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The fishermen as predator : numerical responses of British Columbia gillnet fishermen to salmon abundance Millington, Peter 1984

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THE  FISHERMEN AS PREDATOR:  NUMERICAL RESPONSES OF BRITISH COLUMBIA GILLNET FISHERMEN TO SALMON ABUNDANCE. By PETER MILLINGTON M.  Env.  St., University  Of A d e l a i d e , 1982  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Department, of Zoology) We accept  THE  t h i s t h e s i s as conforming r e q u i r e d standard  to the  UNIVERSITY OF BRITISH COLUMBIA A p r i l 1984 (c^  Peter  M i l l i n g t o n , 1 984  In p r e s e n t i n g  t h i s t h e s i s i n p a r t i a l f u l f i l m e n t of  requirements f o r an advanced degree a t the  the  University  o f B r i t i s h Columbia, I agree t h a t the L i b r a r y s h a l l make it  f r e e l y a v a i l a b l e f o r reference  and  study.  I  further  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 copying 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 department o r by h i s o r her  be  granted by  the head o f  representatives.  my  It i s  understood t h a t copying or p u b l i c a t i o n of t h i s t h e s i s f o r f i n a n c i a l gain  s h a l l not be  allowed w i t h o u t my  permission.  Department o f The U n i v e r s i t y o f B r i t i s h Columbia 1956 Main Mall Vancouver, Canada V6T 1Y3  DE-6  f3/81">  written  ii  ABSTRACT Fishermen are responses  to  predators  prey  can  be  whose studied.  l i t e r a t u r e and d i s c u s s how much of context  of a predator-prey  I  and  review  i t can  be  numerical the f i s h e r y  viewed  i n the  system.  I examined the numerical of  functional  response, aggregation  and movement  boats i n response to f i s h abundance, i n the B r i t i s h  salmon g i l l n e t 1979  fleet  to 1981.  using c a t c h data  In any one year,  by  area  boat)  and  the  number  of  the  period  f o r the whole c o a s t , there was  a strong r e l a t i o n s h i p between the r e t u r n per  for  Columbia  (catch value  gillnet  boats  per  fishing  week  i n the  f o l l o w i n g week. I investigated season along  movement  three  of  hypotheses  the g i l l n e t  to  modified  an  by  individual  innate  learning; return,  pattern movement  explain  movement,  the  sufficient  explain  of behaviour not s u b s t a n t i a l l y by  fishermen  by d i f f e r e n t i a l  to  maximize  to  maximize  f o r a g i n g c o s t s and  area  level,  none of the hypotheses was  t o c o n s i s t e n t l y e x p l a i n the v a r i a t i o n F i x e d movement  movement although  Movement by fishermen consistently  be  sites.  individual  w i t h i n a season.  can  which tends t o e q u a l i z e the r e t u r n per u n i t  i n d i v i d u a l r e t u r n i s modified  At  within  (or s i t e s )  which  time i n a l l s i t e s ; and movement i n which the d r i v e  b e n e f i t s between  the  f l e e t between areas  the c o a s t : f i x e d or t r a d i t i o n a l  equated  to  patterns  do  i n boat numbers not  adequately  they may be u s e f u l i n the short term.  to maximize  individual  returns  d i d not  e x p l a i n movement i n t o and out of p a r t i c u l a r  areas,  and  r e s u l t a n t area  average  return.  returns  not  approach  to maximize t h e i r  areas  variability I  provincial  i n d i d i v i d u a l r e t u r n s such  each area tends to a c h a r a c t e r i s t i c  adjacent  the  S p e c i a l f e a t u r e s of each area appear to modify  fishermen's attempts that  did  in  any  one  year.  r e t u r n with r e s p e c t to  However  there  the  ability  of  the  latter  two  hypotheses  ( e q u a l i z a t i o n of r e t u r n between s i t e s and d i f f e r e n t i a l to  predict  f o l l o w i n g weeks. location  or  cases i t was  boat  boat  numbers  in  as  type  confounded  combination  gillnetters,  may  particular  In some areas with i d e n t i f i a b l e t h i s approach  gillnetters  provide  d r i v i n g the numerical  more  response.  foraging  areas i n the features  of  worked w e l l , but i n most  by t r a d i t i o n a l and economic  A p p l i c a t i o n of these hypotheses such  much  i n these r e t u r n s between y e a r s .  compared  costs)  is  factors.  to components of the and  insight  trollers, into  the  and  fleet, pure  mechanisms  iv  TABLE  OF  CONTENTS  ABSTRACT  i i  LIST  OF  TABLES  LIST  OF  FIGURES  v v i i  ACKNOWLEDGEMENTS  viii  INTRODUCTION  1  LITERATURE  3  REVIEW  Functional Numerical  responses  4  responses  18  METHODS  21  Description  of the F i s h e r y  21  Data  set  25  Data  analysis  29  NUMERICAL  RESPONSES  Numerical RESULTS  AND  OF  FISHERMEN  responses  30  of the salmon  gillnet  fleet  30  DISCUSSION  35  Types  o f movement  35  Fixed  movement  35  Maximizing  individual  Differential Predicting CONCLUSIONS LITERATURE  patterns  costs  movement  boat  return  and b e n e f i t s  between  42 areas  61 69 85  CITED  87  V  LIST OF TABLES  Table I. C l a s s e s of v a r i a b l e s which a f f e c t responses Table  the  functional  of p r e d a t o r s to prey  I I . Percentage  8  c a t c h value by area f o r g i l l n e t  boats  1979-1981  28  Table I I I . Breakdown of g i l l n e t  boat type by area  36  Table IV. P r o b a b i l i t i e s that boats f i s h c e r t a i n area  pairs  - 1979  38  Table V. P r o b a b i l i t i e s that boats  f i s h c e r t a i n area p a i r s -  1980 Table  39 V I . P r o b a b i l i t i e s that boats  f i s h c e r t a i n area  - 1981  pairs  '.  Table V I I . P r o b a b i l i t i e s of  fishing  40  area  pairs  -  Bella 41  Coola Table  VIII.  per boat  Number of boats  i n week N. S i g n i f i c a n t  Table IX. Number of boats boat  i n week N + 1 vs landed  i n week  N.  value  regressions  46  i n week N + 1 vs landed value per  Regression  statistics  &  correlation  coefficients  47  Table X. Average CPUE i n each area each year  66  Table XI. Comparison of approaches to p r e d i c t boat numbers  77  Table  X I I . Weekly  observed  and p r e d i c t e d boat numbers -  1981 Table  78 XIII.  deviations  Contributions  to  50%  of  sum  of  squared 81  Table  XIV.  fished  Mean and median r e t u r n s i n f i r s t  1 979 to 1 981  and l a s t week  vi i  LIST OF FIGURES  Figure  1. Types of f u n c t i o n a l response  5  F i g u r e 2. S t a t i s t i c a l Map - B r i t i s h Columbia  waters  22  F i g u r e 3. Catch value by area 1979 - 1981  26  F i g u r e 4. Number of boats i n week N + 1 vs landed value per boat i n week N - 1979 t o 1981 - B r i t i s h Columbia F i g u r e 5. Number of mobile boats i n week N +  1  31  vs  landed  value per boat week N - 1979 - areas 4 £ 29 Figure  6.  Number  of  stationary  boats  44  i n week N + 1 vs  landed value per boat i n week N - area comparisons F i g u r e 7. Number of s t a t i o n a r y boats i n week  N  vs  50 landed  value per boat i n week N + 1 - F r a s e r River Figure  8.  Number  52  of mobile boats i n week N + 1 vs landed  value per boat i n week N - B e l l a Coola F i g u r e 9. Number of s t a t i o n a r y boats  in  55 week  N  +  1  vs  landed value per boat i n week N - B e l l a Coola Figure  10. Number of boats o p e r a t i n g i n each area each week  - 1979 Figure Figure  57  59  11. T o t a l number of boats o p e r a t i n g 1979 t o 1981 ... 62 12. Value of c a t c h per boat per week i n each area -  1979  64  Figure  13. RPA by area from  1979 t o 1981  Figure  14. Number of boats observed and number p r e d i c t e d by  FORCAST f o r Barkley Sound i n 1981  67  74  vi i i  ACKNOWLEDGEMENTS I would l i k e to thank rny s u p e r v i s o r P r o f e s s o r for  h i s h e l p and ideas d u r i n g the course  a l s o l i k e t o thank N. J . Wilimovsky  the members and  of  my  C. C. Lindsey  Ray  Hilborn  of t h i s work.  I would  Committee, for their  Professors constructive  comments about my t h e s i s . Two British  professional Columbia  fishermen,  Gi'llnet  the b e n e f i t of t h e i r  Moussalli  experience. Linda  Berg,  and Rick T a y l o r who a l l helped  am a l s o g r a t e f u l t o Sue E r t i s and B i l l Webb of Data Centre Finally  of the  A s s o c i a t i o n and Mr T. Sakata gave me  Thanks t o my f e l l o w students Elie  Mr M. W. C. F o r r e s t  Mike  Lapointe,  i n many ways. the  I  Biosciences  f o r t h e i r h e l p when I was stuck. special  thanks t o my wife Donna f o r her p a t i e n c e ,  f o r t i t u d e and encouragement.  1  INTRODUCTION There e x i s t s a l a r g e body of theory concerning prey  i n t e r a c t i o n s developed  f o r n a t u r a l systems which can  a p p l i e d to the study of f i s h i n g and regarded  predator-  fishermen.  F i s h i n g can  as i n d u s t r i a l hunting, with the fisherman  predator, and much of the t h e o r e t i c a l bases of systems can e q u a l l y w e l l be a p p l i e d to  as a  be  super-  predator-prey  man.  Fishermen can be s t u d i e d from at l e a s t frames of r e f e r e n c e ; the f i s h ,  be  the fisherman  three d i f f e r e n t and  the manager.  For e f f e c t i v e management there must be a sound t h e o r e t i c a l underpinning  to e x p l a i n and p r e d i c t changes i n the other  frames of r e f e r e n c e .  There has been much work on  r e s o u r c e s , but there has been r e l a t i v e l y of  little  two  fish  systematic  study  fishermen. Most papers which model f i s h i n g operations ignore or  i m p l i c i t l y assume that the e n t i r e phenomenon i s a system.  Only  a l i m i t e d number of authors have e x p l i c i t l y  d i s c u s s e d t h e i r work i n t h i s context 1979,  predator-prey  Peterman 1980)  (e.g.  Peterman et a l .  or have c o n s i d e r e d the management  i m p l i c a t i o n s of such an approach ( L a r k i n 1 9 7 9 , D i c k i e 1979). The  b a s i c components of such predator-prey  f u n c t i o n a l and density.  The  predator-prey  numerical  e s s e n t i a l and  of the predator  the  to prey  s u b s i d i a r y components of the  system d e s c r i b e d by H o l l i n g  provide a convenient  (1959, 1965,  1966)  framework to study the c h a r a c t e r i s t i c s of a  predator; the fisherman. examine how  responses  systems are  I will  review  the l i t e r a t u r e  much of i t can be c o n s i d e r e d i n t h i s  and  context.  2  Using the B r i t i s h Columbia  (B. C.)  salmon  fishermen as an example, I w i l l examine how response density  (movement and  a numerical predator  aggregation of boats) i s r e l a t e d to prey  (measured in terms of c a t c h  I will  gillnet  i n v e s t i g a t e three  value).  a l t e r n a t i v e hypotheses to  explain  t h i s a g g r e g a t i o n : f i x e d or t r a d i t i o n a l movement, which can equated-to a f i x e d p a t t e r n modified  by  of behaviour not  l e a r n i n g ; movement by  the  r e t u r n per  unit  and  movement i n which the  tendency  to maximize i n d i v i d u a l r e t u r n per  u n i t time i s modified  by  differential I will  (or a r e a s ) ;  substantially  fishermen to maximize  i n d i v i d u a l r e t u r n , which tends to e q u a l i z e time i n a l l s i t e s  be  f o r a g i n g c o s t s and show how  b e n e f i t s between  u s e f u l these hypotheses can  sites. be  as  management t o o l s to p r e d i c t the w i t h i n  season movement of  salmon g i l l n e t  B. C.  Finally considering and  f i s h i n g boats along the  I will  outline briefly  f i s h i n g within  what f u t u r e work can  the  Coast.  the u s e f u l n e s s  of  the context of a predator-prey system  be done in t h i s  area.  3  LITERATURE REVIEW Solomon (1949) used two terms to c a t e g o r i z e the two kinds of responses i n a p r e d a t o r - p r e y system.  Functional  responses  are changes i n the number of a t t a c k s or the consumption  rate of  the p r e d a t o r as a f u n c t i o n of prey or predator d e n s i t y . Numerical responses are changes i n predator d e n s i t y or abundance with changing prey d e n s i t y  (Holling  1959).  T o t a l p r e d a t i o n can  then be expressed i n terms of these two kinds of response. Numerical responses a r i s e  from predator movement i n and out  of an area or changes i n b i r t h or death r a t e s . Oaten it  (1975) p o i n t e d out that  Murdoch and  i f the response i s from movement  could be e i t h e r a numerical or an a g g r e g a t i v e f u n c t i o n a l  response t o prey p a t c h i n e s s .  Which of these i s c o n s i d e r e d more  a p p r o p r i a t e i s a q u e s t i o n of s c a l e , large distances, numerical.  i . e. i f movements are over  i t i s b e t t e r t o c o n s i d e r the response as  Many B. C. salmon g i l l n e t t e r s do move l a r g e  d i s t a n c e s d u r i n g the course of as season, from the Nass River t o the F r a s e r R i v e r , so t h i s  i s the approach  I have adopted.  From the f i s h e r i e s management viewpoint, the numerical response, e. g. an i n c r e a s e i n boat numbers, i s of primary importance.  For example, t o achieve h i s d e s i r e d salmon  escapement goal from the a n t i c i p a t e d run of f i s h , manager must.adjust the opening d u r a t i o n can l i m i t  the k i l l  can be expected.  i n an area as only time  from the f l o o d of p r e d a t o r s (fishermen) which C a l c u l a t i o n of the t o t a l opening time would be  helped i f the number of boats will  the f i s h e r y  (and hence t o t a l e f f o r t )  f i s h an area c o u l d be p r e d i c t e d .  which  4  Even given t h i s viewpoint the g r e a t e r body of work to date has c o n c e n t r a t e d on the f u n c t i o n a l rather than numerical responses of fishermen.  Functional  responses  Holling  (1959) i d e n t i f i e d three b a s i c forms of the  f u n c t i o n a l response: type  1, which produces d e n s i t y  independent  m o r t a l i t y up to a s a t i a t i o n p o i n t ; type 2 which produces  an  i n v e r s e l y density-dependent m o r t a l i t y over a l l ranges of prey abundance; and type 3, which produces  direct  density-dependent  m o r t a l i t y up to point where the predator i s s a t i a t e d or runs out of time.  A m o d i f i e d type 2 (or 'type 4')  response may  occur  when there i s a refuge f o r the prey which p r o t e c t s a p o r t i o n of the p o p u l a t i o n (Figure 1). All  three types of f u n c t i o n a l response c o u l d be, or have  been, observed  i n fishermen.  A type  1 functional  response  i m p l i e s a gear or fisherman whose s e a r c h i n g and/or c a t c h i n g e f f i c i e n c y climbs s t e a d i l y to a s a t u r a t i o n  point.  The type 2 f u n c t i o n a l response has been noted by Peterman (1980) i n the B. C. n a t i v e i n d i a n food f i s h e r y the gear used  f o r salmon, where  i n c l u d e d d i p nets, g a f f s , g i l l n e t s and t r a p s .  The  asymptote i n the type 2 f u n c t i o n a l response a r i s e s from handling time or search e f f i c i e n c y  limitations  (Holling  1959).  The  gear  used by the i n d i a n s can only sweep a l i m i t e d volume of water and h a n d l i n g times at high d e n s i t i e s become important significant  time i s needed to b r i n g  g i l l n e t s and t r a p s (Peterman  1980).  because  i n , empty and reset  the  gure  1. Types of f u n c t i o n a l response. e x p l a n a t i o n of each type see t e x t .  For a  further  6  7  The  type 3 or S-shaped curve  i s t y p i c a l of many v e r t e b r a t e s  where there i s more than one prey s p e c i e s , as the predator  may  not  'switch' to a prey s p e c i e s when i t i s at low d e n s i t y .  That  is,  switching can give a l a r g e r than expected  number of a t t a c k s  when a s p e c i e s i s abundant r e l a t i v e to other prey  (Murdoch  1969) . T h i s type observed  3 response  should be the one most commonly  i n f i s h i n g , where the fisherman  or stock a v a i l a b l e to Holling  has a number of s p e c i e s  him.  (1959) i d e n t i f i e d  f i v e c l a s s e s of v a r i a b l e s which  a f f e c t prey m o r t a l i t y from p r e d a t i o n : 1.  Density of the predator  2.  Density of the prey p o p u l a t i o n  3.  Predator  4.  Prey  5.  Density and q u a l i t y of a l t e r n a t e foods Only  characteristics  characteristics  the f i r s t  prey process  two  (Holling  e s s e n t i a l and  Pacific  f o r the  v a r i a b l e s are e s s e n t i a l  1959).  predator-  These groups i n c l u d e , i n t u r n , subcomponents.  have been a p p l i e d to f i s h  salmon ( L a r k i n 1971,  themselves  Peterman and Gatto  but e x p l i c i t c o n s i d e r a t i o n with respect to man to work by Peterman  predator.  i n the  s u b s i d i a r y components (Table I) and  These f u n c t i o n a l responses e. g.  population  1978),  has been l i m i t e d  (1980).  In f i s h i n g terms, the e x p l o i t a t i o n and i n t e r f e r e n c e f u n c t i o n a l response  components together comprise  between f i s h i n g boats. may  The  competition  e f f e c t s of e x p l o i t a t i o n  competition  i n c l u d e i n s t a n c e s where each u n i t of gear competes with each  TABLE I CLASSES OF VARIABLES  WHICH AFFECT THE FUNCTIONAL RESPONSES OF PREDATORS TO PREY  1. Density of the predator p o p u l a t i o n Essential E x p l o i t a t i o n - as predators compete f o r the same resource, the chance of d i s c o v e r i n g an unattacked prey decreases with i n c r e a s i n g predator d e n s i t y I n t e r f e r e n c e - between competitors Subsidiary S o c i a l f a c i l i t a t i o n - s o c i a l contact can s t i m u l a t e an i n c r e a s e in p r e d a t i o n with i n c r e a s i n g predator d e n s i t y Avoidance l e a r n i n g by prey - the g r e a t e r the predator d e n s i t y the g r e a t e r the chance each prey w i l l a q u i r e an e f f e c t i v e way of a v o i d i n g a t t a c k 2. Density of the prey p o p u l a t i o n Essential Rate of s u c c e s s f u l search - r e a c t i v e d i s t a n c e of predator to prey -speed of predator movement -speed of prey movement -capture success Time exposed t o p r e d a t o r s -non f e e d i n g vs feeding times Time spent h a n d l i n g prey - p u r s u i t time - e a t i n g time -time f o r d i g e s t i v e pause Subsidiary Hunger Learning by predator I n h i b i t i o n by prey -development of defence mechanisms 3. Predator c h a r a c t e r i s t i c s Swimming speed Visual acuity 4. Prey c h a r a c t e r i s t i c s C a l o r i c value of prey Prey exposure time to predator Prey a t t r a c t i v e n e s s to predator -palatability -defence mechanisms S t r e n g t h of stimulus used by predator to l o c a t e prey - s i z e of prey - h a b i t s of prey - c o l o u r s of prey 5. Density and q u a l i t y of a l t e r n a t e foods f o r the p r e d a t o r s Switching From H o l l i n g (1969).  (1959,  1965,  1966);  Griffiths  and  Holli  9  other u n i t of gear  (e. g. t r a w l e r s ) , or where f i s h compete f o r a  u n i t of space on the gear e. g. hooks on l o n g l i n e s  (Rothschild  1977). D e s c r i p t i o n s of i n t e r f e r e n c e competition are mostly anecdotal.  Trawler s k i p p e r s are known to m i s d i r e c t other  s k i p p e r s over unproductive ground or l u r e them i n t o time trips,  (Andersen  and Wadel 1972,  seine s k i p p e r may  Tunstall  sometimes d i r e c t l y  o p e r a t i o n of another v e s s e l  (Orbach  1962)  wasting  and a purse  i n t e r f e r e with the  fishing  1977).  V a r i o u s s o c i a l mechanisms have developed i n f e r e n c e competition  nearby  to m i t i g a t e  (McCay 1978), mostly through spacing  mechanisms such as community r e c o g n i t i o n of r i g h t s to favoured f i s h i n g access p o i n t s (Andersen or a c t i v e l y defended may  territories  and S t i l e s  1973,  (Acheson  1975).  not be g e n e r a l l y accepted, or automatic,  r i g h t s to these access p o i n t s , and f o r them by being f i r s t  long term property  fishermen may  have to compete (and  'ownership') or occupying a l l good  l o c a t i o n s with the gear  Social f a c i l i t a t i o n  1973),  However, there  to f i s h there i n a given season  thus e s t a b l i s h i n g temporary fishing  Breton  (Andersen  and S t i l e s  should i n c r e a s e the average  1973). fisherman's  c a t c h with i n c r e a s i n g c o n c e n t r a t i o n of f i s h i n g boats, and  this  most o f t e n occurs v i a the flow of r a d i o i n f o r m a t i o n between or sub-sets o f , fishermen. information  In many cases c o o p e r a t i v e  flow i s l i m i t e d to kin groups,  f r i e n d s or company  boats through the use of low power r a d i o s (Lofgren 1972) groups (Orbach  1977,  all,  Tunstall  1962).  Information i s an indeterminate, dynamic and  scarce  or code  10  resource  (Andersen  structures another  form  fishermen boats,  appear of  are  but  to  not  also  with  the  skippers  (Tunstall zero  sum  exchanges  on  radio  Tunstall  (Andersen  overall  total  fleet  flow,  information. fishing  reduction The  When  loss  same  1972,  the  these  else's form  other therefore  gain  of  non-committal  information,  1973,  many  their  skipper  over-stated  Andersen  example  company's  to  The  some-one  with-holding catches,  other  relative  takes  For  company;  1973).  is  fish  and  basic  need  large be  outcome  deceptive  catches  or  Orbach  even  1977,  an  search.  even  (1977)  The  affected  disinformation  a l l skippers  slips,  larger  for  contribute of  by  the  a  to  fleet  percentage  learn  avoidance so  they  encounters  may  is  minimal this  flow  withdraws  catch  1973).  social  fishing  of  percentage  Stiles of  is therefore  facilitation  effort called  as this  the  is usually  number  increase  of  a  gear  real units  the  effect.  predators These  a  and  Rothschild  catch  inadvertent  may  in effective  cooperation  gear.  and  (Andersen  increase.  natural  the  there  direct  increase  As  the  performance  in both  however;  information  from  or  under-stated  inefficiencies  of  radio  with  through  1962).  The  not  in  Andersen his  social  facilitation  i s measured  1962,  various  disinformation.  Disinformation  the  silence  social  skippers  game;  1972).  and  in competition  hierachy  (Andersen  strategies,  1972),  mitigate  only  in  a  Wadel  interference -  survival  plays  and  techniques  learn  from  include  from  encounters  encounters flight  from  with trawl  with  fishing gear,  11  p u l l i n g the mouth from hooks, or, once caught,  r e l e a s e as  u n d e r s i z e d or otherwise i l l e g a l . F i s h l e a r n i n g to a v o i d f i s h i n g gear has been most o f t e n observed  i n freshwater a n g l i n g , and  some s p e c i e s (e. g. bass,  M i c r o p t e r u s spp.) appear to l e a r n at a f a s t e r pace than others (e.  g. carp, Cyprinus c a r p i o ) (Beukema 1970a, Hackney and  Linkous  1978,  Schneider  1973).  The  r a t e of l e a r n i n g  m o d i f i e d by the type of stimulus presented e. g. l e a r n i n g r a t e i s g r e a t e r i n pike ( Esox l u c i u s  the  i s also avoidance  ) with s p i n n e r s  than with l i v e b a i t e d hooks (Beukema 1970b). In  commercial  f i s h e r i e s , the common gear type e. g. nets,  g i v e s the f i s h l e s s  'choice' i n i t s response  more p a s s i v e part ( A l l e n response  1963).  Avoidance  and  the f i s h play a  i s more of a  flight  to p r e d a t o r s i n general and capture depends more on the  f i s h size  i . e. whether too l a r g e or too s m a l l f o r the mesh.  Secondly,  f o r marine stocks the s i z e of the area they commonly  occupy means that the encounter be f a i r l y low  r a t e f o r the i n d i v i d u a l w i l l  i n most f i s h e r i e s , with l e s s o p p o r t u n i t y f o r  l e a r n i n g through repeated encounters. e f f i c i e n c y of modern f i s h i n g gear,  T h i r d l y , given the  i t i s not  i n c o n c e i v a b l e that  the p r o p o r t i o n of f i s h escaping, once encountered i s a l s o f a i r l y low. or  hook and  be  line,  at any  time,  However, some gears, such as l o b s t e r  allow f o r l e a r n i n g responses  i n the  pots  fish  s i m i l a r to those of freshwater s p e c i e s s u b j e c t to a n g l i n g . Searching i s a major component of f i s h i n g e s p e c i a l l y i n e n c i r c l e m e n t methods such as purse s e i n i n g , while sub-components such as speed of the predator  ( f i s h i n g boat or g e a r ) , capture  12  success and p e r c e p t i o n d i s t a n c e are c o n s t a n t l y changing improved technology Success  (Mangel and C l a r k 1982).  r a t e s i n l o c a t i n g prey depend on past  time of year, weather e t c . random a c t i v i t y and and Flowers  with  (Orbach  1 977);  experience,  s e a r c h i n g i s not -a  stocks are not randomly d i s t r i b u t e d .  Saila  (1969) a p p l i e d o p e r a t i o n s research methods to model  the search process of f i s h i n g boats t a k i n g t h i s non-random distribution  i n t o account.  Others have developed  stochastic  t h e o r i e s of search, c o n s i d e r i n g c o n t a g i o u s l y or randomly d i s t r i b u t e d prey and a l l o w i n g f o r a v a r i a b l e ' p e r c e p t i o n r a d i u s , escapement and c a p t u r e / h a n d l i n g delays 1964,  Paloheimo 1971 a,  1971b).  (Paloheimo  Success  and D i c k i e  r a t e s were shown to vary  with prey a c t i v i t y and abundance (Mangel and C l a r k 1982). The  time spent h a n d l i n g prey c o n s i d e r e d i n terms of  p u r s u i t , e a t i n g , and d i g e s t i v e pause are, together with the exposed to the prey bound together effort.  The  ( f e e d i n g vs non-feeding  time),  intimately  in the f i s h e r i e s context w i t h i n the concept  in p a r t by simple models of  f o r a g i n g behaviour  (Pyke et a l .  i n animals  1977)  i n these a c t i v i t i e s can be accounted  theoretically  of  optimal a l l o c a t i o n of time spent on these v a r i o u s  components has been addressed  time spent  time  and the a c t u a l  for  i n v a r i o u s search models (Paloheimo  1971a, 1971b).  However, there do not appear to be any authors who  have  c o n s i d e r e d the p r a c t i c a l aspects of these problems i n f i s h i n g boats w i t h i n the context of a predator-prey Shardlow  system.  (1983) i n v e s t i g a t e d the r e l a t i o n s h i p between c a t c h  per u n i t e f f o r t of salmon a n g l e r s and  salmon abundance, where  13  both were estimated s i m u l t a n e o u s l y .  Unlike previous studies  u s i n g h i s t o r i c a l data to estimate abundance, the p r o p o r t i o n of the immediately unit  surrounding p o p u l a t i o n caught  of f i s h i n g e f f o r t , the c a t c h a b i l i t y  by a numerical  c o e f f i c i e n t , was  not  constant but i n c r e a s e d with i n c r e a s i n g salmon abundance. S t u d i e s of the i n t e r a c t i o n that  feeding f a c i l i t a t i o n  mechanism, producing That  i s , one  affect  between f i s h i n g gear and between salmon may  increased c a t c h a b i l i t y  with abundance. others and  the  of t h i s i s g r e a t e r at higher abundance. • (1972, 1977)  has addressed  p r o p e r l y measuring f i s h i n g e f f o r t as hours f i s h e d ,  rather than  the p o p u l a t i o n caught.  the problem of  i n terms of r e a l  changing  effort levels  are c o n s t a n t l y  i . e. d e c r e a s i n g the non-feeding,  differences  (see Pope 1975).  Hunger, or the need to go f i s h i n g has been addressed literature  For  t h e r e f o r e , a l a r g e amount of  to r e l a t e changes i n e f f o r t and  in boats to some standard  eating  i n c r e a s i n g the feeding time.  m o d e l l i n g and management purposes trying  fraction  In most f i s h e r i e s , t e c h n o l o g i c a l  and d i g e s t i v e pause phases and  i s spent  i n p u t s such  i n terms of the numerical  developments, together with i n c r e a s e s i n s k i l l  time  showed  be the u n d e r l y i n g  salmon chasing a l u r e w i l l a t t r a c t  Rothschild  of  fish  from both the s o c i o l o g i c a l  1962), and economic s t a n d p o i n t s (Smith  (Orbach 1981).  1977,  i n the  Tunstall  E x t e r n a l economic  p r e s s u r e s arid i n t e r n a l m o t i v a t i o n s c o n s t i t u t e t h i s  drive.  U n l i k e most c a r n i v o r e s , fishermen do not g e n e r a l l y appear to have a p e r s o n a l s a t i a t i o n p o i n t , other than the l i m i t a t i o n s equipment.  More i s always b e t t e r .  of  14  While i t i s p o s s i b l e to measure the e x t e r n a l pressures to f i s h , e. g. to earn a l i v i n g or pay o f f a debt models are complicated Anderson factor  by non-tangible m o t i v a t i o n s .  any  For example  (1980) i d e n t i f i e d the worker s a t i s f a c t i o n bonus as a  i n economic m o d e l l i n g .  commercial fishermen fishing  load etc.,  This pleasure factor in  means they are w i l l i n g  from other sources,  even unemployment insurance  to s u b s i d i z e t h e i r  i n c l u d i n g o f f - s e a s o n employment or ( F e r r i s and  Plourde  1982).  If the  worker s a t i s f a c t i o n bonus i s p o s i t i v e with respect to other industries,  i t means there may  be i n c r e a s e d p e r c e p t i o n of  b e n e f i t s beyond those c a l c u l a t e d the case coast  from d o l l a r  revenues.  for the salmon f i s h e r y along the western North  (Smith The  l e v e l of a s k i p p e r ' s m o t i v a t i o n and  to take w i t h i n that s t r a t e g y .  c o n s i d e r e d that s k i l l  may  his learning  The  than  the  i n i t s c o n t r i b u t i o n to  but that in many analyses s k i l l  be p r o p e r l y formulated.  the r i s k s he i s  R o t h s c h i l d (1972)  be more important  c a p a b i l i t i e s of the f i s h i n g boat effort,  American  1981).  c a p a b i l i t y a f f e c t both h i s f i s h i n g s t r a t e g y and willing  This i s  was  degree of s k i l l  success w i l l vary with circumstances.  fishing  ignored as i t cannot required for  For example, at low  prey  d e n s i t i e s when the d e c i s i o n environment becomes more complicated the number of d e c i s i o n v a r i a b l e s to be c o n s i d e r e d means a 'skilled'  skipper w i l l  show a r e l a t i v e l y b e t t e r c a t c h than when  the prey d e n s i t i e s are higher  ( R o t h s c h i l d 1977).  Cove (1973) measured d i f f e r e n c e s i n f i s h i n g based on the s k i p p e r ' s assessment of r i s k  strategies  i n three types of  15  fishery.  The  degree  resource,  the  skipper's  gear.  Risks  selection or  good)  boat  measured  of  fishing  and  For  depends  highest  on  e.  this  his  risk  a  low  skipper  degree  motivation  areas  of  on  with and  the  the  the  fishing  versus  capability  near  basis  uncertainty  of  past  gregarious  in a  performance  relative  to  in  Tunstall trawlers  1962).  boat)  low  company  the  risk  taking  only  'skipper'  effect  was  (1973)  the  and  high  hierachy).  explained  42%  fishing  position  in circumstances  (of  on  Cove  (average  habits.  was  capability  features,  company  his  in  the  found of  the  low  motivation  However of  the  the  catches  fishing  survival  of  of  dangerous  skipper's  1973,  taking  varied  included  the  (Andersen  uncertainty, (i.  risk  independent  example,  hierachy  of  the  even  catch  variation. This  Durrenberger fishermen skipper in  effect  and  size  of  can  the  the  catching  1984).  a  boat  B.  power  and  25%  The  therefore  be  quite being  number  C.  he  Icelandic  weak,  in a  the  for  fishing  trips,  of  'skill' to  is his command  Palsson and  They  longline  found  the  differential  in  terms and  of  that  success  larger a  boat  major  ability  to  and  number  the  and  maximize of  the  trips  year.  purse  area  with  by  gillnet  accounted  i s given  between to  studied  analysis techniques.  skipper's  undertake  In  skill  to  skippers  of  amongst  path  greater  component  he  using  certain  size  (1982)  also  seine boats  fleet was  40%  of  skipper  varies considerably  the  attributed  specialization  significance  of  to  differences in skipper  ( H i l b o r n and skill  between  in  the  fisheries.  and  crew  Ledbetter catch  process  16  I n h i b i t i o n by prey takes the form of defence mechanisms, both p h y s i c a l and c h e m i c a l . all  Besides g e n e r a l defences adapted to  types of n a t u r a l p r e d a t o r s  secretions etc.,  e. g. s p i n e s , poisonous  f i s h do not appear  to have developed any  s p e c i f i c defence mechanisms d i r e c t l y as a r e s u l t of p r e d a t i o n by man.  However, there are i n d i c a t i o n s that  f i s h i n g p r e s s u r e has  reduced the mean s i z e of s e v e r a l salmon s p e c i e s (Ricker thus a l t e r i n g In of  t h e i r v u l n e r a b i l i t y to f i s h i n g  fishing  1981),  gear.  the p h y s i c a l predator c h a r a c t e r i s t i c s a r e those  boat and gear type.  In f i s h i n g boats the ' e v o l u t i o n a r y '  p r e s s u r e s are strong and f o r c e r a p i d change from experience i n the f i s h e r y and e x t e r n a l inputs,, both managerial and technological.  The common property nature of the resource i s an  a d d i t i o n a l spur to change as p a r t i c i p a n t s attempt their  t o maximize  i n d i v i d u a l catches i n open c o m p e t i t i o n . R e s u l t s from attempts  t o a t t r i b u t e c a t c h t o boat  c h a r a c t e r i s t i c s have been mixed. Palsson and Durrenberger  As mentioned  (1981) found boat s i z e one of two  f a c t o r s s i g n i f i c a n t l y c o r r e l a t e d with c a t c h . found f i s h i n g boat c h a r a c t e r i s t i c s accounted landed weight Georges Bank. account  previously  C a r l s o n (1975) f o r 50% of the  and 83% of the landed value of US catches from However, H i l b o r n and Ledbetter (1984) c o u l d only  f o r 10% of the d i f f e r e n c e s i n c a t c h from B. C. purse  s e i n e r s by examining  c e r t a i n boat a t t r i b u t e s such as l e n g t h ,  although they d i d not take i n t o account d i f f e r e n c e s i n gear t o e x p l a i n some of these d i f f e r e n c e s . A major d i f f i c u l t y  encountered  i s how to s t a n d a r d i z e a  17  dynamic c h a r a c t e r i s t i c Rothschild  (e. g. see Pope  1975,  1977), e s p e c i a l l y where gross c h a r a c t e r i s t i c s such as  engine power may Michielsen  such as e f f o r t  account f o r some v a r i a t i o n  1975), but where a d i f f i c u l t  c h a r a c t e r i s t i c , such as the hanging have a s i g n i f i c a n t  (Hovart and  to measure  r a t i o of a g i l l n e t , may  also  effect.  The prey c h a r a c t e r i s t i c s c o n s i d e r e d important by H o l l i n g (1959) (Table I) t r a n s l a t e i n f i s h e r i e s terms  i n t o such  ill-  d e f i n e d ones as p a l a t a b i 1 i t y , market demand and market v a l u e . The a c t u a l c a l o r i c value of the product matters l i t t l e ; i t s attractiveness  i n the market p l a c e matters most of  Extreme s e l e c t i v e p r e s s u r e by p r e d a t o r s may c h a r a c t e r i s t i c s such as the exposure  all.  change  time of the prey to the  predator i . e. the p e r i o d d u r i n g which  i t i s v u l n e r a b l e , or the  s i z e , h a b i t s c o l o u r s e t c . of the animals such that the e f f i c i e n c y of the p r e d a t o r ' s method of prey l o c a t i o n The p o s s i b i l i t y  that  i s reduced.  f i s h i n g has caused such changes has merited  serious t h e o r e t i c a l discussion  (Calaprice  1969,  Thorpe  and  Koonce 1981), and the a l t e r a t i o n of run t i m i n g s i n some salmon stocks i s one example Parrish  (Vaughan  1947).  (1963), although p r i m a r i l y c o n s i d e r i n g the  s e l e c t i o n process from the f isherman's viewpoint d i d o u t l i n e the main s e l e c t i o n processes a r i s i n g 1.  Those caused by d i f f e r e n c e s i n the d i s t r i b u t i o n of the  fishing 2.  from f i s h i n g o p e r a t i o n s :  f l e e t and components of the f i s h i n g stock  Those caused by the v a r i a t i o n  i n h a b i t s and behaviour of  components of the f i s h stock i n the e x p l o i t e d area  18  3.  Those caused by the inherent p r o p e r t i e s of the f i s h i n g  gear.  Each of these processes operate to s e l e c t between s p e c i e s in a m u l t i - s p e c i e s f i s h e r y , between abundance l e v e l s of a s i n g l e s p e c i e s , and between d i f f e r e n t components of a stock - s i z e s , ages, sexes e t c . In  a m u l t i - s p e c i e s f i s h e r y , there may  type 3 f u n c t i o n a l response called  (Beddington  fishermen accomplished  1979).  technology a v a i l a b l e to f a c i l i t a t e  optimal f o r a g i n g s t r a t e g y  Larkin  s w i t c h e r s , as man  range of s p e c i e s , uses v a r i o u s f i s h i n g of  be s w i t c h i n g , i . e. a (1979)  consumes a wide  s t y l e s and has an a r s e n a l switching.  (Pyke et a l .  1977)  The theory of  i n d i c a t e s that  s e l e c t i o n of prey items by fishermen i s an important mechanism a f f e c t i n g prey s i z e i n a f i s h i n g system  (Dickie  1979).  A major o b s t a c l e to s t u d y i n g the f u n c t i o n a l responses of fishermen has been to o b t a i n an a c c u r a t e count of prey abundance. now  Modern sonar and underwater  viewing equipment does  allow a simultaneous estimate of prey abundance and predator  ( f i s h boat) numbers and a t t a c k r a t e s  Numerical  (e. g. see Shardlow  1983).  responses  Larkin  (1979) c a t e g o r i z e d approaches  to m o d e l l i n g  fish  p o p u l a t i o n s i n t o 4 broad types: L o t k a - V o l t e r r a models, s i n g l e s p e c i e s models, the ecosystem approach.  Each has  approach, and the  i t s limitations.  functional  Although there has been  some a p p l i c a t i o n of the L o t k a - V o l t e r r a models i n f i s h e r i e s et  al.  1979)  (May  they are not c o n s i d e r e d good p r e d i c t i v e t o o l s f o r  management ( L a r k i n  1979).  The  s i n g l e s p e c i e s models  19  (e.  g. Beverton  and H o l t 1957,  the e f f e c t of man  Schaefer  1957,  Ricker 1975)  lump  the predator under the category of h a r v e s t ,  but have been the most widely used management t o o l s to date, although t h e i r management u t i l i t y evaluated.  The  has not been p r o p e r l y  ecosystem approach i s q u i t e complex  demanding of data, as i s the f u n c t i o n a l approach, management u t i l i t y  and  a few authors have made a s t a r t  on m o d e l l i n g f u n c t i o n a l responses  and a p p l y i n g them to the  (e. g. Peterman  1980).  The  single species  approach c o u l d be regarded as m o d e l l i n g the numerical of  the  of both has to date been u n c l e a r .  I have o u t l i n e d a l r e a d y how  management context  and  response  the prey, u s u a l l y represented by h a r v e s t , to the predator  d e n s i t y but very l i t t l e numerical  response  has been done to i n v e s t i g a t e  of the predator to prey d e n s i t y .  Peterman et a l .  (1979) when examining  B. C. t r o l l e r s on chinook w i t h i n season  f l e e t dynamics of  salmon showed a numerical  aggregation - s i m i l a r  c l e a r bounds on responses  due  numerical  -  to management r e g u l a t i o n s . fleet  They  size,  fleet  e t c . to be e q u i v a l e n t to the p r e d a t o r - r e p r o d u c t i v e • response.  Loucks and S u t c l i f f e f i s h i n g boat m o b i l i t y  (1978) o u t l i n e d a simple model of  (responsive e f f o r t ) to p e r c e i v e d stock  abundance and ocean c l i m a t e , and coast cod  response  to n a t u r a l p r e d a t o r s , with  c o n s i d e r e d that changes i n f i s h i n g e f f i c i e n c y , composition  the  found t h a t the Canadian east  f i s h e r y to be prosecuted by a ' p e r c e p t i v e f i s h i n g  i n d u s t r y a k i n to a n a t u r a l p r e d a t o r - p r e y Ledbetter  system'.  (1981) c o n s i d e r e d t h i s numerical  response  to be  20  b e t t e r understood information  i n the f i s h e r i e s context as a response to  (e. g. past c a t c h per u n i t e f f o r t  was assumed by H i l b o r n and Ledbetter  (1979),  of the movement dynamics of the B. C. purse authors examined a number of hypotheses movement of the f l e e t . its  - CPUE) and t h i s in their  analysis  seine f l e e t .  These  to e x p l a i n and p r e d i c t  I f each v e s s e l i s attempting  to maximize  r e t u r n and there i s optimal boat d i s t r i b u t i o n with respect  to f i s h abundance, the CPUE along the coast should roughly be equal.  However, the CPUEs from the i n d i v i d u a l areas and the  p r o v i n c i a l CPUE were found to be d i f f e r e n t .  T h i s was a t t r i b u t e d  the d i f f e r e n t i a l c o s t s and b e n e f i t s of f i s h i n g c e r t a i n  areas.  Such an, approach does show promise as a management t o o l and I w i l l examine the a p p l i c a b i l i t y B.'C. salmon g i l l n e t  fleet.  of these concepts  to the  21  METHODS  D e s c r i p t i o n of the F i s h e r y The B. C. commencing in o t h e r s .  salmon f i s h e r y  in A p r i l  i s a h i g h l y r e g u l a t e d one  i n some areas and c o n t i n u i n g u n t i l November  For management and s t a t i s t i c a l purposes, the  Department of F i s h e r i e s and Oceans has d i v i d e d the B. C. i n t o 29 main areas b r i e f openings  (Figure 2 ) . The f i s h e r y  i s c h a r a c t e r i z e d by  of a h a l f , one or two days i n these areas or i n  subareas, u s u a l l y s t a r t i n g at 1800 hours on a Sunday. ample scope  coast  f o r movement between these areas between  but u n l e s s the area i s nearby  There i s openings,  or e a s i l y a c c e s s i b l e there i s a  c o n s i d e r a b l e p e n a l t y f o r doing so d u r i n g an opening.  This  f a c t o r , together with the nature of the data s e t , l e d me to c o n s i d e r one week as the b a s i c u n i t of e f f o r t . The fisherman must decide on the b a s i s of h i s recent c a t c h , h i s boat's c a p a b i l i t y , past year's experience, c o n v e r s a t i o n s with other fishermen and i n f o r m a t i o n from the f i s h e r i e s managers, whether to change areas between  openings.  Many of the boats are 'combination' boats i . e. l i c e n c e d both f o r t r o l l i n g and g i l l n e t t i n g .  they are  Thus a combination  l i c e n c e holder must decide on the a p p r o p r i a t e time t o change gear and enter or leave the g i l l n e t  fishery.  Many of the fishermen operate on a p a r t - t i m e b a s i s and are c o n s i d e r a b l y l e s s mobile, o f t e n only f i s h i n g one or two s t a t i s t i c a l areas adjacent to t h e i r p l a c e of r e s i d e n c e or work. Some of the t r o l l e r s may a l s o only g i l l n e t  one a r e a .  These  22  F i g u r e 2. S t a t i s t i c a l Map - B r i t i s h Columbia waters. Source: Canada, Department of F i s h e r i e s and Oceans (1982) .  23  STATISTICAL MAP  25  ' s t a t i o n a r y ' boats can be c o n t r a s t e d with the  'mobile'  ones  D i s t r i b u t i o n of c a t c h value by area f o r the years  1979  which move between a l a r g e r number of a r e a s .  1981  are i l l u s t r a t e d  in F i g u r e 3.  c o n t r i b u t e d an average  The areas which together  of over 76% of the c a t c h value in a l l  years are c o n s i d e r e d in more d e t a i l  (Table I I ) .  p a r t i c u l a r were subject to s p e c i a l examination  Two  in  because of t h e i r  o v e r a l l c o n t r i b u t i o n to the p r o v i n c i a l salmon c a t c h : Area Lower Skeena R i v e r , and Area  29, G u l f / F r a s e r R i v e r .  1981  4,  Together,  catches from these two areas c o n s t i t u t e d between 21 and the p r o v i n c i a l  to  61% of  salmon g i l l n e t c a t c h by value beteween 1979  and  inclusive.  Data set The Department of F i s h e r i e s and Oceans compile data s l i p s made out when a boat w i t h i n one  or two  sells  i t s fish,  days of c a p t u r e .  The  from  which i s u s u a l l y  sales s l i p information  i n c l u d e s the date, boat number, s t a t i s t i c a l a r e a , and c a t c h d e t a i l s such as s p e c i e s , pounds, p i e c e s and value of landed. Wong  fish  T h i s data base has been d e s c r i b e d i n some d e t a i l  by  (1983). Sales s l i p and boat a t t r i b u t e  the years f o r the years increasing  1967  information i s a v a i l a b l e for  to 1981,  but m o b i l i t y has been  i n recent years, with fishermen  boats between areas  (M. W.  road-hauling  C. F o r r e s t , B. C.  Gillnet  A s s o c i a t i o n , p e r s o n a l communication), so l a t e r years are examined i n d e t a i l .  their  26  Figure  3. C a t c h v a l u e by a r e a 1979 - 1981. For a d e s c r i p t i o n of a r e a n u m b e r s see F i g u r e 1 a n d T a b l e I I . 1979 c a t c h ||| ; 1980 c a t c h \ \ \ ; 1981 c a t c h / / / .  TABLE PERCENTAGE  Area Number  CATCH VALUE BY  AREA  II FOR GILLNET  BOATS  A r e a Name  1 Queen C h a r l o t t e I s l a n d s (North) Queen C h a r l o t t e I s l a n d s (W & E) 2 3* N a s s R i v e r 4* Lower S k e e n a Grenvi1le/Principe 5 Butedale 6 7* B e l l a B e l l a 8* B e l l a C o o l a Rivers Inlet 9 Smith I n l e t 10 1 1 S e y m o u r / B e 1i z e 12* A l e r t Bay - J o h n s t o n e S t r a i t s Q u a t h i ask i 13 14 Comox/Oua1icum Beach 15 Powe11 R i v e r Pender Harbour 16 17 Nana i mo/Ladysm i t h 18 Cow i c h a n 19 'V i c t o r i a ' 2 0 * J u a n de F u c a 21 ' O u t e r N i t i n a t ' N i t i n a t Lake 22 2 3 * B a r k l e y Sound 24 C l a y o q u o t Sound 25 N o o t k a Sound 26 K y o q u o t Sound 27 Q u a t s i n o Sound 28 Howe Sound 29* G u l f / F r a s e r R i v e r 31 + O t h e r a r e a s  * = areas c o n s i d e r e d in + = m o s t l y USA w a t e r s .  detail  1979  1979  Year 1980  0 . 14 0 .05 4 . 23 31 .07 1 .4 1 0 . 97 5 . 19 9 . 49 1 . 17 0 . 73 0 .66 2 .84 1 . 28 0 .09 0 .01 0 .02 0 .03 0..01 0 .00 2 , 27 0,.00 0..00 6 . 79 0. 01 0. 0 0 0. 0 0 0. 05 0. 61 29. 69 1.21  0 .92 0 .45 1 .1 42 1 1 .47 5 . 56 4 . 40 1 1 . 10 1 1 . 76 0 . 45 0 .97 0 .80 7 . 36 2 19 0 . 66 0..00 0.. 13 0. 02 0 00 0. 0 0 4 . 21 0. 0 0 0. 79 8 . 99 0 . 21 1. 45 0 . 52 0. 0 0 0 . 01 10. 10 1.81  -  1981  1981  1 . 13 0 .66 4 . 24 28 .40 1 . 74 1 .99 3 .60 7 . 92 2 .69 3 . 57 0 . 74 8 . 56 0 . 89 0 . 84 0 .00 1 . 99 0.. 13 0 01 0. 00 3 .59 . 0..00 O. 0 0 9 . 33 0 . 08 0 . 35 0 . 26 0 . 02 0. 00 16 . 29 0 . 95  Average  0 . 73 0 . 39 6 .63 23 .65 2 .90 2 . 45 6 63 9 .72 1 . 44 1 . 76 0.. 73 6 . 25 1 .45 . 0 . 53 0..00 0..71 0. 06 0 . 01 0. 00 3 . 36 0. 00 0 . 26 8 . 37 0 . 10 0 . 60 0 . 26 0 . 02 0 . 21 18 . 69 1 . 32  29  The data allow at l e a s t three ways of examining catch per unit e f f o r t of  different  expected i.  (CPUE); p i e c e s , weight and salmon s p e c i e s v a r i e s and  dollar  r e t u r n from one  e. an expected  expected  landed v a l u e . fishermen  The  price  trade o f f the  s p e c i e s a g a i n s t that of  another;  high c a t c h of a low value s p e c i e s a g a i n s t an  low catch of a high value one.  the best index of performance was per boat per week of f i s h i n g . used by H i l b o r n and Ledbetter  T h e r e f o r e I considered  to be landed value  in d o l l a r s  T h i s conforms with the methods (1979, 1984).  Data a n a l y s i s A f t e r necessary manipulation i n t o area s p e c i f i c  to break down the data set  i n f o r m a t i o n and boat  type  i n f o r m a t i o n , I used a number of s t a t i s t i c a l r e l a t i o n s h i p s between data,  i n c l u d i n g two  specific techniques  way  a n a l y s i s of  v a r i a n c e , Spearman's rank c o r r e l a t i o n c o e f f i c i e n t and regression.  The a c t u a l t e s t s used and  to t e s t  linear  the assumptions made in  t h e i r a p p l i c a t i o n are o u t l i n e d with the r e s u l t s of each of the tests.  30  NUMERICAL RESPONSES OF  FISHERMEN  Numerical responses of the salmon g i l l n e t The  f l e e t of g i l l n e t  B r i t i s h Columbia coast fleet  which has  by the'recent 1982).  fleet  boats f i s h i n g f o r salmon o f f the  i s one  of three components of a  a great excess c a p a c i t y .  T h i s was  community and  highlighted  Commission on P a c i f i c F i s h e r i e s P o l i c y  A major t o p i c for d i s c u s s i o n w i t h i n the amongst managers i s the  mechanisms as buy-back and  area  fishing  licencing.  these movements, and given  new  through such  For  effective  management, attempts must be made to determine how f l e e t -moves w i t h i n season along  (Pearse  i n t r o d u c t i o n of  management regimes to reduce the o v e r c a p a c i t y  fishing  the  fishing  the B. C. c o a s t , what determines  to p r e d i c t how  they  w i l l change, e s p e c i a l l y  known changes i n f i s h abundance. In a s p e c i f i c  the numerical (salmon),  f i s h e r y H i l b o r n and  Ledbetter  (1979) modelled  response of purse s e i n e r s to prey  but does t h i s a l s o apply  - the g i l l n e t t e r s ?  density  to another group of  These boats p o t e n t i a l l y pose g r e a t e r  a n a l y t i c a l problems as there are two  l e v e l s of  switching  i n v o l v e d ; not only between s p e c i e s of salmon but as many are a l s o l i c e n c e d to A p r e d i c t a b l e numerical evident  f o r 1979  to 1981  between  gears,  troll. response to i n c r e a s e s i n CPUE i s  inclusive  i . e. the number of  boats i n a p a r t i c u l a r week i s r e l a t e d to the d o l l a r boat i n the p r e v i o u s  p r e d a t o rs  week (Figure  gillnet  r e t u r n per  4).  Does t h i s r e l a t i o n s h i p hold f o r p a r t i c u l a r  s i t e s or  areas  31  F i g u r e 4. Number of boats i n week N + 1 vs landed v a l u e per boat i n week N - 1979 to 1981 - B r i t i s h Columbia. 1979 - + ; 1980 - X ; 1981 - l> .  NUMBER  o  o ui.. o o 81  o o zz  a? o o  OF  BQAT5  H>  K  o o o  U1  o o  WEEK  N  r u o o o  +  1  tr /u i o o  33  along the B r i t i s h Columbia c o a s t , and i f so how can i t be explained? H i l b o r n and Ledbetter about m o b i l i t y behaviour  (1979) put forward  of the B. C.  three hypotheses  salmon purse  seine  fleet: Traditional:  fishermen  p r e d i c t i o n of e f f o r t  f i s h i n t r a d i t i o n a l p a t t e r n s and the best  f o r an area w i l l be the h i s t o r i c a l e f f o r t  in that a r e a . Coast  wide e q u a l i z a t i o n : fishermen  w i l l move from area to area  to maximize t h e i r c a t c h , such that the CPUE i n a l l areas approaches approximately Area  the same v a l u e .  s p e c i f i c d e s i r a b i l i t y : each area has i t s own unique c o s t s  and d e s i r a b i l i t y and fishermen maintain  the r e l a t i v e average CPUE's i n the d i f f e r e n t  Traditional 'no an  w i l l move between areas so as to  or f i x e d movement p a t t e r n s can be equated to a  learning' situation  i n other f o r g e r s or p r e d a t o r s ; there i s  innate p a t t e r n of behaviour  in any p a r t i c u l a r Coast situation  not m o d i f i e d by f o r a g i n g success  s i t e or a r e a .  wide e q u a l i z a t i o n of CPUE i s e q u i v a l e n t to a where f o r a g e r s or predators  individually  maximize t h e i r energy r e t u r n , with the r e s u l t r e t u r n f o r each area or s i t e per u n i t equal.  In t h i s case  i n t o account is  areas.  the i n d i v i d u a l  time  move to  that the energy  i s approximately  forager or predator  developments i n adjacent areas  i f such  takes  information  available. Area  s p e c i f i c d e s i r a b i l i t y occurs where s i t e assessment i s  i n f l u e n c e d by d i f f e r e n t i a l c o s t s of f o r a g i n g e. g. there are  34  other f a c t o r s such as the s i t e ' s exposure to other p r e d a t o r s (or weather),  or the degree of a g g r e s s i v e i n t e r a c t i o n with other  f o r a g e r s that must be taken 1977).  Similar  assimilated,  i n t o account  (see Pyke et a l .  i n f o r m a t i o n from adjacent s i t e s  i s also  i f a v a i l a b l e , to weigh the advantages of f u r t h e r  movement. I years  analysed data from the B. C. salmon g i l l n e t  fleet  f o r the  1979 to 1981 i n c l u s i v e to see whether a p r e d i c t a b l e  numerical  response  hypotheses  i s evident and which of these three  best e x p l a i n e d the aggregation of boats  areas at p a r t i c u l a r times. these hypotheses  I then t e s t e d the r e l a t i v e value of  as management t o o l s .  mechanisms which a f f e c t  in particular  the numerical  I d i s c u s s the p o s s i b l e response  of the  g i l l n e t t e r s and how t h i s i n turn m o d i f i e s the outcome of any predict ion.  35  RESULTS AND DISCUSSION  Types of movement As  I was i n v e s t i g a t i n g the aggregation  of f i s h i n g boats i n  response to a n t i c i p a t e d or observed f i s h abundance, I c o n s i d e r e d it  important  to d i f f e r e n t i a t e between boats which moved between  areas and those that d i d not.  L i k e H i l b o r n and Ledbetter  I l a b e l l e d the former mobile, and the l a t t e r S t a t i o n a r y boats by i m p l i c a t i o n react  (1979)  stationary.  in d i f f e r i n g  degrees  to changes i n f i s h d e n s i t y and hence CPUE by e n t e r i n g or l e a v i n g the  f i s h e r y i n t h e i r area or s i t e only  .  Mobile boats are  i n f l u e n c e d by the q u a l i t i e s of other areas and move a c c o r d i n g l y . The'number of boats f i s h i n g each major area 1981  are o u t l i n e d i n Table  fishing  III.  from 1979 to  The t o t a l number of boats  i n any one year averaged about 2500, of which  approximately 30% f i s h e d only one a r e a .  The g r e a t e s t  proportion  of s t a t i o n a r y boats was i n the F r a s e r R i v e r , where the r a t i o of s t a t i o n a r y t o mobile was 1:2.5. (Area  T h i s c o n t r a s t s with the Nass  3) where the r a t i o was 1:200.  F i x e d movement  patterns  Although f i x e d movement p a t t e r n s s i t u a t i o n where there ignored, and  by f i s h i n g boats i m p l i e s a  i s no l e a r n i n g , and much i n f o r m a t i o n i s  a number of c o n s t r a i n t s may r e i n f o r c e such a p a t t e r n  the degree of 'no l e a r n i n g ' i s r e l a t i v e .  The c o n s t r a i n t s  i n c l u d e r e g u l a t o r y a r t i f a c t s such as h i s t o r i c a l  sequences of  TABLE BREAKDOWN OF GILLNET NUMBER OF EACH BOAT TYPE  III BOAT TYPE BY  AREA  FISHING EACH MAJOR  Area  AREA  8  12  20  23  29  1979 al 1 + stats* mob i 1 e  629 3 626  793 42 751  500 1 1 489  603 17 586  455 23 432  279 16 263  353 24 329  1253 462 790  712 13 699  900 28 872  84 1 12 829  640 27 613  878 59 819  329 7 322  618 59 618  1053 321 732  636 16 620  1001 84 917  581 6 575  655 25 .630  698 78 620  217 15 202  635 88 547  1 141 316 825  1980 al 1 + stats* mob i 1 e 1981 al 1 + stats* mob i 1 e  TOTAL NUMBER OF GILLNET  BOATS BY  TYPE BY  YEAR  Year  1979  1980  1981  al 1 + stats* mob i 1 e  2345 677 1668  2570 680 1890  264 1 793 1848  all + a l l = m o b i l e p l u s s t a t i o n a r y v e s s e l s ( i . e. that season). * stats = s t a t i o n a r y boats F o r an e x p l a n a t i o n o f a r e a numbers s e e T a b l e II  vessels  fishing  area  37  openings.  Other  p o s i t i v e reinforcements a r e n a t u r a l c y c l e  p a t t e r n s of salmon abundance which leads to a n t i c i p a t i o n of the run s i z e ,  strengthened  i n t u r n by p a r t i c u l a r good or bad years  in that area. The h y p o t h e s i s that fishermen move a c c o r d i n g to t r a d i t i o n a l p a t t e r n s i s supported (Andersen fleet  by a n t h r o p o l o g i c a l data  (H. Hsu, quoted  i n H i l b o r n and Ledbetter  e x c l u s i o n of other hypotheses. years i s r e q u i r e d to p r e d i c t  satisfactorily  t h i s h y p o t h e s i s i s poor,  1979).  seine However,  except by  A l s o a data s e t c o v e r i n g many  trends and these trends are l i k e l y  to be confounded by changes i n r e g u l a t i o n s .  it  fisheries  and Wadel 1972), and i n the B. C. salmon purse  such an idea i s d i f f i c u l t to t e s t  for  i n other  so i f unexpected  The sytematic base changes do occur  i s o f t e n d i f f i c u l t to i s o l a t e the reason, e. g. such as the  r e g u l a t i o n changes. From the data set i t i s p o s s i b l e to examine the r e l a t i o n s h i p between f i s h i n g areas and determine that a boat was  the p r o b a b i l i t y  f i s h i n g one area w i l l a l s o f i s h another  area.  This  c a l c u l a t e d by d i v i d i n g the t o t a l number of boats which  f i s h e d a p a r t i c u l a r area at some time d u r i n g the year  i n t o the  number which a l s o f i s h e d i n another p a r t i c u l a r area that year. The p r o b a b i l i t y f i s h another 1981  that a boat  f i s h i n g one area i n 1979 w i l l  area that year i s given i n Table IV ( f o r 1980 &  p r o b a b i l i t i e s see Tables V & VI r e s p e c t i v e l y ) .  U n f o r t u n a t e l y I d i d not examine data c o v e r i n g a four year  time  span or longer so that a comparison c o u l d be made between years of  dominant sockeye  salmon runs, where, f o r example I would have  TABLE I V P R O B A B I L I T I E S THAT BOATS F I S H CERTAIN AREA P A I R S  1979  AREA 31  11.0  30  1  1  1  1. 01  1  29 1  1  1  1  I  !  1  1  1  1  !  1  I  1  1  1  1  1  1  1  1  1  1  1  t  1  1  1  1  1  1  1  1  1  1  1  1  1  1  I  1  11. 01 * I • 1  I  1 •  . 21 1  i  1. 171  1 *  1 1 0 1 1 . 01  1  1  1. 0!  i  11. 01  1  !  1  28  1  27  1  1. 601  1  1  . 101  !  !  :  1  26  1  :  i  1  1  1  1  1  1  1  23 1  t  i  1  1  1  t  !  I  24 !  1 1. O i  I  11.0 1. 01  1  1. 0!  1. 30 i . 201. 101. 2 0 1 . 3 0 !  101  !  I  1  !  i  1  1  1  1  1  1  i  !. 4 0 !  1  1. 2 0  !  1  !  1  i . 20!  1. 4 3 !  1 *  1  !. 01 1.01  i  1  1  1  1  1  21  1  1  1  1  1  t  i  1  20  1  1. 761 • j  1  1. 01 . 331  1  11. 01  19 1  1  I  1  i  i  :  1  18 !  1. 601  1  !  . 201  1  17 1  1. 831  1  1  . 231  16 1  11. o:  1  1  :  IS  1  1. 3 0 !  1  1  i  14 1  1. 701  t  13 1  I. 361 • 1  1  12 1  !. 321 * 1 . 01 1  11  1  1. 481  . 021  10 1  1. 331  . 01 1  t  9 1  i . 341 • 1. 01 1  8 1  1. 4 0 ! • 1  7 1  1. 361  6 1  1. 341  1  1. 0 1 1 . 0 1  1. l O  1  . 01 1  1. 0 3 . 091. 191 041. 031. 131. 191 141. 03!. 081. 181. 121 I  !. 74 1 • 1 * :  i  •  11. O i l . 01  22 1  23  . 0 1 . 01  . :  1  1. 01  11.011.01  1  1. 101. 101. 101  I  !  1  !  I  !  I  1  1  1  1  1  1  1  1  1. 201  i . 20!  1. 20!  1. 12 . OBI.19!. 111. 07!. OS!. 231. 181. 041. 02!. 131. 0 3 1. 01 1. 01 1 I 1 1 1 1 I 1 ! 1 I I  . 01  1  1  !  1  1  I  1 . 01*  1. 01 ! . 081. 181 .061. 031. 0 9 ! .241  1  !  I  1  I  I  I  !. 4 0 !  1 1. 0  1  i  1  1  1  1. 201  1  1. 0 8 !  1  1  !  !  i  I  !  1  i  1  1  1  1. 01 .441  :  !. 291  1  . 01  1  . 131  1  1. 10!  1  1. 0 1 1  1  1  .131  1  1. 11 1  1  »  1  t  . 331  1  1. 131  1  . 021. 01 1. 141 .111. 6611. 0 1 . 471. 421. 84!. 47!. 03!. 031. 21 1. 111. 011  1  .201  1  !. 071  1  .021  i  . lOI  t  1. OBI  1  . 141  1  1.11!  1 • !  1  • •  1 1  1 •  3 1  i . 27 t  • S  t  4 1  1. 271 • 1 • I  1  . 131  1  1. I l l  1  1. 01 . 071  1  1. 031  1  . 021  1  I. 031  1  . 06!  1  1. 031  1  1  1  1  I 1 •  1  1  i  !. 0 8 ! .231. 13!  1. 01  1  1. 0  1  .731. 23!. 2 3 !  1  I  1  1  1  !  191 . 031. 041. 16!. 0 8 !  1 1  1  1  I  I  I  1  1  1  !  i  I  1  I  1  1  1. 2 3 !  1  1. 0 8  14!  1  1  I  !  !. 30!. 301. 2 3 !  I  1. 231  !  1  !. 371. 291. 4 3 !  1. 0!  I  1  1. 431  1  11. 01 .221. 37!. 161. 07!. 14 !. 281. 151. 071. 081. 22!. 121  1. 0 2  . 01 1. 101 1. 01. 48!. 061. 06!. 191. 271. 22!. 10!. 081. 26!. 21 1  1 •  . 011 « 1. 081 . 2 2 ! 1 .01. 171. 18!. 27 !. 321. 36!. 131. 131. 32!. 2 3 !  1. 0 3 ! . 101.661. 4311. 01. 31 I. 731. 231. 01 1. 021. 181. 0 9 1 . 011 . 01 1.031 . 141.421. 17!. 2211 .01. 8 B I . 291. 041. 031. 121. 0 7 !  !  •  1. 01  « 1  « 1. 041 . 10!. 391. 161. 13!. 4211 . 01.481. 091. 071. 291. 181 • 1 1 . 0  • I »  1. 031 . 10!.321. 111. 06!. 17!. 3811. 0 1 . 181. 22!. 321. 3 9 I •  1. 2 0  1. 0 4 ! . 121.311. 031. 01 1. 071. 311. 491 1. 0 1 . 281. 901. 7 2 1 . 011. 0 2 1. 021 .031. 16!. 021. 01 I. 02!. 12!. 301. 1411. 0!. 98!. 8 4 ! 1 • 1  » I. 0 4  » 1. 0 2 ! .071. 17!. 031. 0 3 i . 0 4 1. 211. 311. 191. 4 3 ! 1. 0 1 . 7 7 1 . 011. 0 3  3 1  1. 241 • 1 • 1  1  t  . 03(  1  I. 031  1  1. 0 2 .071. 171. 021. 021. 031. 171. 31 I. 21 1. 49!. 9711. 0 1 . 01 1. 0 3  2 1  1. 221  1  1.11 . 221  1  I  1  1  1  1 1  1. 381  1  1  . 091  1  1  !  1  31  30  29  28  27  26  25  24  23  22  21  20  19  18  17  1. 111. 111. 111. 111. 221 221. 111. 111. 361. 3611. 01 1  1. 0 6 .06!. 161  . 03 16  13  14  13  12  11  I 10  1. 131. 231. 131. 411. 731. 691 9  8  7  6  5  4  3  11. 0 2  1  AREA I f a boat f i s h e s an area l i s t e d along t h e s i d e , t h e f i g u r e s l i s t e d a l o n g t h e base. • •= p r o b a b i l i t y l e s s t h a n . 0 1 F o r a n e x p l a n a t i o n o f a r e a numbers s e e T a b l e I I .  given are the p r o b a b i l i t y  that  i t mill  also  fish  an a r e a  00 CO  TABLE V P R O B A B I L I T I E S THAT BOATS F I S H CERTAIN AREA P A I R S - 1 9 8 0 AREA 31 11.0  !. 0 4 1. 0 2 !  1. 02!. 02!. 02!. 0 2 !  30 I  11. o :  I  29  11. 01 »  i  I • I  !  I  , 02! I.021  1. 03!. 03 I  II. O i l . O i l . 011.01  I I . O!  11.01  2 8 11.0  i i . o n . oi  27 I  I. 301  I I . 01. 301. 381  26 I  I. 071  I. 0711. 01. 401. 031. 40S. 1 5 !  30!. 231  11.011.01  I. 121  I. 021  I. 271  I. 391. 01 I. 021. 1311.01. 131.711. 281  I. 431  I. 021. 0 2 !  I. 311. 01 I  1. 241  I. O i l . O i l  23  I. 601 « I, 01 I .041. 191 09 I I . 01. 2 2 I  I . 381 I. 67 1  I. 041. 341 1. 01. 80!. 171  2 2 1.01  I. 60!.01 I. 01 I. 06!. 281 07 1. 83!1. O!  21 I  I  I  I  20  1. 6 B I  t. 021. 221. 031. 721 3 3 !  I •  11.011.01  1. 301. 38 11. 01. 381. 381  2 3 1.01  I. 301  I.97! .0 3 !  03  111  03 1. 49 I. 14! 031. OBI 031. 261. 031  16 I  I. 871  I  13 I  11.01  14 I  !. SO I  13 I •  !. 451 » I. 01 I  I  I  021 0 9 !  I. 2 3 ! . 381  20!  1  I. 13  I. 221. 14!. 03!. 121. 121. 0 2 1. 181. 24 1. 121. 021. 181. 14 1. 031. 08 . 03!. 11 I. 21 1. 071. 071. 0 3 1. 111. 141. 061. 0 1 1. 201. 13 1. 031. 0 3  .03!  21 I. 20!. 331. 131. 14!. 0 2 i. 2 0 i . 3 0 i . 13!. 03!. 261. 2 0 ! . 041. 0 7  * !  I  I  I  « I. 04!. 01 I  . 21 S. 20!. 3 2  121  I _ I  12 S. 04 I. 231. 23!. 11 !. 0 4 !. 231. 161. 0 4 I . 10 I  I. 17! . 17!  71  . 171  I  171  1. 3 4 !  1. 01. 091. 01 i 17!  2 6 ! . 37!.  111. 171 . 34!. 1 4 !. 06 I. 0 6 !  171. 111. 09! . 2 0  1^11 I  . 0811. 01 . 031  471 61 I. 161  161.111 321. 181 . 081 .0 3 !  161. 131. 0 3 1. 26  I  i l l 01 I. 41 I. 2 0 ! 0  I  I 1. O i 1. O i l . O i l . 0 1 1 . 0 1 1 . 0  I. 3 8 ! . 6 3 ! . 1 3 !  I I. 831  IB I  I  I. 631. 301. 271. 021. 3 B I . 431. 131. 131. 23!. 201. 071. 13  19 I 17  I  » I . 0 3 1. 02 ! • I. 12!. 16!. 291. 08!. 0 9 1. 0 3 1. 21 1. 2 2 1 . 0 8 ! . 0 2 1. 02!. 131.03!. 0 7 I  ! 1. O!  I  I  .911. 06 t. 3 0 I . 0 6 1. 0 7 1. 0 3 1. 2 4 I . 191. 0 8 I . 0 2 1 . 181. 121. 0 2 1 . 1 0  I »  2 4 1.01 I *  1. 0 2 i . 021. 02 i . 0 2 1. 021. 0 2 I  I. 0 6 ! . 021. 331. 0 9 !  :  I. 021. 021  i. 02:. 02:  I. 0 2 !  I  I  I. S O U . 01  1  I I . 01 30!  !  I 301  I  I  !  I  I. 391  .031.041  I 1. O 1.311 . 51 I 2 2 !  131. 071 331. 401 16! 071. 331. 291 .081. 17 141. 081 411. 451 . 2 3 !.OBI. 401. 321 .031. 13  061 08! i _ ? I. 3 5 ! « I. 01 !. 041. 061 02!. 21 I 061  I. 181  . 021. 031 . 01 .I 15 1. 0 . 5 7 ! 13!  12 I «  I. 1 2 |  . 01 I. 031 •  11 !  1. 401  .021. 121. 13! 031. 321 14!  1. 2 5 !  . 031. 041  25!  321 . BO I1. 01 . 571. 181 701. 541 . 141.061 39!. 3 4 I 0 2 l . 10  10 I  !. 441  .021 09!. I l l  031. 321 131  ! 22I  . 02!. 0 3 !  16!  311 811. 31 I 1. 0!. 2 4 !.761. 3 7 I . 101.0 6 !  0  2  7  !  L  i 10! . 2 5 1. O! . 141  !. 0 6 ! 471. 5 3 ! . 2 4 !. 101.421. 3 2 ! .071. 13 L  261. 19 1. 01 1. 0 9  9  I. 01  I. S 8 I .01 I  01 I. 031 021. 21 I 0 3 !  I. 161  . 071. 0 4 !  131 331 61 I. 31 I 4711. 01 .911. 3 3 ! 171 .041. 271. 1 1 ! . 021 0 4  8  I *  I. 391 » I  04!. OS! 01 !. 21 ! 0 5 !  I. 12!  . 02!. 021  091 24!  . 0 1 I. 011  7 I  I. 241  . 01 I 031. 051 O i l . 1 7 ! 061  I. 10!  6  I. 1BI  • I 021. 041 O i l . I l l 0 6 !  !. 0 8 !  I •  O i l . 02!  •  I. 01 I  641  171 21 !. 131 1.0!. 621 2 2 ! .031. 321. 21 !. 03 1. 0 9  091 21 I 331  101 081. 0 4 1. 4711. OI . 3 8 !. 131.611. SOI. 161. 13  07!  20!  48!. 051 04!. 0 3 I 321. 7 3 I 1. 01 .281. 82!. 6 3 1 . 26!. 17  3 I »  1. 081 * I  1. 051 031  1. 0 5 !  .011 » I  051  121 341. 0 4 !  0 4 1.02 I 13!. 301 471 1. O! .931. 7 0 I .J32\. I B  4  I «  I. 21 ! * I « I. 02!. 0 3 ! 02!. 1 5 ! 0 5 !  !. 0 9 !  . 01 !. 01 !  071  171 41 i . 0 7 !  051. 031 221. 571 4 0 ! . 2 7 !1.01. 71 I. 21 I. 16  3  I •  I. 1 8 ! • I » I. 021. 031 0 2 1 . 1 1 ! 04!  I. 0 8 !  . 0 1 I. 0 1 !  07!  171 40!. 071 05!. 01 I 191. 5 9 ! 391. 251 9011. O I . 241. 17  2  I •  I. OBI # I  I. 031  .011.01!  061 0 8 !  1.14!  . 031. 041  13!  221 4 9 ! . 0 7 !  07 1. 0 2 I 23!. SOI 3 4 i . 2 0 !  14  13  10  1. 4 7 !  I I * 31  30  29  021 021 O i l . 0 7 ! 031  I • I 04! 28  27  26  06!  01!. 181 0 3 !  25  24  23  22  21  20  19  18  17  16  15  23!. 01 I 01 I. 01 I 081. 3 4 ! 331. 331 74 1. 6 9 I 1 J 5 I . 13  12  11  9  8  7  6  5  62 I . 3 4.! 1611. 0 4  AREA I f a boat f i s h e s an area l i s t e d along t h e s i d e , t h e f i g u r e s l i s t e d a l o n g t h e base. • - p r o b a b i l i t y l e s s t h a n .01 F o r a n e i p l a n a t l o n o f a r e a numbers s e e T a b l e I I .  given are the probability  that  i t will  also  fish  an a r e a  3  2  1  P R O B A B I L I T I E S THAT BOATS F I S H CERTAIN  AREA P A I R S - 1981  AREA 31  I  I  I  I  I  I  30  I  :  I  I  I  I  _ J _ I I  I  I  I  I  I  I  I  J l  I  I  I  I  I  I  I _ I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  I  1. 29!. 071. 281. 041. 131. 131. 2 3 1 . 161. 031. 0 2 1 . 22!. 14 1. 0 2 ! T l 4  29 I  I  11.01  I  I. 011. 041. 021. 331  I  1.13! » I « 1.011.231  28  I  I  I  I  I  I  l_  I  I  I  !  I  !  I  I  I  I  I  27  I  I  I  I  I  I  I  !  I  I  I  I  !  I  I  I  I  1*1  26  I  I  1.261  I  11.02.041  I  1.411  I  t  1.221  23  I  I  1.361  I  I. 0211. 01. 061. 631  I  l._44l  I.Oil.Oil  24  I  I  1.691  I  I  I  1.241  I  23  I  I  1.621  I  1. 031. 081. 0311 01.011  1.311  1711. 01. 7 2 !  I  I  I  ~ I  7~T 7  I  I  I  I  I  l  I » I  I  I  I  I  I • I * I • I • I  I  1. 37!. 111. 691. 261. 261. 111. 261. 281. 201. 041. 431. 2 8 1.117 2 8 1. 361. 101. 27 1. 071. 111. 131. 131. 1 0 ~ 1021. 0 4 1 . 221. 12 1. 211. 16  271  I. 661. 171. 661. 141. 1 4 1 . 2 1 1 . 171. 171. 141. 031. 34 1. 2 4 I  I.071.071  22  I  I  I  I  I  I  I  I  I  11.01  I  I  I  I  I  I  I  I  I  I  I  I  I  II.OI  I  11.011.01  21  I  I  1  1  I  I  l_  I  I  I  I  I  I  I  I  I  I  I  1  I  I  I  I  !  I  I  I  20  I  I  1.671  I  I. 031. 171. 031. 741  I  11.01 • I • I . O i l . 3 0 1  19 I  I  11.01  I  I  I  I  11.01  I  11.011.01  18 I  I  1.671  I  I  1.331  1.671  I  1.331  17 I  I  1.711  I  I  I. 061. 121. 391  I  I. 181.061  I  1  I  11.0 I  1. 301. 071. 281. 061. 111. 12 !. 171. 101. 04 1. 0 3 ! . 221. 13 1. 011. 12  11.011.01  11.01  1.28  1.411 071. 291. 051. 141. 15 1. 19 1. 141. OS 1. 021. I B 1. 10 1.021. 10  I 231 » I • I.021.231  II. O i l . 011.0!  1.331  ! 331  I  1. 331. 291. 6 3 I  11.01.331  I  I  I  1.671.331.331.331.33!  11.01  11.0!  I  I  I  II. 011.0  1  I  I  1. 41 1. 3 S 1. 351. 0 6 1 . 06 1. 06!. 2 4 1. 0 6 1. 121. 24  16 I  I  1.841  I  I. 041. 071. 011. 4 7 !  I  1.211 » I • I.0211.01  1. 46!. 211. 311. 081. 231. 221. 281. 191. 091. 041. 331. 221. 041. 2 3  13 I  I  I  I  I  I  I  I  I  14 I  I  1.361  I. 01 I. 031. 081. 031. 441  I  1.181 • I • I . O i l . 2 4 1  I  I  I  I  |  I  I  I  I  I  I  I  I  I  I  I  !  I  I  I  I  I  11. 01. 14 1. 421. 081. 191. 21 1. 34 1. 271. 091. 061. 331. 2 3 1. 031. 14  13 I  I  1.491  I  I. 031. 031. 031. 271  I  1.091.011  I.031.371  1. 4611.01. 621. 031. 241. 2 9 1 . 411. 241. 131. 031. 391. 28'". 0 3 1 . 1 7  12 I  I  1.461  I • I. 031. 031. 031. 271  I  1.091 • I • I. 021. 221  1. 331. 1311. 01. 11 1. 311. 3 3 1. 481. 31 1. 101. 041. 371. 2 3 1. 031. 14  11 I  I  1.471  I  I. 191. 061. 041. 331  I  1.131  I.Oil  10 I  I  1.331  I  I. 031. 031. 01 I. 3 2 !  I  1.091  I • 1.031.231  1. 411. 131. 771. 1811.01. 6 6 1 . 7 2 1 . 321. 031. 0 3 1 . 1 3 1 . 061. 021. 0 6  I  1.331  I  I. 021. 041. 021. 291  I  1.081  t • I. 021. 201  1. 371. 131. 731. 161. 3311. O 1. 7 2 1 . 361. 061. 031. 261. 13 1. 021. 0 9  9  I  1.231  1. 31!. 091. 8 2 I I . 01. 331. 3 4 1. 381. 321. 061. 061.411. 2 6 1. 07 1. 19  8  I  I  1.441  I  I. 021. 021. 011. 191  I  1.061  I • I . O i l . 131  1. 301. 111. 321. 091. 301. 3711. 01. 351. 101. 03!. 421. 2 7 1. 031. 0 9  7  I  I  1.31!  I  I. 031. O i l . O i l . 151 • I  1041  I  1. 271. 07 1. 371. 03!. 131. 21 1. 6 2 1 1 . OI . 21 I. l O I . 661. 4 7 1. 131. 14  6  I  I  1.231  I • I. 04!. 01 I. 01 I. 11 I  I  1.031 • I  I • I. I l l  1. 191. 081. 231. 021. 06!. 0 8 1. 2 4 1. 431 1. 01. 331. 931. 6 0 !. 191. 16  3 I  I  I. I l l  I  1.061  I  1.041  I • 1.031  I. 151. 041. 131. 031. 031. 0 4 1 . 141. 241 . 3811. 01. 971. 6 4 1 . 231. 13 1. 201. 071. 261. 041. 041. 0 9 1. 271. 381 . 231. 231 1. 01. 61 1. 191. 13  I. O i l . O i l  I • i.101  I  I  I  1.251  I • !. 021. 021. 011. 121 • 1  1.03! » I  I • I. 101  3 7  I  1.231  I • I. 021. 021. 01 I. 101 • I  1.051  I • I. H I  1.211. 08!. 261. 041. 031. 0 7 1. 2 8 1 . 4 3 ! . 261. 241. 9611. 0 1 . 221. 16  2 7  I  1.131  I  I.031.Oil  I  1.01! • I  I.Oil.061  1. 141. 04 I. I S 1. 031. 031. 0 3 1. 151. 3 3 1 . 24 !. 2 6 1 . 871. 6 4 1 1 . 01. 21  1 7  7  7757!  I  I. 061. 051. 031. 2 3 ! » I  1.101 » I  1.021.271  1.311. 111. 36!. 071. 071. 121. 221. 301 . 171. H I . 361. 3 9 1. 171 1. O  4  31  30  29  28  27  26  25  1.061  24  23  22  21  20  I f a b o a t f i s h e s an a r e a l i s t e d a l o n g t h e s i d e , the l i s t e d a l o n g the base. • - p r o b a b i l i t y l e s s t h a n .01 F o r an e x p l a n a t i o n o f a r e a n u m b e r s s e e T a b l e II.  I  19  18  figures  17  given  16  13  14  are  the  probability  13  12  11 that  10 it  9 will  8 also  7  6 fish  3 an  4 area  3  2  1  41  expected pairs,  very s i m i l a r p r o b a b i l i t i e s of f i s h i n g c e r t a i n area  i n response  to a n t i c i p a t e d  fish  numbers.  Consider the r e l a t i o n s h i p between boats ( B e l l a B e l l a ) and (Table V I I ) .  the immediately  The averages  f i s h i n g area 8  surrounding p r o d u c t i v e areas  f o r the immediately  adjacent  Bella  TABLE VII PROBABILITIES OF FISHING AREA PAIRS - BELLA P r o b a b i l i t i e s of f i s h i n g area 8  1979  1 980  Year 1981  If If If  0.29 0.58 0.52  0.22 0.47 0.47  0.48 0.62 0.27  f i s h area 4 f i s h area 7 f i s h area 12  Coola area  (Area 7) are r e l a t i v e l y constant, and owe  more to  immediate g e o g r a p h i c a l p r o x i m i t y than run s t r e n g t h s i n any p a r t i c u l a r year.  The p r o b a b i l i t e s f o r areas 4 (Skeena R i v e r )  and  S t r a i t s ) show no c o n s i s t e n t p a t t e r n and  12 (Johnstone  illustrate for  1978  the need f o r a longer time s e r i e s of data,  where sockeye  especially  and pink salmon catches were high i n a l l  four areas, and were higher than any (Canada  may  of the f o l l o w i n g  years  1981).  T h i s type of approach c o n t a i n s much i n f o r m a t i o n about gross area r e l a t i o n s h i p s e. g. i f an area l i c e n c i n g scheme was c o n s i d e r e d , but  i s not p a r t i c u l a r l y u s e f u l  week boat numbers.  Such an approach may  being  i n p r e d i c t i n g week to  be p o s s i b l e by  f o l l o w i n g s e q u e n t i a l movement of a s u b s t a n t i a l subsample of boats over the weeks for a number of seasons,  but would not  42  e x p l a i n these movements. T h i s type of approach  does have l a r g e r s c a l e long term  b e n e f i t s , as i t s u p p l i e s u s e f u l  i n f o r m a t i o n about  the dependence  of boats on p a r t i c u l a r areas and area combinations example, area l i c e n c i n g  Maximizing  schemes needed to be  i f , for  implemented.  i n d i v i d u a l boat r e t u r n  If each  fisherman  i s f o r a g i n g ( i . e.  moving) to maximize  h i s r e t u r n i n a p a r t i c u l a r week then the net e f f e c t movement between areas or s i t e s u n t i l  should be  the r e t u r n s i n every area  over the medium term are approximately e q u a l .  That i s ,  fishermens' movements along the coast and t h e i r aggregation i n areas a t - c e r t a i n opening  times should l e a d to CPUE i n a l l areas  tending towards a p r o v i n c e wide  average.  If fishermen are p u r e l y economic maximizers responding  in t r a d i t i o n a l  r a t h e r than  f i s h i n g p a t t e r n s they would  respond  q u i c k l y to v a r y i n g l e v e l s of CPUE along the B. C. c o a s t . o b s e r v a t i o n which should r e f l e c t  this  One  i s that movement t o , or  entry i n t o , an area i n c r e a s e s with i n c r e a s i n g CPUE. If I assume that knowledge of the p r e v i o u s weeks' CPUE from an opening  i n an area i s f a i r l y widespread,  I would i n f e r  the number of boats f i s h i n g an area i n the week  that  immediately  a f t e r a c e r t a i n c a t c h would be r e l a t e d to the CPUE of that previous week.  T h i s can be e x p l o r e d by r e g r e s s i n g the  v a r i a b l e s a g a i n s t each o t h e r .  As I showed i n F i g u r e 4,  two this  trend of i n c r e a s i n g numbers with i n c r e a s i n g CPUE i s c e r t a i n l y evident  f o r a l l g i l l n e t t e r s on the B. C. coast i n any one  year.  43  On a s m a l l e r s c a l e t h i s approach was Georgia period and  Strait  commercial t r o l l  f i s h e r y with data from a 10 year  (Ague et a l . 1983), where the r e l a t i o n s h i p between CPUE :  subsequent boat numbers was  purse  a l s o used for the  very s t r o n g , as w e l l as for  s e i n e r s i n e i g h t amalgamated areas on the B. C. coast,  where the f i t was Ledbetter  h i g h l y v a r i a b l e between years  ( H i l b o r n and  1979).  In mobile  gillnet  boats t h i s r e l a t i o n s h i p should  achieved by more boats moving in from surrounding stationary g i l l n e t fishery  boats  boats  the r e s u l t s were mixed.  not  from zero  i n 1981  week was  fishing  the  f o r the season  regressed a g a i n s t the  in that area the subsequent week,  For example, the slope of the  r e g r e s s i o n l i n e for area 29 different  For  trolling.  When the CPUE from one number of mobile  areas.  t h i s means more of them enter  i n t h e i r area, e i t h e r by s t a r t i n g to f i s h  or by s w i t c h i n g from  be  (Fraser R i v e r ) was  (p <0.05) i n 1979  (Tables VIII and  significantly  (Figure 5) and  1980  but  IX).  In comparison, the r e l a t i o n s h i p f o r area 4 (Skeena R i v e r ) was  not s i g n i f i c a n t  following  r e g r e s s i o n a n a l y s i s f o r the mobile  years i n a l l areas d i d the expected  boats  (Table V I I I ) .  r e g r e s s i o n a n a l y s i s to s t a t i o n a r y boats and  Only  for a l l  i n d i c a t e d that only i n area 8 ( B e l l a Coola)  p a t t e r n h o l d f o r a l l three years  boats combined was movement.  (Figure 5) or e i t h e r of the  years.  A similar  Extending  i n 1979  then a l l  s i m i l a r l y unsuccessful in predicting  i n area 8 were a l l the slopes of the r e g r e s s i o n  44  Figure  5. Number o f m o b i l e b o a t s i n week N + 1 v s l a n d e d v a l u e p e r b o a t week N - 1979 - a r e a s 4 & 2 9 . Area 4 S k e e n a R i v e r - + ; A r e a 29 - F r a s e r R i v e r - X .  BOO* ^  h  0.  1  1  1  1  1  1  1  1  1  1  500. 1000. 1500. EOOO. E500. 3000- 3500. 4000. 4500* 5000. V A L U E - B O A T I N WEEK N  CS)  TABLE NUMBER OF BOATS  VIII  IN WEEK N + 1 VS LANDED  SUMMARY TABLE OF SIGNIFICANT  VALUE PER BOAT  IN  WEEK N  REGRESSIONS  Area  All boats  Mobile boats  3  No  (1 )  No  (0)  No  (0)  4  No  (0)  No  (0)  No  (1 )  7  No  (1 )  No  (1 )  No  (0)  8  Yes  Yes  Stationary boats  Yes  12  No  (1 )  No  (0)  No  ( 1 )  20 •  No  (0)  No  (0)  No  (0)  23  No  (1 )  No  (0)  No  ( 1 )  29  No  (2)  No  (2)  No  (2)  The f i g u r e s i n b r a c k e t s a r e t h e number o f y e a r s i n w h i c h t h e s l o p e o f t h e r e g r e s s i o n l i n e f o r number o f b o a t s i n week N + 1 v s l a n d e d v a l u e i n week N i s s i g n i f i c a n t l y d i f f e r e n t f r o m z e r o at p < 0 . 0 5 . Thus : Yes = s l o p e s i g n i f i c a n t l y d i f f e r e n t f r o m z e r o i n a l l t h r e e y e a r s No = s l o p e not s i g n i f i c a n t l y d i f f e r e n t from z e r o i n a l l t h r e e y e a r s F o r a more d e t a i l e d e x p l a n a t i o n s e e t h e t e x t . F o r a n e x p l a n a t i o n o f t h e a r e a numbers s e e T a b l e II. F o r d e t a i l e d s t a t i s t i c s s e e T a b l e IX.  47  TABLE IX NUMBER OF BOATS IN WEEK N + 1 VS LANDED VALUE PER BOAT IN WEEK N REGRESSION STATISTICS & CORRELATION COEFFICIENTS Area  Boat Type Mobile  Stationary  Combined  1 979 3 4 7 8 12 20 23 29  1 .00 (0.06) 0.29 (0.27) 0.02 (0.48) <0.01 (0.52) 1 .00 (<0.01) 1 .00 (0.10) 1 .00 (<0.01) <0.01 (0.64)  1 .00 -(0.01) 0.03 (0.73) 1 .00 (0.02) 0.04 (0.30) 1 .00 (<0.01 ) 1 .00 • (0.12) 1 .00 (<0.01 ) <0.01 (0.58)  1 .00 (0.06) 0.28 (0.27) 0.01 (0.51) <0.01 (0.52) 1 .00 (<0.01) .1.00 (0.08) 1 .00 (0.04) <0.01 (0.69)  1 980 3 4 7 8 12 20 23 29  1 .00 (0.09) 0.35 (0.43) 1 .00 (0.01) <0. 01 (0.59) 1 .00 (0.07) 0.28 (0.13) 0.29 (0.10) <0. 01 (0.43)  1 .00 (0.05) 0.35 (0.42) 1 .00 (0.09) <0.01 (0.44) 1 .00 (0.04) 1 .00 (0.08) <0.01 (0.47) <0.01 (0.47)  1 .00 (0.09) 1 .00 (0.43) 1 .00 (0.01) 1 .00 (0.60) 1 .00 (0.08) 1 .00 (0.16) 1 .00 (0.08) <0.01 (0.49) P.T.O.  48  TABLE IX (continued) Area  Boat Type Mobile  Stationary  Combined  .07 (0.52) .07 (0.60) 1 .00 (<0.01) <0. 01 (0.82) .1 3 (0.19) 1 .00 (0.04) .06 (0.33) 1 .00 (0.04)  1 .00 (0.09) .09 (0.56) 1 .00 (0.10) <0.01 (0.64) <0.01 (0.55) 1 .00 (0.04) 1 .00 (<0.01) 1 . 00 (0.09)  .06 (0.53) .07 (0.60) 1 .00 (<0.01) <0.01 (0.83) .09 (0.24) 1 .00 (0.04) .09 (0.28) 1 .00 (0.06)  1 981 3 4 7 8 12 20 23 29  ( ) = correlation  coefficient r  2  These are the p r o b a b i l i t i e s that a l i n e a r r e g r e s s i o n does not account f o r number of boats appearing i n the week a f t e r a certain catch For an e x p l a n a t i o n of the area numbers see Table I I .  49  lines significantly  different  from zero in a l l years f o r mobile  and s t a t i o n a r y boats and f o r both types combined. These poor  f i t s were caused by l a r g e d e v i a t i o n s from the  the expected number of boats the next week. discussed  i n more d e t a i l  l a t e r where I attempt  good the method i s as a p r e d i c t i v e However, examination  These w i l l  be  to analyse  how  tool.  of the data from s t a t i o n a r y boats d i d  i n d i c a t e some s o r t of s a t u r a t i o n curve, as found by H i l b o r n Ledbetter  (1979).  There  i s a l e v e l of p e r c e i v e d r e t u r n to the  fishermen which must be exceeded  before he i s w i l l i n g  to forgo  a l t e r n a t i v e employment o p p o r t u n i t i e s or shoulder the cost of going f i s h i n g . is  illustrated  burden  T h i s s a t u r a t i o n curve f o r some of the areas  i n F i g u r e 6 f o r 1979.  I t i s apparent  that the CPUE at which s a t u r a t i o n occurs v a r i e s between a r e a s .  and  however,  significantly  In a d d i t i o n t h i s CPUE l e v e l v a r i e s f o r the same  area i n d i f f e r e n t y e a r s . For example the F r a s e r R i v e r has a large number of s t a t i o n a r y boats p a r t i c i p a t i n g There  i s a large  area who  gillnet  i n the f i s h e r y  (Table I I I ) .  ' r e s e r v o i r ' of fishermen w i t h i n the Vancouver only d u r i n g the F r a s e r R i v e r openings.  The  CPUE s a t u r a t i o n l e v e l of s t a t i o n a r y boats v a r i e s c o n s i d e r a b l y , with $200/week a t t r a c t i n g 450 1981 how  ( F i g u r e 7).  boats i n 1979,  but only 220 i n  T h i s type of r e l a t i o n s h i p may  good the season has been f o r t r o l l i n g  i n the  be m o d i f i e d by combination  boats, or a l t e r n a t i v e employment o p p o r t u n i t i e s , e s p e c i a l l y i n the Vancouver a r e a . B e l l a Coola  (area 8) i s c h a r a c t e r i z e d by a c e n t r a l  location  50  Figure  6. Number o f s t a t i o n a r y b o a t s i n week N + 1 v s l a n d e d v a l u e p e r b o a t i n week N - a r e a c o m p a r i s o n s . Area 4 S k e e n a R i v e r - + ; A r e a 7 - B e l l a B e l l a - X ; A r e a 12 Johnstone S t r a i t s - * ; A r e a 20 - J u a n d e F u c a < A r e a 23 - B a r k l e y S o u n d .  SO' ^  40- I  V A L U E - B O A T I N WEEK N  ($)  52  F i g u r e 7. Number of s t a t i o n a r y boats i n week N vs landed value per boat i n week N + 1 - F r a s e r R i v e r . 1979 - + ; 1980 - X ; 1981 - p .  500-  1000.  1500  E000.  E500.  V A L U E - B O A T I N WEEK N  3000-  3500-  4000.  CS) CO  54  (Figure  1) and e a r l y openings.  Movement i n t o t h i s area  approaches the ' i d e a l ' c o n d i t i o n s necessary i.  nearest  f o r the h y p o t h e s i s ,  e. there i s high p r o b a b i l i t y that boats based i n adjacent  areas w i l l  f i s h area 8 (Tables IV, V and V I ) .  why, of a l l areas, the p r e d i c t i o n holds here for a l l years  T h i s may e x p l a i n f o r a l l boat  types  ( F i g u r e s 8 and 9)  O v e r a l l t h e r e f o r e , n e i t h e r an approach r e g r e s s i n g c a t c h and numbers or one determining appears s a t i s f a c t o r y  the s a t u r a t i o n l e v e l  in predicting effort  in a particular  T h i s i s i n marked c o n t r a s t to the very good obtained f o r the whole p r o v i n c e  f o r an area area.  relationship  (Figure 4).  I examined the trends i n CPUE over the whole f i s h i n g  season  to see i f the CPUE i n each area was about the same i n any one week.  T h i s 'is dependent on the c o s t to movement at l e a s t to  a d j o i n i n g areas being s m a l l , the boat  i n q u e s t i o n coping  the c o n d i t i o n s i n a l l areas, and the i n f o r m a t i o n about  with  catches  in adjacent areas being good. Figure  10 i n d i c a t e s the l e v e l of e f f o r t  each week of the year Although  i n boat  i n s e v e r a l of the main areas  small numbers of boats do p a r t i c i p a t e  numbers  i n 1979.  in fisheries in  s e v e r a l areas w i t h i n a week, the f i g u r e s are a good guide  to the  t o t a l number of a c t i v e boats along the coast i n a p a r t i c u l a r week.  The t i m i n g of the f i s h e r i e s are w e l l d i s t r i b u t e d over the  year, a l l o w i n g great o p p o r t u n i t i e s f o r boats to move. demonstrated f o r example i n areas 4, 23 and 29. p a t t e r n s were observed of boats g i l l n e t t i n g  This i s  Similar  f o r 1980 and 1981, and the t o t a l number  i n a p a r t i c u l a r week i s very s i m i l a r  from  55  F i g u r e 8. Number of mobile boats i n week N + 1 vs landed value per boat i n week N - B e l l a C o o l a . 1979 - + • 1980 - X ; 1981 - £> .  NUMBER OF BOATS WEEK N+l  57  F i g u r e 9. Number of s t a t i o n a r y boats i n week N + 1 vs landed value per boat i n week N - B e l l a Coola. 1979 - + ; 1980 - X ; 1981 - t> .  NUMBER OF BOATS WEEK N+l  89  59  Figure  10. Number o f b o a t s o p e r a t i n g i n e a c h a r e a e a c h week - 1979. A r e a s 3 t o 8 as numbered. A r e a 12 - o ; A r e a 20 - $ ; A r e a 23 - + ; A r e a 29 - X .  60  o  "co  UJ uJ  5  "CM  _o M  o CM  o o o  o o  CO  Givoe JO  —I—  o o  CO  uaswnN  o o  o o  CM  61  year t o year effort  ( F i g u r e 11). There i s c o n s i d e r a b l e c o n t r a s t i n  between areas caused  by boats moving i n response to  a c t u a l or a n t i c i p a t e d changes i n f i s h numbers, but these do not affect years  the area d i f f e r e n c e s i n CPUE over the year and between (Figure 12, Table X) , and some areas appear c o n s i s t e n t l y  to have higher CPUE's than others e. g. area 4 vs area 29. Assuming that the u n d e r l y i n g d i s t r i b u t i o n major areas two  i s e s s e n t i a l l y normally d i s t r i b u t e d  way a n a l y s i s of v a r i a n c e of the average  in each year  (Table X ) .  between areas  I undertook a  CPUE f o r each area  I found that although  s i g n i f i c a n t d i f f e r e n c e between years  of CPUE f o r the  there i s no  (p < 0.05) the d i f f e r e n c e s  i n any one year are s i g n i f i c a n t l y d i f f e r e n t .  This  i m p l i e s that there are other f a c t o r s o p e r a t i n g i n a d d i t i o n to attempts  by the fishermen  to maximize t h e i r  r e t u r n per u n i t  time  by moving between areas along the c o a s t .  D i f f e r e n t i a l c o s t s and b e n e f i t s between areas Every area may have unique  f e a t u r e s of l o c a t i o n , weather  e t c . which makes i t p a r t i c u l a r l y d e s i r a b l e and compensates f o r the c o s t s and c o n d i t i o n s experienced.  T h i s i s the area  specific  d e s i r a b i l i t y hypothesis put forward by H i l b o r n and Ledbetter (1979).  The CPUE from each area tends to an average  over the  years which i s r e l a t i v e l y constant with respect t o both the p r o v i n c i a l average  and every other a r e a .  constant p r o p o r t i o n the RPA.  this  The RPA f o r the areas' CPUE  r e l a t i v e t o the p r o v i n c i a l average Although  I have l a b e l l e d  is illustrated  the RPAs do vary, p a r t i c u l a r l y  i n F i g u r e 13.  f o r some of the  62  Figure  11. T o t a l number of boats o p e r a t i n g 1979 - + ; 1980 - X ; 1981 - o .  1979  to  1981.  NUMBER  OF  BOATS  (x1000)  64  Figure  12. Value of c a t c h per boat per 1979. A r e a s 3 t o 8 as numbered. 20 - $ ; A r e a 23 - + ; A r e a 29 - X  week i n e a c h A r e a 12 - o .  area ; Area  CATCH  VALUE  PER  BOAT  ($)  TABLE X AVERAGE CPUE IN EACH AREA EACH YEAR ($/WEEK) Area  Year 1979  1 980  1 981  1 067  1 537  1 201  '4  2792  1 447  2551  7  1604  1 366  1 164  8  1230  1 260  1 402  875  887  1 482  20  1 21 5  1 229  2097  23  1226  1 008  1 364  29  1 129  510  960  Provincial average  1 582  1 225  3  12  1700  For an e x p l a n a t i o n of area numbers see Table I I .  67  Figure  13. Area Area  RPA b y a r e a 4-4 ; Area 20 - X ; A r e a  f r o m 1979 t o 1 9 8 1 . Area 3 7 t> ; A r e a 8 - , ^ ; A r e a 23 - > ; A r e a 29 - ^ .  - + ; 12 - <J  ;  68  69  mid-coast  l o c a t i o n s , the averages appear to stay i n the same  order or rank.  When the c o r r e l a t i o n between the rankings  between areas was compared between years using Spearman's Rank C o r r e l a t i o n c o e f f i c i e n t , t h i s s u p p o s i t i o n d i d not hold up (p < 0.05).  Notwithstanding,  the c o n t r a s t s between the Skeena River  (Area 4) and F r a s e r River The  former i s r e l a t i v e l y  probably  (Area 29) are p a r t i c u l a r l y  striking.  remote compared to the l a t t e r ; i t  r e q u i r e s higher CPUE t o compensate f o r t h i s .  which intermediate i n both  The areas  remoteness and exposure have CPUE  RPAs which are c o r r e s p o n d i n g l y  intermediate.  P r e d i c t i n q movement Applying each of the three hypotheses put forward by H i l b o r n and Ledbetter  (1979) f o r purse  s e i n e r s to e x p l a i n  movement of g i l l n e t t e r s produced mixed r e s u l t s .  F i x e d (or  t r a d i t i o n a l ) movements p a t t e r n s do not s a t i s f a c t o r i l y e x p l a i n movement, although are no dramatic  they may be u s e f u l i n the short term i f there  changes i n the u n d e r l y i n g d r i v i n g  f o r c e s i n the  f i shery. T r y i n g to e x p l a i n movement i n terms of i n d i v i d u a l maximizing t h e i r  r e t u r n worked w e l l f o r the e n t i r e  fishermen  province  (Figure 4) but when a p p l i e d on an area by area b a s i s only i n certain and  l o c a t i o n s with s p e c i a l c h a r a c t e r i s t i c s were c o n s i s t e n t  significant  relationships identified  (e. g. B e l l a C o o l a ) .  S p e c i a l f e a t u r e s of each area appear to modify the d r i v e by fishermen  t o maximize t h e i r  i n d i v i d u a l r e t u r n to the extent that  each area tends to a c h a r a c t e r i s t i c  r e t u r n with respect to  70  adjacent areas locations  i n any  one  year, but except  for p a r t i c u l a r  (e. g. Skeena and F r a s e r ) there i s much v a r i a b i l i t y i n  these r e t u r n s from year to year. The q u e s t i o n remains as to how hypotheses perform  good these l a t t e r  as management t o o l s to p r e d i c t boat  e s p e c i a l l y compared with,  start  season.  i s , I b e l i e v e , a l e g i t i m a t e t e s t of the hypotheses to  from the p o s i t i o n where the only data set 'known' i s that  for  1979,  how  w e l l the hypotheses e x p l a i n the  a p p l y i n g these data as a base and  Given  the l i m i t e d  to p r e d i c t how  1980  going  and  1981  forward data.  a f i s h e r i e s manager, faced with the need  many boats were going to f i s h  i n a given week, i . e  to the a n t i c i p a t e d salmon  in a p a r t i c u l a r  the a n t i c i p a t e d e f f o r t ,  in  wanted to p r e d i c t the number of boats  i n that area  p o s s i b l e c o n t r a s t at l e a s t  approaches, each r e f l e c t i n g a d i f f e r i n g the f i r s t  two  b e n e f i t s i . e.  d e s i r a b i l i t y h y p o t h e s i s , and was  i n the four d i f f e r e n t  l e v e l of knowledge.  the area  in the l a t t e r  specific two  I assumed each  moving i n an attempt to maximize h i s i n d i v i d u a l  r e t u r n , with the r e s u l t that the area CPUE tended p r o v i n c i a l average i . e. hypothesi s.  week and I  I used as the b a s i s of approach that each area  s p e c i f i c c o s t s and  fisherman  response  runs.  When i n f o r m a t i o n i s a v a i l a b l e on the CPUE i n one  f o l l o w i n g week, i t was  to see  set of data examined, I decided to take  the approach that I was  area  numbers,  for example, using the mean number of  boats p r e d i c t e d to be o p e r a t i n g i n each area each It  two  toward a  the coast wide e q u a l i z a t i o n  In has  71  In order fishing  to c a l c u l a t e the number of boats p r e d i c t e d  i n a p a r t i c u l a r area  to be  i n a p a r t i c u l a r week under the area  s p e c i f i c d e s i r a b i l i t y hypothesis,  I used the f o l l o w i n g method.  I m u l t i p l i e d the RPA by the p r o v i n c i a l CPUE to get the CPUE I would p r e d i c t f o r that area  that week.  I then d i v i d e d t h i s  into  the area c a t c h of that week to p r e d i c t the number of boats f o r the next week. For example, i f i n the f i r s t  week of 1980 the p r o v i n c i a l  CPUE was $1200 per week and the RPA f o r Barkley  Sound was 0.75  then: P r e d i c t e d area If  CPUE = 0.75 x 1200 = 900  the c a t c h value  that week was $27 000 then:  P r e d i c t e d number of boats i n Barkley  Sound i n second  week = 27000/900 = 3 0 . For  the coast  appropriate read  wide e q u a l i z a t i o n approach I s u b s t i t u t e d the  CPUE i n t o the r e g r e s s i o n  equation f o r that area and  o f f the p r e d i c t e d number of boats i n the f o l l o w i n g week. The  slope of r e g r e s s i o n  l i n e used may or may not have been  s i g n i f i c a n t l y d i f f e r e n t from zero, but i t was assumed f o r t h i s e x e r c i s e that the r e l a t i o n s h i p , although not n e c e s s a r i l y s i g n i f i c a n t , was v a l i d . o r i g i n a l regression  Note however, that s e v e r a l of the  slopes were negative.  As t h i s d i d not make  sense i n p r a c t i c a l terms ( i . e. the number of boats  decreasing  with i n c r e a s i n g catch) I r e c a l c u l a t e d the r e g r e s s i o n c o n s t r a i n i n g the slope this  ignores  t o pass through the o r i g i n .  the f a c t that every area  Although  undoubtedly has a pool of  boats which c o n s t i t u t e the minimum f i r s t  week's f i s h i n g  boat  72  numbers, t h i s approach at l e a s t from these The The  'marginal'  RPAs and  standardized  regression r e l a t i o n s h i p s .  CPUE f i g u r e s v a r i e d with the approach used.  four d i f f e r e n t approaches were as f o l l o w s : T h i s year's RPA  used the area (1980  and  and  the CPUE of the year  'perfect'  In t h i s I  in q u e s t i o n  the area c a t c h that week, to p r e d i c t the  s i t u a t i o n but does represent  u t i l i z e s a l l the l a t e s t  T h i s i s an  the approach which  i n f o r m a t i o n a v a i l a b l e , hence the  information.  Previous  year's  approach represents both the RPA and  RPA  and  l a s t weeks c a t c h  (FORCAST).  This  true f o r e c a s t i n g in the sense that I used  from the p r e v i o u s  1980  catch and  year  combined f o r 1981)  (1979  and  f o r 1980)  the previous  or  years  week's area  p r o v i n c i a l CPUE.  L i n e a r r e g r e s s i o n of c a t c h versus r e g r e s s i o n equation  used was  r e g r e s s i o n equation  data.  (PERFECT).  thus the number of boats i n that week.  unrealistic  (1979  t h i s week's c a t c h  s p e c i f i c RPA  or 1981), and  CPUE and  The  the p r e d i c t i o n s  f o r that area that year  used was  That i s , i t i s the  numbers where the (PERFLIN).  d e r i v e d from the whole  year's  ' p e r f e c t ' i n f o r m a t i o n case f o r the  coast wide e q u a l i z a t i o n h y p o t h e s i s . L i n e a r r e g r e s s i o n of c a t c h versus r e g r e s s i o n equation  was  that f o r the previous  computed from the lumped data (FORELIN).  numbers where the  T h i s represents  year  from the p r e v i o u s  (1980) or  two  years  (1981)  the t r u e f o r e c a s t i n g approach using  the coast wide CPUE e q u a l i z a t i o n h y p o t h e s i s . The  method of comparison was  to examine the sum  of squared  73 j  d e v i a t i o n s between the observed number of boats f i s h i n g a p a r t i c u l a r area and the number p r e d i c t e d by each of these different  approaches.  For example, the d i f f e r e n c e s i n the observed number of boats i n B a r k l e y Sound i n 1981 and the number of boats p r e d i c t e d by FORCAST are i l l u s t r a t e d  i n F i g u r e 14.  squares would not be p a r t i c u l a r l y  As a t a b l e of sums of  illuminating,  I r e l a t e d these  to a common f a c t o r , the mean number of boats f i s h i n g each week in each a r e a .  That  i s , were the sum of squared d e v i a t i o n s of  observed from p r e d i c t e d  f o r each of the four approaches any  bigger than would have been o b t a i n e d from the sum of squared d e v i a t i o n s of boat numbers from a y e a r l y mean? mean was c a l c u l a t e d fishing  f o r each area from the mean number of boats  i n a l l three years 1979 t o 1981 i n c l u s i v e .  I used an index S  S  2  In t h i s case the  = 1 - (  2  :  E (observed - p r e d i c t e d )  2  /  £ (observed - mean) ) 2  Where : p r e d i c t e d = number of boats p r e d i c t e d that week by that observed = number of boats a c t u a l l y  approach  f i s h i n g that week  mean = mean number of boats f i s h i n g per week i n that area If the S  2  approaches  zero than the denominator,  1, then the numerator and the approach  i s c l o s e r to  i s much b e t t e r than  j u s t using the mean number of b o a t s . If the S  2  i s near 0, then the approach  i s about as good as  j u s t u s i n g the mean number of boats. If the S  2  i s much l e s s than 0 then the approach  i s worse  74  Figure  14. Number of boats observed and number p r e d i c t e d by FORCAST f o r Barkley Sound i n 1981. Observed number of boats - +. Number of boats p r e d i c t e d by FORCAST - X.  S1V08  dO  U3BWDN  76  than  just  considering  boat  numbers.  The None  results  I n most  explaining boats the  fishing  index  that  values  which  and  were  number Juan  Figure very  de  Fuca  Can  these  deviations large  and  predicted  and  deviations Barkley In  Sound  boats.  the  as i n an  and  Barkley  the average  number  i s , most  of  of  approaches  generally  produced  FORCAST.  The  The  worst  20)  and B a r k l e y  due  there  Sound  to i t s  is a  relationships  consistently  values  best  possibly  R i v e r , where  explained  in Figure  14)  otherwise  numbers  further?  large  were  (area  negative,  found 23,  but  often  a  Are the underlying  consistent,  close  having  contribution  trend  large  o r a r e one  between  effect  on  and p r e d i c t e d  to the t o t a l  sum  of  f o r FORELIN  areas  in  or  observed, the  boat  f o r the Skeena R i v e r , B e l l a  the Fraser River  Sound,  the f o r e c a s t i n g  current  average.  be  them,  on m o r e  Coola,  XII are the observed  and  XI.  good a t  'perfect'  the Fraser  results  between  of  of the  based  in Bella  not only  the percentage  the basis  not as  taking  i n Table  That  FORELIN  were  boat  on  a predictor  the seasons.  i s , they  (area  errors  Table  That  found  than  well  by  outperformed  and  ( e . g.  two  both  Strait  14) w h i c h worse  just  across  than  stationary  much  In  were  are outlined  perform  by  as  negative.  FORCAST.  location, of  area  generally  relationships central  than  PERFLIN,  greater  of b o a t s  t h e o u t c o m e s were  were  and  information, FORELIN  cases  number  analyses  approaches  movement  PERFECT  in  of these  of the four  index.  t h e mean  index?  numbers,  squared Coola,  1981.  and PERFLIN,  the  deviations  T A B L E XI COMPARISON  OF A P P R O A C H E S  Area  Year  3  80 81  -  3.. 0 5 0.,41  80 81  -  o..92 .40  80 81  -  4 7  FORELIN  3,.13 1 .08 ,  TO P R E D I C T  BOAT  P E R F L I N FORCAST  —  (  .86 .89  -  .92 .73  -  i  -  .76 2 .09  -  NUMBERS  PERFECT  1 ,45 . *,73  .48 .78  <  0.,37 .10  -  .58 o!.60  .72 o!.63  -  .80 o!.53  8  80 81  12  80 81  -  0,. 9 6 4,.94  -  .42 o!. 2 2  -  20  80 81  -  0,.74 0,. 0 5  -  2 .90 0,. 0 2  - 1 0 , .30 - 1,.00  -  5,. 36 2,.18  23  80 81  -  1 .44 , 0,.91  -  29  80 81  For For  an e x p l a n a t i o n an e x p l a n a t i o n  .72 .47  of each of area  .93 .50  -  0.. 4 5 4.54  .68 .67  4. 1 0 2.. 6 6 -  .67 o!.12  2 .67 .91 -  -  0,. 8 2 5,.48  -  0,. 1 4 3,.08  .37 .02  .88 .28  -  .03 4,. 1 9  -  .16 2,. 5 3  heading numbers  see the t e x t . of Table I I .  TABLE  XII  WEEKLY OBSERVED AND PREDICTED BOAT NUMBERS Area  4 4 4 4 4 4  Week O b s e r v e d FORELIN boat Boats %* numbers +  29 30 31 32 33 34  845 872 716 559 372 375  689 628 544 53 1 524 48 1  Average  623  566  8 8 8 8 8 8 8 8 8 8 8 8 8 8  33 42 52 6 1 56 66 68 206 206 250 2 16 263 375 318  21 22 23 24 25 26 27 28 29 30 31 32 33 34  Average  158  82 84 80 104 98 1 19 120 146 255 188 332 4 15 • 261 177 176  0 0 0 0 0 0  . 1636 .4003 . 1989 .0053 . 1553 .0755  0 .0258 0 .0189 0..0084 0..0198 0 .0189 0..0301 0..0290 0. 0386 0..0258 0. 04 12 0.. 1443 0.. 2478 0. 1394 0. 2 133  PERFLIN Boats %* + .  912 750 528 494 475 362  0 0 0 0 0 0  . 0645 .2139 . 5080 .0607 . 1525 .0024  1981  FORCAST Boats %* +  498 836 707 346 228 174  587  465  72 0 .017 1 74 0 .0115 70 0 0036 93 0..0115 88 0..0115 108 0.,0198 1 100. 0198 136 0. 0 5 5 0 243 0..0154 177 0. 0598 320 0. 12 14 402 0. 2 169 250 0. 1754 166 0. 2593  26 36 46 45 78 52 41 35 85 1 18 105 216 335 343  165  112  0 0 0 0 0 0  . 5269 .0057 .0004 . 1985 .0907 . 1768  PERFECT Boats +  825 0 698 0 34 1 0 225 0 172 0 85 0  .0010 . 0744 . 3458 . 2743 .0984 . 2068  391 0 .0006 0 .0005 0..0005 0 . 0032 0 . 0061 0. 0025 0 . 0091 0 . 3664 0 . 1834 0 . 2183 0 . 1544 0 . 0277 0. 0200 0 . 0078  40 51 49 86 58 45 39 95 131 1 16 239 372 381 228 138  P.T.O  0 .0008 0 .0014 0 .0002 0..0107 0..0001 0..0076 0..0144 0. 2111 0. 0964 0. 3076 0. 0091 0. 2036 0. 0006 0. 1388  Table  XII  continued A r e a Week O b s e r v e d FORELIN boat Boats %* numbers +  23 23 23 23 23 23 23 23 23 23 23  25 26 27 28 29 30 31 34 35 36 40  Average 29 29 29 29 29 29 29 29 29 29 29  28 29 30 31 32 33 34 35 36 37 38  Average  31 1 372 390 267 203 149 43 94 227 223 206  65 168 165 310 203 172 234 173 192 210 62  226  178  502 351 421 420 705 61 1 916 561 610 531 601  501 353 406 423 950 587 715 369 374 394 451  566  502  0 0 0 0 0 0 0 0 0 0 0  . 2754 . 1894 . 2304 .0084 .0000 .0024 . 1660 .0284 .0056 .0008 .0944  PERFLIN Boats %* +  65 168 165 310 203 172 233 172 191 209 62  FORCAST Boats %* +  267 0 527 0 729 0 614 0 447 0 139 0 95 0 52 0 102 0 533 0 82 1 0  0 . 2753 O . 1893 0 . 2303 0 .0084 0 .0000 0 .0024 0 . 1642 0 .0277 0,.0059 0..0009 0..0943  177 0. 0, 0. 0. 0. 0. 0. 0. 0. 0. 0.  0000 0000 0010 0000 2552 0024 17 18 1567 2368 0798 0957  566 535 546 549 659 584 6 10 538 539 543 555  .0024 .0295 . 1409 . 1477 .0730 .0001 , .0033 .0022 .0192 . 1 178 . 4638  393 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  0235 1943 0897 0955 012 1 0042 5374 0030 0289 0008 0121  480 383 62 132 168 1200 6 14 1558 426 514 627  566  * P e r c e n t a g e c o n t r i b u t i o n o f t h a t week t o t h e + B o a t numbers F o r an e x p l a n a t i o n o f a r e a numbers s e e T a b l e  527 0 .0909 729 0 . 2484 614 0 .0978 447 0 .0631 139 0 .0080 95 0 .0057 42 0 .0000 102 0 .0001 533 0.. 1825 607 0 2874 1 19 0 .0147 359  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.  0002 0005 0655 0421 1465 1762 0463 5048 0172 0001 0003  390 0 . 63 0 . 134 0. 17 1 0 . 1221 0 . 625 0 . 1586 0 . 434 0 . 523 0 . 638 0 . 1 192 0 .  0094 0619 0615 0463 1987 0001 3349 0120 0056 0085 2606  634  560  total  PERFECT Boats %* +  deviation  squared.  II.  U3  80  from  t h e mean  occurrence the  in  boat  i n the last  PERFECT  being  these  latter  than  the l i n e a r  week  or small In  methods  Bella  than the  30%. first  almost  28%.  single  approaches.  the largest  Neither  46%  of  occurrence  total was  one  less  single  than  the dynamics  However,  i n none  while  29%.  rather  of the  have  followed  deviation,  followed  f o r a p a r t i c u l a r week  single  method  I n t h e FORCAST,  contributed  Coola  (area  well.  8),  to the t o t a l  In FORELIN t e n weeks,  sum  and  better  approaches  consistently  FORCAST,  in  of the fourteen  of  but again  Coola  the largest  squared  deviations  the contributions  the l a s t The  was  four  weeks  pattern almost  weeks.  was  was  less  were  low i n  variable  in  the deviations  in neither  a consistent  single  contributing  more  a l l of  However,  there  f o r a l l four  cases  PERFLIN  with  and  nor B e l l a  the p r e d i c t i o n s  In a l l f o u r  PERFECT  Sound  well.  a l lthe deviations.  four  than  with  deviations.  worked  contribution  less  approaches  the p r e d i c t i o n s  large  distributed,  numbers  the largest  Both  did  evenly  i n both  observed  event  were  pattern  were  Barkley to the  deviations. Table each  year  weeks  XIII  f o r which  which It  sets  in  the majority  to  the deviations,  the form  index. half  predictions  together  i s evident  o u t t h e number  of cases  only  although season  made,  over  50%  methods one  these  fished  and of  the  weeks  effect one  i n some a r e a s  on  o r two  i n each  t h e number  area  of  deviations.  i n a l l 8 main  o r two  and have a marked  However, the t o t a l  were  contributed  i n a l l four  o f weeks  areas  contribute  that most  the usefulness instances  (Skeena  River),  of  may i n most  TABLE  XIII  CONTRIBUTION TO 50% OF SUM OF SQUARED  Area  Number  DEVIATIONS  Minimum number o f weeks where t o t a l o f c o n t r i b u t i o n t o sum o f s q u a r e d d e v i a t i o n s >= 5 0 % . FORELIN PERFLIN FORCAST PERFECT  3 4 7 8 12 20 23 29  8 4 19 17 14 1 1 13 16  3 4 7 8 12 20 23 29  7 6 6 14 12 6 1 1 1 1  For  an e x p l a n a t i o n  of  area  numbers  see  Table  II  82  they  constitute  'distribution' observed  only of  depend as  the  more  the  index,  the  d e v i a t i o n s are  particular be  any  i.  e.  will  very  dynamics  or  and  in  number  better week  good  these  i s , the  method  of  contributing methods there  the producing  over  in  any  does  not  50%  predict  which  of  of  appear  large deviations will  (PERFLIN  area  XI)  than  but  specific  accurate with  (PERFECT).  to  occur,  the p r e d i c t i o n s  and  information  as  in comparison neither  lag  (FORCAST  the on  but e.  in the  knowledge  of  fleet  do  g.  (PERFECT  follow  see  fleet  fleet  Figure  reflect  when  general mean  methods  P E R F E C T may  boats  in  with  reflect  desirability  fact,  based  FORELIN) a r e  in prediction  a  In  FORCAST  the  may  14) real  have  the  runs  but peak  communication. could  be  react  not  number  of  the  there to  boats  expected  i f the  usually  continue  of  the  between  predictions,  either  these  one  than  However,  when  That  d e v i a t i o n s from  occasions  similar  (Table  less  If  trend  season.  squared area  of  about  The  deviations? do  the  XIII).  their  closely  What  the  very  numbers  immediately  within  the  approaches  FORCAST) a r e  world  of  possible to  well.  dynamics  on  (Table  linear  boat  sum  of  large.  accurate  annual  and  area  i t i s not  The more  the  consistency  be  10-20%  two  are  contributing  l a r g e number  developments that  were  fishing  areas  a  within  boats  is especially the  factors  once  evident that  the  will  mobile.  started  for  these  large  stationary  boats,  in adjoining areas,  area  truely  of  to  the  linear  for  Fraser  not  vary  as  Stationary the  much  which  worked  thus as  boats  season.  River,  approach  and  This is  also  83  consistently The season,  well.  c r i t i c a l entry and e x i t CPUE may a l s o vary during the f u r t h e r c o n t r i b u t i n g to the v a r i a t i o n .  should have a c e r t a i n criteria  fisherman  ' b a s e l i n e ' based on economic  such as o p e r a t i n g c o s t s , which should be exceeded  before the fisherman If  dollar  Each  commences f i s h i n g or c o n t i n u e s to f i s h .  the entry and e x i t CPUE does not vary I would  that the c a t c h per week i n the f i r s t approximately  expect  week should be  the same as that i n the l a s t week.  At f i r s t  glance t h i s does not appear t o be the case as the mean d o l l a r return all  i n the f i r s t  week and l a s t week f i s h e d  three years are d i f f e r e n t  (Table XIV).  by each boat i n  Furthermore, I  performed a c h i - s q u a r e d t e s t comparing the frequency distribution week f i s h e d  of earnings by each boat  i n the f i r s t  week f i s h e d  i n a l l three y e a r s .  That  to the l a s t i s , the  numbers of boats earning between $0-$100, $ 101-$200, $20l-$300 etc., any  in their  one year.  from the f i r s t  first  week of f i s h i n g and l a s t week of f i s h i n g i n  I found not only was the frequency week i n any one year  the l a s t week i n that year and  distribution  significantly different  (p < 0.05), but that the f i r s t  from  weeks  l a s t weeks r e s p e c t i v e l y a c r o s s each year were s i g n i f i c a n t l y  different  from each o t h e r .  These r e s u l t s modified fishermen  imply  i n p a r t that economic c r i t e r i a are  f o r example by ' t r a d i t i o n a l ' behaviour always s t a r t or stops i n a c e r t a i n  after a certain  opening  where the  week or before or  i n an a r e a .  However, even t h i s idea must be q u a l i f i e d ,  i n l i g h t of the  84  TABLE MEAN  AND  Year  MEDIAN  XIV  RETURNS 1979  Week  *Mean  IN F I R S T TO 1981  return  AND  LAST  +Median  WEEK  FISHED  return  ($)  ($)  1979 1979  First Last  582 660  260 377  1980 1980  First Last  827 768  498 512  1981 1981  First Last  850 732  403 390  *Mean d o l l a r e a r n i n g s o f b o a t s t h a t week + M e d i a n d o l l a r e a r n i n g s a b o v e o r b e l o w week w h i c h 5 0 % o f t h e f l e e t enter or leave the fishery F i r s t - d o l l a r earning i n f i r s t week t h a t b o a t fished Last - d o l l a r earning i n l a s t week t h a t b o a t fished  median each (or  return  boat  in  (Table  below)  Although  t o the f l e e t  which  and l a s t  i s the median enter  i n 1979, t h e median  week  dollar  earnings  (or leave) values  fished  the  by  above  fishery.  are almost  equal  1980 a n d 1 9 8 1 .  earnings. fleet  f o r 1979,  million  have  can a l s o  The t o t a l  especially not  This  50% o f t h e f l e e t  different  Deviations  $41  XIV).  i n the f i r s t  reported  1980, a n d  respectively after  taking  information  employment  significant  effect,  fishermen  dollar  1981  were  inflation  landings  fishing.  from  approximately  into faced  opportunities  especially  t o go  to v a r i a b i l i t y in total  i . e. n o t m a r k e d l y  on t h e c o s t s  alternative  forced  be a s c r i b e d  these  $34, $35 a n d  different,  account. by  the g i l l n e t  Although  fishermen may  as the c u r r e n t  have  Idid  or had a  recession  may  have  85  CONCLUSIONS Considering  fishing  convenient  framework  literature  about  With separate (i.  e.  dollar this  limited  That  aquatic  behave  and  and  disparate  resources.  like  may  move  a  a  in geographically  exhibit  i s , they  opening,  provides  c o n s i d e r a t i o n of  duration  may  maximizers  i n an  of  system  energy  the  appropriate  attempt  to maximize  between  sites  to  the  attain  goal. The  density the  of  response.  return  predator-prey  for cohesive  fishermen  economic)  numerical  a  exploitation  openings sites,  as  B.  functional have  C.  The Strait  important  coast  movements  and  thus  purse  gillnetters boat  (CPUE)  i n the  When by  area  some  areas  in  one  certain  week  learn)  and  less  by  with  C.  in  along  Georgia  i t is possible  i s a p p l i e d on  coast,  knowledge  a  finer  predict  boat  numbers  times.  Even  allowing  sufficient  fishermen  for t r o l l e r s  where  was  Gillnet  prey  boat  coast,  advantages,  was  predict  B.  areas,  approach  to  to  least  whole  between  The  ability  Not  the  differences  not  fishermen  of  as  the  well  as  to return  week.  approach to  of  implications.  promise  whole  previous  basis  at  shown  the  responses  effort.  seiners along  numbers  this  area  potential  control has  along  predict  numerical management  i s the  approach  and  and  such  as to  confounded  appear  to  economic  be  their obtain  by  more  criteria  a  i t only  than  on  worked  in  an  specific  reliable  by  i . e.  for consistent  site  number  bound  scale  of  predictions. factors.  tradition  B.C.  purse  (do  seine  not  86  fishermen economic  who  have  incentives  Further fishermen find  out  go  where they  unemployment  there  in  such  as  much  more  fish  later  also  of  Bella  poor  than  than  -  confound  the  and  tendency  expected for  more  to  of  many  test  gear  information).  expected  to  flexibility.  i n moving  to  which  returns. Bella  for  study  these  In  Coola  or  to  In  qualify  for  pure  of  should  a  factors  the  more  of  in  Johnstone  fact  remote  fishermen  comparison,  and  aspects  gillnetters  A n a l y s i s of It  may  encourages  complicating factors. the  some  There  to  stay  central  locations  Straits  offer  flexibility. of' s e v e r a l  these also  trends as  this  The  and  problem  gillnet  the  possible the  emerge,  home p o r t ,  of  fleet  and  boat  how size  may  fleet  has  combination  s e p a r a t e l y may  be  p r o p o r t i o n of  consistent  such  are  (fishing  or • Skeena)  Bella,  scope  movement  with  may  investment  illuminating.  whether  are  face  gillnetters.  the  earlier  fish  Nass  components  factors  the  g.  Further isolate  investment,  movement.  fishing  may  significant (e.  to  capital  insurance.  Location  areas  far higher  complicating  to  contrast,  be  a  to  be  within a  and  troller-  more  follow  these  two  in  detail  season  are  level  to  see  associated of  indebtedness. Finally,  a measure studying  the  be  possible  of  fishermen  simultaneously,  in  real  can  time  by  of  be  the  further  total  numerical  predation and  i f methods refined.  to  i n an  functional estimate  area  should  responses prey  density  87  LITERATURE  CITED  A c h e s o n , J . M. ( 1 9 7 5 ) . T h e l o b s t e r f i e f s : e c o n o m i c a n d e c o l o g i c a l e f f e c t s of t e r r i t o r i a l i t y i n the Maine i n d u s t r y . Human E c o l o g y 3_: 1 8 3 - 2 0 7 .  Allen,  lobster  K. R. ( 1 9 6 3 ) . T h e i n f l u e n c e o f b e h a v i o u r on t h e c a p t u r e o f f i s h w i t h b a i t s . I n t e r . Comm. N o r t h w e s t A t l a n t i c F i s h i n g S p e c . Pub. 5:5-7.  A n d e r s e n , R. R. ( 1 9 7 2 ) . 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