 (')() h~ Nl=2,~0 
21 
TF <I. '" ( Nl > • f-' f" • l H • l A " T 0 7 n 22 6A rn'" 1! r-.iuE 
23 
$TUr'l 24 C F I \I[) !:> f:. C. UN D 
25 
70 NF'=N l+l 26 no li N2=NF,k0 27 IF" N(f\!2) .Eo.lHt) G"' rn 74 28 12 co1-.. r i NuE. 
29 
~TU._,~ 
30 7 4 f\ 1L=r·, ~-l 311 C GET NU~tit:'R 32 r.: AI. l . '°" UMH ( N F , N l • l • ll • ~1 t: R • R N t L l·J ' 
33 JFlf\Jl:.R) 76t~f'.78 34 C HE. ~E ! F 13 LAN K 35 7 6 .,i ~ l f E < b t 7 7 ) t-.:L I NE :.; • l "· 
36 
77 F Ok r..1~\ T C * B L AN t\ F l ~11 N 0 () g WT ' l I NE ~*t I ~ t l )( • 8 0 A 1 ) 37 ~JEkR=NERR+ l 38 f\IFl.b=Q 
39 
GO TU ?0 
40 C*****~.. *~~******* 
41 C HF~f !F HAD 

42 
43 7 8 lr• R! T~ ( 6 t 7~ > NL l NE S•I, r-,1 
44 7 9 F Ok r.; l\1 ( .. RA () C01\1 vt: lo( S I 0 N , L HJ E • 4ao t I 5 • 8 0 A 1 ) 
45 

~ :F'L..l;=q 
46 
~!ft'<li<=NE.KR+ l 
47 GO Tu 20 
48 c GO~() ~l)l~V. FI ~m 3FW c..u .-..i MA 
49 ~0 t-.1 F' c: N ~ + 1 
50 DO tj~ J=NF t ~O 
51 JF(LN(J>.E~.1Ht> GU TO A4 

52 
82 r.or-. rI NUt:: 
53 
~TUF-1.:i 
54 ·c 
GET Avt. WT 
55 
84 ~IL=J• l 
56 CALL NUMA(NFtNL•l•V,NERtRWtLN' 
57 
JF'(Nt.~) 76tA1,,7A 
58 

86 NWl=~~T+INTcR~~kN) 
59 ~IF l Sr-1:NF' I SH + I NT ( ~~' ) 

60 c CHEtK FUR NUMEPTr IN HnOKSt FIND 4TH rOMMA 
61 
NF=.J+ 1 
62 PO HH NL=NF.flO 
63 JF'(LN(NL) •E'>·lH•) c,n To ~~ 
64 BA coN 1 lNUE 

65 D-4 
66 

2 
3 
4 
6 7 8 9 
11 12 13 14 
16 
17 
18 
19 
21 22 23 24 
26 
27 
28 29 
31 32 33 34 
36 
37 
38 39 
41 42 43 
44 
46 47 48 49 
51 
52 53 
54 
56 57 58 
59 
~O Tll 78 89 NL=f'JL-1 
CALL NUMB(NF,~L•l•O-NERtRNtLN) 
If (NtR) 20,~0,78 c HE ~r w~1 r. ~.! cHK c QFr u RNs 90 ~l[t'<f<=Nt:RR+l 
~FLG=9 
rio lu 20 c HE~[ W!fH D 110 CALI. CHl\CC3,t.JER) 
tF\tJLR) 111,ll}•QO 111 DO ti2 J=l,no l l 2 F" ( J > =L N ( ..J ) 
NOi.f-.JlJ+ l 
.JD=JU+ 1 (-,() TU 20 
c 
HE ~f. •I ~ H E:. 120 CALL CHKC(ln,NE~) Jf (NtR) 12~,1?6•121 
"'ER + 
c HERE wl Tti TOO ~.l\NY COMt-14S, WPITE our ST0RE!1 LPJES ON 
C ALSO Hf~E WITH NFLG.GT.A 
121 	•'RJ.lt..(b•l23) 
123 	FOHMAT(* BAA LlNFS WQITTEN*> 
~AD Flu: 
llj R l rt:. (4' 124) 1?4 FOH~1AT(80Al) 
NFLl,::0 NBAU•NRAD+] GO TO 190 
1~6 ~JE=NE:.+ 1 . 
c 
CH~CK FU~ PRES tf<J~.LT•l> 
Jf(Jt3.Lf.l) tf <JD.LTel) I F LJ t • GE • 1 > 
C HER:'. ; MAKt. BLANK 
A• B' ( ( C" ( J, K>1J=l,80 >t 1<= l •JC> , 11 t LN 
OF 
r,n 
GO 
GO 
GO 

C 
Q,d,o,c LTNES To l.;o 
TO 170 
TO l~n 
T 0 l 1 o 
LI NE . 
O O l ~A J =2 • 8 0 
128 	C(J,=HLANK DO l~~ K=ltS J=~..K+l 
129 	C(J>=lH• 
JC=l 

HE~E ~f lER r,ooo Et CHF.CK NFLG 130 !f(NFLA) l~?tl32tl2, 
~RITE [NVIR lTfM SUMMAQy FORM 
132 133 
134 135 
140 
142 
143 
wRJ.H.(9•124> <A<J> • .t:2,en> 
wRJ. rL Cih 124> ca <J> •J•2•80> 
WRJ.T£(8•135) NFI~H,~WT 
FOHMAT(~ TOTALt*t2CT5tlHe)t* t t *> 
TflSHN=TFISMN+NFJSH 

TF!SHW:TFISH~+<FLOAT(NWT>~O.l> 
"'FlSrl:so NWl=O WRlTf(8tl24) fi°'HJ) tJw~•80'> WR1TE(6tl43).(LN<J) tJ:2,80) ,NBATCH,NE 
FO~MAT(79A1e/I3•1H,.y5,1M~) 
NENV=NENV+} 
c 
S~IP Wf(lTING FlJLL OUTF>11T IF JCSw.LT .o 61 148 tF<JCsw> 19n,1so,1sn 62 C WR I TE t UL L ITEMS 63 150 D0 .. 154 K•lt .. JC 
64 
wRLT E ( 8 ' 1 2 4 ) ( A ( ~J ) ' J IS 2 ' ~0 ) • ( 8 ( J ) ' J =~ •A0 ) 
.. 	D-5 
66 
J 	wR.l!l(8"J24' (f(J,~) .. ,1=?,An), (ncJ> •J=2,.C\{I) 
4 
l 5 4 \a4 R.L I' l ' ntl4 1 ) ' L N ( ,j ) • I=2 • R n ) • 1-.J ti A T cH • "·'E 
5 
~1EN'':.:t<Jf. f-.JV +Jr 
c k E S F T::, L1 ll rl ER f 
C ooo~oo~*****O** 

8 
c RE~EI~ 
'.) 160 	Jc•n JA=O 
11 	.rn•u 
12 .Jo=<j 13 
GO I tJ 20 14 l "l'J WRl ·rL (6• l '71) NLHJt:.S."JE l 7 1 FOH M A T ( -11-~.~ T5 <; I ~·~ (; L T~! E 1\1 F f\ R ~ , TS , 4t GR (') l 1P ::: * t TS ) 
16 NE::l"<H=i-.JFi~H +l 1 J c.;o ru l~l 
18 L M 0 ~ R l I t. ( b ' l d l ) 
l<J 
lAl FO~H1~T (0 En~ nN ()'-\Tl\ FlLF '"') I(; R .L Tt. ( b • l 8 3 ) J A , J 11 t .J r. , ,JI) , I "' 21 18~ FOH1Al~T(* JA ...Jp,JC:t~Jn:0.4T4•/.. LAST LJt\JF= .... /.~r\A}) 
2? 	(.,() IV 2 t} 0 
23 l q 0 I t-( l'J f.. R R • G F • NF. M1\ X ) ~11 T0 ? 0 'l 24 
c CH:'.rt< f'JfAL tR~WRS 
25 GO (iJ 1bO 2G C 
27 c 

28 c 
29 t!.OO ,mlTt.(8•20ll ~Ol Fot<r,l>\f ( .L bH n 10 Of l'T F 1-15 n ) 31 ~·' RJ. Tf C 6 • 2 0 1 \ 
32 
wRl Tt ( 6 t 2 0 3 ) 1"1 L I NE S , 1\1 A , f\I ~ , NC t "l D t NE 
33 

2 0 3 F O I"< i-1 ~ T ( * RF f\ n .c• d ~ • .,_ L 1 N r: c; "" • r; T 5 ) 
34 
~i RJ. rt. (b • ?. 0'i ) Nf Rr~ •N~I< I p • NE N v ' ~J AAD 35 ~0 5 F0 I"( i'1 I' r ( u f pp 0 Rs= 0 • T c; • 4t ~I< t pp F n .... T 5 • *L I l\J F ~ RE f 0 RE '-' p IT T!\fr,~ ,/ 36 
l 1h•* ENVTH ITCMS~/•* RAD GQOUPS WRITTEN ON TAPE4= .. tl~\
9 
37 	~p1T~(~•2071 TFlSHN.TFtSHW 
38 
'd1H FO~l'1~f(~ TOTAL FISH ".jUMRFR~ ... ~.o,o WE!l,HT TN 1. RS=••.nhl\ 
39 6 STOP 
FNU 

41 	suu~OUflNE r~KClN•NFQ) 
42 	JNfE~ER COM~A,RLA~K 
43 r OM 11 u NIL I NF: I L ~1 ( H 0 ) 
44 

[>A I /\ C 0 MM A , Rl A I\!!\ I l t-1 , t l H I 
45 

~1ft<=ll 
46 ,,,c=o 
47 c 

Fl\JD LAST BLANK 
48 5. DO lU J=1'Rr, 
49 

~=d1-J 
JflLN(K)eNE.fiLANK) G~ TO 12 
51 10 coNTlNUE 
52 c PU T ON[ C 0 MM A pJ L A S T Rl AN I< UN LE Sc: THFHE I S nf\1E Al~Rf A 0 Y 
53 12 If(Li~(tO.EQ.COMMA) ~n TO 16 
54 c 

NOT cu1"1I·~~ 
55 

JFlK•Gfe80l ~O TO 3" 
55 
l(:K+l 
57 LNlK):COMMA 
58 c C 0 Jl\J T Cl) M ~1 A 
59 lb DO ~u J=ltl< 

!F lLN (J) .EQ.l,OMMI\) "'C=NC+l 
61 ?O to1'4l1MUE 
62 ?.2 JF(i'-JC-N> 24,15d0 
63 c K=!...165 t I-JON BLA~ll< 

64 
24 t<:I\+~ 
65 D-6 
66 

1 
2 

3 
1F ( ~... . en • 8 0 ) G 0 T0 3 ~: 
4 
LN<KJ=CUMMA 
Nr.=NC+l 
6 Go ru 22 
7 c Hf.qr WlfH ERRn~ 
8 30 r-ip<= l 

9 
3~ PE~ll~N 
ENU 11 c;Utj~OUT I NE l\llJMB ( NNF • Ni'IJL t ~CALf F', NE~~'VAi.. , L~fl 12 IN J El;l R LN Cl 4 O> 
13 
roMMUN/SYM~ /Nf) I G ( l 0' • NPF.R. LT' NG T' NA ST' ~.IL~. f\ICO~MA. NHLNK' MT ~lIS' NPLUc; 14 c 
l"i!S \'\IJ8RUUTJ t\1E TURN::) THE SYMBOLS IN LNLJ) ,J:NF ,NL 
· c 
I t\tTU VAl' A S:-P NlJM8f.Q 
n~IA(NOlG(J)tJ=l•10)/lH0,1HltlH2tlH3tlH4tlH5tlH6t1H7tlH~1lH9 I 
17 
OA I A NPER1L T,NGT ,NACiT•NL~ 9 f\JCOM~A,~qLNK,~T"Jl'StNPLIJS/ 18 1 lM••lH<•lH>tlH*•lHt,lHtt]H tlH•tlM+/ 
19 c 
I \J·r TLA L I Z E r-.;F.;1\11'\f 2 1 r-.1L =N1~L 
22 c 
T 0 I N ~IJ LAl t: I\ Gl\ I "I S T Cr11\ 1\.1GF~ S I N NF , NL 23 
MEt-<~=n 
24 VAL:1) • 0 C Ft?ST ~E~OVE ANY BLAN~S F~OM FRONT ANO BAC~ 
26 
NF=l'Jf-1 
27 5 NF=N~+l 28 IF<NF.Gf .NL) GO fl) :'In 
29 
JF(L~(NF).f~.N~LNK> AU TO~ 
~·JL=NL+ I 31 10 Nl_=NL-1 32 t F' ( i-J F • G T • NL > G 0 T0 "' 0 33 C ALT EPEU V ER S I 0 N 0 F NlJ Ml~ T 0 GTVE NE~R=• l F 0 Q I\ 1_ t R L AN K 34 IF<LN(NL).EQ.NBLNK) GO TO 10 
c 
NO~ CHECK FOR 5TG~ IN ~RON~ 
36 
I F <L. I~ ' NF ) • f(~ • M I i\J us ) Al) T 0 1 8 
37 
rF ' L i\J ( NF ) • En • Np Lus ) A 0 T 0 2 0 38 sc=SCALEf 
39 
GO TO t!6 18 sc=-::iCALEF 41 t-.1F=NF+1 
42 r,o rJ 26 
43 20 sc=SCALEF 
44 
f\tF=NF • l 
?.f.. IF<NF.GT.Nt.) c;o TO c;1, 46 DO Ju J=NFtNL 47 JF(LN(J)eEQ.lH.> GO TO 3~ 
48 
DO z::, K=ltln 49 tF(LNCJ).EQ.NDIGCK)) GO TO 27 
?.5 r,QN Tl NUE:: 51 r,o TO Sb 52 21 VAL= VAL01n.n + FLnATCK-1)53 30 coNllNUE 54 AO TO 50 
c 
HE:RE TU CONVE~T DECIMAL FRACTION 56 35 NF=J•1 57 IF<Nt.GTeNL> GO TO ~n 58 No=o 59 no 4ti J=Nf tNL 
"1o=ND+ l 61 no 1i K=1.1n 62 IFlLN(J).EQ.NDIG<~>> GO TO 4~ 
63 
'H CONTI NUt: 
64 GO TO 56 
D-7 
2 
3 
4 
'.) 
c 
r 
.) 8 
Q 
c 
11 c 12 13 
14 
15 
lG 
17 
18 
19 
21 22 23 24 25 26 27 28 29 
31 
32 
33 
34 35 36 37 38 39 
41 
42 
43 
44 
45 
46 
47 
48 
49 
51 
52 
53 
54 
55 
56 
57 
58 
59 
61 
62 
63 
64 
65 
66 
4~ VAL.;; VAL+ (!<-l)U(().liJo*i\Jn) 
4 A C 0 I~ l JN U t. 
Hf ~F. ~ I l H VAL • f\I 0 t R k ' MULT TPLY 
50 VAL=vAL * S<' 

RE '! lJtJN 
~'6 	~,JfkR= l 
PEflJhN HE~F 1F ALL BL~NK MOJ~ MA~CH 14, 7? 
A 0 	r>J E'"' ~ : -l PE I UHt\J [ f'Jl.J 
? V Sr fi I.f F 
D-8 

APPENDIX E 
STATE & HOMETOWN OF PERSONS QUESTIONED 
DURING CREEL CENSUS 

USERS ON LINE 14 
,0'NO. OF ITEMS IN QUERY RESPONSE= 54,.0'1 
NO. OF ITEMS IN THE DATA BANK = 54,0'1 
PERCENTAGE OF RESPONSE/TOTAL DATA BANK= 1/ifi.fiJ'fi 

ALAS 
FAIRBANKS 

ARIZ 
CASHION 
TUSCON 

AUSTRIA 
CALIF 
FRESNO 
LOS ANGELES 

CANADA 
COLO DENVER FLA MIAMI LAKES 
ILL CHICAGO EDWARDSVILLE EVANSTON NORTH LAKE PAXTON STREAMWOOD 
IND BLOOMINGTON EVANSVILLE 
IOWA DES MOINES SIOUX CITY 
KAN LAWRENCE 
LA DE RIDDER NEW ORLEANS 
MASS BROOKFIELD MEX MATAMOROS 
E-1 

MICH 

ADRIAN HOLLAND YPSILANTI 
MINN ST PAUL 
MO JOPLIN KANSAS CITY ST LOUIS 
MONT BILLINGS MONTERREY MONTERREY MEXICO NEB BEATRICE NEW JERSEY WEST BERLIN NC CAPE HATTERAS NJ WEST BERLIN OHIO GRANVILLE 
OKLA ALTUS CARAGEE CARNEGIE CHOCTAW DUKE EDMOND ENID HOLLENVILLE LAWTON MIDWEST CITY MUSTANG OKLAHOMA CITY TULSA 
SAUDI ARABIA DHAHRAN TENN MEMPHIS 
TEX 
ABILINE 
ALICE 
AP 

E-2 

ARLINGTON 
AUSTIN 
BANDERA 
BASTROP 
BEEVILLE 
BELTON 
BISHOP 
BOERNE 
BRACKENRIDGE 
BRADY 
BROWNSVILLE 
BRYAN 
BURKBURNETT 
CALALLEN 
CALDWELL 

cc 
CLEBURNE COLLEGE STATION COMFORT COMMANCHE CONVERSE CORPUS COVE COTULLA DALHART DALLAS DEL RIO DENTON DEVINE 
TEX DRIPPING SPRINGS DUBLIN D#HANNIS EASTLAND EDINBURG EL CAMPO 
EL PASO 
EULESS 
EVERMAN 
FALFURRIAS 
FALL CITY 
FLOUR BLUFF 
FORT STOCKTON 
FREDERICKSBURG 
FREEMONT 
FT WORTH 
FULTON 

E-3 
GALVESTON GARLAND GATESVILLE GEORGETOWN GOLIAD GRAND PRAIRIE GROVES HAMILTON HONDO HOUSTON INGLESIDE IRVING JACKSBORO JOURDANTON KARNES CITY KERRVILLE KILLEEN KINGSVILLE LAGARDO LAMPASAS LAREDO LA COSTE LITTLEFIELD LOCKHART LUBBOCK MARBLE FALLS MARSHALL MATHIS MCALLEN MEXIA MIDLOTHIAN MISSION MT PLEASANT NEW BRAUNFELS NIXON ODEM ODESSA ODUM PA PAWNEE 
TEX PLEASONTON PORTLAND POST POTEET POTSBURO 
E-4 
GUINLAN 
REFUGIO 
REYNOSA 
RICHARDSON 
RIO HONDO 
SAN ANGELO 
SAN BENITO 
SAN DIAGO 
SAN MARCOS 
SAN SABA 
SEQUIN 
SINTON 
SOMERSET 
STEVENVILLE 
STOCKDALE 
ST. AUGUSTINE 
TAFT 
TAYLOR 
TEMPLE 
TEXAS CITY 
THREE RIVERS 
TU LETA 
UNIVERSAL CITY 
UVALDE 
VICTORIA 
WACO 
WAELDER 
WALNUT SPRING 
WEATHERFORD 
WESLACO 
WICHITA FALLS 
YORKTOWN 

VIRGINIA RICHMOND 
WEST VIRGINIA CHARLESTON FAYETTEVILLE 
cc 
ROME ITALY **END FILE NO. 1 STATISTICS TAPE FILE NO.: 1 
HOW MANY HAVE SPECIES, TOTAL AND RESIDENCE ,CC AND NOT RANK ACCESS, 
UNKNOWN* 
,0'HOW MANY HAVE SPECIES, TOTAL AND RESIDENCE, CC AND NOT RANK ACCESS, 
UNKNOWN* 
,0'NO. OF ITEMS IN QUERY RESPONSE = 76 5 

NO. OF ITEMS IN THE DATA BANK = 54,0'1 
PERCENTAGE OF RESPONSE/TOTAL DATA BANK= 14 .16 
E-5 


APPENDIX F CREEL CENSUS DATA REASONS FOR FISHING (24) & COMMENTS (25) 
NO. OF CHARACTERS IN LONGEST STATE: 3 OPTION: NAME NO. OF STATES: 5 NO. OF DELETED STATES: 0 NO. OF DICTIONARY ENTRIES RESERVED: 20 LOS ,DSSA I NOS 5 1851 5 
1 
2 
3 
4 
5 

0 	23. RANK WATER NO. OF CHARACTERS IN LONGEST STATE: 14 OPTION: NAME NO. OF STATES: 6 NO. OF DELETED STATES: 0 NO. OF DICTIONARY ENTRIES RESERVED: 20 LOS,DSSA,NOS 6 1871 6 ONLY PIER OPEN 1 2 3 4 5 
0 	24. RANK OTHER NO. OF CHARACTERS IN LONGEST STATE: 29 OPTION: NAME NO. OF STATES: 106 NO. OF DELETED STATES: 0 NO. OF DICTIONARY ENTRIES RESERVED: 240 LOS ,DSSA,NOS 106 1891 106 BEEN BEFORE BOAT BOOKE : VACATION BOB HALL PIER FULL FIRST TIME FREE FREE + LIGHTED FRIENDS FUN LIGHTED LIGHTED + SAFE NATURE NDMB 
F-1 

NOT AS CROWDED PLAYGROUND FOR KIDS QUIET RECREATION RELATIVES SA.FE SAFE FOR CHILDREN SAFE FOR KIDS SAFE FOR SMALL BOYS VACATION WINTER FISHING 1 1 AREA 1 BEACH 1 BEACHES 1 BOB HALL PIER 1 CABIN HERE 1 COAST 1 ELBOW ROOM 1 ENTERTAINMENT FOR KIDS 1 FAMILY 1 FORMER RESIDENT 1 FREE BEACHES 1 FRIENDS 1 HABIT 1 HOUSE HERE 1 LIGHTED 1 LIGHTED + FREE 
1 LIVED HERE BEFORE 1 NIGHT FISHING 1 NOT AS CROWDED 1 OCEAN 1 PLEASURE 1 RELATIVES 1 RELAXATION 1 SIGHTSEEING 1 SMALL TOWN 1 TRYING OUT 1 TRY OUT 1 VACATION 1 VACATION AREA 1 VARIETY 0 F FISH 1 WORK HERE 1 (VACATION) 
F-2 

2  
2  AREA  
2  BEACH  
2  BEACHES  
2  COAST  
2  FAMILIARITY  
2  FORMERLY STAT HERE  
2  FORMER RESIDENT  
2  FRIENDS  
2  HABIT  
2  HOUSE HERE  
2  PEOPLE  
2  RELAXATION  
2  SAFE FOR KIDS  
2  VACATION  
2  (HABIT)  
3  
3  BEACH  
3  BEACHES  
3  CLEANER AREA  
3  LIGHTED  
3  RECOMMENDATION  
3  RELATIVES  
3  VACATION  
4  
4  BEACHES  
4  FRIENDS  
4  VARIETY OF FISHES  
5  
5  BEACHES  
5  FORMER RESIDENT  
5  FREE  
5  FREE + LIGHTED  
5  GALVESTON NOT LIGHTED  
5  GET AWAY  
5  HABIT  
5  LIGHTED  
5  LONG PIER + NO OBSTRUCTIONS  
5  NEWS  
5  NO TRASH ON BOTTOM  
5  PRICE CHEAPER  
5  QUIETNESS  
5  RELATIVES  
5  RELAXATION  
5  REST  

F-3 

5 SAFE 
5 VACATION 
5 WOMEN 

0 	25. COMMENTS NO. OF CHARACTERS IN LONGEST STATE: 39 OPTION: NAME NO. OF STATES: 123 
NO. OF DELETED STATES: 0 NO. OF DICTIONARY ENTREES RESERVED: 500 
LOS,DSSA,NOS 	123 2131 123 ACCESS ROADS IN POOR CONDITION BAFFINBAY BAFFIN BAY FISHERMEN BAIT AND TACKLE TOO EXPENSIVE BEACHES DIRTIER THIS YEAR BEACH RESIDENT BETTER HERE YESTERDAY-WATER WARMER BETTER THAN AVERAGE DAY BLUE WATER NEAR END OF PIER BUOY ; 3 CALM CALM CLEAR CALM WATER CALM + CLEAR CALM + RAINY CAL + CLEAR CAL+ MUDDY CAMPSITES TOO CROWDED CATCH FOR PREVIOUS NIGHT CLEARWATER CROAKERS CAUGHT IN SURF CUMMINGS CUT DOBBS EXTREMELY WINDY FACILITIES CLEANER THAN AT GALVESTON FACILITIES IMPROVED FAVORITE AREA OF COAST FERRY LINES TOO LONG FIN AND FEATHER FISHING BEST IN BAYS FISHING BETTER IN LAGUNA MADRE FISHING IS USUALLY BETTER FISHING WORSE SINCE CELIA FISHING WORSE THIS YEAR FISH FOUND IN GILL NETS 
F-4 

FLYROD ~HARD TO GET LIVE BAIT J JERRYIS MARINA KINGFISHING GOOD ON CHARTER BOATS LAGUNA SHORES ROAD LIKES JETTIES LOTS OF FLOATING SEAWEED LOTS OF SEAWEED MORE BENCHES MORE CHARTER BOAT INFORMATION MORE LIGHTS MORE RESTROOMS BY PIERS MOSQUITOES MOSQUITO CONTROL MUDDY WALK AFTER RAIN NDMB NEAR BAFFIN BAY NEED ARTIFICIAL REEFS NEED LIGHTS AT NIGHT NEED LIGHTS ON JETTY NEED MORE FISHING PIERS NEED MORE LIGHTS NEED MORE PARKING-CAMPING AREAS NEED MORE PUBLIC SHOWERS NEED MORE RESTROOMS NEED RESTROOM FACILITIES NICE PEOPLE HERE NOT ENOUGH CAMP FAC NO BAY SHRIMPERS 
NO DRINKING WATER ON BEACH NO LIVE BAIT AVAILABLE NO RESTROOMS CLOSE BY + LIMITED PARKING NO SIDEWALKS OR BATHROOM OIL OIL IN CHANNEL OIL ON SURFACE OIL ON WATER OIL SLICK ONLY PIER OPEN PA MORE FISHING ORIENTED THAN GALVESTON PEAT ISLAND PIER CROWDED PIER FISHING WORSE TODAY 
F-5 

PREFERS FISHING ON SOUTH PADRE 
PREFERS INDIAN POINT 
PRICES 	TOO HIGH 
PROBLEM WITH SURFERS 
RAINING 
RAIN SHOWERS 
REALLY 	LIKE FISHING HERE 
REDS THROWN BACK 
RED AND SPECS IN BAY YESTERDAY 
RED FISHING POOR THIS YEAR 
RED THROWN BACK 
RETIRED HERE 
ROADS 	NEED REPAIR 
ROAD TO OSO BRIDGE IS TERRIBLE 
ROUGH 
ROUGH MUDDY 
ROUGH WATER 
ROUGH : MUDDY 
SAIL LINE 
SEVERAL POMPANO THIS MORNING 
SHAMROCK BAY 
SHARK 	RIGS 
SPECS AT FISH PASS ('TATIONED AT PORT ARANSAS STRONG CURRENT 
SUGGEST RENTAL ROWBOATS 
TOO MUCH REPOSE ON BEACH 
TOO MUCH TRASH ON BEACH 
TURBID 
TURBID-CHOPPY 
TURBID CHOPPY 
TURBID + CHOPPY 
UNUSUALLY BIG MACKEREL 
WATER 	LOWER THIS YEAR 
WATER 	MUDDY 
WATER 	VERY CLEAR 
WINDY 
WINDY 	+ ROUGH 
WORSE 	IN JULY AND AUGUST 
1 
3 LARGE SPECS LAST NIGHT 
6-FOOT SWELLS 
0 	26. CODED BY NO. OF CHARACTERS IN LONGEST STATE: 11 
F-6 

OPTION: NAME NO. OF STATES: 10 
NO. OF DELETED STATES: 0 
NO. OF DICTIONARY ENTRIES RESERVED: 120 
LOS ,DSSA,NOS 10 2631 10 
DOBBS 
DONNA MIGET 
LITWIN 
MCNUTT 
MIGET 
M. WOLFE 
NDMB 
TEXAS 

TI 
W"HITE 
0 	27. BATCH NO. OF CHARACTERS IN LONGEST STATE: 3 OPTION: ORDER NO. OF STATES: 101 
FROM 0 
TO 100 
BY 1 
NO LABEL 

0 	28. SHEET NO. OF CHARACTERS IN LONGEST STATE: 4 OPTION: ORDER NO. OF STATES: 5001 
FROM 0 
TO 5000 
BY 1 
NO LABEL 

OEND* OTOTAL RUN TIME IN SECONDS CENTRAL PROCESSOR: 235 .598 PERIPHERAL PROCESSOR: 0. 000 

APPENDIX G GENERAL INSTRUCTIONS (CENSUS SHEET) 
1. 	
For a given category and situation, try to use a descriptor which is already in the dictionary instead of creating a new one. For ex­ample, for the category position: 11 shore 11 is already in the dictionary; do not use "bank" since for our purposes it means the same as 11 shore". 

2. 	
Whenever you find a situation not already defined in the dictionary; 
create a new descriptor, but be sure to add it to the dictionary. 


3. 	
Uniformity in spelling, word order, spacing, and punctuation is neces­
sary. Although "dead shrimp" and 11 shrimp-dead" mean the same thing 
they would be listed as two separate descriptors in the data bank. 


4. 	
The symbols: "," (comma) "and" "or" "for" "with" "not" have special meaning to the ENVIR program. They can't be used within any descriptor. 


11 +11
(a) 	In place of "and 11 , use (plus) 
11 	11
(b) 	In place of , (comma) use "-" (hyphen) 
5. 	
Any descriptor state not known should be left blank. Commas must 
be included between all blanks unless at the end of a line. 


6. 	
The letter "o" should be written "~". The number "zero" is written 
normally. 



The 	number "one" should be written 1. 
The 	number "seven" should be written 7. 
The 	letter "zee" should be written 6. 
7. 	
Print legibly only in #2 pencil (signatures as well). 

8. 	
The following symbols can't be used. 


& 	(ampersand) 
(apostrophe) 

9. 	
Any abbreviations may be used as long as they are recorded as such in the dictionary and explained in the appendix. 

10. 	
Any category using numeric descriptors will not accept words of any 
kind. 


11. 	
The numeric categories: no. of hours fishing, no. caught, no. kept, and no. of hooks should contain only integer numbers. 

12. 	
For the numeric category no. of hours fishing all values less than one hour should be considered one hour. For values greater than one hour: round up if one-half or greater, round down if less than one-half. 

13. 	
For the numeric category weight all values are in units of .1 (one tenth) pounds. Thus an entry of 10 would represent a weight of one pound. All values should be an average for all fish of that species caught. 

14. 	
For the numeric category no. of hooks: each lure, crab net or treble hook would represent one hook. 

15. 	
The category species should contain only the Latin genus and species name, written with only the 1st three letters of the genus and species. 

16. 	
Try to specify no. of each species caught on each type of bait, by using separate lines for each type. 

17. 	
Commas should be placed at the end of all lines. 

18. 	
Make comments brief and relevant to the census. Record them all in the dictionary. 

19. 	
The category time of interview will use a 24 hour clock, in 5 minute intervals. 

20. 	Total duration of trip? 

The appropriate answer to this question involves the total length of this particular outing. In other words, how long will the visitor be away on this trip, from the time he left home till he returns. The answer may be in hours or days. Be sure and note on the survey form whether the number entered is in days or hours. 

21. 	
City and county of residence. 


G-1 
This question is relatively self-explanatory. The answer sought is the permanent residence of the visitor. The county of residence is 
G-2 

very important and should be ascertained if at all possible. For 
out-of-state visitors code the state in the county blank. 
22. Type of Outing: Family or Other. 
TLe question is straightforward. The easiest way to ask the question is "Is this a family outing? 11 If answer is yes, check FAMILY; if no,
• 
check OTHER. Here we are attempting to separate out groups of unre­
lated individuals. 
23. Number of persons in party and number under 18. 

This two-part question is self-explanatory. Number of persons in party includes everyone, even those under 18. In the second blank enter only those under the age of 18. 

24. 
Total expected cost of trip, including food, lodging, bait, and gasoline? 


This figure should repre sent the approximate total outlay of money by the visitor for this trip. Everything should be included: bait, tackle, rentals, food, gas, etc. The figure should be for the visitor's entire group and not just himself. 
G-3 

GENERAL INSTRUCTIONS (CLIMATOLOGICAL DATA) 
1. 	The category time will use a 24 hour clock. 
2 . 	All date categories will use numbers only and include month, day, year in that order. 
3. 	
The category location should correspond exactly to the location of interview category on the census sheets. 

4. 	
The category wind direction will use letter abbreviation (e.g. , NW) and should be as specific as "SSE 11 


• 
5. 	
The category wind velocity should use knots and approximate to the nearest 5 knots (in multiples of 5) . 

6. 	
All temperatures sh:mld be recorded as Farenheit values, with no de­gree (°) symbols used. 

7. 	
No words can be used in any numeric category (e.g., 50F is not acceptable) except for #15. 

8. 	
All climatological readings will be taken at specified areas. 


G-4 

VOCABULARY (CENSUS SHEET) 

(1) Location of Interview 
1 -Caldwell Pier 1 7 -Marina Madre 2 -City Pier 18 -Ocean Drive 3 -ccsc 3 19 -Oso Bridge 4 -ccsc 7 20 -Oso Pier 5 -Fish Pass 21 -Aransas Pass Causeway 6 -Gulf Beach -City 22 -Fin & Feather 7 -Gulf Beach -IA 23 -Hogan's Ramp 8 -Gulf Beach -1 24 -Mom's Bait Stand 9 -Gulf Beach -2 25 -Redfish Bay 
10 -Gulf Beach -3 2 6 -Bahia Marina 11 -PA Jetty 2 7 -Indian Point Pier 12 -PA Marina 28 -Paradise Pier 13 -Station St. Pier 29 -T-head 14 -Bob Hall Pier 30 -L-head 15 -Jerry's Marina 31 -Cole Park Pier 16 -Kennedy Causeway 32 -Portland Causeway Boat Ramp 
(2) Location Where Fishing Done (Biotope) 
1 -Bulkhead 8 -River Mouth 
2 -Channel 9 -Shallow Bay 
3 -Grassflats 10 -Shallow Gulf 
4 -Hypersaline 11 -Shallow Pass 
5 -Inshore Gulf 12 -Surf 
6 -Oil Platform 13 -Open Gulf 
7 -Open Bay 14 -Oyster Reef 

(3) Position 
Bridge 
Boat 
Jetty 
Pier 
Shore 
Wade 

(4) Date of Interview 
Month Day 	Year 
1-12 1-31 	1973 
1974 

G-5 
(5) 
Time of Interview 

0005 -2400 (5 minute intervals) 

(6) 
No. of Hours Fishing 

1-128 (integers) 

(7) 
Species 


Carcharhinu s falciformis Carcharhinus leucas Carcharhinus lumbatus Rhizoprionodon terraenovae Sphyrna lewini Sphyrna tiburo Raja texana Dasyatis sabina Dasyatis sayi Lepisosteus spatula Elops saurus Megalops altantica Anguilla rostrata Ophichthus gomesi Gymnothorax ni.gromarginatus Brevoortia patronus Synodus foetens Galeichthys fe lis Bagre marinus Opsa.nus tau Centropomu s undecimalis Epinephelus migritus Epinephelus itajara Pomatomus saltatrix Rachycentron canadum Caranx hippos Caranx crysos Oligoplite s sauru s Trachinotus carolinus Lutjanus campechanus Lutjanus jocu Rhomboplites aurorubens Conodon nobilis 
Archo sargu s probatocephalus Lagodon rhomboide s Bairdiella chrysura Cynoscion arenarius Cynoscion nebulosus Cynoscion nothus Leiostomus xanthurus Sciaenops ocellata Menticirrhus littoralis Menticirrhus americanus Micropogon undulatu s Umbrina coroides Menticirrhu s saxatilis Chaetodipterus faber Mugil cephalus Polydactylus octonemus 
Trichiurus lepturus Scomberomorus cavalla Scomberomorus maculatus Prionotu s tribulus Paralichthys lethostigma Paralichthys albigutta Balistes capriscus 
Aluteru s schoepfi Lagocephalus laevigatus Chilomycterus shoepfi 
Eel Seriola dumerili 
Trachinotus falcatus Lutjanus griseus Lutjanus analis Lobotes surenamensis Orthopristis chrysoptera 
G-6 

Number caught 
1-500 
Number kept 
1-500 
Weight 
1-15000 
No. of Hooks 
1-250 
Bait 
1 -Chicken 2 -Cut Bait 3 -Dead Shrimp 4 -Dead Mullet 5 -Eel 6 -Jig 7 -Fish heads 8 -Goldspoon 9 -Hootie 
l 0 -Live Mullet 11 -Live Shrimp 12 -Lure 13 -Plastic worms 
City of Residence 
SA -San Antonio CC -Corpus Christi PA -Port Aransas AP -Aransas Pass Ft W¢'rth 
(9) County 
( 1 O) Days Per Year Fish in 
0 -366 
(11) Days Per Year Fish in 
0 -366 
14 -Plastic worm-red 15 -Plastic worm-white 16 -Plastic worm-yellow 17 -Plastic worm-orange 18 -Plastic worm-pink 19 -Ribbonfish 20 -Silverspoon 21 -Spec Rig 22 -Squid 2 3 -Mirror Lure 24 -Live pinfish 2 5 -Bingo lure 
Salt Water 
Fresh Water 
G-7 
(12) Salt or Fresh Water Preference 
S -Salt F -Fresh N¢' -N¢' Preference 
G-8 

VOCABULARY (climat¢'1¢'gical data) 
(1) 	
M¢'nth 
1-12 


(2) 	
Date 
1 -31 


(3) 	
Year 


1973 
1974 

(4) 	
L¢'cati¢'n (same as on census sheets) 

(
5) 	Wind Directi¢'n 

N E s w 
NNE ESE SSW WNW 
NE SE SW NW 
ENE SSE WSW NNW 


(
6) 	Wind Velocity 


5 25 45 65 
10 30 50 70 
15 35 55 75 
20 40 60 80 

(7) 	Cl¢'ud C¢'ver 
1 -Cl¢'udy 3 -Clear 5 -St¢'rm 2 -Hazy 4 -Rain 6 -Partly Cl¢'udy 
(
8) 	Bar¢'meter reading 

(9) 	
Air Temp 
0 -125 



(1 O) Water temp 0 -125 
G-9 

(11) Tidal Fl¢'w 
R -	Rising 
F -	Falling 
S -	Slack 
(12) 	
No. of Pe¢'ple Fishing 0 -500 

(
13) 	No. of Pe¢'ple Interviewed 0 -500 


ABBREVIATION APPENDIX 
SA 	= San Ant¢'ni¢' 
CC = C¢'rpu s Christi 
PA 	=-P¢'rt Aransas 
AP 	Aransas Pass 
Tex 	-· Texas 
Approved Common Name 
Atlantic Croaker Atlantic Cutlassfish Atlantic Sharpnose Shark Atlantic Spadefish Atlantic Stingray Atlantic Threadfin Barred Grunt Bighead Searobin Blackedge Moray Blacktip Shark Black Drum Bluefish Blue Crab Blue Runner Bonnethead Bull Shark Cobia Crevalle Jack Dolphin Fine scale Menhaden Florida Pompano Gafftopsail Catfish Gray Snapper Greater Amberjack Great Barracuda Gulf Kingfish Gulf Toadfish King Mackere1 Ladyfish Least Puffer Northern Kingfish 
Oyster Toadfish Pigfish Pinfish Red Drum Red Snapper Roundel Skate Sand Drum Sand Seatrout Scalloped Hammerhead Sea Catfish Sheepshead 
Local Common Name 
Croaker Ribbonfish Sharpnose Shark Spadefish Stingray Threadfin Barred Grunt Searobin Moray Eel Blacktip Shark Black Drum Bluefish Blue Crab Blue Runner Bonnethead Shark Bull Shark Ling Jackfish Dolphin Menhaden Pompano Sail cat or Gafftop Gray Snapper 
Amberjack Barracuda Whiting Dogfish Kingfish Skipjack Puffer Whiting Dogfish Piggy Perch Pin Perch Redfish Red Snapper Skate Sand Drum Sand Trout Hammerhead Shark Hardhead Sheepshead 
Latin Name 
Micropogon undulatus Trichiurus lepturus Rhizoprionodon terraenovae Chaetodipterus faber Dasyatis sabina Polydactylus octonemus Condon nobilis Prionotus tribulus Gymnothoras nigromarginatus Carcharhinus limbatus Pogonias cromis Pomatomus saltatrix Callinectes sapidus Caranx crysos Sphyrna tiburo Carcharhinus leucas Rachycentron canadum Caranx hippos Coryphaena hippuru s Brevoortia gunteri Trachinotus carolinus Bagre marinus Lutjanus Griseus Seriola dumerili Sphyraena barracuda Menticirrhu s littoralis Opsanus beta Scomberomorus cavalla 
Elops saurus Sphoeroide s parvus Menticirrhus saxatilis Opsanus tau Orthopristis chrysoptera Lagodon rhomboides Sciaenops ocellata Lutjanus campechanus Raja texana Umbrina coroides Cynoscion arenarius Sphyrna lewini Galeichthys felis Archosargus probatocephalus 
G-11 

Approved Common 
Shortfin Mako 
Silver Perch 
Skipjack Tuna 
Smooth Puffer 
Southern Flounder 
Southern Kingfish Southern Stingray Spanish Mackerel Spotted Seatrout Striped Mullet Tripletail Shark sp. Sharksucker Eel Hermit Crab Searobin Name 
Local Common Name 
Mako Shark Silver Trout Tuna Puffer Flounder Whiting Stingray Spanish Mackerel Speckled Trout Black Mullet Tripletail Shark Sharksucker Eel Hermit Crab Searobin Latin Name 
Isurus oxyrinchus Bairdiella chrysura Euthynnus pelamis Lagocephalus laevigatus Paralichthys lethostigma Menticirrhus americanus Dasyatis americana Scomberomorus maculatus Cynoscion nebulosus Mugil cephalus Lobotes surinamensis Carcharhinus sp. Echeneis naucrates Eel Hermit Crab Prionotus sp. 
G-12 

APPENDIX H 
REFERENCES FOR TABLE VI-5 

1 Calabrese, A. 1972. How some pollutants affect embryos and larvae of American oyster and hard-shell clam. Mar. Fish. Rev. 34 (11-12): 66-77. 
2 Butler, P. A. 1964. Commercial fishery investigations. In: Pesticide wildlife studies, 1963. U.S. Fish and Wildlife Service. · Circular 199. p. 5-28. 
3 Daugherty, F. M. 1951. Effects of some chemicals used in oil well 
drilling on marine animals. Sewage and Industrial Wastes. 
23(10):1285-1287. 

4 Eisler, R. 19 6 9. Acute toxicities of insecticides to marine decapod 
crustaceans. Crustaceana. 16:302-310. 

5 Shuster, C. N. and B. H. Pringle. 1969. Trace metal accumulation 
by the American eastern oyster, Crassostrea virginica. Proc. 
Nat. Shellfish. Assoc. 59:91-103. 

6 Butler, P. A. 1966. The problem of pesticides in estuaries. Trans. 
Amer. Fish. Soc. 95(4 suppl.):110-115. 

7 Butler, P.A. 1966. Pesticides in the marine environment and their 
effects on wildlife. J. Appl. Ecol. 3 (suppl.): 2 5 3-25 9 . 

8 Broad, A. C. 1964. Environmental requirements of shrimp. Biol. Prob. in Water Pollution. USDHEW-PHS. Publ. No. 999-WP-25. 
p. 86-91. 
9 Bookhout, C. G., et al. 1972. Effects of Mirex on the larval 
development of two crabs. Water, Air and Soil Pollution. 
1:165-180. 

10 Bryan, G. W. and L. G. Hummerstone. 1971. Adaptation of the polycheate Nereis diversicolor to estuarine sediments con­taining high concentrations of heavy metals. J. Mar. Biol. Assoc., U. K. 51 :845-863. 
11 Hansen, D. J. , et al. 1971. Chronic toxicity, uptake and retention of Arochlor 1254 in two estuarine fishes. Bull. Environ. Contam. Tosicol. 6(2):114-119. 
H-1 

12 Friend, M. , et a 1. 19 7 3 . DDE: Interference with extra-renal salt excretion in the mallard. Bull. Environ. Contam. Toxicol. 9 (1) :49-53. 
13 Epifanio, C. E. 19 71. Effects of dieldrin in seawater on the develop­ment of two species of crab larvae, Leptodius floridanus and Panopeus herbstii. Marine Bio. 11 (4) :356-362. 
14 Epifanio, C . E. 19 7 2 . Effects of dieldrin-contaminated food on the development of Leptodius floridanus larvae. Marine Biology. 13 (2) : 2 9 2 -2 9 7 • 
15 Duke, T. W., et al. 1970. A polychlorinated biphenyl (Arochlor 1254) in the water, sediment and biota of Escambia Bay, Fla. Bull. Environmental Contamination and Toxicology. 5(2):171-180 . 
16 Harriss, R. C., D. B. White and R. B. Mcfarlane. 1970. Mercury compounds reduce photosynthesis by plankton. Science. 170(3959):736-737. 
17 Eisler, R. 1965. Some effects of a synthetic detergent on estuarine fishes. Trans. Arner. Fish Soc. 94 (1): 26-31. 
18 Hemens, J. and R. J. Warwick. 1972. The effects of fluoride on estuarine organisms. Water Research. 6(11):1301-1308. 
19 Davis, H. C. andP. E. Chandley. 1956. Effects of some dissolved substances on bivalve larvae. Proc. Nat. Shellfish. Assoc. 46:59-74. 
20 Nimmo, D. R., et al. 1971. Toxicity and distribution of Arochlor 1254 in the pink shrimp, Penaeus duorarum. Marine Biology. 11 (3) : 19 1-197 . 
21 Parrish, P.R., etal. 1972. EffectsofArochlor 1254, a PCB, on oysters, Crassostrea virginica. ASB Bull. 19(2):90. 
22 Modin, J.C. 1969. Residues in fish, wildlife and estuaries. Pestic. M onit. J. 3: 1-7 . 
23 Marvin, K. T., C. M. Lansford and R. S. Wheeler. 1961. Effects of copper ore on the ecology of a lagoon. Fishery Bull. Fish and Wildl. Serv., U. S., Circular 184:153-160. 
H-2 
24 Price 	T. J. 1964. Accumulation of radionuclides and the effects of radiation on molluscs. Bio. Prob. in water pollution. USDHEW­P HS Pub. 999-WP-25. p. 202-210. 
25 Rabin, E. H. and F. C. Schwartz. 1972. The pollution crisis--official documents. Oceana Pub. New York. 
26 Rabin, E. H. and F. C. Schwartz. 1970. Ocean dumping--a national policy. Rept. to the President by the Council on Environmental Quality. 
2 7 Collier, A., S. Ray and W. B. Wilson. 1956. Some effects of specific organic compounds on marine organisms. Science. 124(3214):220. 
28 Clarke, G. L. 1947. Poisoning and recovery in barnacles and mussels. Biol. Bull. 92:73-91. 
2 9 Shuster, C. N. and B. H. Pringle. 1968. Effects of trace metals on estuarine molluscs. Proc. First Mid-Atlantic Indust. Waste Conf., Univ. Delaware. CE-5:285-304. 
30 Lowe, J. I., et al. 1971. Chronic exposure of oysters to DDT, toxaphene and parathion. Proc. Nat. Shellfish.. Assoc. 61:71-79. 
31 Loosanoff, V. L. 1962. Effects of turbidity on some larval and adult bivalves. 14 Proc. Gulf and Carribb. Fish. Inst. p. 80-94. 
32 Calabrese, A. and H. C. Davis. 1967. Effects of "soft" detergents on embryos and larvae of the American oyster (Crassostrea virginica). Proc. Nat. Shellfisheries Assoc. 57:11-16. 
3 3 Cabrera, J. 1971 . Survival of the oyster_g_. virginica in the laboratory under the effects of oil drilling fluids spilled in the Laguna de Tamiahua, Mexico. Gulf Research Reports, 3(2):197-214. 
34 Lowe, J. I. 1965. Chronic exposure of blue crabs, C. sapidus to sublethal concentrations of DDT. Ecology. 46 :899-900. 
35 Odum, W. E., G. M. Woodwell and C. F. Wurster. 1969. DDT residues absorbed from organic detritus by fiddler crabs. Science. 164:576-577. 
3 6 Gardner, G. R. and G. LaRoche. 1973 . Copper induced lesions in estuarine teleosts. J. Fish. Res. Bd. of Canada. 30(3):363-368. 
H-3 

37 Eisler, R. 19 70 . Latent effects of insecticide intoxication to marine molluscs. Hydrobiologica. 36:345-352. 
38 Eisler, R. and P. H. Edmunds. 1966. Effects of endrin on blood and tissue chemistry of a marine fish. Trans. Amer. Fish. Soc. 9 5 (2) :153-159 . 
39 Eisler, R. 1970. Acute toxicities of organochlorine and organophos­phorus insecticides to estuarine fishes. U.S. Bur. Sport fish. and Wildl. Tech. Paper. 46:1-12. 
40 Woodwell, G. M., C. F. Wurster and P. A. Isaacson. 1967. DDT residues in an east coast estuary: a case of biological concen­tration of a persistant pesticide. Science. 156:821-824. 
41 Lambou, V. W. 1972. U.S. Environmental Protection Agency. Report on the problem of Mercury emissions into the environment of the 
U. S. to the working party on Mercury. Sector group on the unintended occurrence of chemicals in the environment, 0. E. C . D. 
4 2 Ukele s, R. 196 2 . Growth of pure cultures of marine phytoplankton in the presence of toxicants. Appl Microbiol. 10:532-537. 
43 Davis, H. C. 1961. Effects of some pesticides on eggs and larvae of oysters Crassostrea virginica and clams Venus mercenaria. Comm. Fish. Rev. 23(12):8-23. 
44 Hidu, H. 1965. Effects of synthetic surfactants on the larvae of clams M. mercenaria and oysters C. virginica. J. Water Poll. Cont. Fed. 37:262-270. 
45 Lowe, J. I. 1964. Chronic exposure of spot, Leistomus xanthurus, to sublethal concentration of toxaphene in seawater. Trans. Amer. Fish. Soc. 93(4):396-398. 
46 Raymont, J. E. G. and J. Shields. 1964. Toxicity of Copper and Chromium in the marine environment. In E. A. Pearson (ed.) Advances in water pollution research. Vol. 3. Macmillan. New York. p. 275-290. 
47 Chipman, W. A., T. R. Rice and T. J. Price. 1958. Uptake and Accumu­lation of Radioactive Zinc by marine plankton, fish and shellfish. Fi sh. Bull. 13 5, Fishery Bull. Fish and Wildl. Serv. , U. S. 58:279-292. 
4 8 Tucker, R. K. and D. G. Crabtree. 1970. Handbook of toxicity of pesticides to wildlife. Bur. Sport Fish. and Wildl., Denver Wildl. Res. Cen. Resource Pub. 84. 131 p. 
H-4 

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