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UBC Theses and Dissertations

Comparative fish population studies Ni, I-hsun 1978

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COMPARATIVE FISH POPULATION STUDIES BY I-HSUN £1 B.Sc., National Taiwan University, 1S69 B.Sc, National Taiwan University, 1972 A THESIS SUBMITTED IN PABTTAL FULFILIHENT OF THE REQUIREMENTS FOB THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES {Department of Zoology) Be accept t h i s thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA March, 1978 (^T^L-hsun Ni, 1978) In p r e s e n t i n g t h i s t h e s i s in p a r t i a l f u l f i l m e n t o f the r e q u i r e m e n t s f o r an advanced degree at the U n i v e r s i t y o f B r i t i s h Co lumb ia , I a g ree that the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and s tudy . I f u r t h e r agree t h a t p e r m i s s i o n f o r e x t e n s i v e c o p y i n g o f t h i s t h e s i s f o r s c h o l a r l y purposes may be g r a n t e d by the Head o f my Department o r by h i s r e p r e s e n t a t i v e s . It i s u n d e r s t o o d that c o p y i n g o r p u b l i c a t i o n o f t h i s t h e s i s f o r f i n a n c i a l g a i n s h a l l not be a l l o w e d w i thout my w r i t t e n p e r m i s s i o n . Department o f Z o o l o g y  The U n i v e r s i t y o f B r i t i s h Co lumbia 2075 W e s b r o o k P l a c e V a n c o u v e r , C a n a d a V6T 1W5 Date 1978 ABSTRACT T h i s p r o j e c t was de s i g n e d t o s t u d y t h e p a t t e r n s o f v a r i a b i l i t y i n f i s h p o p u l a t i o n s . My h y p o t h e s i s i s t h a t s p e c i f i c p o p u l a t i o n p a t t e r n s s h o u l d be r e l a t e d t o e v o l u t i o n a r y c o n c e p t s ( p h y l o g e n e t i c p a t t e r n s } , z o o g e o g r a p h i c c o n s i d e r a t i o n s (f a u n a l p a t t e r n s ) , and t h e i r v e r t i c a l d i s t r i b u t i o n s . These p a t t e r n s s h o u l d be d e t e c t e d by comparing c e r t a i n p o p u l a t i o n parameters [ g r o w t h parameters (K, L I N F ) , t h e n a t u r a l m o r t a l i t y c o e f f i c i e n t ( H ) f s i z e a t f i r s t m a t u r i t y (LM), age a t f i r s t m a t u r i t y (TM), s i z e a t age 1 (L1) , t h e w e i g h t - l e n g t h e x p o n e n t i a l c o e f f i c i e n t (b) , and l i f e span (T95) ] which a r e i n t r i n s i c b i o l o g i c a l f e a t u r e s of t h e p o p u l a t i o n . Comparative methods were used t o a n a l y z e d a t a from p u b l i s h e d f i s h p o p u l a t i o n s t u d i e s by comparing f i s h p o p u l a t i o n parameters, i n d i v i d u a l l y , i n p a i r s ( r a t i o o r l i n e a r r e g r e s s i o n ) , or grouped t o g e t h e r { d i s c r i m i n a n t a n a l y s i s or C o o l e y and tonnes* c l a s s i f i c a t i o n method), i n o r d e r t o f i n d t h e s i m i l a r i t i e s o r d i f f e r e n c e s among d i f f e r e n t c a t e g o r i e s , and then t o group t h e s e i n t o p a t t e r n s . P u b l i s h e d d a t a p r o v i d e d 682 parameter r e c o r d s from 43 f a m i l i e s (171 s p e c i e s ) o f f i s h e s . Hy f i n d i n g s suggested t h a t more s a t i s f a c t o r y r e s u l t s would be o b t a i n e d from a g r e a t e r volume of d a t a . T h e r e f o r e , a l l t h e a n a l y s e s were based m a i n l y on 15 f a m i l i e s w i t h l a r g e sample s i z e s { B o t h i d a e , C l u p e i d a e , C y p r i n i d a e , E n g r a u l i d a e , Gadidae, H i o d o n t i d a e , Osmeridae, P e r c i d a e , P l e u r o n e c t i d a e , Salmonidae, S c i a e n i d a e , Scombridae, Scorpaenidae, Sparidae, and Sgualidae). Sample si z e s , mean values, standard errors, and c o e f f i c i e n t s of variation for population parameters and r e l a t i v e characters of the 15 families of fishes are l i s t e d i n the summary table. These data would enable the extrapolation of r e s u l t s based on many areas for management of other f i s h stocks where data are lacking. In the majority of families s i g n i f i c a n t l i n e a r regression relationships were found between 1/K—LINF, between LM—LINF, and between M-—K. This means that f i s h having a greater asymptotic length (LINF) also have a larger size at f i r s t maturity (LM), a lower natural mortality c o e f f i c i e n t (M) , and a lower rate (K) at which the asymptotic length i s reached. Using the F-test and the appropriate t-test as a basis for comparison of variances and means of in d i v i d u a l parameters, i t i s evident that i n most cases there are s i g n i f i c a n t differences between fam i l i e s . This confirms one of my hypothesis; namely that differences between families, as shown by population parameters, e x i s t from phylogenetic considerations. By comparing the four characters (K, LINF, LM, and LH/LINF) the f i s h f a m i l i e s can be divided into the following groups: A) Shoaling pelagic fishes - Engraulidae, Clupeidae, and Osmeridae. These families have the highest K values (1.6 f o r Engraulidae, over 0.-4 f o r the others), the smallest LINF, LM, and a very high LM/LINF r a t i o (over 0.7). B) Large pelagic fishes - Scombridae. This family has a moderately high K value (around 0.35) and the largest LINF. i v C) Demersal f i s h e s - Gadidae, P l e u r o n e c t i d a e , S c o r p a e n i d a e , S p a r i d a e e t c . These f a m i l i e s have low K v a l u e s { l e s s t h a n 0.25), i n t e r m e d i a t e LINF s i z e , and lower LM/LINF r a t i o s { l e s s t h a n 0.6). D) F r e s h w a t e r f i s h - C y p r i n i d a e . T h i s f a m i l y has K and LINF v a l u e s which a r e s i m i l a r t o t h o s e o f t h e demersal f i s h e s , but has a s m a l l e r LM l e n g t h and, e s p e c i a l l y , t h e l o w e s t LK/LINF (0.4) and TH/T95 (0.2) r a t i o s . S t e pwise d i s c r i m i n a n t a n a l y s i s based on 7 v a r i a b l e s i n t h e 15 f a m i l i e s showed t h a t over 90% o f t h e 620 c a s e s c o n s i d e r e d i n d e p e n d e n t l y c o u l d be c o r r e c t l y c l a s s i f i e d i n t o the r i g h t f a m i l i e s . C o o l e y and Lohnes* c l a s s i f i c a t i o n method was a l s o u t i l i z e d among s p e c i e s w i t h i n 5 major f a m i l i e s ( C l u p e i d a e , C y p r i n i d a e , Gadidae, P l e u r o n e c t i d a e , and Scombridae). C o r r e c t c l a s s i f i c a t i o n ranged from 5 8.6% ( P l e u r o n e c t i d a e ) t o 87.6% ( C y p r i n i d a e ) . These r e s u l t s f u r t h e r c o n f i r m e d t h e e x i s t e n c e o f p o p u l a t i o n p a t t e r n s by e x a m i n a t i o n of p o p u l a t i o n parameters. C l u s t e r a n a l y s i s based on 7 p o p u l a t i o n p a r a m e t e r s d i s p l a y e d t h e c l o s e n e s s among t h e 15 f a m i l i e s . Dendrograph r e l a t i o n s h i p s b rought out t h e e c o l o g i c a l , r a t h e r t h a n t h e s y s t e m a t i c , a f f i n i t i e s between f a m i l i e s . V TABLE OF CONTENTS ABSTRACT ................... ...... ..................... , . i i TABLE OF CONTENTS . .. V LIST OF FIGURES .. v i i LIST OF TABLES . . . i x ACKNOWLEDGEMENTS . . . . . . . X I INTRODUCTION ...... ....... ..... . .... ............. ... ..... 1 I I BASIC THEORY OF FISHING 5 I I I RESEARCH APPROACH : COMPARATIVE POPULATION STUDIES ....15 1. Ageing Problem ..................................... 15 2. F e a t u r e s of P o p u l a t i o n P arameters .................. 17 3. U n r e a l i t y o f t h e Usage o f P o p u l a t i o n Parameters ....19 IV MATERIALS AND METHODS ......22 1. Research Scheme ...22 2. Survey Data 24 3. M a t e r i a l s ............................ .............. 26 4. Methods o f A n a l y s i s 44 Y CHARACTERISTICS OF POPULATION PARAMETERS ................ 47 1. I n d i v i d u a l Parameters .............................. 47 2. R e l a t i v e C h a r a c t e r s ................................ 65 3. C o r r e l a t i v e C h a r a c t e r s ............................. "75 VI POPULATION PATTERNS .................................... 87 1. Comparison between F a m i l i e s 87 2. Comparison among F a m i l i e s 89 3. C l a s s i f y i n g F a m i l i e s ( D i s c r i m i n a n t A n a l y s i s ) .......97 4. C l a s s i f y i n g F a m i l i e s ( C c o l e y and Lohnes* Method) ... 105 5. C l o s e n e s s among F a m i l i e s ( C l u s t e r A n a l y s i s ) ........108 v i V I I GENERAL DISCUSSION 111 A. Comments on Comparative S t u d i e s 111 B. D i s c u s s i o n o f the R e s u l t s .......................... 114 C. F u t u r e S t u d i e s ......131 V I I I CONCLUSIONS ................134 IX LITERATURE CITED 137 X APPENDICES ................ ...... 138 v i i LIST OF FIGURES F i g u r e Page 1. Research scheme 23 2. The e s t i m a t i o n of growth parameters by f i t t i n g o f von B e r t a l a n f f y growth e q u a t i o n from a g e - l e n g t h d a t a . 25 3. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r t h e i n s t a n t a n e o u s n a t u r a l m o r t a l i t y c o e f f i c i e n t (M) . 48 4. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the growth parameter K 50 5. Mean v a l u e s , 95%. c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the a s y m p t o t i c l e n g t h (LINF) 53 6. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the s i z e a t f i r s t m a t u r i t y (LM) 56 7. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the age at f i r s t m a t u r i t y (TM) 58 8. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the l e n g t h a t age 1 (L1) 60 9. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the w e i g h t - l e n g t h e x p o n e n t i a l c o e f f i c i e n t (b) 61 10. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the ' l i f e span* (T95) 63 11. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r t h e r a t i o M/K 68 12. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r t h e r a t i o LM/LINF 70 13. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r t h e r a t i o L1/LINF 72 14. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample v i i i s i z e s among s p e c i e s w i t h i n f a m i l i e s f c r the r a t i o TM/T95 7 4 15. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the r a t i o T50/T95 7 4 16. L i n e a r r e g r e s s i o n a n a l y s i s between 1/M—T95 w i t h i n f a m i l i e s 7 7 17. L i n e a r r e g r e s s i o n a n a l y s i s between 1 /K—LINF w i t h i n f a m i l i e s 7 9 18. L i n e a r r e g r e s s i o n a n a l y s i s between LM--LINF w i t h i n f a m i l i e s 8 1 19. L i n e a r r e g r e s s i o n a n a l y s i s between L 1 — L I N F w i t h i n f a m i l i e s ^ 2 20. L i n e a r r e g r e s s i o n a n a l y s i s between 1/M—T95 w i t h i n f a m i l i e s 8 4 21. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , ranges and sample s i z e s o f i n d i v i d u a l parameters among 5 f a m i l i e s (group I) 9 0 22. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , ranges and sample s i z e s o f i n d i v i d u a l parameters among 10 f a m i l i e s (group I I ) 9 2 23. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , ranges and sample s i z e s of c o r r e l a t i v e c h a r a c t e r s among 5 f a m i l i e s 9 4 (group I) ^ 24. Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , ranges and sample s i z e s of c o r r e l a t i v e c h a r a c t e r s among 10 f a m i l i e s (group I I ) 9 5 9 9 25. D i s c r i m i n a n t a n a l y s i s i n 5 f a m i l i e s (group I ) 26. D i s c r i m i n a n t a n a l y s i s i n 10 f a m i l i e s (group I I ) 27. D i s c r i m i n a n t a n a l y s i s i n 15 f a m i l i e s (group I and group I I ) 1 0 2 LIST OF TABLES Table Fage 1. C o l l e c t e d data on p o p u l a t i o n parameters i n f i s h j p o p u l a t i o n s . 2. Sample s i z e s of i n d i v i d u a l parameters and r e l a t i v e c h a r a c t e r s i n f a m i l i e s analyzed 3. Summary t a b l e of mean v a l u e s , sample s i z e s , standard e r r o r s , and c o e f f i c i e n t s of v a r i a t i o n f o r •(.f. i n d i v i d u a l parameters i n each f a m i l y a . Summary t a b l e of mean v a l u e s , sample s i z e s , standard e r r o r s , and c o e f f i c i e n t of v a r i a t i o n f o r r a l a t i v e c h a r a c t e r s i n each f a m i l i t y 5. Summary t a b l e of l i n e a r r e g r e s s i o n a n a l y s e s f o r 5 c o r r e l a t i v e c h a r a c t e r s wi t h i n f a m i l i e s .......... 6. ' The c o r r e l a t i o n matrix between parameters f o r combined data o f 15 f a m i l i e s 8 6 8 8 7. The F - t e s t and t h e t - t e s t between f a m i l i e s 9 8 8. D i s c r i m i n a n t a n a l y s i s i n 5 f a m i l i e s (group I) ........ 9 8 9. D i s c r i m i n a n t a n a l y s i s i n 10 f a m i l i e s (group II) ...... 10. D i s c r i m i n a n t a n a l y s i s i n 15 f a m i l i e s (group I and group I I combined) 11. C l a s s i f i c a t i o n f u n c t i o n with 7 v a r i a b l e s f o r each f a m i l y i n d i s c r i m i n a n t a n a l y s i s ................. 12. Summary t a b l e o f d i s c r i m i n a n t a n a l y s i s f o r 15 f a m i l i e s 13. Cooley and Lohnes 1 c l a s s i f i c a t i o n method i n 5 f a m i l i e s (qroup I) 1 0 6 14. Cooley and Lohnes' c l a s s i f i c a t i o n method i n 10 l f ) , f a m i l i e s (group II) 15. Cooley and Lohnes 1 c l a s s i f i c a t i o n method i n 15 i n _ f a m i l i e s (group I I and group II) 16. Summary t a b l e of the r e s u l t s from d i s c r i m i n a n t a n a l y s i s and Cooley and Lohnes* c l a s s i f i c a t i o n , n R method i U B 17. Dendrographic r e l a t i o n s h i p s among 15 f a m i l i e s ^ ® X ACKNOWLEDGEMENTS I would l i k e t o e x p r e s s my deep a p p r e c i a t i o n t o my s u p e r v i s o r . Dr. Norman J . W i l i m o v s k y , f o r always b e i n g w i l l i n g and a b l e t o h e l p me. I am g r a t e f u l a l s o f o r the a d v i c e and c r i t i c i s m o f my t h e s i s committee members. Dr. H. D. F i s h e r , Dr. W. S. Hoar, Dr. G. C. Hughes, and Dr. J . D. M c P h a i l . D uring t h i s s t u d y I r e c e i v e d h e l p and encouragement from many i n d i v i d u a l s . For t h e i r v a l u a b l e s u g g e s t i o n s , I would l i k e t o thank Dr. P. A. L a r k i n , Mr. N. G i l b e r t , Dr. C. J . H a l t e r s , Dr. «3. McLeod and Dr. H. Khoo. For spe n d i n g much t i m e p c r i n g over e a r l i e r d r a f t s o f t h i s t h e s i s w i t h me, l a m e s p e c i a l l y g r a t e f u l t o Ms. Wendy C r a i k , Ms. Judy Mah, and Mr. Sam Gopaul. 1 Fish, protein-rich and renewable, constitutes one of the most important food resources for human beings. Therefore, f i s h i n g i s one of man's oldest occupations and has a continuous history of exploitation of these resources. The study of the dynamics of f i s h populations i s among the oldest and most advanced aspects of man's s c i e n t i f i c studies. There are several important questions that need to be answered when considering the exploitation of f i s h resources. How do we use the resource wisely? What i s the increasingly serious e f f e c t that nan's a c t i v i t i e s , through d i r e c t e x p l o i t a t i o n , are having on f i s h populations? How do we estimate the biomass of f i s h and predict catch in order to obtain a sustained yield? How do we provide an adequate s c i e n t i f i c basis for conservation? I n t e l l i g e n t fishery management reguires sound knowledge of the dynamics of f i s h populations. An essential feature of t h i s theory i s the y i e l d eguation which relates the obtainable y i e l d to such stock parameters as the number of r e c r u i t s , rate of growth at various stages of l i f e , natural mortality rate and int e n s i t y of f i s h i n g . In other words, i t includes an understanding of the b i o l o g i c a l mechanisms by which f i s h stocks are maintained and how the i r yields are regulated, the ef f e c t s of f i s h i n g on a stock, and the kinds, guantities and sizes of f i s h that can be taken on a continuing basis by di f f e r e n t amounts and/or kinds of f i s h i n g . Fishery science also endeavours to explain the causes of h i s t o r i c a l changes i n the f i s h e r i e s and to predict the future state of stocks and yi e l d s for given 2 conditions of the environment and i n t e n s i t i e s of f i s h i n g pressure. "Fishing models1' have already been developed and applied f o r over t h i r t y years. These models have worked f a i r l y well despite the fact that some of the assumptions inherent i n the models are not true. One of the tasks before modern f i s h e r i e s b i o l o g i s t s i s to improve these models or to f i n d new more r e l i a b l e models to predict catch better and to save time in estimating parameters. Many approaches have been t r i e d . As a r e s u l t the question frequently raised i s : " i s i t possible to determine i n which direction the e f f o r t should be concentrated i n order to eliminate randomly directed attempts?" One of the primary i n s t i n c t s of a b i o l o g i s t i s to u t i l i z e comparative methods (e.g. as for comparative anatomy, comparative physiology, etc.) to systematically produce facts i n order that s i m i l a r i t i e s or differences in l i v i n g organisms may be discovered. Peculiar to f i s h e r i e s biology i s the great amount of available data which makes possible comparative methods to dissect the population by analyzing the r e a l i t y and s t a b i l i t y of population parameters. The objective of t h i s comparative population study i s to re-examine the fundamental theory on which f i s h i n g models are based and to determine in which direction the models should be improved and then applied i n p r a c t i c a l situations. The c h a r a c t e r i s t i c s of f i s h populations exhibit both common properties (homogeneity) and v a r i a b i l i t y (heterogeneity). The v a r i a b i l i t y of f i s h populations depends upon genetic 3 constituents as well as envircnmental factors. This project investigated the patterns of v a r i a b i l i t y of f i s h populations. My hypothesis i s that s p e c i f i c population patterns sould be related to evolutionary concepts (phylogenetic patterns), zoogeographic considerations (faunal patterns), and v e r t i c a l d i s t r i b u t i o n s . These patterns were examined by comparing certain population parameters [growth parameters of von Bertalanffy growth model (K, LINF), the natural mortality c o e f f i c i e n t (M), size at f i r s t maturity fLM) , age at f i r s t maturity (TM) , length at age 1 (L1) , and the weight-length r e l a t i o n s h i p exponential c o e f f i c i e n t (b) ] these poulation parameters are i n t r i n s i c b i o l o g i c a l features of the population. The characters chosen are derived mainly from the widely used von Bertalanffy growth model and Beverton-Holt y i e l d model or are r e a d i l y avalable from the l i t e r a t u r e . To elucidate f i s h population patterns, comparative methods were used in the analysis of population parameter data from published f i s h population studies. This was done i n order to determine whether the relationships between the parameters of natural mortality, growth, and size at f i r s t maturity among various f i s h e s have any common properties. The various fishes were then grouped into patterns depending upon t h e i r s i m i l a r i t i e s or differences. The confirmation that s p e c i f i c f i s h population patterns e x i s t would, i n i t s e l f , be s i g n i f i c a n t advancement of ex i s t i n g research and l i t e r a t u r e i n the f i s h e r i e s biology. Further, the phylogenetic pattern based on population parameters would serve as an additional t o o l for systematic studies. The e c o l o g i c a l patterns, such as for faunal and v e r t i c a l d i s t r i b u t i o n s , could be applied to multi-species exploitation models by substituting the generalized c h a r a c t e r i s t i c s of population parameters. In so doing, I had two p r a c t i c a l considerations i n mind. One was to examine the c h a r a c t e r i s t i c s of population parameters with available data i n order to derive ideas about the steps, for modifying f i s h i n g models, which must be taken next. The other was the application of these results i n p r a c t i c a l s ituations by making these findings available f o r management of other f i s h stocks i n those regions where data are lacking. This report f i r s t offers a thorough review of f i s h i n g theories to point out the s i g n i f i c a n c e of population parameters in the Beverton-Holt yield model. This i s followed by a discussion of the background and general ideas of comparative studies. Subsequently, explanations of the research scheme, data c o l l e c t i o n and methods of analysis are provided. Analyses are mainly based on examination of the c h a r a c t e r i s t i c s of population parameters, i n d i v i d u a l l y or i n related pairs (ratios and l i n e a r regressions) and grouping fishes into patterns by considering a l l population parameters simutaneously. F i n a l l y , I have attempted to discuss the r e s u l t s and present my conclusions. 5 IS BASIC 2JEOBY OF FISHING The fisherman and the f i s h are treated as a predator-prey system, with the centre of i n t e r e s t to f i s h e r y researchers l y i n g i n the benefits to the predator. Man cannot do much to replenish the stocks of sea f i s h to compensate for the y i e l d he takes. He has to re l y on the natural r e s i l i e n c e of f i s h populations to make up for the loss caused by f i s h i n g . The main task i s , therefore, to learn about the behaviour of f i s h populations as s t a t i s t i c a l aggregates and, i n p a r t i c u l a r , how they react to the various amounts and kinds of f i s h i n g a c t i v i t y to which they have been, or might i n the future be, subjected. The attention of fishery b i o l o g i s t s concerned with the improvement of f i s h e r i e s has thus become focused on the need to predict catches to ensure that the increased a c t i v i t y w i l l continue to provide at least the same given return. Hence, many bi o l o g i s t s have concentrated on the relationships among population sizes, f i s h i n g a c t i v i t i e s and catches. In practice, they have to begin by making certa i n kinds of s i m p l i f i c a t i o n s to enable them to construct models. The s i m p l i f i c a t i o n s w i l l depend on the kind and amount of data available, as well as the p a r t i c u l a r guestions concerning the population and i t s associated fishery. In what follows, two general types of approach have been developed. The f i r s t method used i s to investigate the population growth pattern of a f i s h stock, or a group of stocks, by considering the growth increment. Increase i n biomass i s the resu l t of the elemental rate of recruitment, growth and natural mortality, and hence capacity to increase, which i s regarded 6 primarily as a function of the s i z e of the stock within the available l i f e span of the f i s h . This approach stems from the concept of the l o g i s t i c law of population growth which may be represented by a sigmoid curve or a derivative of i t , and has been applied to the dynamics of a fishery by Graham (1935, 1939). It has since been further developed by Schaefer (1954, 1957). This model has apparent advantages of s i m p l i c i t y and makes only a small demand on data - good s t a t i s t i c s of catch and e f f o r t . The d i r e c t i o n of improvement for t h i s method i s simply improving sampling techniques. The second method, most widely used in t h e o r e t i c a l population dynamic studies and which provides an explanation of the dynamics and also enables prediction of y i e l d , i s to construct mathematical models of p a r t i c u l a r f i s h stocks i n which rates of recruitment, i n d i v i d u a l growth, and death are represented by functions derived from an analysis of the size and age structure of the population. This a n a l y t i c a l approach, used by Bussell (1931), Thompson and Bell (1934), Bicker (1945 and 1958), and others, has been developed i n d e t a i l by Beverton and Bolt (1957) and w i l l be referred to here as the "Beverton-Holt 1* model. An advantage of t h i s method i s that i t o f f e r s the p o s s i b i l i t y of predicting the e f f e c t s on catches of changes i n f i s h i n g a c t i v i t y by the s e l e c t i v i t y of f i s h i n g operations i n terms of the sizes and ages of f i s h accepted or rejected by the f i s h i n g gears. The Beverton-Holt model can be derived most conveniently by considering the catch obtained from a given year-class 7 throughout i t s l i f e . I t i s simpler to consider f i s h a b l e stock [wherein the f i s h i s older than catchable age (tr)1 instead of the entire age composition of the f i s h population. The following are defined below (following Holt et a l 1959): B« : biomass of f i s h in the exploited phase b : weight-length r e l a t i o n s h i p exponential c o e f f i c i e n t C : t o t a l number of f i s h caught f o r one year-class F : instantaneous f i s h i n g mortality c o e f f i c i e n t f : f i s h i n g e f f o r t K : rate at which f i s h reaches i t s asymptotic length l c (=l t) : length of f i s h at age tc .LINF (=L0o) : asymptotic length of the f i s h It {=lt) ' length of f i s h at age t B : instantaneous natural mortality c o e f f i c i e n t Nt (=Nt) : number of f i s h a l i v e at age t q : c a t c h a b i l i t y c o e f f i c i e n t E : number of r e c r u i t s , i . e . the number of f i s h a l i v e at age t r B' : number of f i s h a l i v e at the age t c at which they are f i r s t retained by the gear i n use tO (=t 0): time at which the f i s h was t h e o r e t i c a l l y at zero s i z e tc (-t ): adjustable age at which f i s h are f i r s t l i a b l e to capture by fishing gear i n use TL (=T^) : maximum age f i s h attained by the f i s h t r (=t r) : age at which f i s h are recruited to fishable stock 8 HINF (=WC0) : asymptotic weight of f i s h wt (=Wt) : average weight of an i n d i v i d u a l f i s h at age t Y : t o t a l weight of f i s h caught f o r one year-class Z : instantaneous t o t a l mortality c o e f f i c i e n t For the pre-exploited period, t r < t < tc dNt = - fl Nt (1) dt i . e . the instantaneous rate at which f i s h are dying of natural causes i s egual to the product of the natural mortality c o e f f i c i e n t times the number of f i s h . Therefore, -H (t-tr) Nt = R e (2) because N = B when t = t r , and when t = tc -H(tc-tr) 8* = R e (3) For the post-exploited period, t > tc, when both f i s h i n g and natural mortalities are operating dNt = - (F + M) Nt = -Z Nt (4) dt An estimate of the instantaneous t o t a l mortality rate (Z) i s 9 Z = - In [ (Nt+1)/Nt ] (5) from the basic equation 7, = M + F = M «• q f <6) we can clot the t o t a l mortality (2) against the f i s h i n g e f f o r t ( f ) , and we w i l l find that a l l the points l i e s on a straight l i n e . The intercept i s H and the slope i s q. from (4) - CF + H) (t -tc) Nt = R* e (7) or -fl (tc-tr)-(F+M) (t-tc) Nt = R e 18) The number of f i s h caught i n the time i n t e r v a l (t, t = dt) w i l l be F*Nt*dt, so that the t o t a l number caught, C, w i l l be given by dividing up the t o t a l time between tc (the age at f i r s t capture) and TL (the maximum age attained) into short i n t e r v a l s , and adding the contributions from each i n t e r v a l . Mathematically, t h i s i s expressed by the i n t e g r a l r TL = J F Nt dt tc <9) 10 The t o t a l weight caught w i l l therefore be given by r T L i = J p st i t at (10) tc Where Wt i s the average weight of an in d i v i d u a l f i s h of age t. The expression for the weight has been developed by von Bertalanffy (1938), namely: -K(t-tO) 3 Wt = WINF [ 1 - € ] (11) i n which WINF i s the asymptotic weight to which f i s h tends with increasing age, K i s a constant which determines the rate at which the asymptotic weight i s reached, and to i s the time at which the f i s h was t h e o r e t i c a l l y at zero s i z e . Or, writing the righ t hand side as a summation, 3 -nK (t-tO) Wt = WINF 5 1 U (n) e (12) n=0 Where U(0) * 1, 0(1) = -3, 0(2) « 3, 0(3) = -1 b assuming weight-length r e l a t i o n s h i p i s W = aL and b = 3 then —K (t-tO) It = LINF [ 1 - e ] (13) 11 Walford (1946) showed that when It + 1 i s plotted against I t , the asymptotic length i s the point where the s t r a i g h t - l i n e r e l a t i o n s h i p cuts the 45° diagonal from the o r i g i n . By substituting Nt, Wt into'.equation (10), then r TL -(F+M)(t-tc) 3 -nK (t-tO) Y = J F R* e WINF^TtHn) e dt (14) tc n=0 Or, writing t - to = (t-tc) - (tc-tO) and rearranging the terms 3 pTL - (F+H+nK) (t-tc) -nK(tc-tO) Y = F R» W I N F 2 I U ( n ) J e e dt (15) n=0 t c on integrating t h i s becomes (16) 3 0(n) -nK(tc-tO) -(F+M+nK) (TL-tc) Y = F R» WINF]TJ e [ 1 - e 1 n=0 F+H+nK Which, i f TL i s s u f f i c i e n t l y large f o r the l a s t term to be deleted, then the y i e l d i s given by -nK (tc-tO) 3 U(n) e Y = F R* WINF^T" ~ — (17) n=0 F • - H + nK or, substituting f o r R* -nK (t-tO) -M(tc-tr) 3 D(n) e Y = F R e WINF ] T • (18) n=0 F + M • nK 12 Because recruitment i s unknown, yi e l d s are normally calculated as " y i e l d per r e c r u i t " . By assuming that H, t r , to, K, WINF are constant and can be estimated from the catch curve and the growth curve, the " y i e l d per r e c r u i t " <Y/R) can be used to compute either the equilibrium r e l a t i o n s h i p between catch and fis h i n g e f f o r t by varying the value of F, or the e f f e c t of changing the s e l e c t i v i t y of the gear by al t e r i n g the value of the aqe tc at which f i s h enter the catchable stock. From the foregoing equation, i n which Y i s determined, the biomass, B», can also be computed of f i s h i n the exploited phase (since the y i e l d i s F times the average biomass of f i s h ) , by using the equation -nK(tc-tO) 3 0(n) e B» = B* WINF (19) n=0 F + H + nK We can reduce the c a l c u l a t i o n s and also take into account a length-weight r e l a t i o n s h i p other than the cubic, by expressing the yield Y in a simple form using the incomplete Beta function (Jones, 1957). Using the transformation g = F/K, m = M/K, -K(tc-tO) l c c = 1 - e = (lc : the length of f i s h at age tc) LINF 13 b : weight-length relationship exponential c o e f f i c i e n t , the y i e l d equation can be derived as M(tr-tO) -g Y = R WINF e g (1 - c) B (ni+g, b + 1) (20) 1-c or i f b = 3 M(tr-tO) -g Y = R WINF e g (1 - c) B (ra + g, H) (21) 1-c This expression for the y i e l d can be considered i n two H (tr-tO) parts, the f i r s t { fi WINF e ] does not contain either of the parameters (F or tc) which depend on the amount or pattern of f i s h i n g . This part can therefore be considered as constant i n studying the e f f e c t on the y i e l d of d i f f e r e n t patterns of fi s h i n g . The second part contains only four parameters, the ra t i o s m = M/K, g = F/K, c = lc/LINF and b. Tables of the incomplete Beta function are available (Jones, 1957; Wilimovsky and Wicklund, 1963). Tables of g(1-c)g have been given by Holt (1957b) . Thus the yi e l d can be expressed as the product of a constant and two quantities obtainable from the tables. Beverton and Holt (1964) deduced the yie l d equation i n a s l i g h t l y d i f f e r e n t form and tabulated d i r e c t l y in terms of M/K (=m) , c, and E f> F/(F • M) = g/(g *• m) ]. If M(tr-tO) Y« * Y/E WINF e , then M/K Y* = E {1 - C) 3 D(n) (1-c) Y: n=0 1+ (nK/M) (1-E) (22) The table of values of Y* (proportional to the y i e l d per recruit) forms the equivalent of a y i e l d isopleth diagram from which the form of the r e l a t i o n s h i p between y i e l d and amount of f i s h i n g or size at f i r s t capture can be determined very e a s i l y . In conclusion, the Beverton-Holt y i e l d model i s based on the information of the natural mortality c o e f f i c i e n t (H) and growth parameters of the von Bertalanffy growth model (K and LINF) by assuming that recruitment i s a constant. Fishing mortality (F) and the age at which f i s h are l i a b l e tc capture (tc) are able to be adjusted in order to obtain the same given y i e l d . To simplify the c a l c u l a t i o n for t h i s y i e l d model requires information on the M/K and Ic/LINF r a t i o s . . Therefore, the comparative f i s h population studies w i l l r e l y on population parameters and r a t i o s but not on adjustable characters. 15 III RESEARCH APPROACH Z_ CGMPA RATI VE POP 01 ATI ON STUBIES Aoeinq Problem The methods used to estimate parameters in the y i e l d equation (18) require data on the age composition of the stock. If the ages of f i s h i n a sample can be determined, then the c o e f f i c i e n t s of mortality may be estimated from the age compositions of catches (equations 5 and 6), and the growth parameters may be estimated from the l i n e a r regression of the logrithmic f i s h s i z e on age (the Walford method for the von Bertalanffy growth equation). This presents d i f f i c u l t i e s in many areas, especially i n t r o p i c a l waters where the aqes of f i s h cannot r e l i a b l y be determined from rings on such hard structures as scales, o t o l i t h s , opercular bones, fin-rays or vertebral centra (Baqenal 1973). Examples of other methods fo r i n f e r r i n g age composition are from polyiaodal length freguency curves or from the seasonal progression of modal sizes. However, these two methods are not universally applicable. Even in the most favourable circumstances the former can be used to distinguish only the few youngest age groups in the population. The l a t t e r method may not be successful when spawning or recruitment i s spread over a very long season. Holt (1960) pointed cut that i f two or more possible age values were suggested by d i f f e r e n t interpretations of the 16 rings on one of the hard structures, i t could be checked by comparison with i t s expected growth parameter (K) value. Even when age can be determined i t i s usually such a time-consuming procedure that there i s a general inter e s t i n finding a means by which to avoid i t or, at l e a s t , to reduce i t s duration.,Holt (1958) showed that i n scombroid f i s h sexual maturity occurs at a size (IM) about two-thirds of the f i n a l length (LINF), when the f i s h i s about one t h i r d of i t s asymptotic weight (UINF). If the weight of a f i s h i s proportional to the cube of i t s length, the size at f i r s t maturity corresponds with the i n f l e x i o n point of the curve of growth i n weight which i s one of the features of the von Eertalanffy growth model. Holt (1959b) then suggested that a comparative study of the re l a t i o n s between size at f i r s t maturity, age and growth parameters be undertaken because determination of the mean or median s i z e at maturity does net require age-determinaticn. , For example, the length at f i r s t maturity of B a s t r e l l i g e r kanagnrta in the Indian f i s h e r i e s i s about 22.5 cm, which would imply that LINF i s about 33 cm. For t h i s stock the length at ages one or two years are known from the movement of modes in length-frequency d i s t r i b u t i o n s . Given LINF and the sizes at the two ages, i t i s possible to determine K, and a value of 0.65 was obtained by t h i s method. This i s not a precise method, but can be most use f u l l y applied for t r o p i c a l fishes. 17 Features of Population Parameters Holt (1957a, 1958, 1959a, 1962a) has shown that i f population parameters are grouped i n a p a r t i c u l a r way, certain generalizations appear i n scombroid f i s h and i n sardines. Sot only are t h e i r LH/LINF r a t i o s r e l a t i v e l y invariable, but these r a t i o s may have a c h a r a c t e r i s t i c limited range of values for groups of fishes up to the taxonomic l e v e l of 'Order'. In other words, i n t r a s p e c i f i c (e.g. sexual) and i n t e r s p e c i f i c relations between these parameters seem generally to be of a similar kind. Beverton and Holt (1959) presented a considerable amount of data and concluded that there i s a r e l a t i o n s h i p between longevity and growth patterns which d i f f e r s quantitatively among certain families. They developed the idea that the value of the natural mortality c o e f f i c i e n t (M) can be gauged from the growth pattern of the species concerned. A f i s h which approaches i t s ultimate length guickly - i . e . has a high K value - i s l i k e l y to have a high natural mortality rate, whereas a f i s h that grows slowly (a low K) i s l i k e l y also to have a low M. The r e l a t i o n between H and K appears to d i f f e r from one group of f i s h to another; thus, for clupeoids, M i s generally between one and two times K, for gadiforms fl i s between two and three times K. I f M/K r a t i o s are further confirmed (i.e. more supporting 18 e v i d e n c e i s found) by d e r i v i n g c h a r a c t e r i s t i c v a l u e s f o r p a r t i c u l a r taxonomic or e c o l o g i c a l groups, then i t becomes t h e o r e t i c a l l y p o s s i b l e t o undertake s t o c k assessment w i t h o u t a s e p a r a t e e s t i m a t i o n o f M and K. T h i s r a t i o a l s o can be u t i l i z e d f o r e s t i m a t i n g f i s h i n g m o r t a l i t y . The method i s not p r e c i s e , but i t i s o f t e n most u s e f u l i n t h e e a r l y s t a g e s of s t u d y i n g a f i s h e r y t o judge whether or not n a t u r a l m o r t a l i t y , o r f i s h i n g m o r t a l i t y , i s l i k e l y t o be t h e dominant element i n t h e t o t a l m o r t a l i t y . B i c k e r (1960) c r i t i c i z e d t h e i r s t u d y f o r not h a v i n g enough data t o s u b s t a n t i a t e the c o n c l u s i o n s . T h e r e f o r e , B e v e r t o n {1963) d i d a more d e t a i l e d s t u d y w i t h i n t h e f a m i l i e s C l u p e i d a e and E n g r a u l i d a e . He c o n s i d e r e d the c h a r a c t e r i s t i c s o f t h e s e two f a m i l i e s and found t h a t the s i z e a t which f i s h e n t e r t h e e x p l o i t e d phase ( l c ) was, i n e f f e c t , the same as t h a t a t which f i r s t m a t u r a t i o n i s reached ( i . e . 1c = LM) . He m o d i f i e d t h e y i e l d e q u a t i o n f o r e g u i l i b r i u m c a t c h per r e c r u i t f o r t h i s s p e c i a l case o f f i s h e r i e s based on mature f i s h . T a y l o r (195 8, 1959b) found t h a t K i n c r e a s e s r o u g h l y p r o p o r t i o n a l l y w i t h t h e l o g a r i t h m o f water t e m p e r a t u r e , and t h e i n s t a n t a n e o u s n a t u r a l m o r t a l i t y c o e f f i c i e n t (M) v a r i e s i n t h e same d i r e c t i o n . H o l t (1959b, 1960) then suggested t h a t t h e i n t e r s p e c i f i c v a r i a b i l i t y of the r a t i o M/K may be l e s s than the v a r i a b i l i t y o f e i t h e r M o r K. M a t h e m a t i c a l l y , i t seems t h a t t h e r e i s no r e l a t i o n s h i p between K and M because they are d e r i v e d i n d e p e n d e n t l y , a l t h o u g h many of the 19 above-mentioned references suggest that there i s a relationship between K and M which may be of considerable b i o l o g i c a l s i g n i f i c a n c e . Holt (1962b) looked at t h i s guesticn from an evolutionary viewpoint. For survival of the population, s u f f i c i e n t f i s h must reach maturity. Even i f M had been determined by predation, K might be adjusted by natural s e l e c t i o n so as to allow enough f i s h to reach breeding stage. 3 U n r e a l i t y of the Usage of Population Parameters The Beverton-Holt model assumes the natural mortality rate to be age-independent. However, Beverton and Holt (1959) showed that M i s not constant but increases with the age of the f i s h , with an especially sharp discontinuity at the onset of maturity, as evidenced by the c u r v i l i n e a r i t y of logarithmic age data. Paulik and Gale (1964) examined r e l a t i v e and absolute differences between the y i e l d isopleths obtained using isometric growth, on which the Beverton-Holt y i e l d model i s based, and those obtained using allometric growth for taxonomic groups with d i f f e r e n t c h a r a c t e r i s t i c growth patterns and l i f e spans. Sicker (1969) suggested when there i s greater mortality among the faster growing members of a year c l a s s , a curve f i t t e d to the observed sizes of the surviving f i s h at successive ages w i l l underestimate the true rate of growth of the f i s h i n the 20 population. However, the M/K r a t i o would remain the same. Because of the s i g n i f i c a n t l i n e a r c o r r e l a t i o n between K and the logrithm of surface water temperature, Taylor (1958, 1959a, 1959b, 1960, 1962) believed that we may be able to eventually abandon some fundamental premises of the f i s h i n g model theory i n favour of more p r o f i t a b l e hypotheses and to direct more attention to the environment, the multitudinous factors of which form a dangerous, large gap between theory and r e a l i t y . Gulland (1972) also suggested that we should be able to f i t environmental factors, p a r t i c u l a r l y such physical c h a r a c t e r i s t i c s cf the surface water as temperature, into the y i e l d model and, hence, achieve a better understanding of what i s l i k e l y to happen i n the immediate future. In summary, the foregoing papers dealt only with the analysis of systematic groups. With the exception of Taylor's studies, they did not take environmental factors into consideration. aside from these papers I could find no other l i t e r a t u r e 1 dealing with more kinds of comparative studies. I t i s reasonable to suppose that not only i n d i v i d u a l animals, but also whole populations are adapted to some degree to t h e i r environment. In the l a t t e r case t h i s would mean that the magnitude of one or more of the v i t a l rates was such that i t i Mitani (1970) did a si m i l a r comparative study by only l i s t i n g a few references. 21 best enabled the population to occupy a pa r t i c u l a r e c o l o g i c a l niche (faunal pattern) and perpetuate i t s e l f . Moreover, f i s h can be grouped according to common developments i n independent categories such as v e r t i c a l d i s t r i b u t i o n . In t h i s case, there may well be some pattern of association between parameters such as growth, maturity and natural mortality which would bring about adaptation of the f i s h population to i t s environment, thereby enabling i t to perpetuate i t s e l f . From above discussion i t follows that the time has come to use comparative studies to examine the s t a b i l i t y of population parameters and the r e a l i t y of f i s h models. In fact, t h i s was suggested by Holt over 15 years ago. I t seems that only f i s h e r i e s research has the necessary accumulation of data to permit t h i s kind of comparative study. 22 II HATEBIALS AND METHODS IJL Research Scheme The f i r s t step was to f i n d available data from published f i s h population studies. Emphasis was put on the information needed for stock assessment and management. Comparative methods were used to examine f i s h population parameters i n d i v i d u a l l y i n order to display their s t a b i l i t y and v a r i a b i l i t y . I nterrelationships between parameters (ratio and correlation) were also examined so as to f i n d the s i m i l a r i t i e s or differences among di f f e r e n t categories. Intra-species studies were based on comparison between sexes and comparison among d i f f e r e n t stocks. This would make known the d i s t r i b u t i o n of f i s h and t h e i r population parameters could be related to l o c a l environmental factors i n order to f i n d species c h a r a c t e r i s t i c s . Inter-species studies were concentrated in three directions - systematics patterns, fauna patterns, and v e r t i c a l d i s t r i b u t i o n s . The f i n a l step of t h i s study i s to apply these patterns to develop or to modify existng f i s h i n g models. (Refer to Figure 1, the flow chart for the entire research scheme) FIND AVAILABLE DATA b i o l o g y o f f i s h s t u d i e s READ IN DATA s p e c i e s , l o c a l i t y , h a b i t a t , f e e d i n g h a b i t s , f i s h i n g g e a r , w a t e r temp., s e x , sample s i z e , sample age r a n g e , n a t u r a l m o r t a l i t y c o e f f i c i e n t , g r o w t h p a r a m e t e r s , m a t u r i t y s i z e and age, l e n g t h a t age 1, l e n g t h - w e i g h t r e l a -t i o n s h i p c o e f f i c i e n t s , a u t h o r INTRASPECIES STUDIES COMPARE ?-< > $ -DISTRIBUTION RELATE TO ENVIRONMENTAL FACTORS FIND SPECIES CHARACTERISTICS INTERSPECIES STUDIES SYSTEMATICS s p e c i e s g e n u s f a m i l y o r d e r FAUNA t r o p i c a l t e m p e r a t e c o l d VERTICAL DISTRIBUTION l a r g e p e l a g i c s h o o l i n g " d e m e r s a l CHARACTERISTICS OF PARAMETERS D E A T H G R O W T H M A T U R I T Y M t95 K '" b ' ~ " (W= a L b ) INTERRELATIONSHIP BETWEEN PARAMETERS WHAT ARE THE - SIMILARITIES - DIFFERENCES FIND SYSTEMATICS PATTERNS FIND FAUNA PATTERNS FIND VERTICAL DISTRBN PATTERNS 'igure 1. Research scheme APPLIED TO FISHING THEORY 24 2 A Survej Data Most t o t a l length values were taken d i r e c t l y from published reports; where either standard length or fork length was reported, these were converted into t o t a l length i f the convertion r a t i o was available. A. Age-length data By using the von Bertalanffy growth equation, 231 age-length data sets were analyzed. A sample run i s shown i n Figure 2. Only a few of these did not provide estimations of K and LINF, in particular the fishes with short l i f e spans (e.g. Engraulidae, Osmeridae, Scombridae, e t c . ) . The estimated K and LINF values were then entered i n the parameter record when there were no estimated K or LINF values in the o r i g i n a l paper, B. Farameter data I have compiled in one record, species names, t h e i r stock areas, sample sizes, natural mortality c o e f f i c i e n t (M), growth parameters (K, LINF), length at f i r s t maturity (LM), age at f i r s t maturity (TM), length at age one (L1), the weight-length exponential c o e f f i c i e n t (b), and author. The weight-length relationship c o e f f i c i e n t s are a and b where: 2'S FITTING OF VON BERTALANFFY GROWTH EQUATION SPARIDAE(CHRY50PHRYS MAJOR) 1 4 . I B 1 1 0 - 5 4 8 8 R 8 8 S 8 8 R 8 ffi R 8 o n ^ r u c u r n 4 " T i n i D i r ) N C D AGE Figure 2. The e s t i m a t i o n o f growth parameters b y f i t t i n q of von B e r t a l a n f f y growth e q u a t i o n from age-Length d a t a (a sample p r i n t out) 26 b W = a L The age at which the f i s h attains 95% of the asymptotic length (T95) i s an index of ' l i f e span', as suggested by Taylor (1959), and i s calculated from K, t o , and LINF by using 1 LINF T95 = - In + tO K LINF - 0.9 5 LINF T50, the age at which the f i s h has attained 501 of the asymptotic length, can be calculated in the same way. 3. Materials Published f i s h population dynamic studies have provided 682 parameter data records from 43 families (171 species) and are l i s t e d i n Table 1. The growth parameters (K and LINF), when not found in the o r i g i n a l papers, are estimated by using the von Bertalanffy growth equation based on age-length data from the paper (refer to Figure 2). Table 2 summarizes sample sizes of data col l e c t e d f o r i n d i v i d u a l parameters and r e l a t i v e characters in 13 fam i l i e s . Because more s a t i s f a c t o r y results are to be obtained from a greater volume of data, a l l the analyses are based mainly on the 15 families having largest sample size s . Group I (5 families) ; Clupeidae - herrings, Cyprinidae - minnows and carps, Gadidae codfishes, Pleuronectidae - righteye flounders, and Scombridae -Table 1 C o l l e c t e d data on population parameters i n f i s h populations Note : (1) H - n a t u r a l m o r t a l i t y c o e f f i c i e n t ; K and LINF are growth parameters; LM = s i z e at f i r s t maturity; TH = age of f i s h at length LM; L l = length of f i s h at age 1; b = weight-length power c o e f f i c i e n t (2) The growth parameters (K and LINF), when not found i n the o r i g i n a l papers, are estimated by using von B e r t a l a n f f y growth equation cased on aqe-length data from the paper (3) Most t o t a l length values were taken d i r e c t l y from published reports; where e i t h e r standard length or fork length was reported, these were converted i n t o t o t a l length i f the convertion r a t i o was a v a i l a b l e . I SP| N0| L. SPECIES NAME AREA I S | |E| M I X| _ i — i I I | LINF | LM | (cm) |(cm) I I I | TM | L1 | I C/r) I (cm) | b IAU1H0R (WITH YEAR 19 ) Acipenseridae Sturgeons 1|Acipenser transmontanus |Canada:Fraser River 1 IAcip^nser transmontanus |Canada:Fraser River 2|Acipenser fulvescens 4|Acipenser n u d i v e n t r i s |Wisconsin:Lake Sturgeon |Europe i -I—T T 1 1 T I f l |0.005| | | |m| |0.035| 216.3| I | |0.01|0.05 | 178.0|112.5| | | |0.04 | 250.0|130.0| |3.15 ISemakula, 63 |3.13 ISemakula, 63 | |Probst*Copper, 54 | IPaccagnella, 48 Airmody tidae Sand lances 1|Ammodytes marinus 1 | Ammodytes marinus 1|Ammodytes marinus 2 | Annnod y t e s d u b i u s 2|Amroodytes d u b i u s 2|Ammodytes d u b i u s 2 | ADimodytes d u b i u s | I r i s h Sea:west INorth A t l a n t i c (North Sea:Faroe |Nova S c o t i a |Emeral:west IBanguerean I Nova S c o t i a Banks 1 I I I |o.289T I I |0.89 | I I I 0.326 | |0.382| 10.228| 15.6| I 2 1.8| I 30. 3| 20.3| 41.7| T 1 1 | 7. 9 | l-Molloy, 67 1.0| |3.169|Reay, 70 2.0) |3.068|Macor, 66 | |3.153|Kohler et a l , | 8.0| IScott, 73 | 5.4) IScott, 73 |14.0| IScott, 63 I I L 1 70 A n g u i l l i d a e Freshwater e e l s 1— • 1 | A n g u i l l a a n g u i l l a |Windermere D i s t r i c t I I |0.02 | 165.01 60.01 9.0| I Frost, 45 Aplochitonidae 1 | L o v e t t i a . s e a l i Whitebait |Tasmania -j. I 6.5| (Blackburn, 50 Argentinidae Argentines 1|Argentina s i l u s 1 | Argentina s i l u s 2|Argentina semifaxiata |Nova S c o t i a INova Sc o t i a I Japan -T—r Ifl I n. | I I |0. 129 | |0. 145 | I 1.2 I 41.4| 37.9| 19.0| -j. x - l r -I 7.5| I 7.5| I I IZukow.ski, 72 IZukowski, 72 lllanyu, 56 Atherinidae 1|Labidesthes s i c c u l u s S i l v e r s i d e s — T I J _ A Blennidae U. Commbtooth blennies I I T3.7 .1 J — -T-I 9.2| 7. 0| 1 _ J J 'Hubbs • 21 1|Blennius p h o l i s |Welsh Coast I 10.9 | 0 . 3 0 | 17. oj 8. 0 J —1— I 1^ IQasim, 57 ...Table 1 continued on next page ] • J • • • 1 1 1 , ,, }. . i # i • • • , 4 1 1 Bothidae Lefteye flounders I I i 1 1|Citharichthys s o r i d i d u s I C a l i f o r n i a | 1|Citharichthys s o r i d i d u s I C a l i f o r n i a I 2|Paralichthys o l i v a c e u s |East China Sea | 3|Pseudorhorabus cinnamoneu|Northwest Kyushu I I i • If 1 1 i 0. 3 |0.3 | 0.3 |0.3 | 1 1 1 1 i i — r -30. 0 | 30.01 1 38. 4 | L_ • • i 1 19.0| 48. 0| 1 1 _ ... ,. ... 1 1 1 3.0| | I 18. 5 ,., — J ,.. .. J | A r o r a , 5 1 |Arora, 5 1 ISaishu, 57 |Matsuura, 61 _ _| | Callicnyraidae 1 T Dragcnets | 1|Callionyraus l y r a I 1ICallionymus l y r a " T " | English Channel '. lE n g l i s h Channel i |m If • 0. 0. 96|0.43 | £610.55 | 25.01 17.5| 17. at I . _ . J T T • • - , IChanq, 51 IChanq, 51,., i. | Carangidae Jacks and pompanos 1 T 1 » I 1|Deca pterus I | | kurroides aka-adsi|Taiwan:Nanfanqao t 1|D. kurroides aka-adsi |Taiwan:Kaohsiung 1 1 1 • i • 1 1 1 1 I 1 1 1 1 1 10.135| I 0.302 | I I i i ' i 1 59.9| 39. 1 | I , \ . — i l 24. 0| 24.0| 1 f . 121.3 | 24.8| i — J IChanq et al,72 ; 1. 054|Chanq+ Shaw,75 1.289|Chanq et al,72 ; |Chanq+Shaw,75 — i — | C i c h l i d a e C i c h l i d s | 0 1 | T i l a p i a melanotheron " r |Nigeria:Lagos Lagoon • 1 i I 0.297| i i 25. 7 J i I 1 i I 13. 9 i |Faqade, 73 no | IE I* Clupeidae Herrings Clupea Clupea Clupea Clupea Clupea Clupea Clupea Clu pea Clupea Clupea Clupea Clupea Clupea Clu pea Clupea Clupea Clupea Clupea Clupea Clu pea Clu pea Clupea harengus harengus harengus harengus harengus harengus harengus harengus harengus harengus harengus harengus harengus ha rengus ha rengus harengus harengus ha rengus harengus harengus harengus ha rengus harengus harengus har eng.us harengus harengus harengus harengus harengus harengus harengus ha rengus harengus harengus harengus hareng us harengus harengus harengus harengus harengus ha reng us harengus T Clupea harengus harengus Clu pea Clupea -}—+ ...Table 1 harengus harengus harengus harengus Noruay:Lusterfjord Dunmore Norwegian,Iceland North Sea North Sea - south I r i s h Sea - north I r i s h Sea - south Gulf St.Lawrence:south Iceland B a l t i c Sea:north Atlanto-Scotian C e l t i c Sea Bay of Fundy B a l t i c Sea:south Gulf St.Lawrence:south Greenland S p a i n : A t l a n t i c coast North A t l a n t i c r C l y d e North A t l a n t i c : H i n c h Fortune Bay (Newfoundl.) Newfoundland Newfoundl.:south west (Autumn spawning) Newfoundl.:south west (Spring spawning) North Sea:north Norwegian Sea + continued on next page -+-| LINK | LM | TH | L l | (CH) | (CM) | (YR) | (CM) 0.7810.65 10.35 | 0.2 35 0.2=10.38 0.2 10.375 10.30 0.2 10.39 10.25 10.25 10.15 0. 16| 0. 17| |0.575 |0.499 | 0. 20 0. 27 | I 0 . .15 |0.35 10.462 10.348 0.20|0.260 0.20|0.282 J0.39 0. 20|0.27 + 21.0 29. 5 36.0 30. 0 29. 29.5 29. 5 36. 36. 5 25. 22.5 32. 8 41. 31.5 31.5 3 5.1 34.5 36.4 35. 2 31. 34.0 24.0| 23. 5| 24. 5 | 24. | 28. | I 18. | I 24.5| 28. | +-I AUTHOR (with year 19 ) Aasen, 52 11.0| IBeverton, 63 8.5| IBeverton, 63 Beverton+Holt, 59 Burd, 62 Bowers, 60a ; Smith, 56 Burd, 59 Lea, 19 ; Day, 57 F r i d r i k s s o n , 50 Hannerz, 56 ICES, 70, 72 ICES, 71 3. 181|Iles, 73 Jensen, 47 Kessieh+Tibbo, 71 Nielsen, 60 O l i v e r , 50 10.5| I P a r r i s h + S a v i l l e , 65 11.5| I P a r r i s h + S a v i l l e , 65 3. 293 | Parsons*ilodder, 73 Parsons*Hodder, 73 2.903|Parsons+Hodder, 75 ; Wintors*Hodder, 75 3. 328| Pdrsonsmoddor , 75 ; Winters + iloddcr, 75 Parrish+Craiq, 63 Runnstrora, 36 co SP| SPECIES NAME | AREA | E| fl | K no| I |x| | | 1|Clupea harengus harengus | Norway | JO. 1*7)0.21 I 1|Clupea harengus harengus|Newfoundland I | |0.25 | 1|Clupea harengus harengusIChaleur Bay I I f 0.30 | 2|Clupea harengus p a l l a s i |Okhosk Sea I | 10.35 | 2|Clupea harengus p a l l a s i |Hokkaido I I I | 2|Clupea harengus p a l l a s i |Canada:west coast I |0.56|0.29 | 2|Clupea harengus p a l l a s i |Alaska I | |0.25 | 2|Clupea harengus p a l l a s i |Japan Sea I 10. 2 |0.19 | 2|Clupea harengus p a l l a s i | E r i t i s h Columbia I | 10.475 | 2|Clupea harengus p a l l a s i |White SearZolotaya I | 10.20 | 2|Clupea harengus p a l l a s i |White Sea: Mezen ' I I I | 2|Clupea harengus p a l l a s i |White Sea:Kandalaksh I | |0.4 j 2|Clupea harengus p a l l a s i |Wliite Sea:Onega I | |0.4 | 2|Clnpea harengus p a l l a s i |White Sea:Dvina | | |0.15 | 3|Sprattus sprattus |Baltic(Bay of Gdansk) I | |0.75 | 3|Sprattus sprattus I North Sea | | |0.8 | 3|Sprattus sprattus IBrittany | | 10.9 | 3|Sprattus sprattus (Swedish Coast ( | (0.45 | 3(Sprattus sprattus |Spain:Atlantic coast I | | 1.2 | 3|Sprattus sprattus |North Sea I | 10.8 | 3|Sprattus sprattus (Norwegian coast I | |0.65 | 4|Sardinops caerulea |San Pedro I | I J 4|sardinops caerulea ( C a l i f o r n i a I |0.15|0.39 | 4|Sardinops caerulea I C a l i f o r n i a I |0.4 |0.45 |05|Sardinops melanosticta INorthwest P a c i f i c | I I | 5|Sardinops melanosticta |Japan | | 10.65 | 6|Sardinops neopichardus ( A u s t r a l i a I | I0.22 | 7(Sardinops o c e l l a t a |South A f r i c a I | (0.45 I 7|Sardinops o c e l l a t a ISouthwest P a c i f i c I | I | R|Sardina p i l c h a r d u s |Mediterranean:Formentera | | |0.5 | 8|Sardina pilchardus |Bay of Biscay I | 10.70 j 8|Sardina pilchardus I P o r t u g a l ( A t l a n t i c coast) | | (0.65 I 8|Sardina p i l c h a r d u s I Mediterranean - west I | |0.55 | 8|Sardina pilchardus (English Channel | | (0.50 | 8|Sardina pilchardus ( A d r i a t i c I I |0.43 | 8|Sardina p i l c h a r d u s I Mediterranean I | I | 9 | S a r d i n e l l a a u r i t a |Aegean I | "0.55 l 0 9 ( S a r d i n e l l a a u r i t a |Lastiglione(Spain) | f | I 109(Sardinella a u r i t a |Lastiglione(Spain) jm| I 1091Sardinella a u r i t a ( A l g e r i a | f | I |09|Sardinella a u r i t a | A l g e r i a |m| I I 9 ( S a r d i n e l l a a u r i t a |Mediterrean - east I | (0.4 ( 9 | S a r d i n e l l a a u r i t a |Mediteran.:Israel coast | | 10.192 I 9 | S a r d i n e l l a a u r i t a IMediterranean:west | | |0.55 I 9 | S a r d i n e l l a a u r i t a I B e l e a r i c Island | | | | 9 | S a r d i n e l l a a u r i t a (Canaries | | | | 9 | S a r d i n e l l a a u r i t a IMoyen Congo I | I |09|Sardinella a u r i t a ISeneqal I | |0.196 I 9 | S a r d i n e l l a a u r i t a |West African:Point Noire | | |0.7 | 9 | S a r d i n e l l a a u r i t a I B r a z i l | | |0.4 |10|Sardinella longiceps l l n d i a n Ocean I | |0.4 111|Sardinella maderensis l l s r a e l I | I |11|Sardinella maderensis |Senegal ( | I ...Table 1 continued cn next page LM (CM) 28. ! 30. 25. 23. 5| 23. 0 | I 25. 01 29. 0 I TH (YR) 3. 5 21. 23. 19. 15.-0| 13.0| 13. 0| 11. 10. 13. 14. 18. 22. 5| 17.0| 20. 0| 10. 23.0| 18.5| 15.01 14. 5| 14.8| 12.51 17. 5| 13.5| 12.0| 16. 5| 19.4| 16.9| 13. 5| 12.0| 15. 12. 5| 17.5| 16.0| 14.0| 22. 0| 22.0| 18.0| 18.0| 1.5 1.5 1.5 2.0 1.5 1.5 2.0 L1 (CM) 15.0 18. 3 16.0 10.4 12.9 14. 1 15.8 14. 0 15.0 15. 5 11.5 15.0 + AUTHOR ( w i t h y e a r 19 ) Sund, 43a, 43b T i b b o , 56, 57 T i b b o , 56, 57 A y u s h i n , 63 Kitahama+Nakayama, 58 H i c k e r , 58 R o u n s e f e l l , 30 T a n a k a , 60 ; A y u s h i n , 63 T e s t e r , 55 Tarabovtsev, 57 Tamhovtsev , 5 7 Tambovtsav, 57 T a u b o v t ^ e v , 57 Tarabo vtsi->v , 57 E l w e r t o w s k i , 60 E l w e r t o w s k i , 61 F a u r e , 50 M o l a n d e r , 43 O l i v e r , 50 R o b e r t s o n , 38 Sund, 11 A h l s t r o m , 60 Beverton«-Holt, 57 C l a r k + M a r r , 55 N a k a i + H a y a s h i , 62 T o k a i R e g . F i s h . R e s . L a b . 60 B l a c k b u r n , 50 D a v i e , 58 ; D e J a g e r Mathews, 60 Andreu et a l , 50 B o u q i s , 52 Rouq i s , '52 L a r r a n e t a , 60 G u s h i n g , 61 M u z i n i c , 54 R o d r i g u e z - R o d a " - L a r r a n e t a , 5 5 A n a n i a d e s , 51 A n d r e u + R o d r i q u e z - r o d a , 51 A n d r e u t R o d r i g u e z - r o d a , 51 B o u n h i o l , 21 B o u r i h i o l , 21 B e n - T u v i a , 60 B e n - T u v i a , 56 N a v a r r o , 32 O l i v e r + N a v a r r o , 52 P o s t e l , 60 P o s t e l , 60 P o s t e l , 55 R o s s i g n o l , 55 R i c h a r d s o n e t a l , 60 Chidambaram, 50 ; N a i r , 60 B e n - T u v i a , 60 P o s t e l , 55 -+- --4 Co11 idae 1|Cottus gobi 1 |-Cottus gobi I | Cottus gobi II Cott us gobi SP| no | SPECIES NAME ASEA 12|Etrumeus microps 13|Konosirus punctata 14| Ethmalosa f i m h r i a t a 14|Ethmalosa f i m b r i a t a 16|Pcmolobus a e s t i v a l i s 17|Pcmolobus pseudoharen 18|Ililsa h i l s a 18|Hilsa h i l s a 18|Hilsa h i l s a 19|Brevoortia tytannus 19|Brevoortia tyrannus 19|Brevoortia tyrannus 19|Brevoortia tyrannus 19\ Brevoortia tyrannus 19|Brevoortia tyrannus | Hiuqa-Nada |Wakasa Bay | N i g e r i a | Nigeria (George Bank | George Bank IRiver Hooghly IRiver Hooghly IGodavari (Indo-Pacific) |North Atla n t i c |Middle A t l a n t i c IChesapeake Bay |North Carolina |North Car o l i n a coast |North Carolina coast x| -+-I I f I m| f I m| LM | T M | L1 | (CM) | (YR) | (CM) | 15.0| 30. 3| 25.01 35.6| 30.0| 113. . 5 | 1 5. I I I I I I 141. I 113. 115. 112. 3| 0151 I 7| 11 81 Sculpins | Windermere I Windermere I River Brathay IRiver Brathay —i If|0.9 !0.4 | I m | 1. 1 | 0 .7 | If|0.8 |0.5 | I m | 0. 9 | 0 . 9 | 7. 3| 4. 21 7. 2| 4. 61 6.5| 5. 0 | 6.5| 5. 0 l I T-1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Cyprinidae Minnows and carps Cyprinus carpio Cyprinus c a r p i o Cyprinus c a r p i o Cyprinus c a r p i o Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Atramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Ahramis brama ^ — + India Indonesia Japan Europe Sweden:Hjalmaren(Lake) Sweden:Toften Sweden:Havqardsjon(Lake) Sweden:Yxtasjon Caspian Sea Poland:Oswin Poland:Wadag Poland:Znin Duzy Poland:Goldopiwo Lake Poland:Goldopiwo Lake Switzerland-germany Denmark:Haderslev Dan Karelia:Samoziero (Lake) US5R:Ladoga (Lake) Danube near Medvedov E a l t i c Arkona 36 north German lakes Dneper,middle course Dneper Delta (river) uSSR:Ilmen (lake) Ural Delta (river) Pskov Reservoir USSR:Itkul (lake) Aral Sea 0. 17 0.047 0.137 0.016 0.092 0. 148 0.045 0.080 0. 135 0.066 0.04 1 0. 209 0.212 0.900 0.429 0.126 0.076 0.257 91.8 67.2 215. 89. 72.2 .Table 1 continued on next page 115. 85. 6 2. 91, 117. 63. 69. 88, 51. 70. 115. 52. 7 4 1 6 9 8 3 0 9 9 3 6 -+ 17. 5 35.0 34.0 42. 5 31.6 31.6 22. 8 35. 4 31. 6 25. 4 26. 5 8.0 7.0 6.0 8. 0 3.7 7. 5 6. 1 5. 4 7. 2 4. 3 8. 2 6.7 6. 1 5.9 10.3 10.9 8. 2 9.6 7.8 11.6 |AUTHOR I -+ (with year 19 ) IYokota+Asarai, 56 IKu wa t a n i , 56, 58 |Lo nqhurst, 6 3 |Longhurst, 6 3 INetzel+Stanek, 66 INetzel+Stanek, 66 IChacko+Gnapati, 49 IChacko+Gnapati, 49 IPillay+Rao, 62 IReintjes, 69 I Re in t ~jes , 69 |R e in t iesy 69 |R e i n t i e s , 69 IRichards, 68 I Rich ar ds, 68 ISmyly , ISmyly, ISmyly, ISmyly, _i 57 57 57 57 | A l i k u n h i , 66 l A l i k u n h i , 66 l A l i k u n h i , 66 l A l i k u n h i , 66 |Alra, 17 |Alm, 19 i Wundsch, 39 ILaskar, 48 IBackiel+Zawisza, 68 IBackiel+Zawisza, 68 |Backiel+Zawisza, 68 IBackiel+Zawisza, 68 IBackiel+Zawisza, 68 |Backiel+Zawisza, 68 (Backiel+Zawisza, 68 1 IBackiel+Zawisza, 68 IBalagurova, 63 IBalagurova, 6 3 |Balon, 63 I Baiich, 6 J IDauch, 63 | B e l y i , 62 I B e l y i , 62 IBerg, 49 IBerq, 49 | Berq, 49 .. |Berg et a l , 49 IBerg et a l , 49 SPECIES NftUE 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 02 2 2 2 2 .2 2 2 3 3 3 3 3 4 4 4 4 4 a a a 4 4 Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Afcramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama Abramis brama R u t i l u s r u t i l u s R u t i l u s r u t i l u s R u t i l u s r u t i l u s R u t i l u s r u t i l u s R u t i l u s r u t i l u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Leuciscus l e u c i s c u s Caspian Sea DSSR:Azov Volgograd Reservoir V i s t u l a F i r t h (lagoon) Germany:Gr.Ploner Ob.Ausgrabensee Vier e r See Hungary:Lake Ferto Netherland (inland) England:Madingley Lake England:Norfolk broads Finland:Onkamo Finland:Tuusula Poland:14 Mazurian lakes Caspian Sea:north -Ammersee Si mssee Greece:Lake V o l v i England:River Wellan DSSR:Aral USSR:Aral USSR:Ilmen Lake uSSR:Ilmen Lake Vitava (river) Rybinsk Reservoir Danube Delta (r i v e r ) Poland:Szczecin F i r t h Karel SSR:Siamozero Karel SSR:Niukozero Netherland (pond) Hommanas-Pellinge DSSR:Volga Azov Sea Muggelsee Lake Langer See V i s t u l a near Warsaw DSSR:Ziemen (ri v e r ) Netherland (inland) Netherland (pond) de Biesbos de Biesbos Lake I j s s e l Willcw Brook England:Afon L l y n f i England:Afon L l y n f i England:River Lugg England:River Lugq River Funshion River Can Netherland (pond) River Thames River Frome River Thames ..Table 1 continued on next page ft 1 u a r | L O (CH) | (CM) 0.255| 53.4| 30.2 38. 0 0.099 | 94.3| 0.109| 60.0| 29.0 0.030 | 201.3 I 38. 0 20. 3 31.6 17. 7 0.122 | 71.41 7. 01 IHofstede, 73 7. 6| IHartley, 47 24. 0| 6. 0 1 | IHartley, 47 0.062 | 106.81 13*. 3| 5. 5| 6. 61 I J a r n e f e l t , 21 15. 2| 6. 0 1 2. 8| I J a r n e f e l t , 21 6. 1 | |Karpinska-walus, 0.233 42. 0 r a (YR) 4. 4.5 6.0 7.0 5.5 6.0 2.5 u i (CM) 9.0 6.5 6.2 1 31.6| 8. 0 ILaskar, 48 1 38.0| 7. 5 | ILaskar, 48 1 | 20. 3 | 3. 0 | |Laskar , 48 1 | 29. 11 5. 0 | |Leeming, 6 3 1 | 35.4| 5. 0 | IMorozova, 52 1 | 31.6| 4. 0 | IMorozova, 52 1 | 34.2| 6. 5 | |Morozova, 52 1 32.9| 6. 0 7.8) |Morozova,52 l O l i v a , 58 0.076 | 79.31 38. | 9. 5 6. 1| |Ostroumov, 55 0.168 | 73.5| 14.2| I Papadopol, 6 3 | | 36.7| 6. 0 | IPeczalska, 63 | | 36. 1| 8. 0 IPotapova, 54 | | 36.7| 9. 0 | IPotapova, 54 0.293 | 47.8| 7. 4 | ISteinmetz, 73 0.056 | 69.7| 3. 2 | |Seqestrale, 33 0.090| 88. 5| 36.7| 7. 0 5. 8 | IShaposhnikova, 48 0. 267 | 57.91 10.1| ITimofeev, 64 0.079 | 54. 1 | 25.3) 6. 5 | |Wundsch, 39 | | 22. 21 6. 0 | JWundsch, 39 0.041 | 132. 3| 7. 1 I |Za wisza, 51 0.111| 97.9| 8. 5| IZhukov, 58 0.199| 33. 1 | 6.0 | 10.01 IHofstede, 73 IHofstede, 73 0.323| 26. 11 6. 3| . | Hofstede, 73 0. 304 | 24.6| 6. 4 | IHofstede, 73 0.057| 100.2| 6.0| IHavinqa, 45 0.371| 22. 9| | |Cragq-nine*Jones, 0.286 | 23.8| | l l i e l l a w e l l , 73 0.317| 24.01 | I H e l l a w e l l , 73 0.361| 22.6| | l l i e l l a w e l l , 73 0.319| 2 3.8| | I H e l l a w e l l , 73 0.693 | 24. 1 | | IHealy, 56 0. 562 | 22. 8 | 12.0| IHartley, 47 IHofstede, 73 0.186 | 21.5| | | Mathevs + ^ . i l l i a m s , 0.301 | 2 5. 3 | | |Mann, 67 0. 198| 20.2| 1 |Williams, 67 Dementeva, 52 Dementeva, 52, 55 E l i z a r o v a , 62 Fil u k , . 57, 62 Geyer, 39 Geyer, 39 Geyer, 39 Geyer+Mann, 39 Lukashov, 61 SP I no | 1—4-01|Drepane a f r i c a n a I L  SPECIES NAME AREA 5|Cotla c o t l a 5|Cotla c o t l a 5|Cotla c o t l a 7|Phoxinus phoxinus i i -4-IRiver Yamuna |River Yamuna IRiver Yamuna I Windermere I x| •4-4-(1 I I 4-LINF | LM (CM) | (CM) 1-I I If I I ml I H I0.28 | |0. 306 | |0-306 | 1 10.55 | 127.51 115.9| 115.91 9.0| | TM | L l | D I (YR) I (CM) | H ' + | 2.0 | |3.15-44. 2 | 44.2| 3. 8| i 2.0| 2.0 | 13 I 3 199 264 Easyatidae Stinq rays 1|Dasyatis a k a j e i 1|Dasyatis a k a j e i Drepani dae | Japan |Ja pa n - T — 1 T Im| |0.1 |f|0.45J0.1 1 T 1-| 105.0| 40.0| | 150.0| 44.0| . j i i _ ) N i g e r i a -^—r I I _x i _ 31.oI~15.57 16.0| u . Embiotocidae Surfperches 01ICymatogaster 0 11Cymatogaster aggregata | Canada:Keates Island aggregata |Canada:Keates Island •i—r~ l f I -T 1 1 r -| 10.0| 2.0| 7.4| | 9.0| 1.5| 7.3| Engraulidae Anchovies 1|Engraulis 1 | En g r a u l i s 1|Engraulis 1|Engraulis 1|Engraulis 2|Engraulis 2|Engraulis 3|Engra u l i s 03|Engraulis 4 | Ce t e n g r a u l i s 4|Cetengrau l i s 4|Cetengraulis 4|Cetengraulis •4|Cetengraulis 4|Cetengrau l i s 4|Cetengraulis 4|Cetengraulis 4|Cetengraulis 4|Cetengraulis 4|cetengraulis 4|Cetengrauli s 4|Cetengraulis encrasicholus e n c r a s i c h o l u s encrasicholus encrasicholus enc r a s i c h o l u s japon icus ja pen i c us mordax mordax mysticetus mysticetus mysticetus mysticetus ra y s t i c e t u s mystice tus mysticetus m yst i c e t u s mysticetus mysticetus mysticetus mysticetus mysticetus | Romania |North Sea | Mediterranean-early spwn | Hediterranean-late spun | C a l i f o r n i a ITohoku and Tokai region | Japan ( C a l i f o r n i a ( C a l i f o r n i a |Almejas Bay IGuaymas Bay |Ahome Point | Banderas Bay IGulf of Fonseca,1952 Fonseca,1954-55 Bay Panama (1 951-60) Panama (196 1-63) Ga didae IGulf of I Monti jo IGulf of IGulf of IColumbia I Ecuador-Peru IGuaymas, Ahome, (Peru Codf ishes Fonseca | 12. 5| 1.0| | ICarausu, 52 1 .0 | 20. 0 | 13. 5| 1 1 IFaqe, 20 1.4 | 16.5| 10.51 1 1 |Faqe, 20 1.8 | 16.0| 11.5| 1 1 |Fage, 20 1.5 | 15.01 11.0| 1 1 IFurnestin, 45 16. 0 | 8. 8| 1.0|10.5| IHayashi, 61 1.6 | 15.0| 10.5| 1 1 IHatanable, 58 0.4 | 21.0| | 1 1 |Clark+ P h i l l i p s , 52 0.5 | 19.0| 15.0| 1 1 |Miller+Wolf, 58 1.23 | 16.6| 12.7) 1.0|12. 1 | I B a y l i f f , 69 2.58 | 14.2| 12.8| 1.0|12.7| I B a y l i f f , 69 2.42 | 14.6| 13.21 1.0|13.2| | B a y l i f f ,"69 | 11.5| 1.0| | I B a y l i f f , 69 2.9 2 | 15. 4 | 12.8| 1.0| I I B a y l i f f , 69 0.90 | 17. 1 | 12. 8| 1.0| | I B a y l i f f , 69 2.42 | 15.9) | | 14.9| I B a y l i f f , 69 2.36 | 15.0| 12.6) 1.0|12.7| I B a y l i f f , 69 1.3 1 | 17.0) 12.6| 1.0 | 13.7| • I B a y l i f f , 69 2.09 | 14.3| 12.6| 1. 0 | 1 2. 71 | B a y l i f f , 69 1.34 | 14.5| 11.8| 1.0|11.8| I B a y l i f f , 69 1.7 | 17.5) 13. 5| I I (Barrett+Howard, 61 1 .3 | 18.0| I I 1 I Barret t+Uoward, 61 AUTHOR ( w i t h y e a r i 1 - ) Jhingran, 68 Natarajan*Jhinqran, 63 Natarajan+Jhinqran, 63 F r o s t , 43 Yokota, 51 Yokota, 51 + Lonqhurst, 63 4, Gordon , 65 Gordon, 65 02|Gadus 02|Gadus 2|Gadus 2|Gadus 2|Gadus 3|Gad us 3|Gadus -J—+-macrocephalus macrocephalus macrocephalus macrocephalus macrocephalus minutus minutu s ...Table 1 continued on next page |Canada:west coast |Canada:west coast |Canada:Pacific coast |Bering Sea IJapan Sea IF.nglish Channel |English Channel .+ m| f l |m|1. 1 If I 0.9 -+H I 0. 42 I 0 .40 93. 0 | 93. 0 | 94. 0 | 91.0| 100.01 20.0| 24.0| +-49.0| 55.0| 55. | I 2.0| 2.5| 3.0|26.0 118.0 I 16.8 11. 13. ( F o r r e s t e r , 6 9 | F o r r e s t e r , 69 IKetchen, 61, 64 | Mo i s e e v , 5 3 ISvetovidov, 49 |Menon, 50 IMenon, 50 SP no !—+ 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 07 7 7 7 7 07 07 07 07 07 07 07 07 07 7 7 8 8 9 10 11 11 11 13 14 14 14 14 14 SPECIES NAME Gadus minutus Gadus morhua Gadus morhua Gadus mcrhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus morhua Gadus virens Melanogrammus a e g l e f i n u s Melanogrammus aeglefinus Melanogrammus ae g l e f i n u s Melanogrammus ae g l e f i n u s Melanogrammus aeglefinus Melanogrammus ae g l e f i n u s Mela nogrammus aeglefinus Melanogrammus aeglefinus Melanogrammus aeglefinus Melanogrammus aeglefinus Melanogrammus ae g l e f i n u s Melanogrammus aeglefinus Melanogrammus aeglefinus Melanogrammus ae g l e f i n u s Melanogrammus aeglefinus Melanogrammus aeglefinus Merluccius merlucius Merluccius merlucius Boreogadus saida Eleginus g r a c i l i s Theragra chalcogramma Theraqra chalcogramma Theragra chalcogramma Gadus merlanqus M i c r c u e s i s t i u s pontasson Microraesistius pontasson Micromesistius pontasson Micromesistius pontasson Micromesistius pontasson — + . +  ...Table 1 continued on next page AREA Mediterran ean ICNAF 4x ICNAF 4VW North Sea West Greenland Eastern Scotian Shelf West Greenland west Newfoundland Labrador east Newfoundland Flemish Ca p Grand Bank S t . P i e r r e Bank Labrador Labrador \ North Sea George Bank Barents Sea Newfoundl.:west(offshore) Newfoundl.:west(inshore) Newfoundland:west Newfoundl.:west (offshore) Newfoundl.:west(offshore) Norwegian Sea Norwegian Coast North Sea ICNAF ' 5 ICNAF 4VW ICNAF 3N0 Eastern Scotian Shelf Southern Nova Sco t i a Southern Nova Scotia Iceladn Arcto-Norwegian North sea-slow growth North sea-fast growth Faroe Faroe North Sea Brown Marmora Sea Marmora Sea A r c t i c Ocean Hokaido:Pacific coast Bering Sea Bering Sea Japan Sea:south I r i s h Sea Western Mediterranea Tuscan Archipelago Fa roe Scot.land:west coast Iceland 2. 3 |0. 10| I 0.20| |0.2 |0.18| I |0. 18 | 10. 15| I I 10.25| I 0.25| |0. 2'. I I 0. 18| I I I 0.201 10.15| I |0. 2 10.201 | 0.20| 10. 25| 10.20| 10.23 I 10.20| 0.15| I |0.20| 10. 6 10. 5 0.2 LINF (CM)' 21.0 105. 0 1 10. 0 132. 0 87. 5 105.0 94. 0 1 15. 0 67. 5 100. 0 100. 0 130. 0 95.0 67. 5 105. 0 112.5 134.0 93. 91.0 1 10. 0 107. 0 53.0 73.0 67. 5 57. 5 77.5 68. 8 67.0 48.3 58. 1 77.9 LM •~M) 70. 85. 45.5 49.7 71. 30. 0 26. TM (YR) 5. 1 6. 1 3.0 33. 5 | 3. 5 67. 5 | | 44. 0 | 23. | 60. 0 | 27. | 22.0| | 40. 0| 30. | 2. 5 20.0 94. 8 ( | 79. 6 | | 55.0 31.3| 3. 5 10.5 27. 9 19.0| 1. 0 19.0 28. 1 19. 3| 1. 0 20.0 33. 4 | 20.01 3. 0 19.5 39. 9 20.0| 3. 0 18.9 43. 2| | 20.4 L1 (CH) 18.0 + AUTHOR (with year 19 ) Vives+Suau, 56 Beverton, 65 Beverton, 65 Beverton+Holt, 57 Hansen, 61-63 3.075|Halliday, 72 Horsted, 69 Kohler, 64 May et a l , 64 May et a l , 64 May et a l , 64 May et a l , 64 May et a l , 64 May, 67 Pinhorn, 7 5 P a r r i s h , 56 Schroeder, 30 Tay l o r , 58 Wiles+May, 68 Wiles+May, 68 Wiles+May, 68 Wiles+May, 68 Wiles+May, 68 G o t t l i e b , 57 Blacker, 7 1 Beverton+Holt, 57 Beverton, 65 Beverton, 65 Beverton, 65 Ha l l i d a y , 72 Hennemuth et a l , 64 Hennemubh et a l , 64 Ices, 69 Ices, 69 Jones, 62 Jones, 62 Jones, 62 Parrish*Jones, 53 P a r r i s h , 56 U.S.Res.Rep, 62 (ICNAF) Beverton+Holt, 59 Beverton + I i o l t , 59 Beverton+Holt, 59 Ilaqa et a l , 57 Hirschhorn, 73 Hirschhoru, 73 Ogata, 56 Garrod, 64 Bas, 6 3 2.970|Matta, 59 P a i t t , 66, 68 H a i t t , 66, 68 R a i t t , 66, 6 8 OJ SPECIES NAME AREA SP| I—+- r 14 |Micromesistius pontasson|North Sea:north IE 15|Trisopterus esmarkii 16|Merluccius b i l i n e a r i s 16|Mcrluccius b i l i n e a r i s 17|Erosrae brosme 17|Brosn\e brosme I North Sea:north ISouthern New England IGulf of Maine IScotian Shelf IScotian Shelf If If I f I m Gasterosteidae Stic k l e b a c k s 11Gasterosteus 11 Gasterosteus aculeatus aculea tus | Cheshire ICheshire (3-spined) (10-spined) tl e x a g r a ra m i d a e Greenlings 01|Ophiodon 0 11Ophiodon elongatus elonga tus |Canada:west coast |Canada:west coast If |m Hiodontidae Mooneyes 1|Hiodon 1 | Hiodon 1|Hiodon 1|Hiodon 1|Hiodo n 11Hiodon alosoides a l o s o i d e s alosoides aloso ides a l o s o i d e s aloso ides |Lake Hinnipegosis |Lake Winnipegosis |Saskatchewan Delta |Saskatchewan Delta |Lake C l a i r e |Lake C l a i r e -l— If |m If I m If I to _ i I c t a l u r i d a e Freshwater c a t f i s h e s 11Ictalurus punctatus ( M i s s i s s i p p i River —JL : I s t i o p h o r i d a e B i l l f i s h e s 1|Istiophorus 2(Tetrapturus 3(Tetrapturus 3|Tetrapturus 3|Tetrapturus Lut -janidae araericanus audax albi d u s albidus a l b i d u s | A t l a n t i c (Western p a c i f i c | A t l a n t i c | A t l a n t i c | A t l a n t i c If I m Snappers 1— 11 Lutjanus sanguineus |South China Sea: G. "Conking | 1|Lutjanus sanguineus ISouth China Sea:southwest| Nemipteridae Threadfin breams 1(Nemipterus 1|Nemipterus 11Nemipterus v i r g a t u s v i r g a t u s v i r g a t u s ISouthern East China I (Taiwan S t r a i t I ISouth China Sea:G.Tonking| Osmeridae Smelts 1|Mallotus v i l l o s u s ( T r i n i t y Bay 1|Mallotus v i l l o s u s I T r i n i t y Bay ...Table 1 continued cn next page | K | LINF | LM | TM | L l | | | (CH) | (CM) | (YR) | (CM) | ..j + 1- + + (--I AUTHOR (with year ) I I I |0.405| 18.9| |0.402| 46.8| |0.181| 63.0| |0.051| 125.5| I I I | | |2.866|Raitt, 66 14.5| 2.0| | I R a i t t , 66 | |11.0| INichy, 69 | I 14.2| INichy, 69 50.7| 6.5| |3.000|Oldham, 72 43.5| a.7| |3.025|01dham, 72 0. 9 1.1 |0.64 | |1.6 | T ~ 6. 7 | • 4. 3| 3. 6J 3. 7 | |Jones+Hynes, 50 |Jones+Hynes, 50 -~i T T r-( 137.0| 70.0| 5.0| | 90.0| 46.01 2.0 | I F o r r e s t a r , 69 | F o r r e s t e r , 69 |0. 352 | |0.535 | |0.171 | I 0.2 33| |0.127| 10.178| 36. 0 | 32. 2 | 44. 4 | 39. 6 | 50.8 | 42. 2| | | |Kennedy*Sprules, 67 | | |Kennedy*Spru l e s , 67 |15.7| |Kennedy^Sprules, 67 (16.5| |Kennedy*-Sprules, 67 | 6.9) |Kennedy+Sprules, 67 | 6.9| |Kennedy+Sprules, 67 _l I L |0.06 | 119 — r -0| 36 —1— 0| |Appelget*Smith, 51 T T T r (1.1 | 236.0| | | | 290. 0 J 190.0| I I |130.0| I I I I I I I I | |De Sylva, 57 | |Ueyanagi+Wares, 75 | 3.915|Mather, Clark + Mason, | 3.0 |De Sylva + Davis, 63 | 3.6 |De Sylva+Davis, 63 75 •T T T r -|0.142( 92.7( | |0.148| 96.51 I J 20.8 J2. 8 0 2 | L a i t L i u . 74 |21.6|2.871|Lai+Liu, 74 I 18.0| I 16.0| I 15.0| |Kjo»Liu, 74 |Kao+Liu, 74 | K.io+Liu, 74 -T-T T 1 1 T 1 T-IfI I I I 16.0| | | I ml I I I 18.0| | | -+-' -i -i + + + +-| J a II q a a r d, 74 IJangaard, 74 + I |SP| SPECIES NAME 1 AREA E| « I K | LINF | LM | TM | L1 I b |AUTHOR (with year 1 no | • i t x 1 1 | (CM)' | (CM) | (YR) I (CM) I 1 1 11 Mallotus v i l l o s u s | Labrador f I 1 o. 48 | 19.01 17. 0| |Templemau, 48 I 11 Mallotus v i l l o s u s |Labrador m | 1 o. 48 | 20.01 18.01 ITempleman, 48 I * I Mallotus v i l l o s u s | Newfoundland f | I | | 3.25 |Winters, 70 t 11 Mallotus v i l l o s u s | Newfoundland m 1 I | | 3. 41 IWinters, 70 I 2| Osmerus eperlanus ILadoga Lake l | | 2.0| 8.0] |Arkhiptzeva, 56 I 21 Osmerus eperlanus IGulf of Ob l I I 4. 5 | |Amstislavsky, 59 1 2| Osmerus eperlanus | Kandalaksha Bay l I 1 3. 5| 4.7 |Belyanina, 69 1 2| Csmerus eperlanus | Huron 1 | I 2.0| IBaldwin, 48 1 2| Csmerus eperlanus |White Sea - Onega bay 10. 061 | 90.1 | | 3.0| 4.7 |Balagurova, 57 1 2| Osmerus eperlanus |Michigan 1 | | 2. 0 | 9.2 ICreaser, 29 1 2| Csmerus eperlanus |Pakovsko-Chudskoya Lake 1 | I 1. 0 | 7.2 IFedorova, 53 1 2 | Osmerus eperlanus IWhite Lake 1 | | 1.5| 6.0 |Fe dorova, 5 3 1 2| Csmerus eperlanus |Neva River 10. 204 | 25.4 | 3.51 7.8 |Kozhevnikbv, 56 ! 2| Osmerus eperlanus |Dvina Bay'white sea) 1 |0. 135 | 40.7| I 3. 5| 4. 1 |Ki r p i c h n i k o v , 35 1 2 | Osmerus eperlanus IChesha Bay |0. 18 | 31.6 I a. 51 3. 5 |Kirpichnikov, 35 1 2| Osmerus eperlanus |Rybinsk 1 | I 1.5| 5. 9 |Lapin , 56 1 2| Osmerus eperlanus |Elbe River I 0. 265| 32.6 1 2.0| 7. 1 I L i l l e l u n d , 61 1 2| Osmerus eperlanus IKurishes Haff:non-migrant 1 | I 1.0| 6. 3 IMarre, 31 1 2| Osmerus eperlanus IKurishes Haff:sea-migrant 1 I 1 2.0| 6. 5 IMarre, 31 1 2| Osmerus eperlanus IPyaozero (Karelia) 1 I 1 5.0| |Melyantzdv, 46 1 2| Osmerus eperlanus JMiramichi River 1 I I 2.0| |Mckenz i e , 64 1 2| Osmerus eperlanus |Yenisey River |0. 264 | 28.5 I 5.5| 4.6 INeiioan, 57 1 2| Osmerus eperlanus |Lena River 1 I 1 7.5| I P i r o j n i k o v , 50 1 2| Osmerus eperlanus |Basin of Maine 1 | 1 2.5 | |Rupp, 59 1 21 Osmerus eperlanus |Onega Lake 10. 372 | 12. 5 I 3.0| 6. 3 | Stefanovskaya, 57 1 2| Csmerus eperlanus IDadey Lake 1 | 1 1.0| 7. 1 I W i l i e r , 26 1 2| Osmerus eperlanus ILazmiaden Lake 1 I 1 2. 5| 8. 2 I W i l l e r , 26 1 3| Hypomesus o l i d u s |Japan:Lake Suwa 11. 65 | 11.5 10.01 I S h i r a i s h i , 57 Percidae Derches 1|Perca f l u v i a t i l i s 1|Perca f l u v i a t i l i s 01|Perca f l u v i a t i l i s 2 | S t i z o s t e d i c n canadense 3|Lucioperca l u c i o p e r c a 3|Lucioperca l u c i o p e r c a 3|Lucioperca l u c i o p e r c a | Sweden |Sweden | Sweden |Canada:Lake Nipigon |24 German lakes (Toften Lake |24 German Lakes i — i 1 — f |0.29|0. m|0.29|0. |0.16 10. |0.44|0. I 10. I 10. I 10. 30.0| 30. 0| 34. 0| 40.0| 78.8| 102.0| 82.4| 16. 0 10.0 "T r-I i I I I I I I |13.0| I 7.0| | 1 5. 0 | _l L. Pleuronectidae 11Hippoglossus s t e n o l e p s i s 1|Hippoglossus s t e n o l e p s i s 02|Isopsetta i s o l e p i s i s o l e p i s i s o l e p i s i s o l e p i s i s o l e p i s i s o l e p i s 02 IIsopsetta 2|Isopsetta 2|Isopsetta 02|Isopsetta 02|Isopsetta 3|Tseudopleuronectes | americanus 4|Hippoglossus v u l g a r i s 4|Hippoglossus v u l g a r i s Righteye flounders |Alaska I North P a c i f i c |Canada:west coast |Canada:west coast |Canada:west coast |Canada:west coast (Hecate S t r a i t IHecate S t r a i t I (St. Mary Bay INorth A t l a n t i c INorth A t l a n t i c _+  I I I I |m| ( f l |m| If I If I |m| I I I0.054 I |0, |0 |0 |0 I I |0.36|0. I f l 10. |m| |0. - + H — — + -36 26 ,244 281 ,4 ,02 .04 214. 9 270. 0 39. 0 45.0 38.0 42.0 41.7 36. 6 250. 0 170.0 10. 0 25.0 18. 0 21.0 132. 0 95. 0 7.4 | 7.6 | 6. 3 | 6.11 |Alm, 52 |Alm, 52 lAlm, 52 IRicker, 49 IBauch, 53 |Mnar, 47 |Ne uhaus, 34 .j 1Southward+Chapman, 65 |Thompson+Cleave, 36 |Fo r r e s t e r , |Forrester, | (Hart, 48 | |Hart,48 6.5|3.094|Kutty, 63 6.1|3.021|Kutty, 63 69 69 I I Dickie+McCracken, 55 |Devoid, 38 |Devoid, 38 ,..Table 1 continued on next page 9 10 11 12 12 13 m m 15 15 15 15 15 15 15 16 18 18 18 18 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 z1 SPECIES NAME Pleuronectes p l a t e s s a Pleuronectes p l a t e s s a Atheresthes evermanni P.einhardtius hippoglossoides R. hippoqlosscides Hippoglossoides dubius Hippoglossoides robustus Cleisthenes h e r z e n s t e i n i Eopsetta g r i g o r j e w i Limanda aspera Limanda aspera Clidoderraa asperrium Glyptocephalus s t e l l e r i Glyptocephalns s t e l l e r i Glyptocephalus cynoglossoides G. cynoglossoides G. cynoglossoides G. cynoglossoides G. cynoglossoides G. cynoglossoides G. cynoglossoides Microstomias achne Eopsetta j o r d a n i Eopsetta j o r d a n i Eopsetta jordani Eopsetta j o r d a n i Hippcglossoides p l a t e s s o i d e s H. p l a t e s s o i d e s H. p l a t e s s o i d e s H. p l a t e s s o i d e s H. p l a t e s s o i d e s II. p l a t e s s o i d e s H. p l a t e s s o i d e s H. p l a t e s s o i d e s H. p l a t e s s o i d e s H. p l a t e s s o i d e s H. p l a t e s s o i d e s II. p l a t e s s o i d e s H. p l a t e s s o i d e s Lepidopsetta b i l i n e a t a Lepidopsetta b i l i n e a t a Lepidopsetta b i l i n e a t a Lepidopsetta b i l i n e a t a Lepidopsetta b i l i n e a t a Lepidopsetta b i l i n e a t a Lepidopsetta b i l i n e a t a Lepidopsetta b i l i n e a t a Limanda ferruginea AREA z1|Limanda ferruginea — + + ...Table 1 continued on next page North Sea North Sea Hokkaido:pacific coast T r i n i t y Bay Bering Sea:Tohoku area Wakasa Bay Bering Sea Sanin D i s t r i c t Japan Sea:south Bering Sea Kamchatka:Okhosk coast o f f Miyako Tohoku Region Hokkaido ' Man I s l e Scotian Shelf Scotian Shelf Nova Scotia:south Nova Scotia:south Nova Scotia:south G.St.Larence off Kinkazan (miyagi Canada:west coast Canaea:west coast Canada:west coast Canada:west coast Scotian Shelf Scotian Shelf New England New England Cape Breton Island Cape.Breton Island East Newfoundland East Newfoundland Grand Bank Grand Bank G.St.Lawrence:south G.St.Lawrence:south G.St.Lawrence:south Canada:west coast Canada:west coast Cox Island Cox Island Western Gulf of Alas Western Gulf of Alas B r i s t a l Bay Norswest B r i s t a l Bay Norswest Sable I s l . Bank ; Ranquerean New England:south E | M | K | L INT | L M | 'rn | L 1 | x| I I | | (CM) | (CM) | (YR) 1 — [ (CM) I j. 1 f | 0.12|0.08 | 70.0| 28. 01 m I 0.22|0.15 | 45.0| 25. 0 | 1 1 80. 0| 4 0. 0 | 7.5| 11.21 1 1 1 1 3. 230 1 1 90.0| 60. 0| 13.0 14.4| I 1 41.5| 25. 5| 5. 5 12.5| 1 1 32. 0 | 19.5| 4.4| 1 1 39.0 | 18.0| 3.0 8. 5 | 1 1 34.0| 20. 5 | 3.5 8.5| 1 1 36.0| 8.0| 1 1 50. 0 | 23* Oj 6.0 3. 5| I I 49. 0| 30. 0| 4 .0 | 11.01 1 1 4 5.0| 30. 0| 6.0 11.01 1 1 13. 0| 6. 7 | 1 1 1 1 40. 0 | 7.7| f 0.1510.07 | m 0.2010.12 | f I0.07 | 83.8| m 10.12 | 60.9| I I 3.649 1 1 3.576 1 1 40.0| 5.6| f 1 1 70. 0| 44. 0 8.0 tn 1 1 53. 0 | 38.0| 7.0 f 0.20|0.167 | 58. 6| 44. | 8.0 ID 0.25|0.160| 49.0 | 38.0 7.0 f 1 1 0.20|0.013| 229.8| m 0.25|0.114| 44.6| f 10.15 | 67.5| m 10.27 | 45. 0 f 10.06 | 78. 1 | 3.423 m |0.10 | 54.4 3. 194 f 0.25|0.06 | 81. 1 m 0.2310.11 | 55. 2| f Q. 1810.11 | 72. 5 m 0.26|0.15 | 58. 51 I I 3. 1 39 f I I 3. 234 m I I 2. 814 f I I 60.0 36. 0 4.0 ro I I 53.0 28.0 4.0 f I I m | 0.254| 40. 1 f |0.158| 41.2 9.5| m |0. 168| 3 5. 9 1 9. 5 | f I 10.1541 44.6 1 7.4| m |0.109| i i 50. 1 7. 2| I 1 0.30 I 0.063 | 77. 9 f 0.22|0.583| 47.3 6.8j A U I M U K iwitn year 11_ +H +-Bevertcn+Uolt, 59 Beverton+Holt, 59 Kasahara, 55 Lear, 69 Mikawa, 63 Yoshiike, 62 Pruter+Alverson, 62 Katanabe, 56a Ouchi, 56 Pruter+Alverson, 62 Polutov,: 67 Kinoe, 52 Hamai+Ishido, 58 Watanabe, 56b Bowers, 60b H a l l i d a y , 73 H a l l i d a y , 73 Powles+Kennedy, 67 Powles+Kennedy, 67 Powles, 67 Powles , 67 Kasahara, 53 F o r r e s t e r , 69 F o r r e s t e r , 69 Ketchen+Forrester, 66 Ketchen+Forrester, 66 H a l l i d a y , 73 H a l l i d a y , 73 Lux, 70 Lux, 7 0 Minet, 73 Minet, 73 P i t t , 73 P i t t , 73 P i t t , 73 P i t t , 73 Powles, 67 Powles, 67 Powles, 67 F o r r e s t e r , 69 F o r r e s t e r , 69 Levinqs, 65 Levinqs, 65 L o v i n g s, 6 5 Levin<js, 65 Levinqs, 6 5 Levinqs, 65 H a l l i d a y , 73 Lux+Nichy, 69 |SP| SPECIES NAME | no | | AREA I i |E| U l i. i T — + |z1|Limanda ferruginea 1 |New England:south i i |m| |z1|Limanda f e r r u g i n e a IGeorge Ank If 1 | z 1 | Litnanda ferruginea IGeorge Ank |m| | 2 11Limanda f e r r u g i n e a |G.St.Lawrence:south 1 1 |22|Parophrys vetulus |Canada:west coast 1 f l |22|Parophrys vetulus |Canada:vest coast |m| | z2|Parophrys vetulus IWashington:Carr Inle If 1 |z2|Parophrys vetulus IWashington:Carr I n l e 1 m| I I -+-10 10 10 10 10 . J I LINF | (CM) -+ ,838 | ,512| ,693| ,243 | ,347 | 39. 50. 42. | LM I (CM) I | TM | L l | I (IfR) I (CM) | I) IAUTHOR (with year 1 "J ) • + — 21 21 I 57. 0| 49.01 4 1.6| 30.7| 30. 25. —+-• 0| • 0| -+—. 6. 3 | 7.0| 7.0| 12. M5. 114. 0| 5| i_. • |Lux*Nichy, |Lux+Nichy, |Lux*Nichy, 829|Powles, 67 |Forrester, |Forrester, l l l o l l a n d , 69 |Holland, 69 69 69 69 69 69 P o e c i l i i d a e Livebearers 1|Gambusia h o l h r o o k i i 1|Gambusia h o l b r o o k i i |Portugal I Portugal | m| | 0. 8 I 1.2 i 6- 2 | 3. 6 | |Franca, 53 |Franca, 53 _ j Polynem idae Th readfins 01|Pentanemus g u i n a r i u s INigeria 02|Galeoides decadadylus (Nigeria I I I I , T T " I 15.0| 40.0| 21.01 •1 T ~ I 8.5| |11.0| ILonghurst, 63 |Longhurst, 6 3 Pcmadasyidae Grunts 01|Pristipoma j u b e i i n i i | Nigeria I I T r | 20.0| ILonghurst, 63 Salmcnidae Trouts Oncorhynchus keta Oncorhynchus keta Oncorhynchus keta Oncorhynchus keta Oncorhynchus keta Oncorhynchus keta Oncorhynchus keta Oncorhynchus keta Oncorhynchus keta Thymallus t h y u a l l u s Coregonus a r t e d i i Coregonus a r t e d i i Coregonus a r t e d i i Coregonus a r t e d i i Ccregonus a r t e d i i Coregonus a r t e d i i Coregonus a r t e d i i Ccregonus a r t e d i i Coregonus clupeaformis Coregonus clupeaformis Coregonus clupeaformis Coregonus clupeaformis Coregonus clupeaformis Coregonus k i y i Coregonus k i y i Ccregonus s a r d i n e l l a S a l v e l v i n u s namaycush Oncorhychus nerka ...Table 1 continued on next |North Paci f i c | j | | | 1 a . o | i IBakkala, 70 |Oregon (tillamook Bay If I | | I 1 1 1 3.0 |Henry, 54 |Oregon (tillamook Bay I m | | | I 1 1 1 3. 2 I Henry, 54 |Canada:Columbia River 1 f |0.30 | 105.01 75.01 1 12.6 IMarr, 43 |Canada:Columbia River 1 m I 0.27 | 120.0| 81.0| 1 12.6 |Marr, 43 |Canada:Columbia River | f 10.39 | 102. 0| 70.0| 1 12.6 |Marr, 43 |Canada:Columbia River 1 mj 10.45 | 106.01 75.0| 1 12.6 IMarr, 4 3-|North P a c i f i c | | | | 1 1 1 3. 2 |Ricker , 64 |Hokk aidoanean | | | | 1 | |2.817|Sella, 29 | Yukoslavia | | 0,335| 46.7| 1 3.0|15.6| |Jankovic, 64 |Wisconsin:Trout Lake |f 1. 1 10.65 | 19.0| 12.5| 1 1 | H i l e , 36 | Wisconsin:Trout Lake 1 m 1. 1 10.65 | 19.0| 12. 5| I I I H i l e , 36 | Wisconsin:Muskellenge L. If 1.2 10.36 | 21.0| 15. 0| 1 1 | H i l e , 36 I Wisconsin:Muskellenge L. 1 n 1. 2 |0.36 | 21.0| 15.0| 1 1 • | H i l e , 36 |Wisconsin:Silver Lake If 0. 9 10.06 | 32.0| 14.01 | | | H i l e , 36 |Wisconsin:Silver Lake 1 m 1. 1 |0.06 I 32.01 14. 0| I I | H i l e , 36 |Wisconsin:Clear Lake I f 0. 3 10.27 | 39.0| 13.0| | I I H i l e , 36 | Wisconsin:Clear Lake 1 m 0.4 |0.27 | 39.0| 13. 0| I I IH i l e , 36 |Canada:Lake Nipigon | 0. 17 10.13 | 50. 0 | 27. 01 I | | Hart, 31 |Canada:Shakospenre Lake | 0.15 |0.09 | 49.0| 27. 0 | I | 111 d r t, 31 |Wisconsin:Trout Lake | | 0.09 | 4 4.0| 23. 0 | | I | II i l o * Deason, 34 |Canada:Lake Opeongo | |0.06 | 70.0 | I 1 1 |Kennedy, 43 |Canada:Lake Opeongo | 1.3 |0.43 | 14.0| I I I 1 47 I USA | f 0. 8 10.51 | 28.0| 18.01 | | | Deason«-Hile, | OS A 1 ro 0. 9 10.51 | 28.0| 18.0| | | | Deason +tlile, 47 |Alaska:Lake Ikroavik I 0. 6 10.4 | 38.0| 1 I | IWohlschlag, 5 4a + b |Canada:Gt.Slave Lake | 0. 6 |0.07 | 56.0| 18.4| | | |Kennedy, 54 |Canada:Cultus Lake 1 |0.58 | 6 9.0| 60.0| I | |Foerster, 29 _ +  + +- + +- 4. 4. —+ page SP| SPECIES NAME no | 1 — + -10|Salmo t r u t t a 1 1|Salvelinus a l p i n u s 11 | S a l v e l i n u s a l p i n u s i AREA I E | M I X I +-+-I |England:L.Windermere |Canada:Daffin Island |Canada:Baffin Island | |0.9U|0.36 |fI 0.24|0.03 ImI 0.24|0. 02 | LINF | LM | TM | L l | | (CM) | (CM) | (YR) | (CM) | H + 1 + +-0| 24.01 | I 0| 60.0| | | 0| I I I 30. 140. 150. I AUTHOR (with year 1 <J_ I IFrost + Srayly, 52 IGrainqer, 53 IGrainger, 53 Sciaenidae \ Drums | East China Sea: ITaiwan S t r a i t 01|Pseudotolithus e l c n g a t u s l N i g e r i a 1|Pseudotolithus elongatus|Sierra Leone 1|Pseudotclithus e l c n g a t u s | S i e r r a Leone 1|Pseudotolithus elongatus|Congo 02|Argyrosomus argentatus |Nigeria 2|Argyrosomus argentatus 2|Argyrosomus argentatus 3|Pseudotolithus | senegalensis 3|P. senegalensis 3|P. senegalensis 3|P. senegalensis 3|P. senegalensis 3 IP. senegalensis 4|Pseudotolithus typus 4|Pseudotolithus typus 4|Pseudotolithus typus 05|Pseudotolithus | brachygnasus INiqeria 6|Cynoscion macdonaldi IMexico south I.Lagos (West Africa) |N iger i a I Congo |Si e r r a Leone, | Ghana ICongo |Lagos (West Africa) INigeria |Conqo I 0. 3 |0. 165| 76.9| 21.01 1 8.5| ILonqhurst, 63 |0.38 | 45. 0 | 32.6| I 15.0| ILonghurst, 61,69 10.31 | 46. 9| 32.6| |16. 1 | |Lonqhurst,69 I0.37 | 42. 0 | | |13.0| |Lonqhurst,69 I I 48. 0 | | 115.0| ILonqhurst, 63 |0.345| 34.01 16. 5| 1.0|16. 3 | 3. 0411Liu+Tzenq,72; Tzenq+Liu,72 |0. 375 | 34.9| 16.5| 1.0|16.2|3. 182|Liu+Tzenq,72; Tzenq + Liu,72 • I I I0.33 | 81.01 s | | |22.9| 1 | Bayaqbona,66 I0.34 | 80. 0 | 35.01 122.31 |Bayaqbona,66; Lonqhurst,69 |0.20 | 28.01 |24.0| |Collignon,60; Lonqhurst,69 | 0. 6 6 | 47. 8| 35.0| |29.4| |Lonqhurst, 61 I0.44 | 54.0 | | |29.0| |Poinsard+Troadec, 66 |0.35 | 52.7| 28.0| |24.5| ITroadec, 66; Lonqhurst,69 | 0.29 | 103. 0 | | I 29. 1 | |Bayaqbona,66; Lonqhurst,69 10.37 | 61.21 48.3| |30.3| ILonqhurst, 63, 69 I I 1 |24.9| i i |Poinsard+Troadec, 66 • I I I I 79.01 35.0| 1 1 1 1 1 |Lonqhu r s t , 6 3 | 0. 3 | 128.0| I 1 1 IBerdeque, 55 Sccmbridae Mackerels and tunas IThunnus thynnus IThunnus thynnus IThunnus thynnus | Thunnus thynnus | Thunnus thynnus IThunnus thynnus IThunnus alalunga IThunnus alalunga IThunnus alalunga IThunnus alalunga IThunnus obesus IThunnus obesus IThunnus obesus IThunnus obesus IThunnus obesus |Thunnus obesus IThunnus obesus lEuthynnus a f f i n i s y a i t o lEuthynnus a f f i n i s y a i t o ISccmber japonicus |Scomber scombrus ISccmber scombrus | Thunnus rsacroyii | Thunnus Ibacares South P a c i f i c If 1 172.01 | 2.98 | South P a c i f i c 1 n | 172.01 I 2.92 | Portuguese coast | | 85. 0 | 2.0 Mediterranean Sea | | 100. | 3. North A t l a n t i c | | 300. 0 97. 5| 3.0 North Sea | | 0. 6 1 270.0| | North P a c i f i c : east | | 13 5.6| 89. 0 | 4. 5 52.0 South P a c i f i c 1 f 1 118.0| | 3. 09 | South P a c i f i c 1 m | 1 18.0| | 2.98 | North P a c i f i c | | 124. 0 90. 0| 6.0 28.0 South P a c i f i c 1 f 1 160.0| I 2.79 | South P a c i f i c 1 n 1 160. 0 I 2.72 | T r o p i c a l Indian Ocean | | 92. | 47.5 T r o p i c a l P a c i f i c : w e s t | | 95. | 2.0 Hawaii I f | 0. 167 | I Hawaii 1 m 1 0. 114| I o f f Peru | | 236.0 | Ph i l i p p i n e s j | 100. 0 45. 0 | Aburatsu | | 63. 0 I 15.0 Tohoku and Tokai Region | | 41.2 30. 0| 2.0 22. 5 C e l t i c Sea I 10.22 | Northwest A t l a n t i c | | 45.0 34. 0| 2.0 26.0 A u s t r a l i a | | 222. 5 120.5| 5.5 South P a c i f i c 1 f 1 156.0 1 2.85 | T " — 4 de Jaeqer, 63 de Jaeqer, 63 Frade+Viiela, 60 Le G a l l , 54 S e l l a , 29 Tiews, 57 Clements, 61a de Jaeqer, 63 de Jaeqer, 63 Yabuta+Yukinawa, 63 de Jaeqer, 63 de Jaeqer, 63 Kume, 62 Nakamura, 65 Shornura+Keala, 6 3 Shomura+Keala, 63 Yuen, 55 Wade, 50 Yahe, 53 K o n d o, 66 ICES, 71 Sette, 43, Robins, 63 de Jaeqer, 63 ...Table 1 continued on next page SP I no | SPECIES NAME 8|Thunnus albacares 8|Thunnus albacares 8)Thunnus albacares 8|Thunnus albacares 8)Thunnus albacares 8|Thunnus albacares R|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 8|Thunnus albacares 9|Katsuwonus pelamis 9|Katsuwonus pelamis 9|Katsuwonus pelamis 09|Katsuwonus pelamis 09|Katsuwonus pelamis 09|Katsuwonus pelamis 09|Katsuwonus pelamis 09|Katsuwonus pelamis 10|Auxis tapeinosoma 11|Scomber tapeinocephalus 12|Pnema tophorus diego 12|Pnematophorus diego 13|Neothunnus macropterus 13|Neothunnus 13|Neothunnus 13|Neothunnus 13|Neothunnus 13|Neothunnus 13|Neothunnus 14 | Cory phaena 1 5 | R a s t r e l l i g e r neglectus 1 5 | R a s t r e l l i g e r neglectus 1 5 | R a s t r e l l i g e r neglectus 16|Pneumatophorus japonicus 17|Parathunnus s i b i 17|Para thunnus 17|Parathunnus 17|Parathunnus 17|Parathunnus 10|Parathunnus 18|Parathunnus 18|Parathunnus 19 | R a s t r e l l i g e r kanagurta 1 9 | R a s t r e l l i g e r kanagurta 1 9 | R a s t r e l l i g e r kanagurta macropterus macropterus macropterus macropterus macrop terus macropterus hippurus s i b i s i b i s i b i s i b i mebachi mebachi mebachi AREA South P a c i f i c North Amer.:west coast North Amer.:west coast Eastern T r o p i c a l P a c i f i c South-west P a c i f i c Indian Ocean:east Indian Ocean:central Indian Ocean:west Southern Taiwan North Araer.:west coast Eastern A t l a n t i c • Eastern A t l a n t i c T r o p i c a l P a c i f i c Paci f i c J a p a n : P a c i f i c coast, Paci f i c Paci f i c A t l a n t i c Southern Taiwan Southern Taiwan Northwest P a c i f i c A u s t r a l i a Hawaii P h i l i p p i n e s P h i l i p p i n e s Sulu Sea Tohoku East China Sea C a l i f o r n i a C a l i f o r n i a Hawaii Paci f i c P h i l i p p i n e s P h i l i p p i n e s P h i l i p p i n e s P h i l i p p i n e s Japan sea Gulf of Tailand-Gulf of Tai l a n d Gulf of Tai l a n d Japan Western P a c i f i c Marshall I s l . s o u t h P h i l i p p i n e s P h i l i p p i n e s P a c i f i c Paci f i c P a c i f i c P a c i f i c Tndia:Cochin India:Andaman Island India:Karwar .Table 1 continued on next page 0.60 0.66 0.136 0.386 0.278 0.290 0. 349 0.333 0.60 0.3 84 0.42 0.33 0.55 0.66 0.356 0. 278 0.302 0.432 0.77 0.7 0.325 0.072 0.30 LINE (CM) 156.0 167. 0 167 326 174 212 215 191 192.8 169. 191.7 194. 8 190.1 190. 1 168. 150. 195. 2 222. 8 103. 6 10 3. 8 105. 5 222. 5 82. 3 175.0 22.0 46. 0 409.0 22.8 LM (CM) 100. 0 46.5 120.0 60. 0 17. 30.5 95.0 93.0 103.0 22.4 TM (YR) 2.0 5.5 3.0 L1 (CM) 55. 0 5 3.9 59.9 55.0 53.6 48.9 63.8 62.2 51.0 54. 3 51.0 45. 9 66. 1 27. 4 3 5.0 0. 131 | 15 3.2! 37. 5| lYokota, et a l , 61 50. 01 27. | 1. 0 25.0 | IHotta, 55 43. 0 | 28.7| 1. 5 28.7| |Oka-ji et a l , 58 39. 6| 29.21 2. 0 23.0 | | F i t c h , 51 0. <* I 40.0| 32. | | |Furnestin, 45 0. 5 I 190.01 54.0 | | Moore, 51 0. 070| 247.81 64. 3| |Nose, Kawatsu+Hiyama, 92. 3 38.0 47.5 35. 5 3. + 18 2. 906 965 096 788 . 736 . 876 . 847 . 146 .123 2. 930 .74 7 . 944 309 174 AUTHOR (with year iy ) de Jaeger, 63 Davidoff, 63 Diaz, 63 Hennemuth, 61 Huang et a l , 73 Huang et a l , 73 Huang et a l , 73 Huang et a l , 73 Huanq+Yanq, 74 Hennemuth, 61 Le Guen et a l , 69 Le Guen+Sakagawa, 73 Nakamura, 65 Yabuta et a l , 60 Yabuta+Yukinawa, 57 Yabuta+Yukinawa, 59 Yang et a l , 69 Yang et a l , 69 Chi+Yang, 73 Chi»Yang, 73 Kawasaki, 65 Robins, 63 Roths c h i l d , 67 Ronguillo, 63 Ronquillo, 63 Ronguillo, 63 Ronquillo, 63 Ronquillo, 63 Ronquillo, 63 Yahuta et a l , 60 Ko-jima , 66 Holt, 59b Vanichkul+Hongskul, 63 Vanichkul+Honqskul, 63 Holt, 58 Iversbn, 55 Kikawa, 53 Ronguillo, 6J Konquillo, 63 Suda, 61 Kikawa, 61 Kikawa, 61 Nose, Kawatsu+Hiyama, George + Bar.er i i , 60 Jones+Silas, 62 Pradhan, 56 SP no I h 19 19 19 zO 20 20 20 20 z1 21 21 22 23 24 24 24 z5 26 26 27 28 29 SPECIES NAME R a s t r e l l i g e r k a n a g u r t a R a s t r e l l i g e r k a n a g n r t a R a s t r e l l i g e r k a n a g u r t a R a s t r e l l i g e r brachysoma R a s t r e l l i g e r brachysoma R a s t r e l l i g e r brachysoma R a s t r e l l i g e r brachvsoma R a s t r e l l i g e r brachysoma Thunnus gerrao Thunnus germo Thunnus germo Euthynnus l i n e a t u s Euthynnus a l l e t t e r a t u s S arda s a r d a S a r d a s a r d a Sarda s a r d a Thunnus o r i e n t a l i s Euthynnus y a i t o E u t h y n n u s y a i t o Gyrr.nosarda nuda K i s h i n o e l l a t o n g g o l Thunnus a t l a n t i c u s AREA E x I n d i a : V a l t a i r I n d i a : V a l t a i r I n d o - P a c i f i c G u l f o f T a i l a n d India:Andaman I s l a n d I n d o - P a c i f i c G u l f of T a i l a n d G u l f of T a i l a n d C a l i f o r n i a c o a s t N o r t h P a c i f i c P a c i f i c ' "• E a s t e r n P a c i f i c A t l a n t i c Marmara and B o s p o r u s E a s t e r n A t l a n t i c ' E a s t e r n A t l a n t i c P a c i f i c Ocean P h i l i p p i n e s P h i l i p p i n e s P h i l i p p i n e s P h i l i p p i n e s Western A t l a n t i c 0.37 0. 38 0.232 0.28 0. 199 0.233 0.169 0.025 0. 176 LINF (CM) 23.9 20.9 22. 9 109. 1 454. 6 63.6 302. 4 LM (CM) 20. 18. 35. 55. 37. 39. TM (YR) + +— 0.6 0.7 2.0 L1 (CM) 57.3 52.0 38.0 50.0 3. 279 3. 263 3. 193 3. 578 2. 880 3. 763 3.038 2. 949 2. 838 3. 206 2. 308 3. 104 AUTHOR ( w i t h y e a r 19 ) Rao, 62 Rao, 62 S u d i a s t a n i , 74 H o n g s k u l , 72 J o n e s + S i l a s , 62 S u d i a s t a n i , 74 V a n i c h k u l + H o n q s k u l , 63 V a n i c h k u l + H o n q s k u l , 63 B e l l , 62 C l e m e n s , 61b Nose, Kawatsu+Hiyama, 57 C a l k i n s + K l a w e , 63 De S y l v a + K a t h i e a , 61 Numann, 55 P o s t e l , 55 P o s t e l , 55 Yamanaka e t a l , R o n q u i l l o , 6 3 R o n q u i l l o , 63 R o n q u i l l o , 63 R o n q u i l l o , 63 I d y l l + d e S y l v a , 63 63 S c o r p a e n i d a e S c o r p i o n f i s h e s 1 T — T - - T ' 1 — T " I 1 I Setastes marinus IICNAF 3NO |0.115| 40.0| I 1 I Sebastes marinus IICNAF 4RST 0. 2 |0. 1 | 45. 0 | I 01 | Setastes marinus |G. St. Lawrence 0. 20|0.1 15 | 40.0| I 1 I Setastes marinus IFlemish Cape I m I0.07 | 45. 0 | I 1 I Sebastes marinus | Flemish Cape | f 10.13 | 47. 8| I 1 ( Sebastes marinus |Hamilton I n l e t Bank 1 o I0.05 | 55.0 | | 11 Sebastes marinus (Hamilton I n l e t Bank | f 10.10 | 60. 0| I 1 i Sebastes marinus |Labra dor 10.1 | 52. 5 | I 11 Setastes marinus IFlemish Cap 10.12 | «5. | 10 2 l Sebastes alutus (North P a c i f i c | | 48. 0 | 1 021 Sebastes alutus |Bering Sea | | 48.0 | | 0 2 J Sebastes alutus (Washington coast |f 1.018 | 43. 2 | | 02 | Sebastes alutus (Washington coast | m I.020 | 40.31 | 0 3 | Sebastes mentella (Gulf of Maine I f I0.09 | 44.3| |03| Setastes mente11a |Gulf of Maine 1 o 10.13 | 3 3.4| 1 3| Sebastes mentella IHermitage Bay I f |0.113| 39. 7 | 1 3| Sebastes mentella IHermitage Bay 1 HI |0.119| 3 5.2| 1 3| Sebastes mentella (Hermitage B:53 year - c l a s s | f 10.10 | 43. 0 | 1 3| Setastes mentella IHermitage B:53 year - c l a s s 1 H |0.17 | 31.01 1 3| Setastes mentella ISouthwest Grand Bank If 10.13 | 34.21 1 3 Sebastes mentella (Southwest Grand Bank 1 m |0.05 | 3 3.0| 1 3| Sebastes ment e l l a |Gulf St.Lawrence | f 10.13 | 38.4| 1 3| Sebastes mentella |Gulf St.Lawrence 1 m |0.06 | 36.0| 1 3| Sebastes mentella |Flem ish Ca pe I f |0.15 | 38.5| 1 3| Sehastes mentella (Flemish Cape 1 m 10.17 | 34.4| 1 3 Setastes mentella (Hamilton I n l e t Bank | f 10.11 | 44.8| 1 3| Sebastes mentella IHamilton I n l e t Bank 1 K 10.16 | 3 8.5| 1 3| •1 » Setastes mentella IHarailton I n l e t Bank | f + -10.16 | 41.51 +-23.5 23.5 4.5 3.119 3.250 B e v e r t o n , 65 B e v e r t o n , 65 K e l l y 4-Wolf , 59 Sandeman, 69 Sandeman, 69 Sandeman, 69 Sandeuari, 69 B e v e r t o n , 65 B e v e r t o n , 65 G r i t z e n k o , 63 P a r a k e t s o v . 63 Westr heim. 58 Westrheim, 58 K e t c h e n , 72 K e t c h e n , 72 Sandeman, 6 9 Sandeman, 69 Sandeman, 69 Sandeman, 6 9 Sandeman, 69 Sand liin.i n. 69 Sandeman, 69 Sa ndem.i n, 69 Sandeman, 6 9 Sandeman, 69 Sandeman, 69 Sandeman, 69 Sandeman, 69 . . . T a b l e 1 c o n t i n u e d on next page SP| no | — h -3|Setastes 3|Setastes 3|Setastes 3 j Sebastes 3|Setastes 3|Sebastes 3|Setastes 3|Setastes 3|Sebastes 3|Sebastes 3|Setastes 4j Setastes 5|Setastes SPECIES NAME AREA I -I mentella IHamilton I n l e t Bank mentella IHamilton I n l e t Bank mentella IHamilton I n l e t Bank mentella I Labrador mentella ILabrador mentella least Newfoundland mentella least Newfoundland mentella IFlemish Cap mentella IFlemish Cap mentella IGrand Bank mentella IGrand Bank inermis |Northwest Kyushu marmorata I Northwest Kyushu S i l l a g i n i d a e 11Sillago sihama Scie idae 1|Solea v u l g a r i s Sparidae 1|Chrysophrys 11Chrysophrys 11 Chrysophrys 11Chrysophrys I |Chrysophrys 1|Chrysophrys 11Chrysophrys 11Chrysophrys 1|Chrysophrys 11Chrysophrys II Chrysophrys 11 Chrysophrys major ma j o r major major major major major major major major major major 02|Pagrus ebrenbergi Sgual idae' 1|Sgualus 01|Squalus 1|Squalus 1|Sgualus 1|Squalus 1|Squalus 1|Squalus 1|Squalus 1|Squalus 1|Squalus 1|Squalus 1|Squalus 1|Squalus acanthias acanthias acanthias acanthias acanthias acanthias acanthias acanthias acanthias acanthias acanthias acanthias acanthias |E| f. U l -+--I— |m| If I |m| If I I m| If I | m| I f l 1 m | I f l |m| I I I I 0.21 0.08 0.02 LINF (CM) 36. 5 52.0 93. 0 28.3 34.5 Sand whiting |India:sout h Soles - T — T 1 1 | | |0.4 | 37.0 _j i i i INorth Sea 7 |0.2sTo.4 | 39.0 . J . 1 I L Porgies "T IWakasa Bay |Taiwan:Pescadores I s l . ITaiwan:Pescadores I s l . | Izu |Hiuchinada |Saganoseki | K i i | amaknsa |Northern Kyushu |East China+Yellov Sea | East China • Yellow Seas | Hiroshima |Nigeria |0.1 13 | |0.077| |0.077 | |0.115| |0.114| |0. 1151 10.12 | |0.127 | |0.136| |0.090| 10.095 | |0.116| I I 71.0 98. 3 98. 3 82.5 77. 8 51.4 97.3 89. 1 80. 3 74.0 74.9 66.2 Dogfish sharks I European waters (Washington coast INorth Sea INorth Sea |Canada:west coast |Canada:west coast |I!ecate S t r a i t |Hecate S t r a i t IGeorge S t r a i t IGeorge S t r a i t (Washington Coast (Washington Coast | Cana<3a: east coast " T —T ~ I I I I I f l I ml I f l I ml I f l I m | I f l I ml t f l |m| | 0.024 | 0.022 10.11 10.21 (0.048 | 0.070 |0.031 |0.0 92 |0.034 |0.067 |0.036 10.071 (0.110 LM (CM) 33. 0 29. 0 28.7 28. 0 29. 27. 0 •24. 23.0 13. 0 12.0 TM | L1 (YR) | (CM) | AUTHOR (with year 19 ) 12.0| 9.5| 10.3| 9.1| 10. 9. 0| 0| 7. 7 | 6.8| 2.0 | 2 .0 | Sandeman, 69 Sandeman, 69 Sandeman, 69 ITokareva, 66 ITokareva, 66 ITokareva, 66 ITokareva, 66 ITokareva, 66 [Tokareva, 66 ITokareva, 66 ITokareva, 66 Mio, 61 I a i o, 6 1 I I _l l_ |Radhakrishnan, 57 | Be vertcn + l i o l t , 57 36.8 31.9 15. 0 5.0 4.0 9.9| lAkazaki, 60 14.6|2.649(Chahg+Chen,72;Huang et al,74 14.6|2. 64 9|Chang+Chen,72 ;Huanq et al,74 8.3| |Ebina, 36 | |Ebina, 40 | |Ebina, 40 8.5| IKawase, 53 21.3| |Murakami:Shindo, 49 13.0| IMio, 62 13.0| |Murakarai+Okada, 67 | |Okada, 70 10.9j |Kang, 37 | |Lonqhurst, 63 216.7| I 29. 1 | |Aasen, 61 186.4| I 39.0| IBonham et a l . 49 101.4| 82.0 11 .0|39.6| |Holden+Meadows , 62 79.7| 60.0 5.0|41.3| | Holden + Meadows , 62 125.3| 93. 5 23.0| | |Ketchen, 72 ; Ketchen, 75 99.8| 72. 14.0| | | Ketchen, 72 ; Ketchen, 75 125.1| 34.0| | | Ketchen, 72 ; Ketchen, 75 84.7| 17.01 | | K e t c Ii e n, 7 2 ; Kotchen, 75 129.1| 103. 2 31.0|31.0| | Ketchen, 72 ; Ketchen, 75 96. 1 | 75. 9 16.0|32.HI | Ketchen, 72 ; Ketchen, 75 152.9| 1 1 IKetchen, 75 101.8| 1 1 IKetchen, 75 1 17. 3 |43.5| |Templeman, 44 ...Table 1 continued on next page 1 7 - ' 1 • r i |S| 1 E| |x| 1 1 1 T | 1 T — -•" r-| | | 1 1 ISP| SPECIES NRSE |no| 1 | AREA 1 1 B | 1 K | LINF | LH (CM) | (CM) • | TM 1 (YR) 1 L1 | b I (CM) | j 1 : — |AUTHOR (with year 19 ) I J ' . • i 1 J _ J _ L J J J 1 1 • | Stichaeidae Pricklebacks • — r •• — — 1— T •• 1 — |01|Anoplarchus purpurescenslVancouver |01|Anoplarchus purpurescens|Vancouver " T T" If I |m| 1 • "' f 1 10 .209| I 1 18.6| i j | 7.1|2.986|Peppar, 65 | 6.5|2.986|Peppar, 65 1 L | Syngnathidae P i p e f i s h e s and seahorses 1 r ) 11Hippocampus hudsonius i | F l o r i d a r i i i X | 1 12 _ | .5 | 14.01 2. • 01 I I A . 1 |Herald+Rakowicz, 51 J — ' , L | T r i c h i u r i d a e C u t l a s s f i s h e s : , — T  | 011Trichiurus l e p t u r u s L L T |East China Sea .. j - T — r i i _ J _ X. T 1 j . -130.0| 97. 1. 5| 3.5|27.0| i i i IMisu, 58,59 j •i -fx CM. 43 Table 2 Sample s i z e s of i n d i v i d u a l parameters and r e l a t i v e c h a r a c t e r s i n f a m i l i e s analyzed N O . P A R A M E T E R S R E L A T I V E C H A R A C T E R S FAMILIES OF M LM L1 TM T50 Sp. M K LINF LM TM Ll b T95 K LINF IINF T95 T95 Clupeidae 19 17 70 82 62 9 22 4 70 12 51 22 70 Cyprinidae 7 2 47 51 38 35 39 4 47 2 10 29 8 47 Gadidae 17 28 45 56 26 5 8 5 45 20 20 7 6 45 Pleuronectidae 22 1 4 38 61 28 19 31 1 1 36 14 27 30 2 36 Scombridae 29 6 36 60 3 2 19 34 34 32 4 19 27 2 33 Bothidae 3 2 2 4 2 1 2 2 2 2 2 Engraulidae 4 20 21 19 12 9 20 8 9 10 20 Hiodontidae 1 6 6 4 6 4 6 Osmerida e 3 10 10 5 23 10 2 10 3 7 7 10 Salmonidae 11 19 28 28 21 2 1 4 28 19 21 28 Sciaenidae 6 1 16 17 12 1 14 2 15 12 15 Scor paenidae 5 2 29 33 12 12 3 2 29 2 11 3 29 Percidae 3 4 7 7 . 2 3 7 4 4 3 7 Sparidae 2 11 1 1 1 8 1 11 8 11 Sgualidae 1 13 13 6 8 7 13 6 3 8 13 Acipenseridae 4 4 3 2 2 3 2 Aromodyt idae 3 5 5 2 4 3 5 4 5 Anquillidae 1 3 1 1 Aplcchitcnidae 1 1 1 1 Argentinidae 3 3 2 3 2 3 Atherinidae 3 3 3 3 3 Blennidae 1 1 1 1 1 1 3 Callienymidae 1 2 2 2 2 2 2 Caranqidae 1 2 2 6 2 2 2 2 Cichlidae 1 1 1 1 1 Cottidae 1 4 4 4 4 4 4 4 4 Dasyatidae 1 2 2 2 2 2 2 Drepanidae 1 1 1 1 1 1 Embiotccidae 1 2 2 2 Gasterosteidae 1 2 2 2 2 2 2 2 2 Hexagrammidae 1 2 2 2 2 Ictaluridae 1 1 1 1 1 Isticphoridae 3 1 2 1 1 1 Luianidae 1 2 2 2 2 2 Nemipteridae 1 3 Poeci l i i d a e 1 2 2 2 2 Polynemidae 2 2 Pomacentridae 1 1 S i l l a g i n i d a e 1 1 1 1 1 Soleidae 1 1 1 1 1 1 Stichaeidae 1 1 1 2 2 1 1 1 Syngrathidae 1 1 1 1 1 1 Trachipteridae 1 1 1 1 1 1 Total 171 105 417 507 297 155 214 81 404 88 213 175 45 402 mackerels and tunas. These have very large sample sizes; 100, 89, 70, 69 and 104 respectively. Group II (10 f a m i l i e s ) ; Bothidae - lefteye flounders, Engraulidae - anchovies, Hiodontidae - mooneyes, Osmeridae - smelts, Salmonidae salmcnids, Sciaenidae - drums, Scorpaenidae - scorpionfishes, Percidae - perches, Sparidae - porgies, and Sgualidae - dogfish sharks. These have r e l a t i v e l y large sample sizes; 5, 22, 6, 30, 33, 19, 41, 7, 12, and 13 respectively. 4. Methods of Analysis The f i r s t step was to examine the c h a r a c t e r i s t i c s of population parameters by looking at the following characters; (a) eight parameters •B = instantaneous natural mortality c o e f f i c i e n t K = curvature of the growth curve, or the rate at which the f i s h reaches i t s asymptotic size LINF <cm) = asymptotic length for which the rate of growth i s zero LH (cm) = size at f i r s t maturity, which i s the length at which 50% of the f i s h are at the maturity stage {or c r i t i c a l length) TH (yr) = age at f i r s t maturity at which f i s h has length LM L1 {cm) = length of f i s h at age 1 b = exponential c o e f f i c i e n t of weight-length r e l a t i o n s h i p T95 (yr) = age at which the f i s l i a ttains 95% of asymptotic length (b) f i v e r e l a t i v e characters (ratios) : M/K, LM/LINF, L1/LINF, TM/T95, T50/T95 where T50 i s the age at which f i s h attains 50% of asymptotic length. (c) fiv e c o r r e l a t i v e characters: H—K, 1/K—LINF, LH—LINF, L1—LINF, 1/M—T95 Estimation of means, standard errors, ranges, and sample sizes of the i n d i v i d u a l parameters and of the r e l a t i v e characters are calculated i n each family. The variation for each character can be compared by the c o e f f i c i e n t of variation (cv), which i s cv = s/x * 100 where x : the mean value of the character s : the standard deviation of the character The c o r r e l a t i o n s between parameters within each family were calculated by l i n e a r regression analysis. Comparisons of variances and mean values between families of the four parameters with large sample sizes (K, LINF, T95, and LH) were based on the F-test and the t - t e s t . I f a s i g n i f i c a n t difference was shown from the F value, Welch*s approximation method was u t i l i z e d instead of the Student t - t e s t . Stepwise discriminant analysis i s used to quantify the means of dif f e r e n t families of fishes by a set of discriminant functions according to their population parameters. It also 46 shows how groups are demarcated i n a multi-dimensional space. The general object of t h i s method i s to f i n d rules of behavior in the assignment of cases into predetermined groups with optimal properties. It i s calculated from the pooled variances and covariances among characters within each group. Discriminant functions are constructed such that the r a t i o within and among matrices i s maximized i n order to discover the smallest number of dimensions in which the population means l i e . analyses were done in group I (5 f a m i l i e s ) , group I I (10 f a m i l i e s ) , and also with groups I and II combined (15 families altogether), Cooley and Lohnes' c l a s s i f i c a t i o n method was also u t i l i z e d . This method i s obtained by xenormalizing the canonical variables in the discriminant analysis. The normalization i s immmaterial as far as c l a s s i f i c a t i o n i s concerned. The analyses were conducted among species within 5 major families (Clupeidae, Cyprinidae, Gadidae, Pleuronectidae, and Scombridae). Dendrograph rel a t i o n s h i p s among 15 families based on population parameters have been surveyed using c l u s t e r analysis. This method clust e r s cases that have the lea s t distance between them. The two cases closest together are amalgamated and treated as one case and then, i n turn clustered with others. V CBABJCTJBISTICS OF POPULATION PARAMETERS The analyses are based mainly on the 15 families »hich have greater sample size s . At t h i s stage, there are s t i l l not enough data to show s i g n i f i c i a n t r e s u l t s in the intra-species studies., 1. Individual Parameters The parameter examined i n d i v i d u a l l y (and the number of data records for a l l families in parentheses) were M (105), K (417), LINF (507), LM (297), TM (155), L1 (21H), b (81), and T95 (404) (Table 2) . Large sample sizes were available for K, LINF, and T95 because of my calculations based on 231 published age-length data sets using the von Bertalanffy growth equation. Figures 3 to 10 show sample sizes, means, 95^ confidence l i m i t s , and ranges of i n d i v i d u a l parameters among species within f a m i l i e s . For species index number refer to Table 1. a. The instantaneous natural mortality c o e f f i c i e n t (M) shows a great deal of v a r i a t i o n . Only in four families (Clupeidae, Gadidae, Pleuronectidae, and Salmonidae) were there enough data to be analyzed. These re s u l t s are shown in Figure 3. Gadidae (n1=28) has the largest O J L F E I G A E F ^ a j R O X e C T I Q A E 1*2. 1.1 . 1*Q. 0-B 0-7 O.K. 0.5. 0.4 0-2. 0-1. O-QJ O. 1. a. 3. 4. 3. 6- 7. GADIDAE: B. 0. 10. U> 12. 13* 14. 13. IS. 17. IB* lfl. S T C I E S I 0.30, 0-27 0*24 0.21 o- in ct o . i a O.CQ o.os o*oa o.oa 0. 1. 2. 3* SALMZNIOAE S- G* 7. 8> 8* lO. 11* 12* 13* 14* 15* 16- 17. 10* 13* so* a . B2% S=ECXES X 2*1 1*0 1-6 1*4 to i O- !• E* 3* -v. 3- G* 7. e- 8. 10* 11* IE. 13. 14. u . IB* 17. SPECIES I 1*3. 1*E 1*0 0.3 c a 0*6. o*s. 0-4. 0.3. 0.1 e • S* F* SPECIES I Figure 3. Mean va l u e s , 95% c o n f i d e n c e l i m i t s , ranges, and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r thf? i n s t a n t a n e o u s n a t u r a l - m o r t a l i t y c o e f f i c i e n t (M) (speci e s index r e f e r to Tabl e 1) 00 49 va r i a t i o n (cv2=125.3%) among species. Pleuronectidae (n=14) shows the least v a r i a t i o n (cv=23. 32%) . Salmonidae has the highest mortality rate (0.755) followed by 0.354 of Gadidae, 0.33 of Clupeidae (n=17) , 0.222 of Pleuronectidae. Apart from these four f a m i l i e s , Scombridae (n=6) also has a high H value (0.478) . b. K i s the rate at which the f i s h reaches i t s asymptotic length. The higher the K value, the sooner the f i s h reaches i t s ultimate length. Eight f a m i l i e s (Clupeidae, Cyprinidae, Engraulidae, Gadidae, Pleuronectidae, Salmonidae, Scombridae, Scorpaenidae) with sample sizes larger than 19 were analysed. The r e s u l t s are shown i n Figure 4. In these eight fa m i l i e s , variations range from 42. 26% (cv) for Engraulidae to 90% for Pleuronectidae. Engraulidae has the highest K value (1.654), followed by Clupeidae (0.431), Scombridae (0.347), Salmonidae (0.329). Demersal fi s h e s [Gadidae (0.243), Pleuronectidae (0.207), Scorpaenidae (0.107)3 and freshwater f i s h fCyprinidae (0.216) ] have lower K values. In other families of f i s h e s ; Osmeridae (n=10) has a K value (0.409) s i l m i l a r to Clupeidae, but with the largest variation (cv=111.83%) ; Sciaenidae (n=16) 1 n i s the sample size 2 cv i s the c o e f f i c i e n t of v a r i a t i o n t*er t't. t.Q o-a 0-7 0-6 o-s; ... j 0-E 0.11 6 S O-oL 0. 1. e. 3. <. 3. 6. 7. e- a- 10. ii. 12. 13. i<. 13. is- 17. us. ia< species 1 CTFWINIOAE e-a. o-ca o-<> 0-E7 i.ua o.ccj srarics 1 S.EL e-s S-3 o.a 0 . & 0 . 3 . GATJ1TJAE o-3a 0 . 1 a 0 > 1 0 e I 1. 3. 6. 7. 0. O- U>. 11. 12- 13- 1«- 15. IS- 17. species 1 F i g u r e 4.1 wean v a l u e s , 95% c o n f i d e n c e l i m i t s , ranges, and ^ sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the o growth parameter K ( s p e c i e s index r e f e r to T a b l e 1) F * L E X J R Q N E C T I O A E SCDveRIDAE 0-75 o . c O.SO C34 CES. 0-17, O. 1. 2> 3. 4. 2. 6. 7. 0-S » J J - ( O N I O A E C-73. 0-O3 O-CD o-sa 0-45 • 1 0 . 11. 12. 13. 14. l i . l£. 17. Ifl. 1 1 ) . SPECIES I e 3. E. SPECIES I *0. 8.77 o.ta o.e. 0.54 ^ o.«l e o. 1* c* 3< 4* 3. 6< 7* a* 0.iu.ii>ti:.t'j>i4.ui.tG.t7.u].uj.ra-t.n.ca.tr3>e4.u3.ct;^ 27.£u^3». SPECIES X S C O R P A E N I D A E 0.2L b-ia 0.17 o.is 0-13 o.u O-CQ O'CG 0-04 o-ce. o.ooj e I a. j . SPECIES I F i g u r e 4.2 Mean va l u e s , 95?. co n f i d e n c e l i m i t s , ranges, sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r growth parameter X (spe c i e s index r e f e r to T a b l e and the 1) 52 has a K value (0.345) s i m i l a r to pelagic f i s h e s ; 0.111 fo r Sparidae (n=11) and 0.179 for Percidae are s i n i l a r to demersal fishes; Sgualidae (n=13) has the smallest K value (0.07) among a l l fishes. Generally speaking, K i s a r e l a t i v e l y stable character. c. LINF i s the asymptotic length for which the growth rate i s zero. Eight f a m i l i e s (Clupeidae, Cyprinidae, Engraulidae, Gadidae, Pleuronectidae, Salmonidae, Scombridae, Scorpaenidae) with sample s i z e s larger than 20 were analyzed. The r e s u l t s are shown in Figure 5. In these eight fa m i l i e s , variations range from 13.943? (cv) for Engraulidae to 101.55S for Pleuronectidae. Scombridae has the greatest asymptotic length (152.7 cm), followed by Pleuronectidae (78.18 cm), Gadidae (75.72 cm), Cyprinidae (73.34 cm), Salmonidae (61.35 cm), Scorpaenidae (43.02 cm), and Clupeidae (28,88 cm) . Engraulidae has the smallest length (16.15 cm). In other families of fishes, Sgualidae (n=13) has the second largest LINF (124.3 cm); Osmeridae (n=10) has an asymptotic length (31.19 cm) sim i l a r to Clupeidae; for Sparidae(n=11) LINF i s 78.44 cm and for Sciaenidae (n=17) i t i s 65.37 cm. Again, the asymptotic length i s a f a i r l y stable character. d. The s i ^ e at f i r s t maturity (LM) i s the length of f i s h at the time when they st a r t to switch t h e i r energy from CULPErOAE ENGRAULIDAE • I O. 1. £• 3* 4. 3. 6- 7. B. 8. 10. 11. 1 £ . 13. 14. 13. IS. 17. 18. Ifl . SPECIES I CYPRINIDAE LL GADIDAE SPECIES I 10 I SPCCIES I e . I O. 1. 2 . 3. A. 3- 6. 7. B- 3- 10- 11. S=ECIES I 12. 13- 14. 13. 1 £ . 17. Figure 5.1 Moan values, 95% confidence l i m i t s , ranqes, sample sizes among species within f a m i l i e s for asymptotic length (LINF) (species index refer Table 1) and the to CO RJELROteCTIOAE SCDMSRIOAE 133' US' s X e • 1 • • • • I 3 n i O- ! • E* ! • - » 3> £• 7. 8' 8- 10- U ' 12' 13- 14< 13' IB' 17- IB. IS- E0- 21. 22« SPECIES I SALMXIOAE e • -ice-u. X 2 so. o- i . e. 3- 3- 6. 7. B- a. ia. u.. SPECIES I «S5-UL I *S3 0* 1* E» 3* 4. S . 6* 7- tt' 9.io.u.l2.l3-14.!5.16'17.ia«13*«0>21'2e.e3-e*'E3'€£-Z7»£B-» SPECIES I SCORPAENIDAE ia a z j e. a< SPECIES I F i g u r e 5.2 Mean v a l u e s , 9 5 % c o n f i d e n c e l i m i t s , r a n g e s , and s a m p l e s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r t h e a s y m p t o t i c l e n g t h ( L I N F ) ( s p e c i e s i n d e x r e f e r to T a b l e 1) ui 55 growth to reproduction. Eight families (Clupeidae, Cyprinidae, Engraulidae, Gadidae, Pleuronectidae, Salmonidae, Scombridae, Scorpaenidae) with sample sizes larger than 11 were analyzed. The r e s u l t s are shown in Figure 6. In these eight families, variations range from 10.88% (cv) of Engraulidae to 76.85? of Salmonidae. Scombridae has the largest LH (62.02 cm), followed by Gadidae (39.12 cm), Salmonidae {32.64 cm), Cyprinidae (30.34 cm), Scorpaenidae (24.48 cm), Clupeidae (19.32 cm). Engraulidae has the smallest size at f i r s t maturity (12.24 cm). In other families of fishes; Sgualidae (n=6) has the largest LM (81.1 cm) among a l l fishes; Osmeridae (n=5) has a LM (15.80 cm) between Clupeidae and Engraulidae; for Sciaenidae (n=12) i t i s 30. 86. The size at f i r s t maturity i s a very stable character. e. TM i s the age at f i r s t maturity at which f i s h has length LH. Four f a m i l i e s (Cyprinidae, Osmeridae, Pleuronectidae, Scombridae) with sample sizes larger than 18 were analyzed. The r e s u l t s are shown in Figure 7. In these four f a m i l i e s , variations range from 36.74% (cv) f o r Cyprinidae to 59.43% f o r Scombridae. Cyprinidae has the highest TH (5.7 years), followed by Pleuronectidae (5.6 y r s ) , Osmeridae (2.8 yrs). Scombridae has the youngest TH (2.7 y r s ) . In other fa m i l i e s of fishes ; Engraulidae {n=12) has the QJLPeiOAE ErGFRAULIDAir °* 1 - 5^ 5 4 . 5 ^ <T7. B. 8. U>. A. 12. 13. 14. 13. ]£. 17. jla, JJJ, I CYPRINIQAE 11 i species i GADIOAIT • Z e. 3. SPECIES I o- i . e. 3. a- G. 7. tl. D. 10. 11. 12. 13. 14. IS- 16. 17. axcics i F i g u r e 6.1 Mean va l u e s , 95% c o n f i d e n c e l i m i t s , r a n g e s , and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the f i r s t m aturity s i z e (LM) (species index r e f e r to Table 1) cn PLE1PCNECTI0AE 1 I O. I. 2. 3. 4. 3- 6. 7. B- 3. 10. 11. 12. 13- 14. 13. 1£. 17. IB. 13. a> 21. 22. SPECIES I SALMQMICAE _i 0. 1. e. 3. 4. 3. G . 7. B* sprits i SCCVeRIDAE 2 -J 72-D ro. 24. 0. 1. 2. 3 . 4. S. 6. 7. 0- B-10.ll.12.l3-14.lS.lj5.17.ia.ia.a3.21.22.23.24. £FECIE5 I SCCRPAENIOAE 25-yS-27.2B-29* -J 2D. G 17-£ 13-10-e • SPECIES I Figure 6.2 Mean v a l u e s , 95% confid e n c e l i m i t s , ranges, and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the f i r s t maturity s i z e ( L H ) ( s p e c i e s index r e f e r to Table 1) C Y P R I N I Q A E PLELRQVJECT I D A E B-6 7 t 6-7 , 3-7 4-G >a 2-a i-a 1 . 0 2 • 2 • o-oL CS/6RinAE ' 5 , 6-7, C.Q. s-e 4 - i 2-2 1-i p-a 0-G SPECIES I SPECIES I F i g u r e 7. Mean values, 95? 0. 1. 2. 3. SCOMBRIDAE 3. 6- 7. 8' e> 10. U . 12. 13. 14. 13. i £ . SFECICS I 17. IB. 13. 20. 21. 22. E-0 5-4. 4-Q 4-E 1. 2. 3. 4. 5. 6. 7- B- 9'l^-ll-l2.0.1<.15.1fi.l7.1B.l£i.£0.fcl.E2.E3.i STOCS I . . confidence l i m i t s , ranges, . and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r t h * f i r s t maturity age (TH) (species index r e f e r to Table 1) c o 59 youngest TH (1.0 y r s ) . Sgualidae (n=8) has the oldest TH (18.8 yrs) among a l l the f i s h e s . f. The length of f i s h at age 1 (L1) shows the speed at which f i s h can grow during the f i r s t year. Four families {Clupeidae, Cyprinidae, Pleuronectidae, Scombridae) with sample sizes larger than 21 were analyzed. The r e s u l t s are shown in Figure 8. In these four f a m i l i e s , variations range from 32.37% (cv) for Cyprinidae to 42.99% for Clupeidae. Scombridae has the largest L1 size (47.34 cm), followed by Clupeidae (14.84 cm), Pleuronectidae (8.4 cm), and Cyprinidae (7.39 cm). In other f a m i l i e s of fishes ; Sgualidae (n=7) has the second largest L1 (3 6. 56 cm) since i t i s ovoviviparous; for Sciaenidae (n=14) i t i s 21.71 cm; for Engraulidae (n=9) i t i s 12.70 cm. g. b i s the exponential c o e f f i c i e n t of the weight-length relationship. Only two families fPleuronectidae (n=11), Scombridae (n=34) ] with large enough sample sizes were analysed. The r e s u l t s are shown in Figure 9. Although the l e a s t data were found for t h i s parameter. I t i s , however, the most stable one (cv l e s s than 10% for a l l the f a m i l i e s ) . It fluctuates around 3 for a l l of the fishes analysed. h. T95, as an index of • l i f e span*, i s the hypothetical -Q C M CD co w 3 (V 0> CD a H> <U T l 3 r t 3 " ro < W PJ OJ H -r t N C <D iD W (fl OJ ^ OJ 3 O 3 LH -Q f t/i "O n —^ >D o n fD "o (/> a, ro ru n *: 3 H " H - n 0) n- rD W 3 " H - 3 M 3 H -CL r t i g (5 P H ' « 3 r t I— 01 n (-"• fD H -r t i fD fD CA f"! n a* 3 r t r-h .Q O O rD H OJ t T r t PJ H" 3 " 3 -a Q 3 z 9 B P R C K-?' B-B <; w a a y A —•—.• PAFW.CTER L l P 6 S 8 4 B 8 8 8 § <6 PARAMETER L l w 0 O )>» ft) u) Ik u © U) H „ M K P 5 P' K ? G ?• P 09 PLEIJRCNETTIDAE 61 3-G. 3-3 e.a 2-6 E-E S i.a. i 1-5 1-1. 0.7. 0.4.. 0.0 0. 1. 2. 3. 4. 5- G. 7. 8- Q. iO- i i . 12. 13. 14. 15. 1G- 17. IB. 13. 20. Hi- 22-SPECIES I SCOMBRIDAE 3-e 3-4. 3-Q E £•3. i -a. --•s. i - t . o-a. 0.4. . -4 1-+ 2 T 3 -4—i—i—^ 2 X I I E. 3- 4. 5. 6. 7. B. a.iO.l'l. 12.13.14.l!s.lS.17.1fl.i3.i.2i.s.s.24.25.BS.27.i.29. SPECIES I Figure 9. Mean values, 95% confidence l i m i t s , ranges, and sample sizes among species within families for thf» weight-length exponential c o e f f i c i e n t (b) ( s p — i e s index refer to Table 1) 62 age of f i s h on reaching 95% of i t s asymptotic length. Eight families (Clupeidae, Cyprinidae, Engraulidae, Gadidae, Pleuronectidae, Salmonidae, Scombridae, Scorpaenidae) with sample sizes larger than 19 were analysed. The r e s u l t s are shown i n Figure 10. In these eight f a m i l i e s , v a r i a ti on ranges from -44.95% (cv) for Clupeidae to 141% f o r Scombridae. Scorpaenidae has the largest T95 {41.25 years), followed by Pleuronectidae (32.29 y r s ) , Cyprinidae (28.58 yr s ) , Salmonidae (24.00 yrs) , Gadidae (16.82 y r s ) , Scombridae {14.64 yr s ) , and Clupeidae (8.29 yrs). Engraulidae has the youngest T95 (only 2.359 yrs) of the other families of fishes while Sgualidae (n=10) has the oldest T95 (57.77 yrs) ; for Sciaenidae (n=15) T95 i s 9.144 years; and for Sparidae (n=11) i t i s 27.51 years. Generally speaking, t h i s character has the largest variation of a l l the parameters. In summary, the weight-length exponential c o e f f i c i e n t appears to be the most stable character. The LM, TH, and L1 are also stable characters. The growth parameters (K, LINF) are f a i r l y stable because of the large sample sizes. The natural mortality c o e f f i c i e n t (M) shows large v a r i a t i o n . T95, a t h e o r e t i c a l age, has the greatest variation. less variation among species i s displayed by cer t a i n families: Engraulidae, Clupeidae, Cyprinidae, Gadidae, Scorpaenidae, and Sciaenidae. The suggested means and standard errors of cLipeioAH ENGRAULIDAE I * o- i . a. s. 4. s. 6. C Y P R I M I Q A E 8- B- !£>•.11. li?. 13- 14. IS. 1£. 17. 16• l a . STXIES I 6.7, 6.0, J. . IA 0 1 I- 4-S. f 3-0. S T C I C S I GADIDAE C S3-SB-, J7-. 10 T I SRXIES I SB-IA (- 33-s • I 0. 1* 2« 3. 4. 5. . 6* 7. B. 3. lO* 11-5PECTC5 I 13- 14. 13- 16- 17. Figure 10.1 Mean v a l u e s , 95X c o n f i d e n c e l i m i t s , ranges, and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the • l i f e span' (T95) (species index r e f e r to Tab l e 1) OJ PLEXROECTIDAE- St OOP I DAE 1 B04-I IA 1A I- 70. U 3 J sa.. I • O- .. £• 3-SALMONIDAE i . G. 7. U. 3. ID. It. IT. 13. 14. 15- UG- 17. l f l . 13. SPECIES I 0. 1. £. 3. 4. 5' 6* 7. B. 9.10.11.li!.l3.14.1i.lC.17.1B.13.aD.cn-a?.E3.e4.Zi.ai.E7.aB-i aracs i SCORPAENIDAE ISO-, 13S-, 120.J srccics I Ul CI £ CG-E. 3-SPECIES I Figure 10.2 Mean values, 95" confidence l i m i t s , ranqes, and sample sizes among species within families f o r the • l i f e span' (T95) (species index refer to Table 1) means for i n d i v i d u a l parameters of 15 major families are summarized i n Table 3. Relative Characterg For 5 r e l a t i v e characters (ratios), M/K, LM/1INF, TM/T95, T50/T95, the sample sizes are 88, 213, 175, 45, and 402 data records respectively (Table 2). Figures 11 to 15 show sample sizes, means, 95% confidence l i m i t s , and ranges of r e l a t i v e characters among species within f a m i l i e s . Refer to Table 1 for species index number. a. The M/K r a t i o shows a great v a r i a t i o n . Only four families (Clupeidae, Gadidae, Pleuronectidae, and Salmonidae) with sample sizes larger than 11 were analysed. The results are shown in Figure 11. In these four f a m i l i e s , variations range from 50.46% for Clupeidae to 123.4% for Pleuronectidae. Salmonidae has the highest r a t i o (4.708), followed by Pleuronectidae (3.018), Gadidae (1. 686), and Clupeidae (0.937). In other families of f i s h e s , the r a t i o i s 1.348 for Scombridae (n=4) ; Percidae (n=4) has a r a t i o (1.818) si m i l a r to Gadidae. b. The LM/LINF r a t i o i s a very stable character. Eight f a m i l i e s (Clupeidae, Cyprinidae, Engraulidae, Gadidae, Pleuronectidae, Salmonidae, Scombridae, and Sciaenidae) Table 3.1 Summary t a b l e of mean va lues , sample s i z e s , s tandard e r r o r s , and c o e f f i c i e n t s of v a r i a t i o n f o r popu la t ion parameters (M, K, LINF, LB) in f a m i l i e s ana lysed j Family 1 a i K """" 1 LINF 1 1 i • 1 Hean LM N SE }. cv | Mean t) SE i cv | Hean H SE cv Hean H SE cv —'"i IC lupeidae i 0. 330 17 0. 068 85.45 0. 431 20 0. 024 45. 99 28.88 82 0. 879 27. - r 57| 19. 32 £2 0. 781 31.821 1Cypr in idae 1 0. 625 2 0. 475 107.5 0. 216 ±1 0. 027 84. 68 173.34 51 6. 397 62. 28 30. 34 33 1. 409 28.621 (Gadidae | 0. 354 22 0. 084 125.3 0. 24 3 11 0. 026 71. 80 75.72 56. 4. 271 42. 21 39. 12 2S- 4. 026 52.481 IP leuronect idae! 0. 222 l i 0. 014 23.32 0. 207 38 0. 030 90. 37 178. 18 51 10 1.6 101 . 5 35. 62 2§- 4. 750 70.551 IScombridae 1 0. 478 6 0. 122 62.51 0. 347 26 0. 033 56. 54 1 52.7 6.0 12 .14 61. 59 62. 02 32 6. 089 55.54| IBotbidae j 0. 300 2 0 0 0. 300 2 0 0 44.60 4 11 .96 53. 65 33. 50 2 14 .50 61.211 IEngraul idae 1 0 1. 654 20 0. 155 42. 26 16.15 21 0. 492 13. 94j 12. 24 12 0. 305 10.88) IHiodontidae 1 0 0. 266 • 6 0. 062 57. 48 40.87 6 2. 657 15. 98: 0 1 IOsmeridae | 0 0. 409 10 0. 145 111 .8 31.19 10 7. 161 72. 60 15. 80 5 1. 497 21.18| (Salmonidae | 0. 755 11 0. 092 52.88 0. 329 2§ 0. 042 68. 25 6 1.35 23 7. 940 68. 48 32. 64 21 5. 474 76.851 (Sc iaen idae 1 0 0. 345 16 0. 027 31. 30 65.37 17 6. 548 41. 07 | 30. 86 12 3. 281 36.831 IScorpaenidae 1 0. 200 2 0 0 0. 107 29 ?• 009 44. 47 43.02 13 1. 999 26. 70] 24. 48 12 1. 830 25.901 IPerc idae | 0. 295 74 0. 057 38.81 0. 179 .7 0. 028 40. 67 56.74 7 11 .36 52. 97 0 1 ISparidae | 0 0. 111 11 0. 005 15. 36 78.44 11 4. 104 17. 35 0 ! (Sgual idae | 0 0. 071 13 0. 014 72. 75 124.3 13 11 .11 32. 23 81. 10 6 6. 319 19.091 t. - L L i i . — i j N : sample s i z e (with underscor ing shows there i s a co r re spond ing f i g u r e ) SE : s tandard e r r o r of mean cv : c o e f f i c i e n t of v a r i a t i o n Table 3.2 summary tab le of mean va lues , sample s i z e s , standard e r r o r s , and c o e f f i c i e n t s o f v a r i a t i o n f o r popu lat ion paramentrs (TH, L l , b, T95) in f a m i l i e s analyzed f I I I I Family I I I I IC lupeidae I ICypr in idae I |Gadidae I |P leuronect idae I )Scombridae I 1Bothidae I IEngraul idae I IHiodontidae I |Osmeridae I ISalmonidae t ISc iaenidae I IScorpaen idae I |Percidae I ISparidae I ISqual idae I i ,i T8 L1 [lean N SE 1 cv | Bean N 4-I I SE cv | Bean N I 6 E cv 0 b 1.B33 9 0.221 36.08|14.84 22 1.360 42.99|3.176 4 0.096 6.07 5.694 35 0.354 36.74)7.390 39 0.383 32.37|3.266 4 0.125 7.64 3.427 15 0.442 49.90|18.78 8 1.385 20.86)2.987 5 0.035 2.61 15.605 19 0.635 49.36)8.400 31 0.523 34.64|3.201 11 0.081 8.43 2.700 19 09368 59.43|47.34 34 3.181 39.19|3.014 34 0.045 8.70 0 I 17.55 2 0.95 7.66| 1.000 12 0.000 0 |12.70 9 0.409 9.68| 0 I 11 .50 4 2.661 46.28| 0 (2.848 23 0.333 56.13|5.720 10 6.456 25.21|3.330 2 0.080 3.40 i 3.500 2 0.500 20.201 0 13.000 4 0. 141 9. 43 0 121.71 14 1.872 32.25|3.112 2 0.071 3.20 7.617 12 0.896 40.76)5.833 3 0.677 20.09)3.185 2 0.066 2.90 0 111.67 3 2.404 35.69| 0 112.43 8 1.493 33.991 18.88 8 3.492 52.33136.56 7 2.125 15.38| _J L 0 0 T95 Bean N SE 8.29 70 0.445 |29.58 47 4.685 16.82 45- 1. 424 32.29 36 7.119 14.64 3.2 3.658 9.98 2 0 2.35 20 0.367 13.74 6 2. 522 14.69 10 4.273 24.00 23 6.244 9.14 15 0.764 41.25 22 7.290 19.54 7 3.284 27.51 11 1.442 I57.77 13 10.31 I cv | I 4 I 44.951 I 108.61 I 56.791 I 132.31 I 141.3| I 0 I I 69.481 I 44.98| I 91.981 I 137.71 I 32.361 I 95.17| I 44.451 I 17.381 I 64.331 I N : sample s i z e {with underscor ing shows there i s a corresponding f i qu re ) SE : standard e r r o r of mean cv : c o e f f i c i e n t of v a r i a t i o n C L L P E I D A E f=LELRdrcCTIDA£ o 3 5 1,ar 1-7 t>5 t-4 i .e . l -o. 0-0 0-£ 0-4 O-E o-oj O. 1. E- 5- 4. 5- 6-GADIDAE e. a. ID. ii. iB. is . i4. I J . ]!e. jj7. jig. 1a. gTTTTS I 8-0. 4- 5 4 * 5- 5 5-Q 8-3 B-Q 1-5 1-Q o-oj 3 O- 1. £. 3- 4. 5. 6. 7. B. 9- 10- 11. 12. 13. 14. IS. 16. 17; S T C I E S I 2 IS.. 14. 12-i i . 9 e< E. 0. 1. e. 3. 4. s. C. 7. B. B. SALMDNiOAE lO. U. 12. 13. 14. 13. IE. 17. IB. 13. BO. a . E>. JFECIES j s. s-F i g u r e 11. Mean va l u e s , 95%, c o n f i d e n c e l i m i t s , ranges, and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the r a t i o M/K ( s p e c i e s index r e f e r t o Table 1) CTi CO 6 9 with sample sizes larger than 9 were analyzed. The re s u l t s are shown i n Figure 12. In these eight fam i l i e s , v a r i a t i o n ranges from 12.17% for Engraulidae to 41.95% for Sciaenidae. Engraulidae has the highest r a t i o (0.766), followed by Clupeidae (0. 707), Scombridae (0.629), Salmonidae (0.590), Gadidae (0.588), Pleuronectidae (0.57a), Sciaenidae (0.513). Cyprinidae has the lowest IM/LINF r a t i o (0.110). In other families of fis h e s , Osmeridae (n=3) has the highest value (0.88); Sgualidae (n=6) also has a high r a t i o (0.77) since i t i s ovoviviparous. c. The L1/LINF r a t i o shows the amount that the f i s h can grow during i t s f i r s t year of l i f e . Four families (Clupeidae, Cyprinidae, Pleuronectidae, and Scombridae) with sample sizes larger than 11 were analyzed. The results are shown in Figure 13. In these four fa m i l i e s , v a r i a t i o n ranges from 28.521 f o r Clupeidae to 58.91% for Cyprinidae. Clupeidae has the highest r a t i o (0.456), followed by Sciaenidae (0.364), Scombridae (0.323). Cyprinidae has the lowest L1/LINF r a t i o (0.112); In other families of fis h e s , Engraulidae |n=9) has the highest value (0.831) among a l l fishes; for Squalidae (n=7) i t i s (0.314). d. The TH/T95 r a t i o indicates the proportion of the f i s h ' s l i f e prior to attaining i t s maturity. Cyprinidae and 0-B5. 0.7C . 0.O1 O.S3 0-S1 0-42 0-34 o-a C 1 7 o-on COPeiDAE ENGRAULIDAE C X' •*• S- *• 5- &* ?• 6' e. 10. U - 12- 13. 14. 13. 16• 17. is. IS* CYPRINIDAE C S 7 0.45 C O 0.34 O'ER O-Q 0-17. C U . o-osi -o-cc. e x 3- 4. 4-4 3 3-a S 2-7. i.e. l-l 0-5 J-Oj 11 I bHO- lLb I • GADIDAE 0.T7 C S S 0.61 . Z 0-54 5 0-4S 3 0-33 C 3 1 0-23. 0-1S O-CQ -o.ccj •I 3. 4. S. 6- 7' 6. 9. 10. 11. 12. 13. 14. 15- 1£> 17. SPECIES I Figure 12.1 Mean v a l u e s , 95% c o n f i d e n c e l i m i t s , ranges, and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the r a t i o LM/LINF {species index r e f e r t o Tab l e 1) o PLEURONECTIDAE e r a . 0.70 C 5 4 i e e 0- 1. P* 3- 4. 5 . 6. 7. B. B. to- 11- 12. 13. 1«- IS. IB- 17. 10- 13- 2D- E l . E2» SFECIES I SALMXTQAE SCIAENIDAE a 0.7SL 0.71. 0-E3 0-5S 0.47 0-33 , 0-3E C E V O.lfi o.oa cooJ a- 3. i r t i m i ' S C O . G R I D A E O . B 7 0-7B. 0-70. 0-81 . 0 .3S. o . a a . 0-17. o.oai. o.oa a. e. 0. 1* 2. 3* 4- 5' T . 7- 0. 9 . 1 0 . 1 1 . i e . l 3 . 1 4 . 1 5 . 1 £ . 1 7 . 1 0 - 1 3 - a 3 . E 1 . 2 D . e 3 . 2 4 . 2 5 . E G - B ' . f f l . a i . SPC:IES i Figure 12.2 Wean va l u e s , 9 5% confidence l i m i t s , ranges, sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r r a t i o LM/LINF (species index r e f e r t o Table 1) and the CLUPEIDAE PLEURONECTIDAE 0.72^  0.S7, 0-50. O - O 0-23 0-22. 0-14 O. 1- 2. 3-CYPRINIDAE S. 6- ?• B' B« lO. 11. J2. .13. 14. 13- IS. 17. IB. 13. SPECIES t 0-43 0-33 J 0-33 5 o-aa S o £• o 0-14 o-oa o-os o-co, O- 1. 2. 3. 4. 3- b- 7. S. 9. JO. 11- 12- 13- 14. 15. IS. 17. Ifl. 13. 20. CI-SPECIES I 5CQUGRIDAE 0-23 0-21 0-0& 0-03 -o-ooi. e o - J s U 0.401 0-201 2 T S. £. 7. B. B-10.11-J2-1314.15-16-17-13-liCD-2l-ti.,2)-24.2i.tC-e7.r3-23. SPECIES I Figure 13. Mean values, 95% confidence l i m i t s , ranges, and sample sizes among species within f a m i l i e s f o r the r a t i o L1/LINF (species index refer to Table 1) 73 Engraulidae with sample sizes larger than 7 were analyzed. The re s u l t s are shown in Figure 14, Variation ranges from 16.85% f o r Sgualidae (n=8) to 59.09% for Osmeridae (n=7). Engraulidae (n=10) has the highest r a t i o (0.649), followed by Sgualidae (0.426) and Osmeridae (0. 249). Cyprinidae (n=8) has the lowest TM/T95 r a t i o (0.190). Gadidae (n=6) has a r a t i o of 0.222. e. The T50/T95 r a t i o i s a very stable character. This shows how much time the f i s h needs t c grow to half of i t s asymptotic length r e l a t i v e to the time tc grow to i t s f u l l length. Two fam i l i e s (Clupeidae, and Cyprinidae) with sample sizes larger than 46 were analyzed. The res u l t s are shown i n Figure 15. The r a t i o i s around 0.23 for a l l fishes except the ovoviviparous Sgualidae (0.145). This means most of the fishes need less than one guarter of the i r l i f e span to grow to half of t h e i r ultimate s i z e . In summary, the T50/T95 r a t i o has the least v a r i a t i o n , although the LH/LINF r a t i o has the greatest b i o l o g i c a l significance because of the energy-spending strategies of fishes for growth and/or for reproduction. The fl/K, the L1/LINF and the TM/T95 rati o s show large v a r i a t i o n s . The suggested means and standard errors of means for r e l a t i v e 74 CYPMNICAE ENGRAU.IOAE o.»[ ©•E7, 3 0*30 a cu] 0-13 0-1T, o.tr e.oa coo. 0.31 o.« 0-31 o-ea o.in o.ia EPfXIES I SFVTJCS i F i g u r e 14. Mean va l u e s , 95$ confidence l i m i t s , ranges, and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r the r a t i o TM/T95 ( s p e c i e s index r e f e r to Table 1) cupeirjAE CYPRINIDAE «? • • I 0-31 cm ° ' E < »  » o-v civ O'Ul O'CP 0-CQ O-ce l * r> j . tf *• V. '«. o. ID. U> UB« 13* 14. IS- Ii. 17- i j . ia. C H L c-er, ca 0-17. B o-i-4 I o-co 0-CG CCQ o-co I I SPECIES I F i g u r e 15. Mean v a l u e s , 95« confidence l i m i t s , ranges, and sample s i z e s among s p e c i e s w i t h i n f a m i l i e s f o r t h -r a t i o T50/T95 (sp e c i e s index r e f e r to Table 1) 75 characters (ratios) of 15 f a m i l i e s are summarized i n Table 4. 3. .Correlative Characters Five c o r r e l a t i v e characters (H—K, 1/K--LINF, LH—LINF, 11—LINF, 1/.H—T95) were examined by l i n e a r regression analysis. Nine f a m i l i e s : Clupeidae, Cyprinidae, Engraulidae, Gadidae, Pleuronectidae, Salmonidae, Sciaenidae, Scombridae, and Scorpaenidae were analyzed. a. For the M—K co r r e l a t i o n , four f a m i l i e s (Clupeidae, Gadidae, pleuronectidae, Salmonidae) with sample size larger than 11 were analysed. The results are shown i n Figure 16. Except for Pleuronectidae, the ether three families show a s i g n i f i c a n t l i n e a r c o r r e l a t i o n between M--K. The slope of these regression l i n e s i s different from the coresponding r a t i o s . Without considering the intercept of the regression l i n e on the M-axis, in Clupeidae Dl i s about 1.66 4 times K and K/K i s 0.937; i n Gadidae M i s 2.454 times K and H/K i s 1.686; i n Salmonidae M i s 1.047 times K and H/K i s 4.7 (refer to Table 4) . b. For the c o r r e l a t i o n between 1/K—LINF, nine families (Clupeidae, Cyprinidae, Engraulidae, Gadidae, Pleuronectidae, Salmonidae, Sciaenidae, Scombridae, and Table 4 Summary table of mean values, sample s i z e s , standard e r r o r s , and c o e f f i c i e n t s of v a r i a t i o n f o r r e l a t i v e characters in f a m i l i e s analysed i— f 1 T 1 T H/K LH/LIHF L1/LINF ! TB/T95 T50/T95 1 Family f. J I Bean }o.937 N SE cv Bean N SE cv I Mean N SE cv | Bean N SE cv Bean N SE cv | IClu peidae 1 2 0. 137 50. 46 0. 707 51 0.014 14. 38 10. 456 22 0. 028 28.521 0 0,218 2 2 0.006 22.581 ICyprinidae I 1.322 2 0. 678 72.55 0. 410 1 2 0.033 25. 29 10. 112 29 0. 012 58.9110.190 8 0.028 42. 19 0.234 ±1 0. 002 5.521 1Gadidae I 1.686 20 0. 270 71.63 0. 588 20 0.021 16.24 1 o . 383 7 0. 090 62.3510.222 6 0.026 29. 16 0.202 45 0. 010 34.62J I pleuronectidae 13.018 14 0. 995 123.4 0. 574 22 0.022 20. 15 |0. 181 30 0. 017 51.27|0.409 2 0.036 12.44 0.236 36 0.008 19.421 IScombridae I 1.348 4 0. 362 53.65 0. 629 1 2 0.036 25.01 t o . 323 2 2 0. 029 46.56|0.047 2 0.000 0.04 0.234 33 0. 008 18.701 IBothidae | 1.000 2 0. 000 0 0 10. 345 2 0. 137 56.27| 0 0.231 2 0. 000 0 I IEngraulidae 0 0. 756 11 0.023 12. 17 10. 831 79 0. 030 10.9310.649 1 2 0.073 35.43 0.231 20 0. 000 0 I IHiodontidae 0 0 0. 267 4 0.069 51.911 0 0.218 76 0.027. 30.67] IOsmeridae 0 0. 888 3 0.009 1 .84 0. 208 7 0.059 74.8210.249 7 0.056 59.09 0.222 10 0. 012 17.11) ISalmonidae I 4.708 11 1. 189 110.0 0.590 2 1 0.034 26.65 0 1 0 0.231 28 0. 000 0.06) ISciaenidae 0 0. 513 11 0.065 4 1. 95 10. 364 12 0. 040 38.18| 0 0.231 15 0. 001 1.94) 1Scorpaenidae I 1.870 2 0. 130 9.87 0. 447 4 0.034. 15.'09 0. 170 3 0.039 39.781 •' 0 0.226 29 0.012 27.31) |Percidae I 1.818 4 0. 445 08. 89 0 t o . 139 3 0. 035 44. 191 0 0.236 7 0. 004 4.541 ISparldae 0 0 t o . 152 8 0. 017 30.361 0 0.225 1 1 0. 004 6.26| ISqualidae 0 0. 770 6 0.014 4.50 10. 314 7 0. 049 41.0810.426 8 0.025 16.85 0. 145 13 0. 007 17.871 N : sample s i z e (with underscoring shows there i s a corresponding fiqure) SE : standard e r r o r of mean cv : c o e f f i c i e n t of v a r i a t i o n 7 7 CLLPCIOAE " FPOQ OT 3-OPE BEING ZERO IS 0.73SE-O3 »i ©-? tvt, • • 5 • M O - l e - i 0-00 0-0* C L 0-21 o-eo 0-31 0-42 0--4 0O(. 0-61 GADIDAE M o -0.182 • CN= 50) PRCS CF SLOP ECIN3 2EFQ IS 0-OOCG 00 FLOJRON'ECTIOAE M 0-SC8 • 0<O37K (N* 15) PRCO OF ELCP EETING ZERO IS O-IOCE 01 0 - 1 - . . o - e r j • - o a t C-OQ c e o o o . o - i 2 o - i ? c - r j O-LQ o - : n o - « i o - - i > o - u o - u . SALWONIOAE • M e 0.125 * l.o<C K 'N= IS) «\ PR03.CF SLOPS BEIN3 ZERD IS 0.337E>CH o c o o - u > o - i 3 o - a 0 - 3 3 o - < j £ M OSD T i F " o-ol J U > j - a i c - i i c - i 3 <..JO . - . j i oT5 £aj ^ 7 1 Figure 16. Linear regression analysis betw-en 1 fami l i e s /M--T95 within 78 Scorpaenidae) with sample sizes larger than 14 sere analyzed. The r e s u l t s are shown in Figure 17. . ftll the other eight families except Sciaenidae show s i g n i f i c a n t l i n e a r correlations between 1/K—LIHF, This shows that the higher the K value, the smaller the asymptotic length. c. For the c o r r e l a t i o n between TH/T95, six families (Clupeidae, Cyprinidae, Gadidae, Pleuronectidae, Salmonidae, Scombridae) were analysed. The results are shown i n Figure 18. A l l of them show s i g n i f i c a n t l i n e a r c o r r e l a t i o n s between Lfl—LINF. Slopes of regression l i n e s in Clupeidae, Gadidae, Pleuronectidae, Salmonidae, and Scombridae are si m i l a r to the corresponding 1H/LINF r a t i o s . Cyprinidae show inconsistency between the slope of 0.16 6 and the r a t i o of 0.410. d. For the c o r r e l a t i o n between L1—LINF, six families (Clupeidae, Cyprinidae, Engraulidae, Pleuronectidae, Sciaenidae, Scombridae) were analysed. The r e s u l t s are shown i n Figure 19. Only two families (Clupeidae and Scombridae) out cf six show s i g n i f i c a n t l i n e a r correlations between L1--LINF. Hy r a t i o n a l i z a t i o n for t h i s , not having good l i n e a r regression, i s because L1 has large variation. For Cyprinidae the slope i s similar to the L1/LIHF r a t i o , but f o r Scombridae the I I I 79 Q-lPEIOAE 1/K = 0"D3 * 0-O33 LXfF tN* 70) .r. FR33 OF SLOPE EEIN3 ZERO 15 O-ISCE-CS «-<4 C Y P R I N I D A E i/K -4-033 • O-aCGLlNF i[N= -17) PRCS DF SLOPE EEIN3 ZEFO IS 0«S2GE-Q7 ENGRAULIDAE 1/K -e.cos * o.ira.iNF <N, S O t.» PROB Q- ELCP EEIN3 ZLK) IS 0-2CGE-O3 i t »•» i*r »-c ••5 »•* 6-C . • • lO- IJ. i s . GADIDAE 1 / K •= 1 . 5 5 3 • 0 « C S 3 L I N F CN= -45) PRCS O F S L O P B E I N G ZERO I S . F i g u r e 17.1 l i n e a r r e g r e s s i o n a n a l y s i s between 1/K—LINF w i t h i n t a r a i l r e s PLEIJFOCCTIOAE ^ Cf HUP ^ G ^ IS o.OOaf CO TS* 100' R3. eo« SALWCNIDAZ ^ c 0-897 * CHS LI>T (N=20) RR03 DF ELCPE EEIK3 2ER0 IS O-IEBE-Ol 80 SCOvBRIOAE JVK -1-EG7 • O'CCQLINF <N» 33) PRD3 CF SUP EEIN3 ZERO IS 0-2Q7E-O3 uc. a^ > »v j . na. 3^4- <n. *o* SCORPAENIDAE: ^ e" -3-108 • 0-534 LINT tN= 29) PRD3 DF EIXFE EEIN3 ZERO IS O-UEET-OI x F i g u r e 1 7 . 2 L i n e a r r e g r e s s i o n a n a l y s i s between 1 / K — L I N F w i t h i n f a m i l i e s 81 QJLFEIOAE LM a O-OIO * 0-7CG LTtF CN" 51) t>> 4* •• 12. Hi. 139. XI- V* 31. 33. 3Q-CYPRINIDAE: LM 1G-EE2 * O-IOGLIN^  CN= 10) PRCQ CF SLEFEEEIN3 ZERO IS 0-1SQE-01 tv co> o- co» ea* joi. ibi» nn. eoi. GADIDAE: LM a O'D-VB • 0-5S2 LIN=- <N= 50) [ FTOO Or SLCP EEirc 2ERQ is O-COOZ 00 * PLEURONECTIDAE: O W. 7i-> ICO. V3. UO. COO. O-SALMDNIDAE LM = -1-303 + LINF (N= 21) 0* 1^ * JD' -C« 5G* ft*. TO. tU. uiV> I'C* SCCVBRIQAE LM 14-612 • 0-335LrN=" CNa IS) x to. uo. l O - uo. pjo-Figure 18. Linear regression analysis between LH—LINF within families CLUPEIDAE U- c - 1 - 7 3 S * 0 - 5 0 5 L I N F (Na 2 2 ) FRD3 O F SLOPE: E E I N 3 ZERO I S 0-83CG.-C3 30- . o . o . CYPRINIDAE U. = 8 -'tis * - 0 - 0 1 4 L I N T (N= S3) PROB O F S L O P E E O N S ZERO I S 0 - 1 0 C E 0 0 X •* l c . -a- to. i a j - 129. i i i - 172. i v * . e i s . ENGRAULIDAE L l 1 0 - 8 4 3 * C 1 2 1 L I N F CN* 9 ) 1^ F T O 0 O F S L O P E E I N G ZERO I S 0 - 1 0 C E 0 1 o* i * ». »- • • 10* « • M ' 13* S7-8 2 PLEURONECTIDAE Ll 8 - E 3 3 * -O.0O4Jt>F CN* 30) P H 0 3 O F S L O P E E I N 3 ZERO I S O - I O O E 0 1 -TTK — X un> 216. SCIAENIDAE Ll «= 1 5 - 1 2 9 * 0 - 1 0 0 LI>F .CN= 12) °RCB O F aXPC B E I N G ZERO I S 0 - 1 O X 0 1 o* io- n - <a> 11. c SCOMBRIDAE Ll 3 0 - 6 5 5 * 0 - 0 7 3 L I N ? (N= 27) PRD3 O F S L O P B E I N G ZERO I S 0 - 3 7 C G - 0 3 X X * ?!' EC* B>- fXO X * 01. i x . i u . - » n y . c v j . j t o . •co. c a . Figure 19. Linear regression analysis between L1—LINF within f a m i l i e s 83 slope (0.073) i s much smaller than the r a t i o (0.323). e. For the c o r r e l a t i o n between 1/M--T95, only four fa m i l i e s (Clupeidae, Gadidae, Pleuronectidae, Scombridae) were analyzed and the r e s u l t s are shown i n Figure 20. None of them shows a s i g n i f i c a n t l i n e a r c o r r e l a t i o n between 1/M-—T95. In conclusion, there are s i g n i f i c a n t l i n e a r regression relationships (slope b i s s i g n i f i c a n t l y d i f f e r e n t from 0) between H—K, beween 1/K—LINF, and between LH—LINF in most of the families analysed except Sciaenidae. These re s u l t s show that f i s h having a larger LINF also have a larger LM and lower K and H values. A l l these results together with sample sizes are summarized i n Table 5. Combining the data records from the 15 major fa m i l i e s , a c o r r e l a t i o n matrix was calculated to show the o v e r a l l c o r r e l a t i o n between characters. The r e s u l t s are shown i n Table 6. Once again, i t suggests that f i s h having a higher mortality c o e f f i c i e n t have a higher K value, a lower LH and a smaller LINF. 84 CLLFEIOAE: 1/M a 1-3CG » 0-307 T033 CN» IH) PTC33 C T E L C F G GEIfGLZOTa IS 0-&VE-01 GADIQAE = 2 » 7 3 S * 0 - 0 3 4 T O S S C N = S O ) *' ' TFR33 C T S U P E E I N 3 Z E R O I S 0 - 1 7 5 E C O *-? o.q » • ! 1-3 ••7 .... PLEIJRONECTIOAE J V M . '.•-••CM * O-OOHTCQS CN" 13) , 5t no3 or txxr> core Z E R O I S o-iocr: 01 7.4 •« 1 1 t . 5 • •7 SALMONIDAE f^ J-^M = 1.543 • 0-021 T035 CN= 13) " C R 0 8 C F EUPE EEIN3 ZERO IS 0.531E-01 . . c l 4.7. 1 3 S . Figure 20. Linear regression analysis between fa rallies 1/M—T95 within 85 T a b l e 5 Summary t a b l e of l i n e a r r e q r e s s i o n a n a l y s e s f o r 5 c o r r e l a t i v e c h a r a c t e r s w i t h i n f a m i l i e s (sample s i z e s w i t h i n b r a c k e t ) F a m i l i e s - M— K 1/K--LINF L M — L I N F L 1 — L I N F 1/M—T9'5 C l u p e i d a e ** *# ** ** ns (12) (70) (51) (22) (12) C y p r i n i d a e — ** * ns — (Hi) (10) (29) E n g r a u l i d a e — ** — — ns — _ (20) (9) Gadidae ** ** ** ns (15) (45) (20) (20) P l e u r o n e c t i d a e ns ** ** ns ns (15) (36) (27) (30) (13) Salmonidae ** * ** — ns (15) (28) (21) (19) S c i a e n i d a e — ns — n s _ _ (15) (12) Scombridae — ** ** ** — _ (33) (19) (27) S c o r p a e n i d a e — * — — — (29) ** : 1% l e v e l f o r l i n e a r r e g r e s s i o n c o e f f i c i e n t b w i t h 0 * : 5% l e v e l f o r l i n e a r r e g r e s s i o n c o e f f i c i e n t b w i t h 0 ns : no s i g n i f i c a n t d i f f e r e n c e between b and 0 — : sample s i z e l e s s than 10 Table 6. T h e c o r r e l a t i o n matrix between combined data of 15 f a m i l i e s parameters f o r W I T H I N GROUPS C O R R E L A T I O N M A T R I X V A R I & S I F S GROUP M K L I N F LM V A R I A B L E GROUP 1 - 0 0 0 0 M 0 . 0 1. 0 0 0 0 K 0 . 0 0 . 2 2 2 9 3 1 . 0 0 0 0 L I N F 0 . 0 - 0 . 1 2 5 4 5 - 0 . 2 0 9 7 4 1 . 0 0 0 0 LM 0 . 0 - 0 . 9 6 6 2 0 E - 01 - 0 . 3 3 2 8 6 E - 01 0 . 4 1 9 7 1 2. . 0 0 0 0 TM 0 . 0 0 . 3 6 3 4 1 E - 02 - 0 . 4 5 9 8 1 E - 01 0 . 1 2 7 9 7 0 . 2 3 5 7 2 * L l 0 . 0 0 . 8 4 0 3 4 E - 05 0 . 2 1 7 9 4 E - 01 0. 1 6 4 6 6 0. 1 0 6 4 4 B 0 - 0 0 . 1 5 3 4 3 E -02 - 0 . 4 0 4 9 2 E - 02 - 0 . 5 3 2 9 3 E - 0 2 - 0 . 2 69 52E TM 1 . 0 0 0 0 - 0 . 1 8 7 1 2 E - 0 1 - 0 . 4 2 2 9 9 E - 0 2 L l 1 . 0 0 0 0 0 . 2 6 3 8 1 E - 0 2 1.0000 oo CTl 87 II POPULATION PATTERNS 1.. Ccmjgarison between Families The comparisons of standard deviations and mean values of the four parameters with large sample sizes (K, LINF, T95, and LH) between families were conducted for 11 families: Clupeidae, Cyprinidae, Engraulidae, Gadidae, Osmeridae, Pleuronectidae, Salmonidae, Sciaenidae, Scombridae, Scorpaenidae, and Sgualidae. By using the F-test and the appropriate t - t e s t 1 , s i g n i f i c a n t differences between families were found in most of the cases. The seguence of families was rearranged using the methods of numerical taxonomy in order to bring s i m i l a r families adjacent to each other and the r e s u l t s are shown i n Table 7. Each small box in Table 7 displays the r e s u l t s cf comparison between two families. The f i r s t two columns show the r e s u l t s from the F-test and the next two columns show the r e s u l t s from the appropriate t - t e s t . The four rows in the small box indicate four parmeters (K, LINF, T95, and LH) on which analyses were based. The attempt to group families into patterns f a i l e d by t h i s method. But t h i s confirms that one of my hypotheses, that s i g n i f i c a n t differences exist from phylogeretic 1 I f a s i g n i f i c a n t difference was shown from F value, the Welch ts approximation method was u t i l i z e d instead of the Student t- t e s t . 88 C l u p e i d a e O s m e r i d a e C y p r i n i d a e E n q r a u l i d a e i ~x 1****1 1****1 I * * ! |** | C l u p e i d a e r +~ -i j * * j * * | j * * | * * j |***|** | I I |Osmeridae r H 4 1 1****1 * * | * * * | I****I****I * * j 1** * *1* * * *1 * j 1** * *1* * * *1 1 C y p r i n i d a e T a b l e 7 The F - t e s t and t h e a p p r o p r i a t e t - t e s t between f a m i l i e s F t r-K I L I N F | T 9 5 | LM I S a l m c n i d a e I * * * * j * * * | * * j * * 1 * * * * 1 * * * * | * * * | I * * * * | * * * | I * I * j * * * | I * * | ****| *** j ** | j * * * * j * ***I * * j P l e u r o n e c t i d a e | * * * * | * * * * | * ) * * j * * | * * * * | I * * 1****1 * * j * * j i i ** : *\% l e v e l * : 5% l e v e l — : sa m p l e s i z e l e s s t h an 10 I I | S a l m o n i d a e — i I G a d i d a e | * * | * * * I** * | S c i a e n i d a e j * * * * l * * * * l * * * * l * * I****I****I** | * * I * * j * * * * j I * * i + + H H | * * * * j * | * * | * * | * j * * * * j * * * * | * * | I 1 * * * * 1 * * |** | * * * * j * * I * j * * * * | I * * | * J P l e u r o n e c t i d a e I** | j * * * | | ( G a d i d a e +  I * *j * * | S c o r p a e n i d a e S c o m b r i d a e S q u a l i d a e * | * * * * j * * * * | 1 j * * * * j sciaenidae 1 * * * * 1 * * * * 1 * * | * * * * | * * * * | * * * * | * * * * | * * * * | 1 * * * * 1 * * * * 1 * * | * * * * | * * * j * * * * | * * * * | * * * * j 1** * *1* * * *1 * * * | * * | I 1 * * * *1* * * *1 I** i * * * | 1 *|** |** * ] * * * * ) * | s c o r p a e n i d a e 1****1 * *| * * | * * | I * *| * j 1****1 l * * * * j * * * * l * * * | * * * * | * * * * | * * * * | * * * * | * * * * | * * * * j 1 * * * * 1 * * | | * | * * | * * * | * * | 1****1 I * * * | * * * * | 1****1 **| **| ***| * * * | * * * * | s c o m b r i d a e 1****1 * * * | * * * | * * * | * * * * j * * * * j * * * | 1** * *1* * * *1 * * | * * * * | * * j * * * | * * * * | 1 * * * * 1 * * * * 1 * * * * 1 * * * * 1 * * * | -I I I ***| * * | **** ***!****! * j * * * * j * * * * j I * * * * I I 89 considerations, i s true. Comparison among Families The mean and i t s 95% confidence l i m i t f o r each family i s shown (a) in Figure 21 and 22 for each parameter, (b) i n Figure 23 and 24 for each r e l a t i v e character. (Fig. 21 and Fig. 23 show the results from group I: 5 f a m i l i e s ; Fig. 22 and Fig. 24 show the resu l t from group II: 10 f a m i l i e s ) . Together with Tables 3 and 4, which show the standard error of the mean and the c o e f f i c i e n t of va r i a t i o n , these figures permitted the grouping of fishes into four d i f f e r e n t categories by comparing the four stable characters such as K, LINF, LIS, and the r a t i o LM/LINF, as follows: A) Engraulidae, Clupeidae, and Osmeridae. They have the highest K (1.-6 for Engraulidae, over 0.4 for the others), the smallest LINF, LM, and a very high LK/LINF ra t i o (over 0.7). Common c h a r a c t e r i s t i c s of t h i s group of fishes are small s i z e , pelagic habitat, and schocling behavior. B) Scombridae has f a i r l y high K value (around 0.35), although i t i s lower than pelagic shoaling f i s h e s . This family has the largest LINF (152.7 cm) and L1 (47.34 cm). Salmonidae and Sciaenidae seem to show similar c h a r a c t e r i s t i c s , especially for t h e i r K values (0.329 e-a. M. t-o. t.s. 1.4, 1-1 O-B, ©•7 . o-a. C l u p e C y p r i G a d i d P l e u r o Scombr a. C l u p e C y p r i G a d i d P l e u r o Scombr o-a 0-7 . 0-6. o-a. 0-4. O.E C I . 0-OL C l u p e C y p r i G a d i d P l e u r o Scombr C l u p e C y p r i G a d i d P l e u r o Scombr Figure 21.1 Mean values, 95£ confidence l i m i t s , ranges and sample sizes of i n d i v i d u a l parameters among 5 families (group I) o Clupe Cypri. Gadid Pleuro Scombr i.e. u.v E.G. •° e.a. C i - n . £ i . s . o . a 0.4 -o-oj Clupe Cypri Gadid Pleuro Scombr eo. To-il T Clupe Cypri Gadid Pleuro Scombr in o. I-D U 3 ' £ 01. ED « • 0. 70 — t — Clupe Cypri Gadid Pleuro Scombr Figure 2 1 . 2 Mean values, 95% confidence l i m i t s , ranges and sample s i z e s of i n d i v i d u a l parameters among 5 f a m i l i e s (group I) t—* 1 > sr 1-8 1-0. o-a, o-a 0-6 0-3. o-«. 0-3 0-1 e B 0-OL B o t h H i o d ^ Salm . S c o r S p a r E n g r Osme S c i a P e r c p Squa 173-U . IS?. - I 130. W i c 0' £ B7-ss. E l a B o t h v „ „ K i o d o Salrn . S c o r S p a r E n g r Osme S c i a P e r c F Squa e-ay i-a J.-S. i-e. O.E 0-K e • u •a B o t h H i o d ^ SalirT . S c o r S p a r E n g r Osme S c i a P e r c p Squa B o t h H i o d „ Salm . S c o r S p a r E n g r Osme S c i a P e r c p Squa F i g u r e 22.1 Wean v a l u e s . 95% c o n f i d e n c e l i m i t s , r a n g e s and sample s i z e s of i n d i v i d u a l p a r a m e t e r s among 10 f a m i l i e s (group I I ) E7-. 10-1 7-. 3-J 13 B B o t h H i o d Salm . S c o r S p a r E n g r Osme S c i a P e r c Squa 3 ' \ 1-Q. 0-7 . 0-3. I f i -O'OL B o t h H i o d ^ Salm. . S c o r S p a r E n g r Osme S c i a P e r c Squa 13-i 1E3. l -O. 83. 50. B o t h H i o d ^ Salin . S c o r S p a r E n g r Osme S c i a P e r c Squa 13 w * Both. H i o d ^ Salin . S c o r S p a r E n g r Osme S c i a P e r c Squa Figure 22.2 Mean values, 95? confidence l i m i t s , ranges and sample sizes of i n d i v i d u a l parameters among 10 fam i l i e s (group II) RATIO l_M/Ca> RATIO M/K A o o o o o o o o o o fj p p y ft g P P ,* P 5 t 03 d n t o 01 g H- CD N 0) CD 3 01 ' < O OJ Hi M d o ro o 01 K -l-« fD M Ul 0) o\P ft < CD O O 3 Hi O H-0) CD K. 3 OJ O O CD r+ CD H 01 0) 3 . o 02 3 H-rt 01 M 0) ui 3 . 0} Hi CD OJ 01 3 H- (1) H- Cu CD 01 01 •Q) — 3 iQ ' 0 O CD d H o d CD o TJ o P> H-TJ I—' CD d O cn o o o d TJ-CD TJ O OJ TJ H CD d n o cn o o —x «B «8 «3 'to RATIO TM/T95 o o o o o o o o to o o* . P E P P P f r F ' r ' . p - p p o d TJ-CD n TJ. o 0). f i . TJ h-1 CD , d H O cn o o o (-• c TJ CD O TJ • H H-,0> H-Di TJ h-1 CD d n o cn o o •x+x «K «8 RATIO Ll/Uo O 3 pi p JK } i g iL X 1 K fr6 IB ie-12' 13-u. z o 8-i -S. ;>.. s-«.. e. S.S. 4.a. •»•« a-a ? S fl' RAT 1-6 0-5 t>*0 B o t h ' H i o d ^ Salm . S c o r S p a r < Engr Osme S c i a P e r c P Squa IT .1 t I B ° t h H i o d Salm S c o r •• q n s r E n g r Osme S c i a b C O r P e r c ° P a r Squa 0-£H C 7 S , 0 .66 . C S S . 5 C37. o .sa. o.ia 0-OQ coo. 12 3 O-tlT. o.oa. 0-7B. H| o f i O-EQ. 8 C<1. 0.33. o-aa. o-ia. 0.10.. O'CO. B o t h H i o d ^ Salm ."Scor Soar E n g r Osme S c i a P e r c P Squa B O T H T T ^ V H i o d r > Salm,, . S c o r S p a r E n g r .• Osme S c i a P e r c F Squa «> Figure 24. Mean values, 95% confidence l i m i t s , ranges, and sample sizes of co r r e l a t i v e characters among 10 families (group II) 9 6 for Salmonidae, 0.345 f o r Sciaenidae). These fishes are large, pelagic, and migratory. C) Gadidae, Pleuronectidae, Scorpaenidae, Sparidae etc. They have lower K values |less than 0.25), intermediate 1IHF, and lower LH/LINF ra t i o s (less than 0.6). These fishes l i v e on or close to the continental s h e l f , have a longer l i f e span and a large asymptotic length, but are slow growing. D) Freshwater f i s h - Cyprinidae has K and LIWF values which are s i m i l a r to those of the group C fi s h e s , but has a smaller LH and, especially, the lowest LM/1IFF (0.4) and TM/T95 (0.2) r a t i o . They s t a r t reproduction early and have a high mortality rate (0.625, the second highest among 15 families, next to Salmonidae), The re s u l t s of calculations for the other 28 fa m i l i e s , with i n s u f f i c i e n t data support, are l i s t e d i n appendix 1 and 2. 97 C l a s s i f y i n g Families (Discriminant Ana3.ysis) For 7 variables (M, K, LINF, LH, TM, L1 , b) , stepwise discriminant analysis not only elegantly guantifies the means of d i f f e r e n t groups by a set of discriminant functions according to t h e i r underlying a f f i n i t i e s but also shows how groups d i f f e r in a multi-dimensional space. Table 8 and Figure 25 show the re s u l t of discriminant analysis i n group I (among 5 f a m i l i e s , which have very large sample sizes) i n which 93.291 of the 432 cases, when considered independently, were corre c t l y c l a s s i f i e d . Table 9 and Figure 26 show the r e s u l t s of analysis in group I I (among 10 families which have f a i r l y large sample sizes) i n which 90.963? of the 188 cases were c o r r e c t l y c l a s s i f i e d . Further, the r e s u l t s are shown i n Table 10 and Figure 27 of group I and group II combined (15 families altogether) in which 90.48^ of the 620 cases were c o r r e c t l y c l a s s i f i e d . These tables also show the percentage of cases c l a s s i f i e d into different groups. Thus, they indicate the s i m i l a r i t y among groups. For example, in Table 10, 12.2051 of Scorpaenidae were c l a s s i f i e d into Pleuronectidae. The calculated c l a s s i f i c a t i o n function for 15 families based on seven variables, i s shown in Table 11. Table 12 presents a summary of the r e s u l t s of the discriminant analysis by l i s t i n g the canonical roots (eigen-values), canonical variables (eigen-vectors) , and the group means (based on eigen-values) for I i I Table 8. 98 D i s c r i m i n a n t a n a l y s i s i n 5 f a m i l i e s (cjroup I) 93.29% CF THE CASFS HERE CORRECTLY CLASSIFIED NUMBER OF CASES CLASSIFIED INTO GROUP -PREDJCTFD KLUPEI CYPRIN GADIDA PL.SURO SCOMBR TOTAL OBSERVED KLUPEI 57 I 1 0 I 100 CYPRIN 3 85 0 1 0 89 CADI DA 3 1 63 1 1 69 PLEURO 5 0 3 6? 0 ' 70 SCOMPR 0 0 8 0 96 104 I OF CASES CLASSIFIED INTO EACH GROUP • PREOICTEC KLUPEI CYPRIM GADIDA PLEURO SCOMPR IBSFRVF.D KLUPFI 97.00 1.00 1.00 0.0 1.00 CYPRIN 3.37 95.51 0.0 1.12 0.0 GADIGA 4.35 1.45 91.3 0 1.4 5 1.4 5 PLEURO 7.14 0.0 4.29 88.57 0.0 SCOMBR 0.0 0.0 7.69 0.0 92.31 Table 9. D i s c r i m i n a n t a n a l y s i s i n 10 f a m i l i e s (qroup II) 90.56? OF THF CASES WERF CORRECTLY CLASSIFIED NU^ eSR OF CASES CLASSIFIED INTO GROUP -PREDICTED 6E0THI 7ENGRA 8HI0DC 9QSMER OSALMO 1SCIAE 2SC0RP 3PERCI 4 SPAR I 5SCUAL TOTAL OBSERVED 6B0THI c 0 0 0 0 0 0 0 0 0 5 7ENGRA 0 20 2 0 0 0 0 0 0 0 22 8HIJOO 0 0 6 0 0 0 0 0 0 0 6 90SHER 0 0 0 30 0 0 a 0 0 0 30 OS ALMO 3 0 2 0 24 0 0 3 1 0 33 1SCIAF 5 0 0 0 0 13 c 1 0 0 19 2SC0RP 0 0 0 0 0 0 41 0 0 0 41 3°ERCI C 0 0 0 0 0 0 7 0 0 7 4SPARI 0 0 0 0 0 0 0 0 12 . 0 12 5S0U4L 0 0 0 0 0 0 0 0 0 13 13 1 Of- CASES CLASSIFIED INTO EACH GROUP PPEDICTFQ 6B01HI 7ENGRA 8HI0DO 9OSMER OSALMO 1SCIAE 2SC0RP 3PFRCI 4SPARI 5SQUAL OBSERVFO 630THI 1C0.0C 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 CC 7ENGPA 0.0 90.91 9.09 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8HIOD0 0.0 0.0 100.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 90SHER CO CC 0.0 ICO.CO 0.0 0.0 0.0 CO 0.0 0.0 OSALMO 5.C5 CO 6.06 O.C 72.73 O.C 0.0 9.05 3.03 0.0 1SCIAE 26.32 0.0 0.0 0.0 0.0 68. 42 CO 5.26 0.0 O.C 2SC0PP CO 0.0 0.0 CC 0.0 0.0 100.00 0.0 0.0 0.0 3PERCI 0.0 0.0 0.0 0.0 0.0 CC CO 100.oc 0.0 0.0 4SPARI CC CO 0.0 O.C 0.0 0.0 0.0 CO 100.00 CC 5S0UAL 0.0 0.0 0.0 0.0 0.0 0.0 CO 0.0 0.0 100.00 99 8.000 • 7.667 • 7.333 7.000 • 6.667 6.333 a 6.000 5.667 # 5.333 5.000 4.667 4.33? 4.000 3.667 • 3.333 3.0CC 2.667 2.333 2.000 1.667 1.333 1.000 0.667 0.333 -0.000 * -0.333 / s -0.667 / sss -1.000 / SS 5 -1.333 / s s SS -1.667 • Is s s s sss -2.000 \ s -2.333 # -2.667 a \ s s -3.000 -3.333 -3.667 -4.0C0 • -4.333 -<i.667 -5.000 -5.333 • -5.667 -6.000 -6.3?2 -6.667 -7.000 -7.333 * -7.667 -8.000 -a.333 -8.667 -9.0CO -9.333 -9.667 -10.000 • S.000 7.667 7.333 7.000 6.667 6.333 6.000 5.667 5.333 5.000 4.667 4.333 4.000 3.667 3.333 •3.000 2.667 2.333 2.000 1.667 1.333 1.000 0.667 0.333 -0.000 -0.333 -0.667 -1.000 -1.333 -1.667 -2.000 -2.333 -2.667 -3.000 -3.333 - 3 . 667 -4.000 -4.333 -4 .667 -5.000 -5.333 -5.667 -6.000 -6.333 -6.667 -7.000 -7.333 -7.667 -8.000 -8.333 -3.667 -5.000 -9.333 -0.667 - !0.000 -8.COO -4.000 -0.000 4.000 8.000 -10.000 -6.000 -2.000 2.000 6.000 F i g u r e 25. D i s c r i m i n a n t a n a l y s i s i n 5 f a m i l i e s (group I) K : C l u p e i d a e C : C y p r i n i d a e G : Gadidae P : P l e u r o n e c t i d a e S : Scombridae 1 0 0 13. 12. 12. 11. 11 . 10. 10. •>•. s . 8, 8. 7, 7. 6. 6c 5. 5. <t. <• . 3, 3. 2. 2. 1 • 1. 0, 0 . - 0 . - 0 . - 1 . - 1 . -2. -2 . -3 , -3 . -•». -<i , -5 . -5 . -O -6 -7 -7 -8 -6 -i -4 -10 -10 - l i -11 -12 - !2 -!-> -13 2 C C 7C0 200 7CC 200 700 20C 700 200 700 200 7C0 200 700 200 700 2CC 700 200 70C 200 700 2C0 700 20C 700 200 30C BOO 3C0 800 300 800 300 800 300 800 300 BOO ,?00 ,800 ,300 ,800 ,3CC ,800 ,300 ,800 .300 ,800 .300 ,800 .300 ,noo ,3CC .300 13.; 12. 12. 11. 11. 10. 10. 9. S. 8. 8. 7. 7. 6. 6. 5. 5. 4. 4. 3. 3. 2. 2. 1. 1. 0. 0. - c . - 0 . -1. -1. - 2 . - 2 . - 3 . - 3 . -<.. -1. - 5 . -5. - 6 . - 6 . - 7 . - 7 . - 8 . - 8 . -9. - 5 . - 1 0 . - IC. - 1 1 . - 1 1 . -12 . -12. -13 . - 1 3 . -1C.800 -13.800 800 1.200 7.200 •7.8C0 -1.8C0 *.2C0 10.200 13.200 F i g u r e 26. D i s c r i m i n a n t a n a l y s i s i n 10 f a m i l i e s (group I I ) 6 : B o t h i d a e 1 : S c i a enidae 7 : E n g r a u l i d a e 2 : Scor paen i d a e 8 : Hiodont i d a e 3 : P e r c i d a e 9 : Osmerid ae 4 : Spar i d a e 0 : Salmonidae 5 : S g u a l i d a e T a b l e 10. D i s c r i m i n a n t a n a l y s i s i n 15 f a m i l i e s (group I and group I I combined) 90.16? OF THE CASES WERE CCRRECTLY CLASSIFIED NUMBER OF CASES CLASSIFIED INTO GROUP -PREDICTED KLUPEI CYPRIN GADIDA PLEUNC SCOMBR 6B0THI 7ENGRA 8HIO0O 90SMER OSALMO 1SCIAE 2SC0RP 3PERCI 4SPARI 5S0UAL TOTAL OBSERVED KLLPEI 97 0 1 0 2 0 0 c c 0 C 0 0 0 0 100 CYPR IN C 83 0 5 0 0 0 0 1 0 0 0 0 0 0 89 GADIDA 0 0 62 0 2 0 0 c 0 5 C 0 0 0 0 69 PLEUNC C 1 0 66 0 0 0 0 0 1 0 1 1 0 C 70 SCOMBR 0 0 3 0 95 0 0 0 0 0 1 0 0 0 1 104 6ROT HI 0 0 0 0 0 3 0 0 c 2 c 0 0 0 C 5 7ENGRA 1 0 1 0 0 0 20 0 0 0 0 0 0 0 0 22 8HI0D0 0 0 0 3 0 0 0 3 c 0 c 0 0 0 0 6 90SMER 0 0 0 4 0 0 0 0 26 0 0 0 0 0 • • 0 30 OSALMO 0 0 7 0 1 0 0 c 0 25 0 0 0 0 0 33 1SCIA? 5 0 1 0 1 0 0 0 0 0 12 0 C 0 0 19 2SCORP C 0 0 5 0 0 c 0 0 0 0 36 0 0 0 41 3PERCI 0 0 0 4 0 0 0 0 0 0 c 0 3 0 0 7 4SPARI C 0 0 0 0 0 0 0 0 1 0 0 0 11 0 12 5SCUAL 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 S OF CASES CLASSIFIED INTO EACH GRCUP PRECICTEC KLUPEI CYPRIN GADICA PLEUNC SCOMBR 6B0THI 7ENGRA 8HI0D0 90SMER OSALMO 1SCIAE 2SC0RP 3PERCI 4S PAR I 5 SOUAL OBSERVED KLUPEI 97.OC 0.0 1.00 0.0 2.0C C. C C O C C 0.0 0.0 0.0 0.0 0.0 0.0 0.0 CYPRIN 0.0 93.26 0.0 5.62 0.0 0.0 C O 0.0 1.12 0. C C O 0. 0 0.0 0.0 0.0 GADIDA 0.0 0.0 89.86 0.0 2.90 0. c 0.0 0.0 0.0 7.25 0.0 0.0 0.0 0.0 0.0 PLEUNC 0.0 1 .43 0.0 94.29 0.0 O.C C O c. c 0. 0 1.43 0.0 1.43 1.43 0.0 0.0 SCOMBR 0. 0 C O 2.88 C O 95.19 0.0 C O C O 0.0 0.0 0.96 0. 0 0.0 0. 0 0.96 6B0IHI 0.0 0.0 0.0 0.0 0.0 60. CO C O C O 0.0 40.00 0.0 0.0 0.0 0.0 0.0 7ENGRA 4.55 C O 4.55 C O 0.0 0.0 50.91 0.0 C O 0. C 0. C C. 0 0.0 0.0 0.0 8HIODO 0.0 0.0 0. 0 50. CC 0.0 0. 0 0.0 50.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 90 SH ER 0.0 0.0 • 0.0 13.33 0.0 O.C C O 0. 0 86.67 0.0 0.0 C O 0.0 0.0 0.0 OSALMO 0.0 0.0 21.21 0.0 3.03 0.0 0.0 C O 0.0 75. 76 0.0 C O 0.0 O.C 0. 0 1SCIAE 26 .32 0.0 5.26 C O 5.26 O.C C O c. 0 C O 0.0 63 .16 0.0 0.0 O.C 0.0 2SC0RP 0.0 0.0 0.0 12.20 0.0 0.0 0.0 C C C O C O C O 87. 30 C O 0.0 0.0 3PFRCI C C C O 0.0 57. 14 0.0 0. c 0.0 0.0 o.o • 0.0 0.0 0.0 42.96 O.C C O 4SPARI 0.0 0.0 0.0 0.0 0.0 O.C C O C O 0.0 8.33 0.0 0.0 0 .0 91 .67 0.0 5SQJ AL O.C 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 C. C C O C O 0.0 O.C ICO.00 102 •9.SCO -6.900 -3.SCO 5.ICO ...» 11.100 -12.900 -C.9C0 SOC 2.100 S.100 Figure 27. Discriminant analysis in (group T and group i i ) 15 fam i l i e s K : Clupeidae C : Cyprinidae G : Gadidae P : Pleuronectidae S : Scombridae 6 : Bothidae 7 : Engraulidae 8 : Hiodontidae 9 : Osmeridae 0 : Salmonidae 1 : Sciaenidae 2 : Scorpaenidae 3 : Percidae 4 : Sparidae 5 : Sgualidae Table 11. C l a s s i f i c a t i o n f u n c t i o n w i t h 7 v a r i a b l e s f o r 15 f a m i l i e s i n d i s c r i m i n a n t a n a l y s i s CLASSIFICATION FUNCTION FOR EACH GROUP VARIABLE KLUPEI CYPRIN GAOIDA PLEUNC SCOMBR 6B0THI 7ENGRA M 16.811 37.310 2C.958 12.700 29.167 16.620 11.062 K 11.686 4.4406 6.6172 7.466C 8.5276 8.1035 48.885 LINF 0.11014E-02 C.27205E-01 0.99545E-02 0.12221E-01 0.3C012E-01 -0.7589 1F-02 0.33069E LM 0.20755 0.22790 0.259C6 0.25642 0.4C226 0.28995 0.19807 TM 0.70422 2.4101 1.2445 2.3852 0.65046 1.1265 0.4C951 Ll 0.55912 0.20869 0.70223 C. 26260 1.8851 0.67281 0.39242 B 536.02 551.32 504.54 540.49 505.42 519.59 519.71 .CNSTANT - 866.C8 -927.17 -775.77 . -882.62 -839 .40 -818.06 -349.56 -01 -VARIABLE CLASSIFICATION FUNCTION FOR EACH GROUP OSALMO 1SCIAE 2 SCORP 3PFRCI 4SPARI 5SCUAL M 44.534 16.699 11.084 16.566 24.791 25.028 K 5.5853 9.6869 4.2365 5.4356 2.6624 2.2282 LINF 0.15026E-01 0.11456E-01 -0.23150E-C3 0.20C32F-01 0.38354!! -01 -0. 24 25Cli LM 0.27710 C.27C36 0.16440 C. 11062 0.7 1506F -01 0.38277 TM 1.2849 0.12965 3.5925 2.2735 2.2535 8.8680 Ll C.564C5 0.82627 C.2C306 0.43634 0.46032 1.5457 B 506.57 525.30 53 7.4 5 519.CO 446.74 520.32 »ONSTANT - 791.92 -637.70 -876.07 -813.60 -610 .06 -934.43 8HIO0O 23.666 6.4889 0. 55370E-C2 0.27299 2.0388 0.41766 519.60 -819.55 90SMER 22.088 11.104 0.13379E-01 0.17933 1.1929 0.15457 56 1.92 •548. O co Table 12. Summary table of discriminant analysis f o r 15 families EIGENVALUES 10.93254 4.61658 1.77785 1.66485 1.21548 0.18683 0.10185 CUMULATIVE PROPORTION OF TOTAL DISPERSION 0.53340 0.75e64 0.84538 C.92661 0.98592 0.99503 1.00000 CANONICAL CORRELATIONS 0.55718 0.90662 C.800C1 0.79041 0.74070 0.39676 0.30403 COEFFICIENTS FOR CANONICAL VARIABLE -STANDARDIZED VARIABLE M -0.15938 K -0.24347E-01 LINF -0.84822E-C1 LM -0.43159 TM C.4344C L l -2.6811 B 0.62483 •0.22035 0.86798 C.52660E •0.16636 -1.8741 -0.41136 •0.60580E-01 01 -0.67732 0.33379 0.136C0 0.29306 0.56'+19 0.46582 -1.62C4 -C.86523 1.2811 -0.14040 , 0. 10140 0.86 129 0.73039 0.75413 -1.0685 -0.94059 -0.30161 0. 15624! -0.55722 0.51212 0.34618 -01 -0.21184 0. 2475C 0.64078 1.1389 -0.47681 -1.3784 -0.54456E-01 0.16588 -0.17855 -1.4764 1.3506 -0.27383 -0.85485E -0.63832E CONSTANT 66.854 14.264 31. 554 -12.712 13.909 -60.155 63. 476 GROUP CANONICAL VARIABLES EVALU4T ED AT GROUP M<=ANS KLUPEI 1.CC71 1.7959 -0.189C9 0.24561 0.77437 -0. 58817 0.17730 CYPRIN 2.7153 -1.2023 -1.7239 -C.76C55 -1 .1287 0. 12142 -0.19832 GADIJA -0.76258 0.23073 1.3697 -C.53035 0.36086 C. 43853 0.33232 PLEUNC 2.4642 -C.78368 0.223eo 0.66685 0.96300 0. 75983 -0.35517E -01 SC0M3R -6.5213 -C.60642E-01 -0.76132 0. 18788 0.18429 0. 50755E-01 -0.18380 6B0THI -C.22874E-01 C.68154 0.74728 -C. 1A-880 0.88781 -0. 14207E-01 0 .68485 7ENGKA 0.77563 5.4761 1.£239 3.7654 -3.2186 C. 3011 9 -0.15014 8HIODO 1.2355 -C.44997 0.85978 -0.56214 -0. 1 2325 C. 30979E-C1 0.7 1820 90S ME R 3.41 62 1.2700 -1.6668 C. 393? 8 0.24279 -0. 28903 -0 .13423 OSALMO -0.30077 0.833595-01 0.2C453 -2.3853 -2.C293 -0. 13422 0.56171 ISC IA E -C.66736 1.9537 -0.7E917E -01 -0.20364 1 .2204 -0. 44330E-02 0.80640E -01 2SC0RP 3.4244 -2.0745 1.C268 0.72173 1.0139 -0. 28201 -0.22519 3PERC I 1.6371 -C.33334 1.3727 -0.49515 0.65750 -C. 7195 7 -0.34442 4 S 0 AR I -0.39766 -C.499 1 2 5.5098 -3.4647 -0.8 703 3 -0. 51134 -1 . 1490 5S0UAL -2.4350 -10.130 1.4804 3.7218 -1.5787 -0. 76065 0.5t249 105 each of t h e f a m i l i e s which make up F i g u r e 27. The a n a l y s e s were a l s o conducted among s p e c i e s w i t h i n 5 major f a m i l i e s [ C l u p e i d a e {sample s i z e n=100) , C y p r i n i d a e (n=89) , Gadidae (n=69), P l e u r o n e c t i d a e (n=70) , and Scombridae (n=104)]. C o r r e c t c l a s s i f i c a t i o n ranged from 23.08? (Scombridae 28 s p e c i e s , ) t o 76.40% ( C y p r i n i d a e , 5 s p e c i e s ) . C l a s s i f y i n g F a m i l i e s ( C c o l e y and Lohnes' Method}, Cooley and Lohnes n o r m a l i z e t h e c a n o n i c a l r o o t s o f t h e above method t o u n i t magnitude. T h i s method p r o v i d e d more s u c c e s s f u l c l a s s i f i c a t i o n s . T a b l e 13 shows 96.761? of t h e c a s e s were c o r r e c t l y c l a s s i f i e d i n group I . T a b l e 14 shows 93.62% of t h e c a s e s were c o r r e c t l y c l a s s i f i e d i n group I I . F u r t h e r , T a b l e 15 shows 90.16% o f the c a s e s were c o r r e c t e d c l a s s i f i e d f o r t h e combined d a t a of group I and group I I . A summary T a b l e o f t h e c a l c u l a t i o n f o r 15 f a m i l i e s i s l i s t e d i n Appendix 3. The a n a l y s e s were a l s o conducted among s p e c i e s w i t h i n 5 major f a m i l i e s ( C l u p e i d a e , C y p r i n i d a e , Gadidae, P l e u r o n e c t i d a e , and Scombridae). C o r r e c t c l a s s i f i c a t i o n ranged from 58.6% ( P l e u r o n e c t i d a e ) t o 87.6% ( C y p r i n i d a e ) . B e s u l t s f o r c l a s s i f y i n g f a m i l i e s a r e summarized i n T a b l e j 106 T a b l e 1 3 . C o o l e y a n d Lo lines' c l a s s i f i c a t i o n m e t h o d i n 5 f a m i l i e s ( g r o u p 1) 9 6 . 7 6 * OF THF CAS^S WERF C 0 P R c C T L Y C L A S S I F I E D NUMBER OF CASES CLASS IF I ED INTO GROUP -PREOICTED KLUPE I CYPRIN GAOIGrA PLEURO SCOMRR TOTAL OBSERVFD KLUPEI 95 1 0 3- 1 100 CYPRIN 1 87 0 J. 0 89 GAOIQ4 C 0 66 0 3 69 PLEURO 0 0 1 69 0 70 SCCMBR 0 0 3 0 101 104 : OF CASES CLASS IF I ED INTO EACH GR?UP PREC IC TED KLUPEI CYPRIN GADI'DA PLFIJQQ S C 0 « 3 R IBSGRVHD KLUPEI 95.OC 1 .00 0 .0 3.00 1.00 CYPRIN 1.12 9 7 . 7 5 0 .0 1.12 0 .0 GACIDA 0 . 0 0 . 0 9 5 . 6 5 0 .0 4 .35 PLEURO 0 .0 0 .0 1 .43 98 . 57 0 .0 SCOMBR O.C 0. 0 2.83 0.0 9 7 . 12 T a b l e 1 4 . C o o l e y a n d L o h n e s ' c l a s s i f i c a t i o n m e t h o d i n TO f a m i l i e s ( g r o u p I I ) 9 3 . 6 2 ? OF THE CA,ScS WERff C3RRECTLY CLASS IF IED NUMBER OF CASES CLASS IF I ED INTO GROUP -PREDICTED 6E0THI 7ENGRA 8HI0DC 9CSMER OSALMO 1SCIA E 2SC0RP 3PFRCI 4 SP AR I 5S0UAL TOTAL OBSERVED 6B0THI 3 0 1 0 1 0 C 0 0 0 5 7ENGRA 0 21 0 0 1 0 0 0 C 0 22 8HIO0O 0 0 4 0 0 1 0 1 0 0 6 90SMER . C 0 0 30 0 0 C 0 0 0 30 0S/1LH0 c 0 0 0 33 0 0 0 0 0 33 1SC IAE 0 0 0 0 2 17 c 0 .0 0 19 2SC0PP 0 0 0 1 1 0 39 0 0 0 41 3P&RCI c 0 0 0 2 0 0 5 0 0 7 4S PAR I 0 0 0 0 1 0 0 0 11 0 12 5SQUAL 0 0 0 0 , 0 0 0 0 0 13 13 % Of- CASES C L A S S I F I E D INTO EACH GROUP PRECICTEC 6 BOTH I 7ENGRA 8HI0D0 9'0SMFR OSAIMC 1SCIA E 2SC0RP 3PE.RCI 4SPARI 5S0UAL OBSERVED 690TH1 6 0 . 0 0 0 . 0 2 0 . 0 0 0 . 0 20 .00 O.C C O C C C C 0 .0 7EN6RA 0. 0 95 .45 C O 0 .0 4 .55 0.0 C O C O 0 .0 0 . 0 8HIGD0 0 . 0 0 .0 6 6 . 6 7 0 .0 0 .0 1 6 . 6 7 C O 16 .67 0 .0 C O 9CSKER C O 0 .0 0 . 0 ICO.00 C O 0 .0 C O O.C 0 .0 C C OSALHO 0 . 0 C O C O 0 . 0 100 .00 C O C O C O 0 . 0 0 .0 1SC IAE O.C C O 0 .0 0 . 0 10 .53 8 9 . 4 7 C O O.C C O C O 2SC0RP C C C O 0 . 0 2.44 2.44 0 .0 95 .12 C O C O 0.0 3PERCI 0 . 0 0 .0 0 .0 C O 28. 57 C C C O 71.43 C O 0.0 4 S PARI 0.0 0 . 0 0.0 0 . 0 8.33 0 .0 C O 0 .0 9 1 . 6 7 C O 5S0UAL C C 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 .0 100 .00 T a b l e 15. Cooley and Lohnes 1 c l a s s i f i c a t i o n method i n 15 f a m i l i e s (group I I and group I I ) and C o o l e y and Lohnes' c l a s s i f i c a t i o n method OBSERVED K L L P F I C Y P R I N G 4 0 I D A PLEUNC SCCMBR 6 R 0 T H I 7ENGRA 8HIODO 90SMER OSALMO I S C I « E 2SC0RP 3 P EKCI 4 SPAR I 5S0UAL 9 0 . 4 8 V 0 F THE C A S E S WERE CORRECTLY C L A S S I F I E D NUMBER OF CASES C L A S S I F I E D INTO GROUP -P R E D I C T E D K L U P E I C Y P R I N GADICA PLEUNC 55 0 C 2 0 c 4 0 c 0 3 2 0 0 0 0 8 5 0 0 0 C 0 0 0 0 C 0 0 0 0 1 0 60 1 6 0 0 0 0 2 0 0 0 0 0 0 1 0 57 0 0 0 0 0 0 0 0 0 0 0 * OF C A S E S C L A S S I F I E D INTO EACH GRCUP PREC ICTFD OBSERVED K L U P E I C Y P K I N G4CICA PLEUNC SCOMBR 6 B 0 T H I 7ENGRA 8 H I 0 D 0 9GSMER OS AL MQ I S C I A E 2 S C J R P 3 ° E R C I 4 S PAR I 5 SQJAL SCOMBR 6B0THI 7ENGRA 8HICDO 90SMER OSALMO I S C I A F 2SCORP 3 P E R C I 4S PAR I 5SOUAL TOTAL i 0 1 0 0 2 0 0 0 0 0 100 0 0 0 1 2 0 0 0 0 0 0 89 0 3 0 0 0 4 2 0 0 0 0 69 0 0 0 1 2 0 0 6 0 1 0 70 95 0 0 0 0 0 2 0 0 1 0 104 0 5 0 0 0 0 0 0 0 0 0 5 C 0 1 8 0 0 0 0 0 0 0 0 22 0 0 0 6 0 0 C 0 0 0 0 6 0 0 1 0 28 0 0 1 0 0 0 30 0 2 0 0 0 26 0 0 2 1 0 33 0 0 0 0 0 0 16 0 0 0 C 19 0 0 0 0 0 0 0 39 0 0 0 41 0 0 0 0 0 0 C 0 7 0 0 7 0 0 0 0 0 0 0 0 0 12 0 12 1 0 0 0 0 0 0 0 0 0 12 13 SC0M3R 6B0TH I 7ENGRA 8HI0D0 90SMER OSALMO 1 S C I A E 2SCORP 3 P C R C I 4S PARI 5S0UAL 9 5 . 0 0 0.0 0.0 2.86 0.0 0.0 1 8 . 1 8 0.0 0.0 O.C 1 5 . 7 9 4 . 6 8 0.0 0.0 C C 0.0 9 5 . 5 1 0.0 0.0 0.0 C O 0.0 0.0 0.0 0.0 C O 1.00 0.0 8 6 . 9 6 1.43 5.77 0.0 C O C O 0.0 6. 06 0.0 0.0 0.0 0.0 0.0 C O 1.12 0.0 8 1 . 4 3 l . C C C O 0.0 C O 91 . 3 5 0. 0 0.0 4.35 0.0 0.0 C O 0.0 0.0 C O 0.0 C O 0.0 0.0 7.69 0. 100. 0. 0. 0. 6. 0. 0. 0. 0 00 0 c 0 C6 0 0 0 l . O C C O C O C O 0.0 C O 8 1 . 3 2 C O 2.33 0.0 C. C 1.12 0.0 1.43 0 0 0 100 C C O O.C 0 0 0 00 C 0.0 C. 0 C O 0.0 O.C 0.0 0.0 2.25 0.0 2.86 0.0 0.0 0.0 0.0 53.33 0 .0 C O 0.0 0.0 0.0 0.0 2.00 0. C 5.80 O.C 0 0 C 0 0 70 0 O.C 0.0 0.0 0.0 0 0 C 0 c 79 0 C O 0. 0 2.90 0.0 1 .92 C O 0.0 0. 0. 0. 84. 0. 0. 0. 0. 0 0 0 21 C 0 0 0 C O C O 0.0 8. 57 C O 0.0 0. 0 0.0 3. 33 0.0 0.0 9 5 . 12 0.0 C O 0. 0 0.0 C O 0.0 0.0 0.0 0.0 0.0 0.0 C O 6.06 0.0 0.0 100.00 0.0 C O 0.0 0. 0 0.0 1.43 0.56 0.0 0. 0 0.0 0.0 3.03 0. 0. 0. ICO. 0. 0 0 0 00 C 0.0 C O 0.0 0.0 0.0 0.0 C O 0.0 0.0 C O 0.0 0. 0 0.0 0.0 92.31 o 108 Tab l e 16 Summary t a b l e of the r e s u l t s from the d i s c r i m i n a n t a n a l y s i s and Cooley and Lohnes* c l a s s i f i c a t i o n method (The % of cases were c o r r e c t l y c l a s s i f i e d ) 1 Data Group I (5 f a m i l i e s ) Group I I (10 f a m i l i e s ) Group I and I I (15 f a m i l i e s ) C l u p e i d a e (1*3 s p e c i e s ) C y p r i n i d a e (5 s p e c i e s ) Ga didae (16 s p e c i e s ) P l e u r o n e c t i d a e (22 s p e c i e s ) Sccmbridae (28 s p e c i e s ) Cases 4 3 2 188 620 + _ 100 89 + 69 70 104 D i s c r i m i n a n t Method 93.2 9% 90.96% 90.4 73.0 0f6 76.4 0% 44.93% 24.2 9% + 23.08% C c c l e y and Lohnes' C l a s s i f i c a t i o n 96.76% 9 3.62% 90. 16% 80.00% 87.64% 68. 12% 58.57% 68.27% j Group I : C l u p e i d a e , C y p r i n i d a e , Gadidae, P l e u r c n e c t i d a e , Sccrabridae. Group I I : B o t h i d a e , E n q r a u l i d a e , H i o d o n t i d a e , Osmeridae, Salmonidae, S c i a e n i d a e , S c o r p a e n i d a e , P e r c i d a e , S p a r i d a e , and S g u a l i d a e . 109 16. 5._ Closeness among Families (Cluster Analy.sisX Dendrograph rel a t i o n s h i p s among 15 fa m i l i e s have been examined using c l u s t e r analysis. This method cl u s t e r s cases that have the least distance between them. The two cases closest together are amalgamated and treated as one case and then, i n turn clustered with others. The analyses were based on average values of 7 variables in 15 f a m i l i e s . In Table 17, except for Hiodontidae, Bothidae, Percidae, which have smaller sample si z e s , a l l other fishes show the i r b i o l o g i c a l r e l a t i o n s h i p s . Scorpaenidae r e l a t e s c l o s e l y with Pleuronectidae; s i m i l a r l y Sciaenidae with Gadidae; Clupeidae with Osmeridae; Sgualidae with Scombridae. The r e s u l t shows the eco l o g i c a l a f f i n i t i e s between families rather than the systemic relatedness. 110 Table 17. Dendroqraphic relationships among 15 families C N A S 0 1 i i . i i • 1 2 4 fi 3 3 1 3 0 7 5 5 L C c s P & H P S G s S c E S s A L S c L 0 I E c A p A Y N Q c B U M 0 £ T 0 I D A M P G U 0 E P E R U H D C A I R 0 R I? A M L E R P R I 0 I E D I N I A L B I I A 0 D N P N A 0 I N U I R D D N N A T A I E A D I L D I A A I e E I E D E 0 D I A D AMALf>. DISTANCE * * * * * * * # * * * * * # * 0.701 I I I I - + - T T T T T T T T I 1.125 T I - + - T I I I T I T \ T T 1.138 I I I T T - + - T T T T I T 1 .1 50 I I I - — I I y T I I T 1.133 - + - 1 T T I J T I I I 1 .419 1 I + _ I I T T T T 1.8 61 - - + — T I T I T I T 1.7 30 - + I T T T T T 1.923 T I - + - I I I 2.460 - + T I I ! 2.761 f I J I ft .064 .'- + - -- I I 4. 300 T - + -4 .6 50 + 111 III GENERAL DISCOSSION Comments on Comparative Studies I h i l e gross comparisons, such as t o t a l stock size i n r e l a t i o n to systematic c l a s s i f i c a t i o n , might be useful (e.g. clupeoids seem to have larger populations than perciforms), "dissection" of the population i s l i k e l y to produce more useful r e s u l t s and be le s s misleading, i n the same way that i n t e r n a l anatomy i s more useful than gross morphology i n comparing i n d i v i d u a l organisms. However, to eventually r e l a t e parameters for mortality, maturity and growth to population s i z e , i t w i l l be necessary to make comparisons between these parameters and s p e c i f i c fecundity and recruitment rates. Thus, comparative f i s h population studies are only a t o o l . Methods of analysis provide us with a more p r a c t i c a l common basis. The population parameters, r e l a t i v e characters, and co r r e l a t i v e characters were chosen mainly because either they were variables in the Beverton-Holt y i e l d model or they were of b i o l o g i c a l s ignificance and were re a d i l y a v a i l a b l e . This does not mean that I am f u l l y convinced by the r e a l i t y of parameters used i n the models but the parameters are useful f o r comparative purposes. One of my aims in t h i s study i s to re-examine these population parameters, since they have been used for such a long time. Comparative studies can be used to display the v a r i a b i l i t i e s of 112 population parameters and also to detect the r e a l i t i e s of their usage i n y i e l d models in order to determine i n which dire c t i o n improvement should be pursued so as to eliminate randomly directed t r i e s . In other words, I only f e e l that the r e s u l t s of these comparative studies provide descriptive summaries of data rather than laws of nature, although the res u l t s might indicate of the l a t t e r . There are, however, three q u a l i f i c a t i o n s which must be made. One ari s e s out of the dependence upon published data. A considerable element of personal judgement i n evaluation and interpretation of the o r i g i n a l papers was necessary, especially as the true meaning of parameters in th e i r o r i g i n a l context was not f u l l y defined; for example, maturity determination (size at f i r s t maturity or c r i t i c a l s i z e ) , body lengths (total length, fork length, or standard length) and the problem for estimation of parameters. The second q u a l i f i c a t i o n i s that some degree of selection of examples has been unavoidable although I have kept t h i s to a minimum. For example, most of the references used i n t h i s studies are mainly based on the papers i n English, i n Chinese, and in Japanese. Since one of the p r a c t i c a l objects i s to make predictions, based on many areas, to f i s h stocks i n other areas for which data are lacking, references from a l l over the world ( i . e . in other languages) have tc be taken into consideration. Therefore, references i n ether languages l i s t e d in the publications from the Food and agriculture Organization of the United Nations (e.g. cited 113 in t he f i s h e r i e s synopsis) or cited by the well-known scholars were considered. The thi r d i s a re s u l t of the fcias of available data which are taken mainly from f i s h e r i e s biology studies. These data tend to be based more on economically important, large fishes than on the smaller representatives of the family. Therefore, the c a l c u l a t i o n of mean value for each family i s larger than the mean value would be. For example, the calculated mean value of the asymptotic length i n Cyprinidae (estimated from ocly 7 species) i s much too large for most of the sttall c yprinids. These d i f f i c u l t i e s created by the personal inter p r e t a t i o n , data s e l e c t i o n , and biased samples should not, however, hamper attempts to formulate hypotheses which may unite into a single entity the considerable volume of accumulated data pertaining to f i s h populations. Organizing the data required to test hypotheses i s a massive project - and i t i s only the great amount of available data peculiar to f i s h e r i e s biology which Bakes possible the u t i l i z a t i o n of comparative methods. This, i n i t s e l f , i s an ind i c a t i o n of the large amount of research reguired to compile and analyze the banks of data. Every population parameter was estimated from thousands of specimens by diff e r e n t authors, and about one thousand diff e r e n t papers were u t i l i z e d . Therefore, t h i s research i s based on information from millions of specimens which comprise many f i s h stocks i n various areas of the world. 114 l i 2i££JissioB of the Jesuits 1. Population parameters: A major concern of population dynamics has always been to determine, by experiment and observation, those parameters of populations dealing with n a t a l i t y and mortality, so as to be able to assess the capacity of populations to increase numerically with time. By considering biomass and y i e l d , growth i s brought into focus. Therefore, the population parameters are divided into three groups for discussion: §1 Mortality M: Present estimations of the natural mortality c o e f f i c i e n t are too imprecise to be s a t i s f a c t o r y in properly evalulating t h i s important component of population dynamics. Host authors give such a wide range of sampling error as to make t h e i r r e s u l t s useless. Also, r e s u l t s are freguently inconsistent with information from other sources or s i m i l a r populations. An alternative method, using an alternative parameter (e.g. T95) or a function (by adding other factors) must be found to estimate adeguately the death rate of f i s h populations. Es p e c i a l l y i n short-lived species, a 115 constant mortality rate i s not a p a r t i c u l a r l y useful measure where mortality i s highly age-specific. I t i s only s a t i s f a c t o r y in species with longer l i f e spans which have a nearly l i n e a r s u r v i v a l curve. T95: T95, as an index of ' l i f e span*, suggested by Taylor (1959) i s the age at which f i s h reach 95% of their asymptotic length. The reason for using t h i s instead of the r e a l l i f e span i s because the maximum age recorded is highly dependent on the sample size (Beverton 1963). Different sample sizes tend to provide di f f e r e n t maximum ages. TS5 (calculated from K and LINF) i s a theoretical age, for comparative purposes, although the l i f e span i s determined by mortality, not the rate of growth. Engraulidae has the shortest T95 (2.4 years) and Sgualidae has the longest T95 (57.8 years). But, for certain kinds of fishes (e.g. Salmonidae, T95 i s calculated to be 24 years - which i s obviously un r e a l i s t i c ) so we must also take fecundity information into consideration i n order to better estimate the " l i f e span*. b l growth The reason f o r choosing growth parameters from the von Bertalanffy growth eguation instead of from other growth eguations [although some short-lived f i s h e s show a growth pattern as described by Parker and Larkin (1959) '], i s mainly 116 because i t i s most widely used in f i s h population studies, especially for comparative purposes. This si t u a t i o n c a l l s for the need to develop a more generalized equation, using a more widely applicable form of the growth curve. Length i s used as the measure of body s i z e rather than weight because growth i n length nearly always follows a simple curve without an i n f l e c t i o n . Besides, stcBach contents and gonad maturation contribute to greater variation i n the estimation of weight. K: The growth parameter K i s a r e l a t i v e l y stable character. The higher the K value, the sooner the f i s h reach th e i r asymptotic length. This also implies that the f i s h has a short l i f e - s p a n , among shoaling pelagic fish e s , Engraulidae has an extremely high K value (1.6) , followed by Clupeidae and Osmeridae (both are over 0.4), and then, the large pelagic f i s h : Scombridae (0.347). It seems that Sciaenidae (0.345) and Salmonidae (0.3 29) are s i m i l a r to large pelagic f i s h . Host of the demersal fishes have r e l a t i v e l y lower K values (below 0.3). In p a r t i c u l a r , the ca r t i l a g i n o u s Sgualidae have the lowest K value (0.07). LINF: The asymptotic length i s a t h e o r e t i c a l value showing the largest s i z e that a f i s h can a t t a i n . This has a r e l a t i v e l y l a r g e r variation but i s s t i l l very stable within f a m i l i e s , a f i s h with a longer LINF (e.g. 117 Scombridae) has a faster growth rate only i f i t also has a larger K value. Sgualidae has a very large IINF but a very low K value. Engraulidae has an extremely high K value but the lowest LINF, so that they are not the fast growing f i s h e s . The growth rate i s dependent on both K and LINF, as shown by the following mathematical expression, after Gulland (1969), d l — = K (LINF - 1) where 1 i s the length of f i s h dt The length of a f i s h at age 1 i s used to i l l u s t r a t e the speed at which f i s h can grow during the f i r s t year. Part of the reason for choosing t h i s character i s to indicate p a r t i a l l y the fis h a b l e size of f i s h . L1 i s a f a i r l y stable character. My r a t i o n a l i z a t i o n for i t s variation i s that fishes spawn at d i f f e r e n t times of the year. The exponential c o e f f i c i e n t of the weight-length relationship i s the most stable parameter, although there are the least data for i t . The von Bertalanffy growth eguation assumes that catabolic processes are proportional to the weight of the f i s h , whereas anabolic processes are assumed to be proportional to the surface area or to the two th i r d s power of the weight, but i n practice t h i s relatonship freguently does not hold. This can be shown by the fac t that b 118 values fluctuate around 3. Marr (1960) indicated that the weight of f i s h at a given length varies inversely with stock s i z e . I f t h i s i s a result of competition for food i t can be proved by induction of changes i n the weight-length r e l a t i o n by a l t e r i n g the diet i n an experiment. In other studies of f i s h growth, Eddy and Carlander (1940) found a c o r r e l a t i o n between the length of the growing season and the growth rate f o r a number of species. Gerking (1966) investigated populations of b l u e g i l l sunfish i n eight Indiana lakes and found that the length of the growing season varied between the d i f f e r e n t lakes and that the most rapidly growing f i s h occurred i n the lakes with the longer growing seasons. Investigations have also shown that both of the von Bertalanffy c o e f f i c i e n t s , K and LINF, are influenced by temperature. For example, cod growth parameters have been determined throughout t h e i r geographical range, and correlated with surface water temperature by Taylor (1558). Holt (1959b) then suggested that K ought to increase logarithmically with temperature, and that LINF should decrease slowly with temperature. Kinne (1960) did a more direc t investigation of the ef f e c t of temperature on f i s h growth. He kept f i s h at d i f f e r e n t temperatures i n aguaria and his r e s u l t s show how the length at age of i n d i v i d u a l f i s h varied with temperature. The growth parameters are l i n e a r l y related to temperature, but only within a certain 119 range, when plotted on a semi-logarithmic basis. a l l these findings suggest that growth of f i s h i s highly correlated with environmental f a c t o r s , e s p e c i a l l y with water temperature. Eddy and Carlander (1940) suggested that heredity could account for differences i n growth rate between species and races but that within species i t i s the environment that i s important i n determing the growth rate. Therefore, one of my furture approaches w i l l be to analyse the c o r r e l a t i o n between growth parameters and water temperature in d i f f e r e n t f i s h e s . In so doing, I hope I can add another variable, the water temperature, into the growth eguation. £l maturity. K fecundity, and recruitment In a study of the natural regulation of animal numbers. Lack (1946) concluded that the reproductive rate was determined by natural s e l e c t i o n ; in p a r t i c u l a r , the clutch size that i s selected i s the one which maximizes the number of offspring per female that survive u n t i l maturity. In f i s h e s , the same re s u l t i s generally achieved with very high fecundity. Information regarding recruitment i s one of the biggest problems i n f i s h e r i e s biology. The formal study of stock and recruitment started with Bicker's (1954) paper, followed by Bicker (1958), Larkin, Baleigh, and Wilimcvsky (1964), Cushing (1971, 1973), Cushing and Harris (1973), and Larkin (1973). However, because recruitment i s highly variable, and since i t may take a considerable time to show 1 2 0 that the downward trend i s a re s u l t of overfishing, i t may be too l a t e for decisive action to be taken. Even so, Cushing (1977) suggested that i n the future i t may appear that management decisions based on the growth model were precise but prone to f a i l u r e , whereas those based cn a recruitment model were les s accurate but more h e l p f u l . To estimate best y i e l d s from multi-aged stocks i t i s obviously desirable to combine stock-recruitment rel a t i o n s h i p s with a y i e l d - p e r - r e c r u i t analysis. Some progress has been made in t h i s d i r e c t i o n . Beverton and Holt (1957) proposed a "self-regenerating" model that combines a recruitment curve with t h e i r y i e l d - p e r - r e c r u i t analysis. Halters (1969) formulated an a n a l y t i c a l combination model that can handle any recruitment curve and any type of growth, and he also developed the corresponding computer programs. Silliman (1967,1969) developed a f i s h population model with an analog computer program which consists of the growth pattern, stock-recruitment r e l a t i o n s h i p , and mortality rate. None of these proposals, however, has been used very much with actual stocks. Thus, there are no available population parameters for recruitment which can be universally applied., Larkin (pers. comm.) suggests more recruitment information should be considered in the comparative f i s h population studies. Not only are the parameters of recruitment highly variable, but the sample sizes for these parameters are very limited. Although I have collected some fecundity data, useful analysis of these data 121 i s complicated by the variation between fis h e s i n spawning freguency. Therefore, I could not find any reproduction parameters other than the size at f i r s t maturity (LM) and the age at f i r s t maturity s i z e (TM) for comparative studies. LM: The s i z e at f i r s t maturity i s a very stable parameter. I t s v a r i a t i o n i s partly due to the inaccuracy of quoted maturity sizes (either the c r i t i c a l s i z e at which 50% of the f i s h have reached maturity or the mean length attained when f i r s t spawning begins). Maturity i s a factor l i k e l y to influence f i s h growth, because, after the onset of maturity, energy that might have been used for growth w i l l be required f o r gonad maturation and i n some cases, for making spawning migrations. Growth can be expected to be more reduced after the onset of maturity than i t otherwise would have been. However, the amount of energy the f i s h expends i n reproduction i s an unknown quantity. Not only i s i t d i f f i c u l t to estimate the fecundity of f i s h , but information about spawning freguency i s also reguired. An a l t e r a t i v e method might be to compare the difference of the K values before and after the s i z e at f i r s t maturity. The di f f e r e n t K values w i l l indicate d i f f e r e n t strategies of f i s h e s i n u t i l i s i n g t h e i r energy for growth and/or reproduction. TM: The age at f i r s t maturity has more var i a t i o n than LM. 122 This may be attributed to fewer samples. A few authors determined TM by detecting *spawning checks' on scales without even knowing the size at f i r s t maturity. In conclusion, the most complete information i s available for growth. Improvement of the growth model should take the environmental factors such as water temperature into consideration. The only stable and r e l i a b l e parameter related to n a t a l i t y i s the size at f i r s t maturity. He have to emphasize the development of techniques to better estimate the fecundity of f i s h , e s p e c i a l l y with respect to recruitment. The estimation cf mortality would s i m i l a r l y be affected, p a r t i c u l a r l y for the short-lived species. The suggested mean and i t s 95% confidence l i m i t for each parameter for d i f f e r e n t families are l i s t e d i n Table 3. This can provide the information for other f i s h stocks where data are lacking. These variat i o n s are always certainly wider than the true ranges, as pointed out by Holt (1959a), because the accuracy of any p a r t i c u l a r parameter estimate i s rather lew. 2. Belative characters ( r a t i o s ) : M/K The M/K r a t i o , a most important character and one which has been strongly recommended by Holt (196 2), not only because of i t s evolutionary meaning but also because i t i s necessary for the s i m p l i f i c a t i o n of c a l c u l a t i o n i n 123 t h e y i e l d e q u a t i o n , shows a g r e a t d e a l o f v a r i a t i o n . T h i s l a r g e v a r i a t i o n i s due t o i n a c c u r a c i e s o r u n c e r t a i n t i e s i n t h e v a l u e o f M. That M/K has l e s s v a r i a t i o n than e i t h e r M or K, as sug g e s t e d by H o l t (1962), has not been shown by my r e s u l t s . But c e r t a i n t r e n d s a r e e v i d e n t i n the f o u r f a m i l i e s a n a l y s e d : f o r C l u p e i d a e H i s n e a r l y e g u a l t o K; f o r Gadidae H i s about 2 t i m e s K; f o r P l e u r o n e c t i d a e M i t i s about 3 t i m e s K, and about 5 t i m e s K f o r Salmonidae. These M/K r a t i o s f u r t h e r c o n f i r m e d B e v e r t o n and H o l t ' s (1959) s u g g e s t i o n s t h a t " t h e r e l a t i o n between H and K appears t o d i f f e r from one group o f f i s h t o a n o t h e r ; f o r c l u p e o i d s , fl i s g e n e r a l l y between one and two t i m e s K, f o r g a d i f o r m s M i s between two and t h r e e t i m e s K. M. Thus i t becomes t h e o r e t i c a l l y p o s s i b l e t o undertake s t o c k assessment f o r taxonomic groups w i t h o u t a s e p a r a t e e s t i m a t i o n o f M and K. L8/LINF The r a t i o LM/LINF shows t h e l e a s t v a r i a t i o n of the r a t i o s . T h i s c h a r a c t e r p a r t l y i n d i c a t e s the energy spent by t h e f i s h f o r r e p r o d u c t i o n . The h i g h e r the v a l u e o f t h i s r a t i o , the more energy the f i s h spends f o r r e p r o d u c t i o n a f t e r i t r e a c h e s i t s f i r s t m a t u r i t y s i z e (LM). The range of mean v a l u e s i s from 0,41 (Scorpaenidae) t o 0.89 (Osmeridae). These r e f u t e H o l t ' s (1959b) s u g g e s t i o n t h a t t h e r a t i o f o r many s p e c i e s c o u l d be about 0.6 t o 0.7 ( i . e . two t h i r d s o f 124 asymptotic length). Beverton and Holt (1959) suggested that short-lived f i s h also have a higher LM/LINF value. In the study, t h i s was found to be true for Engraulidae and Clupeidae, but not for Salmonidae and Sciaenidae. Holt (1962) also suggested a correlation between LM/LINF and K, v i z . that f i s h which grow rapidly towards t h e i r asymptotic size (high K value) mature at a size which i s larger, r e l a t i v e l y to that asymptote, than that of f i s h which approach the asymptotic si z e r e l a t i v e l y more gradually (low K value). This i s true from my calculations i n s p i t e of an apparent inverse li n e a r relationship between K and LINF and a positive l i n e a r relationship between LM—-LINF. (Refer to Tables 3, 4, and 5). The r e l a t i o n s h i p between LM/LINF and K should be investigated in order to expose the underlying b i o l o g i c a l meaning of energy spend strategies. L1/LINF The r a t i o L1/LINF shows a great deal of variation. This i s due to the large v a r i a t i o n of L1. The reason fo r chosing t h i s character i s to p a r t i a l l y indicate the lc/LINF r a t i o , which i s necessary for s i m p l i f i c a t i o n of the y i e l d c a l c u l a t i o n . Thus, i f L1 i s egual to the fishable s i z e , L1/LINF w i l l be the same as lc/LINF. This also indicates the growth strategy of f i s h . For example: Engraulidae grow to four f i f t h s of t h e i r maximum siz e during t h e i r f i r s t year but Cyprinidae can 125 only grow to one tenth of t h e i r maximum size. TM/T95 The r a t i o TM/T95 also shows a great variation. The absence of worthwhile r e s u l t s from t h i s r a t i o may be due to i n s u f f i c e n t TM data. The smaller the proportion of time remaining after the f i s h reaches maturity, the greater energy the f i s h has spent i n reproduction, figain, Engraulidae has the highest r a t i o here., T50/T95 For almost a l l fishes (except ovoviviparous Sgualidae) the r a t i o T50/T95 i s around 0,23.,This indicates that most f i s h grow to half their asymptotic length before they have reached one quarter of t h e i r entire l i f e span. The r e l a t i v e characters sometimes display rather more bi o l o g i c a l meaning than the i n d i v i d u a l parameters. For example, compared with other fis h e s , Cyprinidae has a r e l a t i v e l y high TM (5.69 y r s ) , but a very low TM/T95 r a t i o (0.19). This explains why carps only take one f i f t h of their l i f e span to reach maturity i n order to sustain the species i n the unstable freshwater environment. The suggested mean and standard errors of the means for each r a t i o i n different families are l i s t e d i n Table «|. This table supplies very useful information when these values are substituted into the s i m p l i f i e d y i e l d equation (equation 20 p.13) 3. Correlative characters: 126 There are s i g n i f i c a n t linear regression relationships in a l l of the f a m i l i e s (except Sciaenidae) between 1/K— LINF, between LM—LINF, and between M—K. This means that f i s h having a larger LINF also have a larger LM, and lower K and M values. This supports Beverton and Hclt*s (1959) suggestion that a f i s h with a high K value i s associated with both a low maximum length and a high mortality. This r e l a t i o n holds for both i n t r a - f a m i l y and i n t e r - f a m i l i e s comparisons. One p a r t i c u l a r d i f f i c u l t y i n interpreting apparent inverse relationships between LINF and K i s that i n determining these parameters from curves, a chance ro t a t i o n of the regression l i n e of lt+1 on I t (Walford method) which would tend to increase K would always decrease LINF, and vice versa. The intra-species and inter-species relationships indicated above could not, however, have teen generated i n t h i s way. My r a t i o n a l i z a t i o n for the difference between the slope of the regression l i n e (Figure 16 to 20) and the r a t i o (Table 4) that the slope may show the c h a r a c t e r i s t i c s of the whole family while the r a t i o represents the average ch a r a c t e r i s t i c s of the species in the family. Therefore, the greater the difference among species the larger the variation for the family. It i s very disappointing that the r e s u l t s did not show a s i g n i f i c a n t l i n e a r c o r r e l a t i o n between 1/M—T95 because i t could reasonably be expected that f i s h having a longer l i f e span (T95), would also have a lower natural mortality rate 127 (M). The I d e a of using T95 instead of M to indicate the mortality rate has not been shown to be f e a s i b l e . Osing the P-test and the appropriate t-test as a basis for comparison of means and standard errors of the means i t i s evident that there are s i g n i f i c a n t differences between families i n most cases. This confirmed one of my hypotheses, namely, that differences e x i s t from phylogenetic considerations. Since t h i s method only takes one variable at a time, although four characters were considered (Table 7), the attempt to group families into patterns f a i l e d by t h i s method. I t would appear that by comparing the more stable characters such as growth parameters (K, LINF), LM, and the r a t i o LM/LINF, the families can be divided into groups as follows: A) Shoaling pelagic fishes - Engraulidae, Clupeidae, and Osmeridae. These fishes have the highest K values (1,6 for Engraulidae, over 0.4 for the others), the smallest LINF and LM, as well as a very high LM/LINF r a t i o (over 0.7). They reach t h e i r asymptotic length very quickly and also spend a qreat deal of energy in reproduction. This r e s u l t s in t h e i r lower LM and LINF values, B) Large pelagic fishes - Sccobridae have a f a i r l y high K value (around 0.35) and the largest LINF. These f i s h 128 reach t h e i r asymptotic length r e l a t i v e l y quickly and grow to a very large s i z e . This kind of f i s h would constitute the most valuable f i s h protein resource. C) Demersal fishes - Gadidae, Pleuronectidae, Scorpaenidae, Sparidae etc. They have lower K values (less than 0.25), intermediate LINF (smaller than large pelagic f i s h e s , but bigger than shoaling pelagic fishes) , and lower LM/LINF r a t i o s (less than 0.6). They have r e l a t i v e l y longer l i f e spans. They grow slowly and spend less energy, but over a longer period, i n reproduction. D) Freshwater fishes - Cyprinidae have K and LINF values which are close to these of the demersal f i s h e s , but have a smaller LM and, especially, the lowest LM/LINF (0.4) and TM/T95 (0.2) r a t i o s . The f i s h s t a r t s reproduction at a very early stage. Due to the unstable freshwater environment, the freshwater fishes would have a high mortality rate (0.625 for Cyprinidae, the second highest among 15 families next to Salmonidae). In order to sustain the species, the f i s h has to start reproduction at a very early stage. This shows in the lowest LM/LINF and TM/T95 r a t i o s . 6. C l a s s i f i c a t i o n methods make i t possible for researchers who have data based on common measurements from many populations 129 to demonstrate the v a l i d i t y of the measurements for predicting membership i n the populations. This gives r i s e to discriminant analysis, the general object of which i s to fi n d rules of behaviour in the assignment of i n d i v i d u a l cases to predetermined classes with optimal properties. The c l a s s i f i c a t i o n function in Table 11 defines the predetermined classes. The larger the absolute value of canonical variable (Table 12) for the population parameter, the greater the influence of c l a s s i f i c a t i o n . Therefore, the most useful characters for c l a s s i f i c a t i o n i n 15 families are, i n decending order, L1, TH, b, K, M, LM, and LINF. The f a m i l i e s with small sample sizes such as Bothidae (n=5), Hiodontidae (n=6), and Percidae (n=7) made re s u l t s hard to interpret. This again shows the importance of large sample sizes in these kinds of comparative studies, among 5 major families shown in Table 15, i t seems that the greater the number of species in the fa m i l i e s , the lower the c l a s s i f i c a t i o n power. There are differences between the canonical variables in d i s c r i n i n a n t analysis (Table 12) and the normalized canonical variables in cooley and Lohnes* c l a s s i f i c a t i o n method (appendix 2). It seems that the fcrmer method emphasises one parameter at a time, while the l a t t e r method consider parameters more evenly. Dendrograph relationships based on 7 population parameters among 15 f a m i l i e s have been surveyed using c l u s t e r analysis. S t r i c t l y speaking, t h i s r e s u l t i s not precise enough to show 130 population patterns. The main reason for t h i s i s the use of only the mean value for each group without taking the variation of parameters into consideration. The program of UBC BHD P 2 H cannot handle such a large sample i f i t i s based on i n d i v i d u a l cases instead of groups (f a m i l i e s ) . In conclusion, comparative population studies are hiqhly dependent on sample sizes. The results cf the comparison among species within families show that more supportive data i s required. This being the case, i f we want to better understand and estimate population s i z e , more attention should be paid to the population l e v e l , e s p e c i a l l y n a t a l i t y and mortality. I t can not be said which parameter i s best when based on a single analysis. Population parameters have d i f f e r e n t weights with d i f f e r e n t analyses. For example, i f we consider s t a b i l i t y or r e l i a b i l i t y , K, LINF, and LH are very useful characters, especially by using them tc group f i s h into d i f f e r e n t ecological patterns. But, i n terras of c l a s s i f y i n g fishes into systematic groups, L1 and TH have more s i g n i f i c a n t meaning. Therefore, comparative f i s h population studies not only depend on sample siz e s , but also r e l y on the number and kinds of population parameters employed. 1 3 1 future Studies My findings suggest that more worthwhile re s u l t s are to be obtained from a greater volume of data. In order to undertake comparison among species, comparison between sexes, and comparison among di f f e r e n t stocks of the same species, the c o l l e c t i o n of data then, i s an ongoing and necessary exercise. For example, only by having enough data, can one prove or disprove Holt*s (1959a) suggestions that "In most fishes, i t seems that when there are i n t r a - s p e c i f i c sexual differences, these are that K and M i s higher and LINF i s lower in males than in females". Modification of the single species model to a multi-species •family* y i e l d model based on family s t a t i s t i c s should be undertaken. Also, introduction the v a r i a t i o n of these parameters into the y i e l d model should be considered i n order to detect the s e n s i t i v i t y of the model to such variation. The cause for variation of characters very l i k e l y encompasses environmental factors, p a r t i c u l a r l y water temperature. But, t h i s approach i s hampered as most papers do not mention v e r t i c a l d i s t r i b u t i o n s of fishes. I t seems the only alternative i s to use surface water temperature. 132 However i t would be of great value i f a l l workers endeavoured to obtain data on v e r t i c a l d i s t r i b u t i o n s when c o l l e c t i n g information. Aside from surface water temperature, an additonal method for examination of the faunal pattern i s the comparison cf geographical variations with species for which there are suitable data. Eoth growth parameters (K and LINF) and size at f i r s t maturity (LH) are f a i r l y stable. However, to re l a t e the mortality, maturity and growth parameters eventually to population size, i t w i l l be necessary to make comparisons between these parameters and s p e c i f i c fecundity and recruitment rates. These can serve as indexes for grouping fishes into d i f f e r e n t categories with d i f f e r e n t strategies for spending t h e i r energy i n growth and/or reproduction. I w i l l endeavour to employ additional a n a l y t i c a l methods to datermine the patterns for di f f e r e n t strategies of energy u l t i l i z a t i o n . It w i l l be easier to find population patterns by using the most stable characters. But, i n terms of applying the res u l t s to f i s h e r i e s models, the in t e r r e l a t i o n s h i p s between (or among) parameters s t i l l need to be established even though they show great v a r i a t i o n (e.g. M, T95, etc.). Perhaps by obtaining larger sample sizes the variation can 133 be neutralized. Once t h i s i s done, I can induce the ch a r a c t e r i s t i c s of population parameters and, at the same time, deduce b i o l o g i c a l meaning with mathematical support. For example, for the M/K r a t i o s u f f i c i e n t f i s h must reach maturity for s u r v i v a l of the population. Even i f M sere determined by predaticn, the f i s h should have a high K value, which i s a re s u l t of natural s e l e c t i o n , in order to reach the breeding state. The best r e s u l t s I can obtain are from growth parameteters, and they do e x i h i b i t s i g n i f i c a n t differences among fam i l i e s . This being the case, i t may be possible to consider applying d i f f e r e n t population growth strategies of r and K selection. 134 VIII CONCLUSIONS For i n d i v i d u a l parameters, the weight-length exponential c o e f f i c i e n t <b) i s the most stable character. The siz e at f i r s t maturity (LM), the age at f i r s t maturity (TH), the growth parameters (K and LINF) , and the length at age 1 (L1) are f a i r l y stable characters. The natural mortality c o e f f i c i e n t (M) and the age on reaching 95% of asymptotic length (T95, as an index of l i f e span) show large variation. The suggested means and standard errors for each parameter in 15 major fa m i l i e s are l i s t e d in Table 3. This provides the information for other f i s h stocks where data are lacking. For r e l a t i v e characters ( r a t i o s ) , the LM/LINF i s the most stable character although the T50/T95 r a t i o shows the least variation. The other three characters: M/K, L1/LINF, and TM/T95 show large v a r i a t i o n . The suggested means and standard errors for each r a t i o i n 15 families are l i s t e d i n Table 4. For c o r r e l a t i v e characters, there are s i g n i f i c a n t l i n e a r regression relationships (between 1/K—LINF, between LM— LINF, and between M—K) i n a l l of the f a m i l i e s except Sciaenidae. This means that a f i s h having a greater LINF also has a larger LM, a lower K and a lower M. There are no s i g n i f i c a n t l i n e a r c o r r e l a t i o n s between L1—LINF and between 135 1/M--T95. By comparing four stable characters together (K, LINF, LM, and LM/LINF) I can divide the families into groups: A) Shoaling pelagic fishes - Engraulidae, Clupeidae, and Osmeridae. They have the highest K (1.6 for Engraulidae, over 0.4 for the others), the smallest LINF, LM, and a very high LM/LINF ratio (over 0.7). B) Large pelagic fishes - Scombridae, has f a i r l y high K value (around 0.35) and the largest LINF. C) Demersal fishes - Gadidae, Pleuronectidae, Scorpaenidae, Sparidae etc. They have lower K value (less than 0.25), intermediate LINF s i z e , and lower LM/LINF r a t i o s (less than 0.6). D) Freshwater fishes - Cyprinidae has K and LINF values which are s i m i l a r to those of the demersal f i s h e s , but has a smaller LM and, especially, the lowest LS/LINF (0.4) and TM/T95 (0.2) r a t i o s . By using the F-test and the appropriate t - t e s t as a basis for comparison of the mean and the standard deviation, of four population parameters (K, LINF, T95, and LM) , 136 s i g n i f i c a n t differences are found between families i n most cases. Although the attempt to group families i n t o patterns by t h i s method f a i l e d , i t does confirm one of my hypotheses; namely that differences between fa m i l i e s , as shown by population parameters, exist from phylogenetic considerations. 6. Stepwise discriminant analysis based on 7 population parameters (M, K, LINF, LH, TH, L1, and b) were conducted in group I (among 5 fam i l i e s , which have very large sample s i z e s ) , group II (among 10 fa m i l i e s which have f a i r l y large sample s i z e s ) , and also with group I and group II combined (15 families altogether). Over 90% of the cases when considered independently were corre c t l y c l a s s i f i e d i n a l l of the analyses. 7. Cooley and Lohnes* c l a s s i f i c a t i o n method was also u t i l i z e d and i t gave even better r e s u l t s . The analyses were conducted among species within 5 major families (Clupeidae, Cyprinidae, Gadidae, Pleuronectidae, and Scombridae). Correct c l a s s i f i c a t i o n ranged from 58.6% (Pleuronectidae) to 87.6% (Cyprinidae). 8. A dendrograph based on 7 population parameters among 15 families using c l u s t e r analysis shows the ecological relationship to be more s i g n i f i c a n t than the phylogenetic relationship among fa m i l i e s . 137 I X L I T E R A T U R E laser), O. 1952. F i s k e r i d i r . Skr. Havundersok. 10 (2) . (quoted in Beverton and Holt 1959) Aasen, 0. 1961. Pigghaundersokelsene, Fiskets Gang, 47 Arg. Nr.2, p.36-44. (age-length data quoted in Holden and Meadows 1962) Ahlstrcm, E. H. 1960. Synopsis on the biology of the P a c i f i c sardine (Sardinops caerulea). In Proc. world s c i . meeting on the b i o l . of sarine and related species. 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Spec. s c i . Bep. 0. S. F i s h . W i l d l . S e r v . - F i s h . 150, 30p. Z a w i s z a , J . 1951. The growth r a t e o f bream, b a r b e l , Vimba yjmba and white-bream i n the middle reaches cf t h e V i s t u l a near S a r s a n . ( P o l i s h , En and Ru summary). Bocz. Nauk r o l n . 57:237-271. Zhukcv, P. I . 1958. Ryby b a s s e i n a Nemana. Minsk, I z d a E e l o r u s k o i SSR, 191pp. 179 Zukowski, C. 1972. Growth ana m o r t a l i t y of a t l a n t i c a r g e n t i n e , A r g e n t i n a s i I u s A s c a n i u s , on t h e Nova S c o t i a Banks. ICNAF Bes. B u l l . 9:109-115. X APPENDICES C O o Appendix 1. Mean values, 95% confidence l i m i t s , ranges and sample sizes of i n d i v i d u a l parameters among 28 families (group III) 1: Acipenseridae 11: Cottidae 21: Poecilidae 2 : Ammodytidae 12: Dasyatidae 22 : Polynemidae 3: Anguillidae 13: Drepanidae 23: Pomacentridae 4: Aplochitonidae 14: Embiotocidae 24: S i l l a g i n i d a e 5 : Argentinidae 15: Gasterosteidae 25: Soleidae 6 : Atherinidae 16: Hexagrammidae 26 : Stichaeidae 7: Blennidae 17: Ictaluridae 27: Syngrathidae 8: Callionymidae 18: Istiophoridae 28: Trachipteridae 9 : Carangidae 19: Lujanidae 10 : Cichlidae 20: Nemipteridae 18 si 8 a a i i • M a i a a a 3 B & S s 2 a 8 Q ° W1 >Qi3tVWd fl ' ru 0 (a a a a a a a j 5 a I •a " a 2 ? ru a Fi fl •8 •a 3 •a •a •a ?5 •a i i <s 182 -5)—r-ii 6 i a a ? « a ?S ?! a 8 I s K i a 3 -j ?s ?! a a a C M X •rH G CD Ci I 3.o e.« C I Y i-a N r S t 1-5 o - a . 0-6. c a . O.Q C 1. E. 3. 4. S- B. 7. 6- 9-10-11.12.l3.i«.i5.1fi.i7-lB.ia.eb.Ei.S.S-E4.3-ai-E7.Sa. FAMILY I e r a , . C S 7 e s t . J \ 0-3Q. D o - 3 a , M o - i a . o - i a . C O G O-CO, 0. 1. t - 3. 4. S. 6. 7. 6. S.10.11.1E-13-14.1S.16-17.IB.13.20-21.E2.23.24.2S.2S .27.2a. FAMILY I O.BCL 0-77 CBa. 0-60 •J- nl 5-3 3-EGl j-cal o - o o s 1 * ' B > 3 " * • s " c * 8. a - i o . i l . 1 2 . 1 3 - 1 4 . 1 5 . l f i - I 7 . i . i . 2 0 . 2 1 . £ E . E 3 . 2 4 . E 5 . e r i . 2 7 . a 3 . O-SO. o - s a . 0.4Q , § 0-42. p 0-3S S o . a a . 51 0-B4 . c i a . o - i a . c o s . - o - o a _ U E ' 3 " *' 3- 6. >. B- C 1 0 . 1 1.i2.i3 . 1 4 . 1 S . 1 6 . 1 7 . i B . j l 3 . i . i . 2 2 . S 3 . 2 l 4.E3 . 2 S . i FAMILY I Appendix 2. Mean values, 95% confidence l i m i t s , ranges and sample sizes of cor r e l a t i v e characters among 2 8 families (group I I I ) 184 Appendix 3. Summary t a b l e of Cooley and Lohnes' c l a s s i f i c a t i o n method C O O L t Y £ LCHNES C A L L Tt-E C A N C M C A L V A R I A B L E S D I S C R I M I N A N T F U N C T I O N S . T HEY PPRFPR TC NflRM AL I ZF THESE E I GL : N-VECTOR S TO UN IT MAGNITUDE. AN A R B I T R A R Y - 1 . 0 IS INCLUDED IN THE RFNORMAL IZAT ION O R I G I N A L I 2 3 V A R I A B L E M C. 1 8 4 8 6 0 . 3 7 6 1 8 0 . 2 9 3 8 4 K 0 . 1 7 C 2 4 F - C 1 -c. 892 28 - 0 . 8 7 2 9 4 E - 01 L I N F 0 . 3 4 1 8 5 5-C3 - 0 . 5 4 9 6 4 F - 0 3 0 . 2 0 5 C 0 C - 03 LM C. 4 9 2 Z C E - C2 c. 2 7 9 2 4 ^ - 0 2 0 . 1 2 5 C O E - 02 T v. - 0 . 2 1 7 2 3 E - CI c. 2 0 1 4 4 - 0 . 1 5 4 1 0 F - 01 L l 0 . C I 0 . 9 3 5 6 9 = - 0 2 0 . 2 6 9 2 5 F - 02 B - 0 . 9 8 1 2 2 c. 1 4 C 9 5 0 . 951 73 I 2 3 GROUP K L U P c I - 2 . 3 6 6 7 0 . 7 3 3 4 7 3 . 1241 C Y P R I N - 2 . 7 6 5 2 1 . 7 6 3 0 3 . 2 5 8 0 GAD IDA - 1 . 9 7 3 8 1 . 2 7 0 9 2 . 9861 PLEUNC - 2 . 7 2 6 7 1 . 6 1 9 2 3 # 0 8 6 1 SCOMciR - C . 6 3 0 2 2 1 . 3 7 1 0 • 3 . 1740 6 Q 0 T H I - 2 . 1 4 6 4 1 . 1 1 6 1 — * C424 7ENGRA - 2 . 3 3 3 6 - C . 5 3 0 2 4 2 . 9 4 8 4 8 H I C J 0 - 2 . 4 4 C 1 1 . 5 0 4 7 3 . C326 ^ O S M i R - 2 . 9 4 6 8 C . 9 1 4 0 4 3 . 2 530 OSALMO - 2 . C 8 1 6 1.3 215 3 . C897 1 S C I A E - 1 . 9 4 9 4 C . 6 9 6 1 7 •5 — . 1145 2SCQRP - 2 . 9 5 0 7 2 . 0 6 2 5 3 . 0 1 8 0 3PERC I - 2 . 5 3 3 7 1 . 4 8 3 5 2 . 9 8 7 8 4 SPAR I - 2 . 0 5 9 0 1 . 5 2 1 5 2. 6 2 6 9 5SQUAL - 1 . 5 8 3 7 4 . 8 2 8 5 2. 9 7 8 4 ERRATA No data has been entered i n Table 1 for species no. (Gadus c a l l a r i a s ) as i t i s a synonym for species no. (Gadus morhua). 

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