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An investigation of a potential carrying capacity of coho and chinook salmon in the Georgia Strait Mountain, Scot Alexander 1996

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A N INVESTIGATION O F A P O T E N T I A L C A R R Y I N G C A P A C I T Y O F C O H O A N D C H I N O O K S A L M O N IN T H E G E O R G I A STRAIT by SCOT A L E X A N D E R MOUNTAIN B . S c , The University of British Columbia, 1992  A THESIS S U B M I T T E D IN P A R T I A L F U L F I L L M E N T O F THE REQUIREMENTS FOR T H E D E G R E E OF M A S T E R O F SCIENCE in T H E F A C U L T Y O F G R A D U A T E STUDIES DEPARTMENT OF Z O O L O G Y  We accept this thesis as conforming to the required standard  T H E UNIVERSITY OF BRITISH C O L U M B I A July, 1996 © Scot A . Mountain, 1996  In  presenting  degree  at the  this  thesis  in  University of  partial  fulfilment  of  of  department  this thesis for or  by  his  or  scholarly purposes may be her  representatives.  permission.  of  ^ o o logjv.^  The University of British Columbia Vancouver, Canada  Date  DE-6 (2/88)  YVJ  ST*  . yPl !^ • 0  for  an advanced  Library shall make it  agree that permission for extensive  It  publication of this thesis for financial gain shall not  Department  requirements  British Columbia, I agree that the  freely available for reference and study. I further copying  the  is  granted  by the  understood  that  head of my copying  or  be allowed without my written  ABSTRACT  Stable or decreasing catches in conjunction with increasing hatchery releases have suggested decreasing marine survival rates for populations o f Pacific salmon (Oncorhynchus sp.) in the G e o r g i a Strait. I examined the possibility that a carrying capacity is imposing limits on the populations o f coho (Onchorhynchus kisutch) and chinook (Oncorhynchus tshawytschd) salmon. T w o investigations were carried out; the first involved an examination o f the impact that juvenile salmon have on their food supply. The second used a computer m o d e l to predict the possible results that a hatchery based fisheries manipulation might produce under different experimental protocols. The feeding study suggested that juvenile salmon might be having, a much greater impact on their available food supply than has previously been suspected. Overall, it was estimated that chinook and coho together consume an average o f 4 % to 6 % o f their main foods daily. I f these impacts are taken together with those o f other species, this suggests that a carrying capacity might w e l l be important. A hatchery manipulation experiment is one obvious way to test for a marine survival limit as i m p l i e d by a carrying capacity. U s i n g a metagaming approach to model such an experiment, insights were obtained into h o w it could be performed most efficiently. The results suggest that, depending on the required outcome, it w o u l d be advisable to maintain current exploitation rates o f both coho and chinook stocks during such an experiment. Other factors that w o u l d favor a rapid conclusion to the experiment are extreme as opposed to conservative manipulations, and m i n i m a l attempts to rebuild stocks through other means. However, even i f these recommendations are heeded, the m o d e l suggests that a hatchery experiment might need to be a long term project. W i t h reductions i n hatchery releases as h i g h as 75% every second year, average times to produce conclusive results were on the order o f a decade or more.  TABLE OF CONTENTS  Page TITLE PAGE  i  ABSTRACT  ii  LIST OF T A B L E S  v  LIST OF FIGURES  vi  ACKNOWLEDGMENTS  viii  C H A P T E R 1: G E N E R A L I N T R O D U C T I O N C H A P T E R 2: A D I R E C T I N V E S T I G A T I O N 2.1.  1 OF FOOD SUPPLY  6  INTRODUCTION  6  2.2. F E E D I N G S T U D Y  7  2.2.1. Materials and Methods  2.3.  7  2.2.2. Results  H  2.2.3. Discussion o f Stomach Contents Patterns  17  2.2.4. Feeding Study Conclusions  22  VIRTUAL POPULATION ANALYSIS  24  2.3.1. Introduction  24  2.3.2. Materials and Methods  25  2.3.3. Discussion o f V P A Results  32  2.4. B I O E N E R G E T I C S  2.5.  ••  36  2.4.1. Materials and Methods  36  2.4.2. Discussion o f Results  48  FOOD AVAILABILITY STUDY  60  2.5.1. Discussion  61  2.6. S U M M A R Y  67  iii  C H A P T E R 3: M E T A G A M E - A C O M P U T E R M O D E L T O A S S I S T I N D E S I G N I N G A L A R G E S C A L E FISHERIES E X P E R I M E N T  68  3.1. I N T R O D U C T I O N  68  3.1.1. What the "Game" in the Metagame Program Does  70  3.2. M A T E R I A L S A N D M E T H O D S  72  3.2.1. The Bayesian Assessment M e t h o d  72  3.2.2. The Stock Production M o d e l  74  3.2.3. U s i n g the Metagame  76  3.2.4. The Questions that Were Addressed  83  3.2.5. Simulations R u n  84  3.3. R E S U L T S A N D D I S C U S S I O N  85  3.3.1. Manipulation o f Stocking Reduction Levels, Hypothesis 4 (Marine C a r r y i n g Capacity) True  85  3.3.2. M a n i p u l a t i o n o f W i l d Catch Retention, Hypothesis 4 True  91  3.3.3. Reducing Exploitation Rates, Hypothesis 4 True  93  3.3.4. Increasing Exploitation Rates, Hypothesis 4 True  93  3.3.5. The Other Hypotheses  96  3.3.6. Conclusions  100  C H A P T E R 4: G E N E R A L C O N C L U S I O N S  ,  REFERENCES  102 104  iv  LIST O F T A B L E S Table  Page Table 1. Frequency o f occurrence and abundance by weight o f chinook stomach contents for five sampling periods during the summer o f 1993  11  Table 2. Frequency o f occurrence and abundance by weight o f coho stomach contents for five sampling periods during the summer o f 1993  12  Table 3. A taxonomic listing o f items identified in smolts' stomachs  13  Table 4. Release o f chinook juveniles from S E P facilities  27  Table 5. C h i n o o k salmon: adult hatchery catch contributions broken d o w n by smolt year and catch year....28 Table 6. C h i n o o k salmon; percent o f each hatchery smolt year represented in each catch (representation coefficients)  29  Table 7. C h i n o o k salmon; percent representation o f w i l d smolts in catches, assuming that hatchery smolts survive 8 0 % as w e l l as w i l d smolts to be caught as adults  30  Table 8. C h i n o o k salmon; number o f adults in each catch year that originated in each smolt year  31  Table 9. C h i n o o k smolts; estimates o f w i l d smolt population sizes derived from catch statistics and representation coefficients  32  Table 10. Prey consumption (cal/m-^/day) vs. abundance estimates (cal/rn-^)  61  Table 11. The parameter settings w h i c h defined the manipulations tested  85  v  LIST O F FIGURES Figure  Page Figure 1. Decreasing catches o f w i l d coho and chinook in conjunction with increasing releases from hatchery operations have ocurred in the Georgia Strait  2  Figure 2. F l o w chart showing the procedures involved in the direct investigation o f the food supply  7  Figure 3. Location o f the stomach sampling cruises on the Georgia Strait  9  Figure 4. C o h o : Stomach abundance by weight o f major prey items during the summer o f 1993  14  Figure 5. Chinook: Stomach abundance by weight o f major prey items during the summer o f 1993  16  Figure 6. Feeding regimes used in the bioenergetics model  23  Figure 7. F l o w chart showing the procedure followed in the modified V P A  25  Figure 8. C h i n o o k salmon; upper bound estimates o f number o f smolts entering the G e o r g i a Strait, assuming l  8 0 % ratio o f hatchery to w i l d survival  34  Figure 9. C o h o salmon; upper bound estimates o f number o f smolts entering the G e o r g i a Strait, assuming 8 0 % ratio o f hatchery to w i l d survival  35  Figure 10. Temperature regime used in the bioenergetics model  42  Figure 11. Range o f consumption estimates for major prey items (in calories/cubic metre/day)  53  Figure 12. The Metagame main user interface  76  Figure 13. The multitrial plots form shows the patterns in probabilities placed on the various hypotheses....78 Figure 14. The graph control form allows the metagame user to plot up to four graphs, each showing different information  80  Figure 15. The Game Parameters F o r m shows the various parameters and default values that affect the running o f a metagame  81  Figure 16. The M o d e l Parameters Form shows how the Metagame defines population production under the four different hypotheses  82  vi  Figure 17. The proportion o f trials that showed the probability o f hypothesis 4 being true to be greater than 9 0 % when hypothesis 4 was i n fact true  87  Figure 18. The proportion o f trials that showed the probability o f hypothesis 4 being true to be greater than 9 0 % when hypothesis 4 was true  89  Figure 19. The proportion o f trials that showed a specific likelihood o f hypothesis 4 being true  91  Figure 20. The proportion o f trials that showed the probability o f hypothesis 4 being true to be greater than 9 0 % when hypothesis 4 was true  95  Figure 21. Chinook: decline in the estimated probabilities o f hypothesis 4 being true when the other hypotheses were true  97  Figure 22. C o h o : decline in the estimated probabilities o f hypothesis 4 being true when the other hypotheses were true  98  vii  ACKNOWLEDGMENTS  This thesis w o u l d not have been completed without the assistance and support o f many people. I w o u l d like to thank m y supervisor, Dr. C a r l J. Walters, for suggesting, obtaining funding, and providing support for the project. Funding was provided v i a an N S E R C science subvention grant, and through a grant from the Ocean Production Enhancement Network ( O P E N ) . D r . Walters also conceived and produced the heart o f the Metagame model. Particularly the population production and the Bayesian assessment sub-models remained virtually unchanged from his original version. The staff o f the Pacific B i o l o g i c a l Station were instrumental in helping me obtain stomach samples from smolts collected in conjunction with their sampling cruises. I am especially grateful to Barb Thompson, Chris N e v i l l e and D r . Richard Beamish for their assistance. Identification o f stomach contents w o u l d not have been possible without the longterm and patient efforts o f Martha Haro. D r . J . D . M c P h a i l also provided some welcome assistance in identifying many specimens o f half digested fish larvae. The ecology graduate students o f U B C provided support throughout the project. Joe D e G i s i , Leonardo Huato, and Joel Sawada were always ready with sage advice on any topic. Chris Schell acted as a sounding board for m y ideas on many occasions. A n d the spruce grouse club provided me with the humor that was necessary to maintain m y perspective. Finally, thanks to T o m and Diane for getting me started and keeping me going. I couldn't have done it without y o u . A n d Sara, y o u ' v e kept me sane through some crazy times, while making some o f the saner times pretty crazy. Thanks for your support, love, and friendship.  viii  C H A P T E R 1: GENERAL INTRODUCTION  The coho (Oncorhynchus kisutch) and chinook (Oncorhynchus tshawytschd) fisheries in the G e o r g i a Strait are among Canada's most economically important natural resources. F r o m 1987 to 1990, the commercial catch o f these two species generated an average o f $63 m i l l i o n per year ( D F O 1992 a). However, this figure pales i n comparison to the income generated by tourism related to the extensive sport fisheries based i n the G e o r g i a Strait. Historically, recruitment to these fisheries came primarily from w i l d fish that spawned in the streams and rivers around the Strait. However, in the early 1970's hatcheries supported by the Canadian Salmonid Enhancement Program began to contribute a significant proportion o f fish to the total catch. Since then, the proportion o f hatchery fish in Georgia Strait catches has been steadily increasing, while the w i l d fish proportion has been declining. B y 1992, hatchery fish constituted up to 2 0 % o f the chinook catch (Cross et al. 1991), and h a l f o f the total coho catch (Walters 1993). The increase in hatchery releases and catches has not been accompanied by a proportional increase in the total numbers o f fish caught in the Strait. In the case o f coho, overall catches have remained more or less constant over the last thirty years (Walters 1993). However, proportions o f w i l d fish in the catch are significantly lower than historical levels . G e o r g i a Strait chinook catches have shown a significant decline from 1978 to 1989 (Cross et al. 1991). This decline in total catches is mirrored almost exactly by the decline in numbers o f w i l d fish being caught. Figure 1 illustrates the increasing hatchery releases, and the concurrent decreasing w i l d catches o f both species. The stable or decreasing catches, in conjunction with increasing hatchery releases into the Strait, suggests a major negative impact on the productivity o f w i l d salmon stocks. Concern among British Columbia's fisheries managers and scientists has led to several investigations o f possible causes o f the w i l d stock decline. Initial  1  recommendations from the Department o f Fisheries and Oceans ( D F O ) for reversing the trend focussed on fishing restrictions, habitat restoration, and continued hatchery production to enhance the failing stocks ( D F O 1992 b).  Figure 1. Decreasing catches of wild coho and chinook in conjunction with increasing releases from hatchery operations have ocurred in the Georgia Strait. Data from Cross et al. 1991. Chinook Salmon  (> /  T3  1s SJ  "rez£ 2 d)  § 3I SI  1980  1982  Year  2  M o r e recently, it has been suggested that the combination o f increasing hatchery releases with stable or reduced returns may in fact suggest the existence o f a carrying capacity limit that imposes an upper threshold on the numbers o f salmon the Strait can produce. Thus, it may be that the decline in w i l d stocks is a result o f competition with hatchery fish for limited resources. Other attempts to explain the decline have concentrated on environmental conditions such as ocean temperatures and pollution. In total, four main hypotheses have been advanced to explain the declines in w i l d coho and chinook abundances (Walters, 1993). These hypotheses are; 1. O v e r f i s h i n g . 2. Freshwater rearing habitat limitation. 3. Changing oceanographic conditions. 4. M a r i n e carrying capacity. Each o f these explanations has plausible arguments for and against it. The overfishing hypothesis is one that is commonly touted by both the media and D F O . In fact, there is supporting evidence to suggest that a very low proportion o f w i l d fish that recruit to the fishery survives to spawn. However, serving as evidence against this hypothesis is the lack o f information relating spawning stock sizes to recruitment. In fact, there are indications that some salmon fisheries have survived under m u c h higher exploitation rates. F o r example, Fraser R i v e r sockeye have been shown to sustain and even increase their populations under fishing impacts that are similar to those experienced by coho and chinook in the Georgia Strait (Walters 1993). Another popular theory is that the decline in w i l d stocks is the result o f a loss o f rearing habitat in freshwater streams and rivers. There are certainly some major impacts on these habitats due to human activities such as forestry, urban development and mining. However, it is not clear whether these activities impact rearing habitat positively, negatively or not at all from the fish's point o f view. In fact, evidence exists that habitat disturbances caused by logging operations may actually be associated with increased smolt production (Holtby  3  1988). E v e n i f habitat impacts are negatively affecting smolt production, it is highly unlikely that the amount o f habitat destruction that has occurred could account for the large reductions in w i l d abundance (Walters 1993). Further evidence against the overfishing and habitat destruction hypotheses is given by the apparently reduced marine survival rates observed for salmon in the Georgia Strait. These estimates come from coded wire tag ( C W T ) data summarized by Cross et al. (1991). This suggestion o f reduced survival implies that the smolt numbers entering the Strait must have stayed the same, or even increased, in order to produce the observed catches. H o w e v e r , this w o u l d not be the case i f overfishing or habitat limitation was occurring. I f either o f these hypotheses were correct, then smolt production must have declined, and marine survival must have remained constant or even increased. Unfortunately, the reliability o f the marine survival estimates is questionable, especially for w i l d stocks. Thus, they cannot be taken as definitive evidence against the overfishing or habitat limitation hypotheses. C W T data also provide evidence that any limit on salmon survival is impacting the young fish, i n their first year at sea. This conclusion can be drawn from the fact that the proportion o f w i l d one year o l d "jacks" in the catches has not decreased relative to the older fish (Cross et al 1991). Therefore, there does not appear to have been any reduction in survival between one year o f age and later years. This means that whatever is reducing survival rates is probably acting on the smolts shortly after they go to sea. The two hypotheses that are most consistent with decreased marine survival in the Strait are poor oceanographic conditions, and a marine carrying capacity limit. It is impossible, with the historical data, to either prove or disprove that an oceanographic influence has caused the decline in salmon survival. Environmental conditions such as water temperature and salinity are constantly changing, and these changes undoubtedly impact the resident species both positively and negatively. In particular, sea surface water temperatures have increased somewhat since the late seventies, and subsequent E l N i n o s have been responsible for higher than normal temperatures in the early 1990s. However, because o f the complexity o f the interactions i n the Strait in response to changing environmental conditions, it is not an enlightening excercise to hypothesize h o w these conditions might negatively impact salmon survival.  4  The hypothesis that appears to best fit the observations is that o f a marine carrying capacity limit. This hypothesis claims that, due to limited available resources, a restricted number o f salmon can be reared in the Strait each year. Thus, the proportion o f w i l d salmon in the total stock is being reduced as more hatchery fish claim a share o f the limited total capacity for adult production. The most obvious factor that might impose such a restriction is the limited availability o f food resources. G i v e n that the annual productivity in the Strait must be finite, it is plausible that the Strait can only produce a circumscribed number o f individuals o f each resident species. There is another reason that the possiblity o f a carrying capacity limit i n the Strait should be investigated. O f the four possible hypotheses defining the situation i n the Strait, a carrying capacity limitation w o u l d be the most easily corrected by fisheries managers. A simple reduction in hatchery outputs to appropriate levels should be enough to improve the survival o f w i l d stocks in the Strait. Therefore, it is important to analyze the G e o r g i a Strait fisheries for any evidence that the hatchery program may be the direct cause o f the destruction o f w i l d salmon stocks. The focus o f m y study was two-fold. Initially, I attempted to investigate the food supply o f G e o r g i a Strait smolts to see whether or not food could be imposing a carrying capacity limit on production. The second part o f my project i n v o l v e d using a computer model to help design a hatchery based experiment that could aid fisheries managers in differentiating w h i c h o f the four hypotheses is governing the current situation in the G e o r g i a Strait. The concept o f this experiment is to directly manipulate total hatchery smolt production on a large scale, to determine whether marine survival rates respond positively to hatchery smolt reductions as predicted by the carrying capacity hypothesis.  5  C H A P T E R 2: A DIRECT INVESTIGATION O F FOOD SUPPLY  2.1. I N T R O D U C T I O N This chapter details m y attempt to determine whether or not there is evidence o f a food limited carrying capacity for coho and chinook smolts in the Georgia Strait. The investigation proceeded v i a five steps (Figure 2). The first step was a detailed analysis o f the main food items being taken by smolts in the G e o r g i a Strait. The stomach contents o f nearly 600 smolts were excised and identified in order to determine what prey were preferred. U p o n completion o f the feeding study, a modified Virtual Population A n a l y s i s was performed, using existing catch statistics for Georgia Strait fisheries. This analysis provided an estimation o f the total numbers o f fish o f various sizes present in the Strait over time. H a v i n g obtained detailed information on what the smolts were eating, and how many o f them were present, a bioenergetics model was used to combine this information with smolt growth data. This model provideds an estimation o f the total food consumption needed to produce the coho and chinook populations in the G e o r g i a Strait. These food requirements were apportioned out to specific food items in appropriate proportions as indicated by the feeding study. The final step was to compare the estimates o f the amount o f major food items being consumed with estimates o f food availability. Due to a lack o f directly relevant food availability data, abundance and production estimates for the major prey items were obtained from historical oceanographic studies o f the G e o r g i a Strait and nearby coastal environments. This step was the constraining factor in m y ability to draw firm conclusions about the carrying capacity in the Strait. Because o f this difficulty, the results o f the feeding study may best be used to guide further research. Nevertheless, the final result o f this procedure was a comparison o f the amount o f food being eaten by coho and chinook with the amount o f food apparently available to them. This comparison was examined to ascertain whether or not it suggested the existence o f a carrying capacity limit.  6  Figure 2. Flow chart showing the procedures involved in the direct investigation of the food supply. Results of the feeding study and VP A were used in the bioenergetics model. Bioenergetics results were compared with abundance estimates to produce an estimate of overall exploitation.  Feeding Study  VPA  Used: Stomach dissection and historical data. Produced: Information on preferred food items and early marine growth rates.  Used: Data on hatchery releases and catch-at -age for wild and hatchery fish. Produced: Estimates of numbers of smolts necessary to produce observed catches.  \  Bioenergetics  Food Availability Study  Used: Computer bioenergetics model of coho and chinook growth and physiology. Produced: Amount of calories of each food type necessary to produce observed growth.  Used: Data on nekton abundance and productivity from historical studies. Produced: Values to compare with bioenergetics output.  Final Product Estimation of the impact young coho and chinook have on the available food supply.  2.2. F E E D I N G S T U D Y This section describes the stomach content analysis o f the salmon smolts. This procedure was carried out in order to gain a better understanding o f the food items that are important to y o u n g coho and chinook in the marine environment. It was necessary to obtain this information in order to allow a comparison o f the foods the salmon were eating w i t h the available amounts.  2.2.1. Materials and Methods In the summer o f 1993, juvenile coho and chinook salmon were collected in conjunction w i t h the Georgia Strait Juvenile Salmon Survey being carried out by the Pacific B i o l o g i c a l Station in N a n a i m o , B . C . Fish were sampled from four separate cruises, on M a y 25 through 28, June 14 through 17, June 22 through July 9 and July 5 through 8. Three o f the cruises followed a preset series o f transects that crossed the strait, and extended from the Fraser R i v e r plume ( 1 2 3 ° 2 3 ' W latitude, 4 9 ° 2 ' N longitude) in the south to Q u a l i c u m B a y ( 1 2 4 ° 3 7 ' W latitude, 49  7  ° 2 6 ' N longitude) on the northern end (Figure 3). Fishing on the fourth cruise was concentrated in the area o f the Fraser R i v e r plume. Sets were generally from 30 to 60 minutes in length, with the shorter time interval being used when the nets were filling more quickly. M o s t o f the sets occurred during daylight hours. 25 o f the sets on the Fraser R i v e r P l u m e Cruise occurred at night. F i s h were caught from the charter fishing vessel Qualicum Producer, using a dual beam trawl design. T w o nets, each with a mouth opening o f approximately nine metres circumference, were trailed from outriggers located amidships . The nets were trailed o f f the stern, well clear o f the wake o f the vessel. Sampling extended from the surface to a depth o f 6 to 7.5 metres. M e s h size on the cod end o f the nets was 2.5 centimetres. A liner with a mesh size o f 1.5 centimetres was used in the nets. Salmon were rapidly sorted from the catch and identified as to species. They were then measured, and either frozen or stored i n 10% formalin for later analysis in the lab.  Subsampling The first three cruises, w h i c h covered the entire strait, were divided geographically into six main areas depending on their depth and proximity to land (Figure 3). F o r every cruise, up to 30 fish o f both species were randomly sampled from the catch from each area. If fewer than 30 o f a species were caught in an area, then a l l o f that species from that area were analyzed. F o r example, if, during cruise one, 75 chinook were caught in area three, then 30 were randomly selected to have their stomachs excised. If only 12 coho were caught in the same area, a l l o f their stomachs were analyzed. For the Fraser R i v e r Plume cruise, chinook were randomly drawn from a total o f 84 separate sets. Since only 21 coho were caught on the entire cruise, all o f their stomach contents were analyzed.  8  Figure 3. Location of the stomach sampling cruises on the Georgia Strait. Three of the cruises followed the transects shown. These were further subdivided into areas as shown. The fourth cruise concentrated on the Fraser River plume.  9  to  Stomachs were excised from preserved fish in Nanaimo, and stored in 10% formalin for transportation back to Vancouver. Once in the Vancouver lab, stomachs were blotted dry and weighed on an electronic balance to the nearest 1 x 1 0 " ^ grams. Stomachs were then dissected, and the contents were emptied, rinsed, and examined under a dissecting microscope. The contents were identified and sorted into taxonomic categories. After being sorted into taxonomic groupings, the items in each group were blotted dry and weighed separately. The empty stomachs were also blotted and weighed. In total, stomachs from 575 juvenile salmon were analyzed. O f these, 335 were chinook and 240 were coho. After the stomach contents o f each fish were quantified individually, fish were pooled into five groupings. Each group consisted o f fish that had been caught within four day (ninety-six hour) time periods. This allowed a temporal comparison o f their diet compositions over the summer. 2.2.2. Results F o r each time period, the average frequency o f occurrence and the average numerical abundance b y weight o f each prey item was calculated (Tables 1 and 2).  Table 1. Frequency of occurrence and abundance by weight of chinook stomach contents for five sampling periods during the summer of 1993. Italics show sample size. May 25-28 (109) Prey  FO(%)* AW(%)*  June 13-16 (93)  June 21-24 (41)  FO(%) AW(%) FO(%) AW (%)  June 26-29 (28) FO (%)  AW (%)  July 4-7 (54) FO (%)  AW (%)  Fish larvae  22.9  12.6  26.9  12.5  48.8  17.4  32.1  13.3  33.3  14.0  Digested matter  38.5  23.1  72.0  25.6  97.6  42.9  82.1  33.2  75.9  41.1  Insecta  78.0  59.4  38.7  11.1  46.3  5.7  42.9  12.4  33.3  19.3  Gammaridean Amphipods  21.1  1.4  18.3  3.7  2.4  0.4  0.0  0.0  3.7  0.3  0.0  0.0  5.4  0.1  4.9  0.0  7.1  0.4  9.3  0.6  Cancer sp. larvae  27.5  1.6  40.9  12.8  65.9  28.6  64.3  21.6  55.6  23.6  Porcellanid larvae  0.9  0.9  40.9  33.3  4.9  2.2  50.0  19.1  1.9  0.6  Euphausiacea  1.8  0.1  3.2  0.9  0.0  0.0  0.0  0.0  1.9  0.2  14.7  0.1  23.7  0.1  26.8  2.9  7.1  0.0  27.8  0.2  Hyperidean Amphipods  Other identifiable matter  * Frequency of occurrence (FO) indicates the number of nonempty stomachs in which the prey item was present in any amount. Abundance by weight (A W) indicates the percent of the total weight of stomach contents constituted by the prey item.  11  A n y prey item that consistently made up more than 5% o f the diet (by weight) was considered to be a major prey item. These included fish larvae, terrestrial insects and cancer sp. larvae for coho, and the same items, with the addition o f porcellanid crab larvae, for chinook. The average percent abundance by weight was plotted for each o f these items for each species (Figures 4 and 5).  Table 2. Frequency of occurrence and abundance by weight of coho stomach contents for five sampling periods during the summer of 1993. Italics indicate sample size. May 24-27 (89) Prey  FO(%)* AW(%)*  June 13-16 (7/)  June 21-24 (//)  June 26-29 (8)  FO(%) AW(%) FO(%) AW (%)  July 4-7 (51)  FO (%)  AW (%)  FO (%)  AW (%)  Fish larvae  50.6  21.4  40.8  11.2  63.6  16.8  62.5  22.5  21.6  Digested matter  82.0  33.2  81.7  27.5  81.8  18.6  75.0  18.4  80.4  18.3  Insecta  92.1  34.8  39.4  10.8  72.7  10.3  0.0  0.0  5.9  0.3  Gammaridean Amphipods  49.4  4.3  5.6  0.1  9.1  1.3  0.0  0.0  2.0  0.0  1.1  0.1  19.7  2.2  18.2  2.2  0.0  0.0  17.6  1.9  Hyperidean Amphipods Cancer sp. larvae  5.2  65.2  5.7  74.6  45.8  81.8  50.9  87.5  59.1  94.1  74.1  Porcellanid larvae  0.0  0.0  4.2  0.8  0.0  0.0  25.0  0.0  3.9  0.0  Euphausiacea  0.0  0.0  5.6  1.3  0.0  0.0  0.0  0.0  0.0  0.0  15.7  0.6  25.4  0.3  18.2  0.0  25.0  0.1  37.3  0.1  Other identifiable matter  * Frequency of occurrence (FO) indicates the number of nonempty stomachs in which the prey item was present in any amount. Abundance by weight (A W) indicates the percent of the total weight of stomach contents constituted by the prey item.  T a x o n o m i c identification o f prey items in the fish was limited due to the digested nature o f many o f the stomach contents. Because o f this, most o f the contents were grouped into fairly broad categories, generally not proceeding beyond the level o f class or order. However, when a w e l l preserved specimen was encountered, attempts were made to classify it as specifically as possible. Table 3 is a taxonomic list o f all the items that were found in the stomachs o f both species o f juvenile salmon. Detailed identification o f items to the species level is presented where possible. H o w e v e r , the species presented should be considered as examples, and not a complete list. For the more specific taxonomic groups (family, genus, and species) the inclusion o f one group does not imply that other groups were not consumed. They may have been present, and simply not identified due to their advanced state o f digestion.  12  Stomach Content Change Over Sampling Period: Coho O n examination o f the food items taken by coho at different dates over the summer (Figure 4), two readily apparent changes in diet composition are seen. Early in the summer, terrestrial insects made up a large proportion o f the coho diet (35% by the end o f M a y ) .  Table 3. A taxonomic listing of items identified in smolts' stomachs  Hemigrapsus nudus (larvae) Hemigrapsus sp. (larvae) Section A n o m u r a F a m i l y Porcellanidae (larvae) Class Arachnida Class Insecta Order Coleoptera (beetles) Order C o l l e m b o l a (springtails) Order Diptera (flies) F a m i l y Tabanidae (larvae) Order Hymenoptera (wasps)  PHYLUM COELENTERATA Class Hydrozoa P H Y L U M N E M A T O D A (as parasites) Anisakis sp. PHYLUM ARTHROPODA Class Crustacea Subclass Ostracoda Conchoecia sp. Subclass Copepoda Order Calanoida Acartia sp. Epilabidocera sp. Candacia sp. Order Cumacea  PHYLUM MOLLUSCA Class Cephalopoda Loligo sp.  Lamprops sp. Order Isopoda Gnorimosphaeroma sp. Order A m p h i p o d a  PHYLUM CHORD ATA Class Osteichthyes (larvae) F a m i l y Clupeidae (herrings) Clupea harengus pallasi (pacific  SubOrder Gammaridea Elasmopus sp. Stenothoides sp. Stenothoides burbanki Suborder Caprellidae Suborder Hyperiidea Hyperia sp. Hyperiella sp. Hyperiella macronyx Order Euphausiacea  herring) F a m i l y Scorpaenidae (scorpionfishes) Sebastes sp. F a m i l y Hexagrammidae (greenlings) Family A m m o d y t i d a e (sandlances) Ammodytes hexapterus F a m i l y Salmonidae (salmonids) F a m i l y Pleuronectidae (righteye flounders) PLANT MATTER Phaeophyta (brown algae) Fucus sp.  Euphausia pacifica Order Decapoda Suborder Reptantia Section Brachyura F a m i l y Cancridae  INORGANIC M A T T E R Wood Plastics Fishing L i n e  Cancer sp (larvae). Family Grapsidae 13  A s the summer continued and insects became less important, brachyuran crab larvae became increasingly important, m a k i n g up as m u c h as 7 4 % o f the coho diet by the end o f the sampling period. L a r v a l fish comprised the other main constituent o f the juvenile coho diet. These appeared to be an important food item throughout the sampling period. W h i l e the amount o f fish larvae being eaten varied somewhat by date, they never made up less than 5% o f the total diet by weight at any time.  Figure 4. Coho: Stomach abundance by weight of major prey items during the summer of1993. Italics indicate sample sizes. Terrestrial Insects  Fish larvae 43  36,  o>  „ 35 .c  £30  OJ  5 '5 * sr n  | 30 >. » u  8 r C  |c  (0  •o  I' 1  2 5  20  in 15  nto  s 10 Oi u  llllllll  V c D_ O •  u 0)  o- 5 : # ^lllflliSK«-  0  . 1SM=y  S 10  4-Jtn  12-Jm  ZXlr  28-Xn  I_ t 6JJ  14JJ  0 1M/ty  27-My  40n  12-Jin  ZkJm  2SOn 6 J J  Date  Decapod Larvae „70 jz CT) >. u TJ  C  Jl30<o  £20, t) 0)  O-100 19My  27+%  "kin  12-JLn  2Ckln  Since coho fork length increased regularly over the summer, the pattern o f diet composition with respect to length is similar to diet composition with respect to date. Terrestrial insects were an important diet item in smaller coho, but as the fish got larger, they appeared to stop eating insects altogether. Conversely, the larger fish appeared  14  to be eating more brachyuran crab larvae than did smaller fish. Fish larvae were a significant diet component for almost all sizes o f juvenile coho. There is some suggestion that the very largest fish were eating almost exclusively crab larvae, and that fish larvae were a relatively unimportant diet item for these fish. However, it should be noted that the sample sizes o f fish at the tails o f the length distributions were small. Thus, the reduction in diet variation might have been due to the fact that, in some cases, only one or two fish were used to determine an average diet. In the middle size ranges, up to twenty five fish contributed to the sample o f stomach contents.  Stomach Content Change Over Sampling Period: Chinook The pattern o f food utilization was not as clear for juvenile chinook as it was for coho (Figure 5). L i k e the coho, early in the summer chinook appeared to be utilizing terrestrial insects to satisfy a high proportion o f their dietary needs (60%). This proportion dropped off considerably as the summer continued, but at the latest sampling dates it increased again to 2 0 % . Overall, it appears that the pattern o f eating large amounts o f terrestrial insects early in the summer, and fewer later on occurred in chinook as w e l l as coho. However, the chinook ate more terrestrial insects at the earliest and latest sampling dates than the coho did. A l t h o u g h it is not as regular or as dramatic as the pattern seen in the coho, juvenile chinook also appeared to be eating an increasing amount o f brachyuran crab larvae as the summer continued. U n l i k e the coho however, the chinook also ate anomuran crab larvae (of the family porcellanidae) in significant amounts in the middle o f the sampling period (up to 3 3 % o f the diet, by weight). Since the size and shape o f the porcellanid larvae are considerably different than that o f the brachyurans, it is assumed that juvenile salmonids w o u l d perceive these two species as distinct diet items. W i t h respect to fish larvae i n the diets o f the juvenile chinook salmon, more or less the same pattern occurred as was seen in the coho. Fish larvae continued to constitute a significant proportion o f the diet throughout the sampling period. Figure 5 summarizes the variation in major prey items taken by chinook over the sampling period.  15  Figure 5. Chinook: Stomach abundance by weight of major prey items during the summer of 1993. Italics indicate sample sizes.  Decapod larvae  Porcellanid Crab Larvae  The increase in chinook fork length was not as regular over the sampling period as it was for coho. This may have been due to the confounding effects o f 0+ and 1+ fish being m i x e d in the same samples. Nevertheless, when comparing diet composition with respect to fork length for chinook, it becomes clear that larger fish consumed fewer terrestrial insects than smaller fish. The only exception to this obvious pattern occurs at a fork length o f 180- 185mm, but since only one fish was sampled i n this category, it is unclear as to whether or not this exception indicates a general pattern. The data for other fish around the same size suggest that terrestrial insects are not a c o m m o n diet item for larger fish. The increasing consumption o f brachyuran crab larvae with increased chinook fork length is also quite evident from the data. W h i l e this pattern is not as pronounced as that seen for juvenile coho, it does appear that crab 16  larvae become a more important diet item as fish grow. Utilization o f porcellanid crab larvae by the chinook appears to occur generally at the smaller size classes, but is coincident with the reduced use o f insects as a food item. The largest size classes did not appear to be eating porcellanids. Except for the very smallest chinook, most size classes appeared to be eating a significant proportion o f fish larvae. Thus it appears that salmon o f most o f the sizes sampled were eating fish larvae throughout the entire sampling period. Probably the most striking pattern that is apparent i n the chinook diet composition vs. fork length comparison is the general increase in digested matter in the fish gut with increasing size. This may be due to the preservation method chosen, in that digestive enzymes in a larger fish may take longer to freeze and be inactivated than those in a smaller fish. However, the fact that a similar pattern was not seen in juvenile coho over a similar size range suggests that this is not the case. A simpler explanation presents itself when one considers the length/frequency distribution o f juvenile chinook. Increased amounts o f digested matter were found i n size ranges where fewer fish were sampled. Therefore this is probably another reflection o f reduced variability i n diet composition due to a reduced number o f guts contributing to the pool o f sampled stomach contents.  2.2.3. Discussion of Stomach Contents Patterns Historically, most salmonid feeding studies have concentrated on fish at ages either older or younger than the target age for this study. Thus, many publications exist detailing dietary habits o f adult salmon at sea, and juveniles in the fresh water or estuarine environment. However, there are relatively few that deal with juvenile salmon in the marine environment, and even fewer that concentrate on the Georgia Strait. Nevertheless, in order to carry out bioenergetics calculations, it was necessary to get a broader idea o f normal feeding patterns for juvenile coho and chinook salmon in the Georgia Strait. Therefore, I compared the results o f m y feeding study with others similar to it, in order to see i f some overall pattern could be discerned (Foskett 1950, Shapovalov 1954, Prakash 1962, LeBrasseur 1966, M a n z e r 1968 and 1969, Robinson 1969, M y e r s 1979, Healey 1980, Brodeur 1989).  17  Terrestrial Insects The utilization o f a large proportion o f insects is not unheard o f among juvenile salmon feeding studies. However, insects have rarely been shown to be as important for both coho and chinook as they were in the current study. A s early as 1950, Foskett reported a surprisingly large proportion o f terrestrial insects i n both coho and chinook caught in the N a n a i m o area. Robinson (1969) showed juvenile fish in the Georgia Strait to be consuming an assemblage o f insects remarkably similar to that in the current study. However, M a n z e r (1969) found diets o f y o u n g coho i n the Chatham sound area to have only about 5% insects. M o r e recently, off the coast o f Washington and Oregon, Brodeur (1989) found insects to constitute a high proportion o f coho diets, similar to that shown in m y study. However, he d i d not find that juvenile chinook were eating insects to any significant degree. Healey (1980), in the most comprehensive study o f juvenile salmon in the Georgia Strait'to date, did not find insects to be an important diet item for coho or chinook, with the exception o f chinook rearing in the estuary. Thus, the general picture with respect to the use o f insects by juvenile salmon appears somewhat murky. N o previous study has shown them to be utilized to the same degree, or for as m u c h o f the summer as was found in this study. This is particularly true for chinook, who have seldom been shown to be eating any significant proportion o f terrestrial insects at all. A possible explanation was proposed by Brodeur (1989). H e suggested that terrestrial insects were particularly abundant in salmon stomachs in times o f unusual w i n d patterns. Presumably, insects were b l o w n offshore, and trapped on the surface layer o f salmon feeding grounds. It should also be noted that in the current study, insect use was much higher for both species in the early summer. This may represent a latent preference for insects in fish that have recently left the estuarine environment, where it has been shown that insects make up a large proportion o f their diet (Healey, 1980, Anderson et al., 1981, B r o w n et al. 1987, M a c d o n a l d et al. 1987). Finally, the fact that a very large proportion o f fish in the current study were caught either in or near to the Fraser R i v e r Plume may mean that they were presented with an unusually high proportion o f terrestrial insects that were being carried out into the marine environment from further inland.  18  Fish Larvae Historically, the utilization o f fish larvae as a diet item for juvenile salmonids is w e l l documented. However, both the extent and timing o f the consumption o f fish are quite variable. In 1950, Foskett found that chinook and coho i n the N a n a i m o area started off eating smaller organisms, and then graduated to fish (mainly herring) as they grew in size. LeBrasseur (1966) showed that coho diets in coastal zones depended on fish for almost h a l f o f their prey. M a n z e r (1969) found that fish made up a very large proportion o f juvenile coho diets (up to 70%) throughout the summer. Healey (1980) found that the utilization o f fish was quite variable both by year and b y area. C h i n o o k were shown to feed more heavily on fish in late summer 1975 (63%) than i n late summer 1976 (29%). H e explained this difference as a reduction i n availability o f fish and showed it to be mirrored in other species. H e also showed that chinook ate fish most heavily in the G u l f Islands (79%), while they were less important i n the Fraser R i v e r Plume (37%) and the Central Strait. The same year to year variation was shown for coho, although overall fish were a less important contributor to their diet than to that o f chinook. F o r both species, invertebrates were more important than fish earlier in the summer. Conversely, Brodeur (1989) found that coho ate more fish i n June (19%) than they d i d i n either July (5%) or September (6%). W h i l e chinook ate more fish at all three months than coho did, they showed the same pattern o f decreasing amounts as the summer continued (34%, 3 0 % and 16% for June, July and September respectively). A g a i n , it is difficult to draw a general conclusion from existing studies. It appears that fish are an important diet component, but their availability can be quite variable. Historically, it appears that chinook have eaten fish to a greater extent than coho, although the current study does not follow this pattern. It is interesting to note that more recent studies (including the current one) show lower overall levels o f larval fish i n the salmonid diets. This may simply be a random manifestation o f the normal variability in fish availability. H o w e v e r , it is also possible that larval fish are being more heavily impacted as a food source as increasing numbers o f juvenile salmon are released into the Strait every year from hatchery operations. It is also interesting to note that the general pattern observed i n earlier studies, in w h i c h larval fish became an increasingly important food item as the summer wore on, is not borne out in the two most recent studies (Brodeur, 1989 and this one). Brodeur showed either a constant or  19  decreasing utilization o f fish for both species, while the current study found a more or less constant use o f larval fish throughout the summer.  Decapod Larvae and Crustaceans Decapod larvae, particularly anomuran and brachyuran crab larvae, are the only food item for w h i c h a relatively consistent pattern o f consumption has been shown by feeding studies. Foskett (1950) defined the general pattern when he reported that juvenile salmon in the Nanaimo area gradually transferred to crustaceans as they graduated from insects and smaller prey. Prakash (1962) supported this pattern when he found crustaceans to be the main early summer diet o f coho and chinook, followed by fish in later months. M a n z e r (1969) d i d not report this kind o f progression, but did find decapod larvae to be one o f the principle diet items o f juvenile coho in Chatham Sound. M y e r s (1979) found that hatchery coho in channel areas fed on crab larvae to a greater extent than their w i l d counterparts. In June, Healey (1980) found that coho consumed crab larvae in June o f 1968, but not 1966. Later in the summer he found Cancer gracilis megalops to be an important component in both o f his study years. Healey showed that chinook ate decapod larvae throughout the summer, especially when fish larvae were scarce in their diet. F o r both species, crab larvae were more important i n the G u l f Islands and the Fraser R i v e r Plume, and less important in fish that were caught further north in the Strait. Brodeur's (1989) study o f f the Washington coast runs somewhat contrary to the general pattern, in that he found coho to be eating decapod larvae at very high levels (67%) i n June, with these levels falling o f f in July and September. C h i n o o k were also seen to eat many decapods early on, and w h i l e the levels fell o f f somewhat, they remained relatively high throughout the summer. Overall, except for Brodeur's (1989) study it appears that a general pattern can be discerned. Decapod larvae seem to replace the early invertebrate diet items (such as insects) as the juvenile salmon grow during the summer. In this study, and in Brodeur's, coho appeared to utilize decapod larvae to a greater degree than chinook, although Healey (1980) found chinook in some areas to have a very high proportion o f crab megalops i n their diet. Thus, in an otherwise variable diet picture, it appears that decapod larvae make up a consistently important fraction o f juvenile coho and chinook diets.  20  Other diet itmes The diet items discussed so far were the main prey o f the fish analyzed in the current study. T a k e n together, these items generally comprised 9 0 % or more o f the identifiable prey in the juvenile salmon stomachs. N o other individual prey consistently made up more than 5% o f the diet o f either species. However, there are a few prey species that have been important in a number o f historical studies, and were represented in m y samples, w h i c h are worthy o f mention. Euphausiids have long been established as a major food item for adult coho and chinook salmon (Prakash, 1962, LeBrasseur, 1966, Manzer, 1968, Graham and Argue, 1972). Therefore, it is not surprising that they have appeared as an important item in some studies o f juvenile salmon stomach contents as w e l l . Healey (1980) found that in June, coho in the Saanich inlet used euphausiids as their main diet item. C o h o showed a preference for euphausiids when their consumption levels were compared with those o f other fish species. Brodeur (1989) showed euphausiids to be important for both coho and chinook. U n l i k e Healey, however, he found that euphausiids increased i n importance later in the summer. B y September they made up 2 4 % o f coho diets, and 4 1 % o f chinook diets. In light o f the fact that euphausiids appear to be primarily an adult salmon food, this second pattern is more intuitive than that found by Healey. In the current study, euphausiids, while frequently present, were generally unimportant. C h i n o o k appeared to eat a small amount (less than 1%) consistently, while coho were only seen to consume them early in the summer, when they made up 1% o f the total diet. A m p h i p o d s are another diet item that, while frequently observed in m y samples, never made up a large proportion o f the salmon diets by weight. Nevertheless, they too have been important i n past studies o f juvenile salmonid diets. Healey (1980) found them to be important for coho throughout the summer. A m p h i p o d s appeared to be eaten as a replacement for unavailable diet items, such as decapod larvae and fish. A m p h i p o d s were also important throughout the summer for chinook, again replacing fish when fish were unavailable. This was particularly true for the Fraser River Plume and the North Strait. In the G u l f Islands, amphipods were less important. Brodeur (1989) showed amphipods to be an increasingly important component o f coho diets as the summer continued, m a k i n g up over half o f the volume o f stomach contents i n September. C h i n o o k showed the  21  same general pattern, although amphipods did not attain as great an importance. Nevertheless, by September they composed 14% o f the the stomach contents. It is w e l l established that copepods are an important food o f juvenile salmonids before they enter the marine environment (Anderson et al., 1981, M a c d o n a l d et. al. 1987). Therefore, it is not surprising that some studies have found them to be o f continued importance in the early ocean phase o f the salmonid life cycle. B o t h Healey (1980) and Brodeur (1989) found that chinook and coho consumed copepods early in the summer i n the marine environment. Interestingly, Healey found that copepods were less important to chinook than to coho, pinks, chums, and sockeye. Brodeur, on the other hand, saw copepods making up to 3 8 % o f chinook diets in July, while copepods never made up more than 7% o f coho diets. Nonetheless, copepods were a much more important item in these studies than in the current one. Finally, it should be noted that the pteropod mollusc Limacina was seen to be an important diet item for coho by both Healey (1978) and Brodeur (1989). This was particularly true in Healey (1980) for fish that were caught in the N o r t h Strait, where pteropods made up 2 0 % o f the coho diet. N o Limacina were found in the stomachs o f juvenile salmonids in the current study.  2.2.4. Feeding Study Conclusions G i v e n the available studies o f juvenile coho and chinook salmon feeding, it is difficult to define any specific pattern o f diet composition in the early months at sea. The overall picture is one o f variability. This reinforces the generally held belief that salmonids are opportunistic feeders (Prakash 1962, Sandercock 1991). In the historical studies, the variability in diet compositions appears to follow availability o f food items. H o w e v e r , using these studies, it is possible to define a few apparently preferred food items, and in this w a y produce a broad general picture o f the feeding patterns.  22  Figure 6. Feeding regimes used in the bioenergetics model. Chinook  July  August  September  October  Month Coho  c  s (D Q-  July  August  September  October  Month  M o s t studies have shown a general pattern o f starting with smaller invertebrates in the estuary, and m o v i n g through larger marine invertebrates to a more piscivorous diet as the summer continues (Shapovalov et al. 1954). The main exceptions to this pattern show that fish may be an important diet item throughout the summer. F o o d items that seem to be important in a majority o f studies include terrestrial insects, decapod larvae, fish larvae, and,  23  to a lesser extent amphipods. The occurrence o f these items is consistent for most studies. M o s t o f the variability in consumption levels o f any one prey type appears to occur in response to the availability o f one or more o f the other types. Because o f their key importance in the diets o f juvenile salmonids in the Georgia Strait, the diet items used to construct an idealized feeding regime were insects, copepods, decapods, fish and amphipods. This regime was used to define the consumption patterns incorporated in the bioenergetics model (section 2.4). Figure 6 illustrates these patterns for both coho and chinook.  2.3. V I R T U A L P O P U L A T I O N A N A L Y S I S 2.3.1. Introduction V i r t u a l Population Analysis ( V P A ) is a straightforward technique that is used to estimate historical numbers o f fish based on currently observed population sizes and removals. V P A relies on catch-at-age data to recursively calculate stock sizes based on catches. Calculations for each cohort are done individually. The standard procedure is to calculate the number o f fish alive in each separate cohort for each past year. This is done based on the numbers o f fish k n o w n to be alive in a given year, and assumed values o f natural mortality and catch . The simple relationship used for each cohort is as follows (Hilborn and Walters, 1992):  N  = N  t  t  +  ]  + C  t  + M  t  where: N N  = the number o f fish alive this year.  t  t +  j = the number o f fish still alive next year.  C = this year's catch. t  M  t  = this year's losses due to natural mortality.  Note that the above equation does not account directly for immigration and emigration, w h i c h C W T data show are important for most Georgia Strait stocks. T o some extent emigration losses are balanced by immigration 24  from Puget Sound stocks; other emigration losses were included as part o f the natural mortality figures used in the V P A procedure, so no confounding effects should have resulted from fish exiting the Strait. In the current study, a modified form o f the V P A was used to calculate the number o f smolts that must have been present i n the Strait to account for observed catch-at-age data. This modified V P A used "representation coefficients" in place o f natural mortality estimates to recursively calculate smolt numbers. This method was considered superior to using estimates o f smolt-to-adult mortality due to the variability and inherent biases in historical estimation procedures (Ricker 1976). W h i l e more recent studies have improved on instantaneous estimates o f marine mortality ( M c G u r k 1996), there are still few empirical estimates for chinook (Bradford 1995). The complete V P A procedure that was used is outlined below, and summarized in Figure 7.  Figure 7. Flow chart showing the procedure followed in the modified VPA. See text for further explanation.  A. Hatchery smolt releases (from Cross et al. 1991)  B. % Hatchery smolts caught as adults i.e. representation coefficient (calculated)  C. Hatchery fish caught as adults in the Georgia Strait, (from Cross et al. 1991)  B / 50% (lower estimate) B / 80% (upper estimate)  F. Wild smolt estimates (calculated) G. Total Smolts (sum of hatchery releases + wild estimates)  D. % Wild smolts caught as adults; upper and lower estimates (i.e. wild representation coefficients)  E. Wild fish caught as adults in the Georgia Strait, (from Cross et al. 1991)  2.3.2. Materials and Methods The protocol for the modified V P A was based on data obtained from Cross et al. 1991. This report provides recent, relatively complete data for estimating hatchery smolt numbers entering the Strait. Included in it are detailed hatchery release data, Georgia Strait catch statistics from all fisheries (divided into hatchery and w i l d  25  contributions), and catch at age data for the Georgia Strait fisheries. A l l the data are presented for ten years or more, spanning from the m i d seventies to the late eighties. The first step in the modified V P A was to obtain an estimate o f the numbers o f hatchery smolts being released into the Georgia Strait ( " A " in Figure 7, Table 4). The detailed hatchery release information in Cross et al. provided the numbers o f fish released from a l l the hatcheries that enter the Strait. However, not all o f these fish were released as smolts. Some were released as fed fry, and others as unfed fry. Therefore, released numbers o f fry were multiplied by appropriate survival rates, to convert them to "smolt equivalents." F o r chinook, fry to smolt survivals were estimated at 3 0 % for fed, and 10% for unfed fry (Healey 1991). F o r coho, fry to smolt survival was estimated at 1% for unfed, and at 7% for fed fry (Sandercock 1991). A l l the chinook fry were assumed to be oceantype fish who migrate downstream to the estuary in the same year that they are hatched. Therefore, they were added to the smolt numbers in the same year that they were released. Coho, on the other hand, generally spend a year or more i n fresh water before they enter the marine environment as smolts, so any fry released in a given year were survived and added into the total smolt releases for the following year. A n example o f the calculation o f hatchery smolt releases for chinook is shown in Table 4. The next step in the modified V P A was to determine the number o f hatchery smolts from each year that contributed to later catch statistics drawn from Cross et al. (1991). This produced an adult "sample" (through catches) o f hatchery smolts that allowed an estimation o f "survival" o f smolts to be caught as adults ( " C " in Figure 7). Smolts can go on to be caught as adults at more than one age. F o r example, chinook can be caught as 2, 3, 4 or 5 year o l d adults. Because o f this, it was necessary to combine data showing the hatchery contribution to Georgia Strait catches with data that showed catch-at-age. B y doing this, it was possible to apportion the hatchery contribution to catch into smolt production years. Thus, for each catch year it was possible to calculate how many smolts from each hatchery production year had contributed to it. A n example o f this procedure as performed for chinook smolts is shown in Table 5.  26  Table 4. Release of chinook juveniles from SEP facilities. Data from Cross et al. (1991) Smolt Year Region Inside  Stage  1977  1976  1978  1980  1979  1981  1982  1983  1984  unfed fry  0  0  0  310160  0  0  0  222  0  fed fry  0  0  726  70046  257855  270027  436942  263860  568620  smolts  2466174  5329193  4661154  7785011  9423722  7523551  9607293  1.2E+07  1.3E+07  effective smolts  2466174  5329193  4661372  7837041  9501079  7604559  9738376  1.2E+07  1.3E+07  unfed fry  0  0  0  0  0  2500  45000  0  0  (survived fry) Upper Fraser/ Thompson fed fry  0  16319  12963  34111  158857  198631  1470250  230091  915255  smolts  0  0  0  14417  17753  56083  285620  1644009  3233352  effective smolts  0  4895.7  3888.9  24650.3  65410.1  115922  731195  1713036  3507929  2466174  5334089  4665261  7861691  9566489  7720481  1E+07  1.4E+07  1.7E+07  (survived fry) Total Strait Smolts  The third step in the modified V P A was to produce a "representation coefficient" that served the same purpose as natural mortality rates do in a standard V P A ( " B " in Figure 7). The representation coefficient method was preferable to using mortality rate estimates, since existing mortality estimates are quite variable, and not specific to the salmon populations in the Georgia Strait. The coefficient was produced by d i v i d i n g hatchery contributions to catches by the numbers o f hatchery smolts released. Thus, it provided an index as to h o w likely it was that a smolt from a given year w o u l d turn up in the Georgia Strait catch in a later year. In reality, it is a composite that represents loss o f smolts to many different agents, probably the most important o f w h i c h is natural mortality. However, other agents that might prevent a smolt from being represented in later G e o r g i a Strait catches include escapement to spawning, catches in other fisheries outside the Georgia Strait, and straying to other river systems (i.e. emigration). Nonetheless, the representation coefficient provides a direct index o f the likelihood o f a given fish making it from the smolt stage into the catch "sample" as an adult. It should be noted that the coefficient was calculated for the likelihood that any smolt w o u l d end up in any catch year. For example, a hatchery chinook smolt from 1981 had a 0.11% chance o f showing up in the 1982 catch, a 0.23% chance in the 1983 catch, a 0.31% chance in the 1984 catch and a 0.05% chance o f being caught in 1985. C h i n o o k smolts from 1981 did not show up in catches from any other year. Table 6 shows an example o f representation coefficients as calculated for chinook  27  hatchery smolts. It can be seen from the format that Table 6 is simply derived from the information presented in Tables 4 and 5.  Table 5. Chinook salmon: adult hatchery catch contributions broken down by smolt year and catch year. Data from Cross etal (1991) Catch Year Smolt  1978  1979  1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  Year  Total Caught **  1974  4881  1975  23796  4435  1976  20135  21620  3572  1977  12203  18294  17411  4063  11087  14733  19806  3518  16759  17152  4386  10157  14513  21382  4883  8796  18092  23802  3596  10965  20141  17529  3962  12206  14833  19316  2678  8989  16344  13056  2561  9905  11047  12483  3992  6695  10562  19462  6401  16468  **  9981  **  1978 1979 1980 1981 1982  **  8929  ** 51971 49144  1983 1984 1985 1986 1987 1988  47225 50934 54286 52597 49033 40950 37427 **  **note that for the first three and last three smolt years, the total number of smolts respresented in catches could not be calculated. This is because chinook are caught as two, three, four, andfiveyear-olds. Therefore, somefishfrom each of these smolt years would be caught in years for which catch statistics were not available.  Once these representation coefficients had been calculated, they were used in conjunction with the w i l d contributions to each catch year (Cross et al. 1991, " E " in Figure 7) to back-calculate the number o f w i l d smolts that must have been present to produce the observed catches. It was tempting to simply use the same representation coefficient values as were calculated for the hatchery smolts. However, it is c o m m o n l y accepted that hatchery smolts have a lower survival to adult stages than w i l d smolts do (Nickelson 1986, Cross et al. 1991). A m o n g studies that have attempted to quantify this reduction in hatchery smolt to adult survival, most have found that hatchery survival is somewhere in the range o f 50 - 8 0 % that o f w i l d smolts (Cross et al. 1991, Parkinson 1995).  28  Table 6. Chinook salmon; percent of each hatchery smolt year represented in each catch (representation coefficients). Catch Year Smolt  1978  1979  1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  Year 1974  Total % Caught  1.21%  1975  1.16%  0.22%  1976  0.82%  0.88%  0.14%  1977  0.23%  0.34%  0.33%  0.08%  0.24%  0.32%  0.42%  0.08%  0.11%  0.21%  0.22%  0.06%  0.11%  0.15%  0.22%  0.05%  0.11%  0.23%  0.31%  0.05%  0.10%  0.19%  0.17%  0.04%  0.09%  0.11%  0.14%  0.02%  0.05%  0.10%  0.08%  0.02%  0.04%  0.05%  0.06%  0.02%  0.02%  0.04%  0.07%  0.02%  0.05%  1978 1979 1980 1981 1982  0.97% 1.05%  1983 1984 1985 1986 1987 1988  0.60% 0.53% 0.70% 0.50% 0.36% 0.24% 0.17%  0.03%  Therefore, to avoid over-estimating the numbers o f w i l d smolts, the representation coefficients were adjusted to reflect the superior w i l d survival rates. In order to define a set o f boundaries on the possible w i l d smolt population, representation coefficients were divided by both 0.5 and 0.8 ( " D " in Figure 7). Table 7 shows representation coefficients for w i l d chinook, assuming hatchery survival to be 8 0 % that o f w i l d . Note that these values are simply those presented in Table 6, divided by 0.8. Thus, two sets o f w i l d smolt estimates were produced. The first was an upper boundary, defining a large population, in w h i c h the hatchery smolt to adult survival was 8 0 % that o f the w i l d smolts. The second described a lower boundary defining a smaller smolt population, in w h i c h the hatchery smolt to adult survival was 5 0 % that o f the w i l d smolts. It should be re-emphasized that the representation coefficients are comprised o f all the factors that w o u l d reduce a smolt's chances o f later being represented in the Georgia Strait catch, and not just natural mortality. However, natural mortality is the only agent that should be different for w i l d and hatchery smolts. In other words, both types o f smolts should have more or less the same chances o f being caught in another fishery, or escaping to  29  spawn. Therefore, by adjusting the representation coefficients as though they only reflected natural mortality, I ignored the importance o f these other agents. This means that the resulting w i l d smolt population estimates are probably a conservative under-estimation. This is especially true for the coho stocks. This however, is preferable to an over-estimation, i n light o f the fact that these numbers were used to investigate a potential carrying capacity limit. Nevertheless, it means that the large population estimates are probably a better estimate o f the true coho numbers than the small population estimates, as both w o u l d be conservative to begin with. There is some possibility that w i l d chinook stocks were not subject to the same declining trend as evident for hatchery fish (Walters 1995). I f this is true, then the chinook smolt estimates w o u l d not be as conservative as the coho estimates.  Table 7. Chinook salmon; percent representation of wild smolts in catches, assuming that hatchery smolts survive 80% as well as wild smolts to be caught as adults. Catch Year Smolt  1978  1979  1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  Year  Total % Caught  1974  1.51%  1975  1.45%  1976  1.02%  1.10%  0.18%  1977  0.29%  0.43%  0.41%  0.10%  0.30%  0.39%  0.53%  0.09%  0.14%  0.27%  0.27%  0.07%  0.13%  0.19%  0.28%  0.06%  0.14%  0.29%  0.39%  0.06%  0.13%  0.24%  0.21%  1978 1979 1980 1981 1982  0.27% 1.22% 1.32% 0.75%  0.11%  1983 1984 1985 1986 1987  0.67% 0.88% 0.05%  0.63%  0.14%  0.18%  0.02%  0.45%  0.07%  0.12%  0.10%  0.02%  0.06%  0.06%  0.07%  0.02%  0.03%  0.05%  0.08%  0.02%  0.06%  0.30% 0.21%  0.04%  1988  Once the upper and lower boundary representation coefficients had been produced for the w i l d smolt populations, it was a simple matter to back-calculate from the catch-at-age data to produce estimates o f the w i l d smolt populations in the earlier years ("F" in Figure 7). Table 8 shows the catch-at-age-data used for chinook adults (Cross et. al 1991).  30  Table 8. Chinook salmon; number of adults in each catch year that originated in each smolt year. Data adapted from Cross et. al 1991. Catch Year Smolt  1978  1979  1980  1981  1982  1984  1983  1985  1986  1987  1988  1989  Year 1974  209501  1975  233674 200817  1976  221588 223988  176809  1977  141010 212403  197210 141017  1978  135165 187009 157288  691639 99293  1979  119006 149152 110750  1980  94915  1981  Total Caught  578755 87651  466559  105022  97765 125639  66832  92708 140136  423341 96061  395737  1982  58996 132888 107145  75996  1983  84565 101603  84765  44103  1984  64656  80380  49191  51151  1985 1986 1987  375025 315035 35786  230013  46647  39915  52745 190458  29684  37850  58831  24086  55788 35501  1988  Since each catch year was comprised o f fish that were smolts in several different years, and representation coefficients were produced for each smolt year to each catch year, several estimates o f w i l d smolt population size were produced for each smolt year. F o r the most part, these estimates were reasonably close to each other. Therefore, they were averaged to produce a single estimate, and it was this w i l d smolt estimate that was added to the published hatchery smolt releases to produce a total estimate o f the numbers o f smolts that were present in the Strait in any given year ( " G " in Figure7). Table 9 shows the estimates produced for chinook smolts.  31  Table 9. Chinook smolts; estimates of wild smolt population sizes derived from catch statistics and representation coefficients. Catch Year Smolt  1978  1979  1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  Year  estimate  1974  4279141  1975  2.2E+07 2.3E+07  1976  2.6E+07 2.7E+07  1977  5.6E+07  1978  Average  3E+07  5.9E+07 6.5E+07 4.6E+07  5.7E+07  5.2E+07  4.5E+07  1979  5.7E+07  4E+07 3.2E+07  9.6E+07 6.7E+07  5.5E+07 3.9E+07  6.4E+07  1980  8.2E+07 6.6E+07 4.7E+07  6E+07  1981  5.4E+07 3.8E+07  4.9E+07  1982  6.4E+07 5.1E+07  4.8E+07  5.2E+07 6.6E+07 6.9E+07 4.9E+07  1983  8.6E+07  5.9E+07  9E+07 6.4E+07  5.5E+07  1984  1.1E+08 7.9E+07  6.8E+07  5.8E+07  7.9E+07  1985  1.1 E+08  9.1E+07  7.7E+07  7.3E+07 8.7E+07  1986  1.2E+08  1987  7.4E+07  1E+08 9.4E+07 1.2E+08  1988  1.1 E+08 1.1 E+08  2.3.3. Discussion of V P A Results Chinook The primary objective in performing the V P A was to produce an estimate o f total G e o r g i a Strait smolt numbers that could be used in conjunction with a bioenergetics model in order to calculate total amounts o f food required b y the smolts. This task was made somewhat more complicated in that instead o f producing a static picture o f the numbers o f smolts entering the Strait over the years, obvious trends in those numbers became apparent for both coho and chinook. The chinook estimates for the upper boundary, in particular, showed an interesting pattern. F r o m 1977 to 1985 the estimated numbers o f smolts necessary to produce observed catches has increased somewhat (Figure 8). This is in spite o f the fact that overall chinook catches have decreased substantially over that same period (Figure 1). This implies that smolt to adult survivals could have been decreasing even more dramatically than one w o u l d expect given the relative amounts o f k n o w n smolt releases and adult catches. In other words, survivals w o u l d have to have  32  decreased to such an extent that not only are more smolts necessary to produce a constant number o f adults, but more smolts are necessary to produce even fewer adults. It is possible that this result is simply an artifact o f the estimation method chosen, i f in fact the ratio o f hatchery to w i l d representation coefficients was not constant over this period. This could only be the case i f the ratio o f hatchery to w i l d survivals was decreasing during this time, as w o u l d occur i f survival rates have not declined as violently for w i l d chinook as for the hatchery smolts. A n y other agent that reduced representation coefficients w o u l d be expected to affect both hatchery and w i l d stocks equally, and so the ratio o f the two w o u l d stay the same. F o r example, i f more hatchery fish were being caught in fisheries outside the Strait, the same w o u l d be true o f w i l d fish, so that the relationship between their representation coefficients w o u l d be the same. In the case o f natural survival however, it is possible that hatchery smolt survivals were becoming poorer relative to w i l d smolts over this period, so that the increase in hatchery smolts necessary to produce observed hatchery adults should not be mirrored by w i l d stocks. A n alarming pattern that was observed in m y estimates o f smolt numbers involved the ratio o f w i l d to hatchery smolts entering the Strait over the years. While both hatchery and w i l d smolts entries were seen to increase, hatchery smolt numbers have been increasing at a faster rate than w i l d smolt numbers. This means that, i f we use the large smolt population estimates, the ratio o f w i l d to hatchery smolts decreased from 8 5 % w i l d and 15% hatchery in 1977 to 7 1 % w i l d and 2 8 % hatchery by 1985. I f this rate has remained constant, we could expect a ratio o f 64% w i l d smolts and 36% hatchery smolts entering the Strait in 1995. This pattern becomes even more pronounced i f it is not accepted that w i l d survivals have decreased as much as hatchery survivals in the Strait. I f w i l d chinook survivals are assumed to be constant, then the w i l d smolt population estimates are reduced. Therefore the ratio o f hatchery to w i l d smolts is increased. The overall picture then, is one o f total adult chinook numbers in the Strait falling. A t the same time smolt to adult survivals also appear to be falling at an extremely rapid rate, at least for the hatchery stocks. It is unclear whether or not the w i l d survivals have decreased to the same extent. Either way, the result is that the estimates i n the total number o f smolts necessary to produce the observed adults has been increasing.  33  Figure 8. Chinook salmon; upper bound estimates of number of smolts entering the Georgia Strait, assuming 80% ratio of hatchery to wild survival.  1.20E+08  O.OOE+00 t 1977  |  '  , 1979  1981  —,  - i 1983  1985  Year  Because o f this increasing trend, I used the most recent estimates o f smolt numbers in the bioenergetics model. These estimates were assumed to be most representative o f the current situation. A t the same time, they were among the highest over the entire period. Thus the situation they model is one in which a carrying capacity limit is most likely to be encountered. The estimates used in the bioenergetics model for chinook smolt numbers in the Strait were 100 m i l l i o n smolts in the small population estimate, and 150 m i l l i o n in the large population estimate.  Coho In estimating coho smolt numbers entering the Georgia Strait, the trends in population parameters were not as consistent as those seen for chinook. The declining catch statistics seen for chinook were mirrored by the coho only until 1984. After that, coho catches in the Strait recovered and stabilized somewhat. The coho smolt estimates were similar to the chinook estimates in that they showed some increase from 1976 to 1988. However, this increase was not as consistent or as extreme as that estimated for chinook. The most striking feature o f the smolt estimates is the extreme spike that occurs around 1984. This peak is due to the  34  recovery in coho catch coupled with a year in which the recruitment to the catch (as indexed by the representation coefficient) was quite low. This peak estimate suggests a total o f up to 30 million smolts entered the Strait in 1984. The proportion o f hatchery/wild smolts entering the strait shows an increase that is even more dramatic than that seen in the chinook population. For the coho population, the increasing number o f smolts entering the Strait appears to be almost entirely due to an increase in the number o f hatchery smolts being released. The w i l d smolt estimates fluctuate over the same period, but do not show any consistent trend towards an increase or a decrease. The result o f this is that, i f we use the large population estimates, the proportions o f coho smolts entering the Strait have gone from about 8 5 % w i l d and 15% hatchery in 1977 to about 5 5 % w i l d and 4 5 % hatchery i n 1986. U s i n g the small population estimates, the 1986 smolts were more or less composed o f equal parts hatchery and w i l d fish.  Figure 9. Coho salmon; upper bound estimates of number of smolts entering the Georgia Strait, assuming 80% ratio of hatchery to wild survival.  3.50E+07  0.00E+00 -| 1977  1978  |  |  1979  1980  1 1981  ,  ,  1982  1983  ,  1  ,  1984  1985  i 1986  Year  Once again, the overall pattern appears to be one o f increasing numbers o f smolts entering the Strait each year. In contrast to the chinook situation however, this increase does not appear to be due as much to decreasing survival rates o f the smolts. Instead, most o f it is explained by the increasing numbers o f coho smolts being  35  released by hatchery operations around the Strait. L i k e the situation seen with the chinook population, the decrease in the estimated proportion o f w i l d coho smolts entering the Strait appears to be significant. The coho smolt numbers chosen to be used in the bioenergetics model were as follows: 15 m i l l i o n for the lower estimate, and 30 m i l l i o n for the upper estimate. These values encompass the variation seen in the coho smolt estimates in recent years. Thus, they were assumed to be the best available estimates o f the current situation.  2.4.  BIOENERGETICS  A bioenergetics modelling procedure was used to estimate the amount o f food energy that a smolt w o u l d need to consume i n order to produce observed growth rates. This estimation was based on the balanced energy equation ( K i t c h e l l 1983, N e y 1990): Consumption = G r o w t h + Respiration + Egestion + Excretion This equation is a mathematical expression o f the idea that all the food consumed by an organism can be apportioned out to the physiological processes occurring in that organism. Therefore, food is either incorporated into biomass (growth) or energy (respiration), or is removed as waste (egestion and excretion). G i v e n that all o f the the physiological parameters on the right side o f the equation can be estimated from field or laboratory data, the model was used to produce consumption estimates specifically for coho and chinook smolts in the G e o r g i a Strait. Since the V P A had produced estimates o f total smolt numbers in the Strait, it was then possible to extrapolate and estimate the total amount o f calories being consumed by salmonid smolts in the G e o r g i a Strait. The bioenergetics modelling incorporated the first six months o f the smolt's marine life, since it is during this time that they are consuming a juvenile diet consistent with that found in the stomach content analysis.  2.4.1. Materials and Methods The bioenergetics model used was "Fish Bioenergetics 2" produced by Hewett and Johnson o f the University o f Wisconsin. This versatile model comes equipped with physiological parameters for 20 different fishes. A m o n g these taxa are many o f the major Pacific salmonids, including coho, chinook, pink, sockeye, and lake trout. A complete description o f the parameters used for all o f the taxa, and their alternatives, is provided in 36  Chapter 3 of the Fish Bioenergetics User's M a n u a l (Hewett and Johnson, 1992). In the following sections, I w i l l describe the equations that were used for the coho and chinook modelling runs as they appear in that chapter. I w i l l also describe the parameters that are not included by default in the model, such as feeding patterns and ocean temperatures. These were obtained from separate sources, and input to the model as external data.  Consumption Several options for consumption equations are included in the model. Their general form is to calculate the daily m a x i m u m specific feeding rate as a function o f weight (g prey/g body wt.). This m a x i m u m rate is then modified by a temperature dependence function (f(T)), and by an index o f prey availability, expressed as a proportionality constant, or P-value. This P-value is a proportion o f the m a x i m u m feeding rate actually exhibited by fish, w h i c h depends upon factors like food availability and activity level. The basic form o f the overall consumption equation is: Consumption = M a x . consumption x P-value x f(T) The bioenergetics model includes three options for the water temperature dependence function, f(T). The actual equation used was the default included in the model for both coho and chinook. This is the Thornton and Lessem (1978) function, which provides a good fit for cold water species at low water temperatures. It calculates temperature effects as the product o f two sigmoid curves; one half fits the increasing portion o f the water temperature dependence curve ( K A ) , and the other half the decreasing portion ( K B ) . Thus the general form o f the curve is: f(T) = K A  xKB  The general result o f using this algorithm is to increase consumption as temperature is increased from some low level. This continues up to an optimum temperature, after w h i c h consumption falls o f f rapidly as water temperatures increase to stressful levels. Details o f the form o f the two curves K A and K B , and the parameters used, can be found in Chapter 3 and A p p e n d i x 3 o f the user's manual. The same curve, and parameters, were used for both coho and chinook, as was done by Stewart et al. (1981).  37  Respiration and Specific Dynamic Action Respiration is an expression o f the amount o f energy that a fish uses for metabolism. In the bioenergetics model, it is determined by calculating resting metabolism as a function o f fish weight. This value is then corrected by a water temperature dependent factor, and another factor that represents activity. Finally, the energy lost to specific dynamic action is calculated ( S D A ) . This is an expression o f the metabolic heat lost from the digestion and transformation o f food. This is added to the respiration value to produce a total metabolic rate. "Fish bioenergetics 2" models specific dynamic action as a constant proportion o f the assimilated energy (i.e. that energy not lost to egestion or excretion). Thus, the basic equation for determing total metabolic rate is:  R = ( a x W P x f(T) x A c t i v i t y ) + Energy lost to S D A  where: R = the total metabolic rate W = fish weight a = intercept o f the weight function P = slope o f the weight function f(T) = water temperature dependence function A c t i v i t y = increment for active metabolism  The bioenergetics model provides two temperature dependence functions, o f w h i c h the first was used for both coho and chinook. This algorithm calculates water temperature dependence as a simple exponential function (Hewett and Johnson. 1992, Chapter 3). The model computes activity by using the s w i m m i n g speed function developed by Stewart et al. (1983).  38  Egestion and excretion - losses due to waste Egestion is an expression o f fecal waste, while excretion is the nitrogenous portion o f the waste. The bioenergetics model allows these waste losses to be calculated in one o f two ways: either as a function o f water temperature and consumption, or more simply as a constant proportion o f consumption. For both chinook and coho the first method was used. The equation used also allowed for corrections depending on the digestibility o f the prey item eaten. Prey items are entered into a separate file, and read into this equation. This allows the model to vary egestion and excretion depending on the prey that is being consumed. The equation that was used is described i n detail i n Chapter 3 o f the User's M a n u a l , and also in Stewart et al. 1983.  Growth Once the energy losses due to metabolism, egestion, and excretion have been accounted for, the rest o f the consumed energy is transferred into growth o f the fish. The balance o f energy can be either positive or negative in the model, so that fish can either grow or lose weight, depending on the rest o f the energy budget. In the specific case o f m y modelling runs, growth was calculated using start and end weights that I supplied to the model as external parameters. The first step in estimating these start and end weights was to decide on corresponding lengths that were representative for chinook and coho populations. F r o m m y 1993 catches o f chinook in the Strait, the average length o f smolts entering the Strait was 70 m m . Healey (1980) found the average length o f chinook entering the Strait to be 80 m m . Thus, for the chinook start length, an average o f these two was used, producing a value o f 75 m m . Healey (1980) also found that during their first six months in the Strait, chinook grow an average o f about 0.8 m m per day. Therefore, at a start length o f 70 m m , after six months in the Strait an average chinook w i l l have reached a length o f 214 m m . U s i n g the same data, Healey defined a length/weight relationship for chinook as follows:  Weight(g) = 3.53 x 10" x L e n g t h ( m m ) 6  3  39  282  This relationship was used to convert the observed start length,and the calculated end length into weights. The start and end weights that were used for chinook in the bioenergetics modelling runs were 4.01 g and 157.11 g The same process was used to determine start and end weights for coho salmon. Once again using Healey's and m y data, the start length for coho smolts entering the Strait was estimated at an average o f 100 m m . The best estimates o f coho growth over the range 100-280 m m are about l m m / d a y (Healey, 1980). Thus after six months, the end length w o u l d be 280 m m . Healey defined the length to weight relationship for coho smolts as:  Weight (g) = 1.62 x 10" x L e n g t h 6  3  4 2  Thus the start weight (at 100 mm) for coho is 11.21 g. The end weight (at 280 m m ) is 379.14 g. The bioenergetics model uses these start and end weights, over a user-specified time period, to calculate growth as a rate. This rate is expressed as grams o f growth per day. The weight gain o f the fish can be converted to an energy expression using an energy density (calories/gram, wet weight). The fish bioenergetics m o d e l calculates the energy density o f both coho and chinook as a function o f their body weight, using a simple linear regression, o f the form:  Energy density = a + P x Weight  where:  a = the intercept  P = the  slope  In fact, two separate sets o f a and p values are supplied, in order to define different caloric densities for the predators as they enter different size ranges. However, the default cut off between the size ranges is 4000 g. Since I  40  only modelled fish w h i c h were smaller than this size, their energy densities were all defined by the same linear regression, using an intercept o f 1377 cal/g, and a slope o f 0.2356 for both species.  External data T o this point, I have described the equations and parameters that are used as defaults in the fish bioenergetics model. A l l o f these defaults are set by the model programmers according to the current state o f knowledge regarding the general physiology o f chinook and coho salmon. However, there are several sets o f situation specific parameters that must be supplied by the user in order to perform a bioenergetics m o d e l l i n g run.  Temperature A s already mentioned, the consumption, respiration, and egestion/excretion equations all have a temperature dependent expression involved in them. Thus, in order to make these expressions perform in a realistic fashion, it is necessary to have as detailed information on the temperature regimes i n the smolt's environment as possible. This information was obtained from lighthouse data collected around the G e o r g i a Strait. Data collected from 38 lighthouses, some spanning up to 65 years, was averaged to produce a representative yearly temperature regime for the G e o r g i a Strait (see Figure 10). The detail provided by this large amount o f information allowed the temperatures used in the model to be remarkably realistic. However, it should be noted that no weighting o f values was done; that is, older data were given the same emphasis as more recent values. Thus, any possible effects o f ocean warming trends were not considered. If temperatures in the Strait are increasingly significantly, or i f a single year experiences higher than normal temperatures due to an E l N i n o event, observable differences in the bioenergetics model output w o u l d result. The temperature dependent factors w o u l d increase energy lost to respiration, egestion, and excretion. The net result w o u l d be an increased consumption o f prey items in order to produce the same amount o f growth in coho and chinook smolts. Therefore, i f such warming effects are occurring, m y estimates o f prey consumption by salmon smolts w o u l d be low. This is important, since higher rates o f prey consumption are more likely to produce a 41  carrying capacity limit. However, it is as yet unclear whether increased ocean temperatures are a permanent effect, or simply a transient variation in a long term pattern. Therefore, I chose to take a longer term view o f the temperature patterns in the Strait, thereby modelling a more general situation. A l s o , by using the long term average, I maintained m y policy o f erring on the conservative side in all estimates that w o u l d contribute to the final consumption values. Another reason for using the lower temperature regime dictated by the long term average is the probability that fish are seldom feeding right on the surface where the temperature data were obtained. Brodeur and Francis (1992) used temperatures that were several degrees below that at the surface, to simulate the temperature at a depth o f 10 m , w h i c h was the midpoint o f their sampling depth. Since the midpoint o f m y sampling depth was just under four metres, I d i d not feel that it w o u l d be valid to lower m y temperature regime by an equivalent amount. Nevertheless, I feel that this further justifies not incorporating the average 1°C rise in temperature observed in local waters since the late '70s.  Figure 10. Temperature regime used in the bioenergetics model.  4  -illS^^lli^^^^^^^^HEj^^^^^^te'''' ^ I I ^ ^ B ^ ^ i i l i l p l l -j'  0 Jan  Feb  f—— Mar  1 Apr  •• • --••••| May  1 Jun  1  ,  =(  Jul  Aug  Sep  Month  42  •  .  !  Oct  Nov  ,— Dec  Diet patterns A s already stated the goal o f the using the bioenergetics model was to produce estimates o f prey consumption from observed growth patterns o f coho and chinook salmon. In order to do this, it was necessary to produce a representative diet regime for both coho and chinook. A s explained above, estimation o f the diet patterns o f coho and chinook smolts was accomplished by analyzing the stomach contents o f smolts from the G e o r g i a Strait, and c o m b i n i n g these results with the results o f past sampling projects. The idealized feeding regimes produced followed the general pattern already described: an early emphasis on smaller food organisms such as insects and copepods, being replaced by larger organisms such as decapod and fish larvae, later in the summer (see Figure 6). A m p h i p o d s were used to represent smaller crustaceans being consumed before the later emphasis on the larger decapods, m a i n l y in the form o f crab larvae.  Prey Energy Densities Since the bioenergetics model converts wet weights o f prey into energy used by the predator fish, it was necessary to provide the program with estimates o f energy density (calories/gram wet weight) for the prey items used. A l l o f the prey energy densities used were obtained from an extensive calorimetry study in the literature, in w h i c h thousands o f representative organisms were burned to determine their caloric content ( C u m m i n s and W u y c h e c k , 1971). This source was also used to double check the default energy densities used by the m o d e l for the coho and chinook smolts that were feeding on these prey. A n added advantage to modelling prey as simple bundles o f calories is that it reduces the variability necessary i n the diet pattern used. This is due to the fact that many small marine invertebrates have similar energy densities. F o r example, the amphipods used in the idealized feeding pattern are intended to represent all similarly sized crustacean invertebrates. Thus, the five prey items actually used are not meant to be a complete list o f everything a coho or chinook smolt might eat during their first six months at sea. Instead, they are representatives o f the changes i n prey energy densities thought to be consumed by smolts during their early ocean residence. Nevertheless, these five prey items do tend to comprise most o f the stomach contents o f coho and chinook examined in smolt feeding studies. 43  Mortality Estimates B y using the modified V P A described earlier, estimates o f the total numbers o f smolts entering the Strait in a representative year were produced. However, it w o u l d be unrealistic to assume that all o f these smolts continue to feed throughout the summer months. In fact, Healey (1991) suggests that most o f the mortality that affects chinook salmon between their smolt and adult stages impacts the fishin their first year at sea. Parker (1962) supports this idea with his observation that predation mortality is concentrated in the coastal zone, where smolts i n their early ocean life spend most o f their time feeding. H e also points out that salmon at smaller sizes are more susceptible to predation than larger ones. This idea is echoed by M c G u r k (1996) in his study o f the allometric relationship between mortality and salmon size. Because o f the undoubtedly high mortality o f y o u n g salmon, it was necessary to include estimates o f mortality for both coho and chinook during the six months over w h i c h they were modelled. This allowed the model to reduce the numbers o f fish feeding in the Strait by appropriate amounts each day. U s i n g tag and recovery data, many authors have attempted to estimate the mortality o f coho and chinook during their ocean lives (Shapovalov and Taft, 1954, Parker and Kirkness 1956, R i c k e r 1976, Fraser et al. 1983). Sandercock and Healey (1991) have attempted to summarize the results o f such studies for coho and chinook stocks, respectively. However, due to the variability in both the methods and the results o f such studies, it is extremely difficult to establish a generally representative value for mortality rates o f either species. T o further complicate matters, almost all o f the existing studies report an estimate o f either overall smolt to adult mortality, or an average yearly mortality rate during ocean residence. It is generally held that such average marine mortality rates overestimate the actual mortality o f adults, and underestimate that o f smolts ( M c G u r k 1996). Other than several suggestions that mortalities are probably much higher than average during early ocean residences (Shapovalov and Taft 1954, Parker 1962, Henry 1978, Healey 1991), very few studies have attempted to actually estimate a value for the mortality rate o f coho and chinook smolts during their first six months in the ocean. Mathews and B u c k l e y (1976), w o r k i n g with coho in the Puget sound produced one o f the few attempts at estimating mortality for coho in their first six months at sea. U s i n g mark and recovery methodology, they estimated that o n l y 13% o f coho smolts survive their first six months in the ocean. This translates into an 87% mortality rate. 44  Converting this to a daily instantaneous mortality rate to be used in the bioenergetics model for coho smolts produced a value o f 0.0113. In the case o f chinook smolts, no such study was available to provide an estimate o f early ocean mortality. In fact, it is believed that no past studies have provided direct estimates for the marine survival rate o f chinook stocks (Bradford 1995). In a summary o f several studies producing estimates o f annual mortality, Healey (1991) suggested that over their ocean life, chinook probably suffer a yearly mortality o f 20%. However, this estimate was intended to represent an average o f ocean going chinook o f all ages. A s stated above, such averages are k n o w n to underestimate smolt mortality ( M c G u r k 1996). A l s o , the corresponding estimate derived for the coho mortality (above) was much higher, and it was suspected that the true chinook value w o u l d be somewhat closer to the coho estimate. Therefore it was assumed that the mortality rate in the first six months o f life w o u l d be much higher than 2 0 % . Thus, this value was adjusted upward to 67%. This was chosen as a reasonable compromise using the somewhat scant information available. This mortality rate translated into a daily instantaneous mortality rate o f 0.00616.  Modelling Procedure Once I had specified all o f the necessary parameter values, I conducted a set o f modelling runs. The first step in this procedure was to divide the smolts entering the strait into cohorts. This was necessary since the values produced by the V P A estimated total smolt entries into the Strait for a given year. W h i l e the majority o f these smolts enter the Strait early in late spring or early summer, they do not all enter the Strait at the same time. Healey (1980) found that most coho smolts enter the Strait in M a y and June. In the case o f chinook smolts, he found that they also started to show up in the Strait i n M a y . Therefore, in order to spread out the impact o f smolts on their food resources, and more realistically model the natural system, the total numbers o f smolts for each population were split into three seasonal cohorts. The first and third cohorts were both 1/4 o f the total estimated smolt populations. The middle cohort was the largest, representing the median entry date o f smolts. It was composed o f the remaining 1/2 o f the total smolt population. The entry t i m i n g o f these cohorts to the modelled population was spread out over three months, starting in M a y . 45  Thus, the program modelled a situation in w h i c h 1/4 o f the smolts entered the Strait at the beginning o f M a y , 1/2 at the beginning o f June, and the remaining 1/4 at the beginning o f July. W h i l e this is still an obviously artificial representation, it was assumed that this w o u l d spread the impact on the food supply out enough to reasonably model prey consumption in the Strait. A n y misrepresentation w o u l d have been inconsequential later in the summer, since after July all the smolts are present and feeding in both the model and the actual G e o r g i a Strait. A s already noted, all modelling runs were performed for two population sizes o f both species. The smaller population represented a lower boundary estimate on the number o f smolts entering the Strait each year, and therefore a lower estimate o f the impact on food resources. The larger population was the upper boundary estimate, m o d e l l i n g a higher impact on food resources. Therefore, these estimates define a probable range o f smolt numbers and their resulting impact on prey populations. It should be noted that this range is produced from a very conservative V P A procedure. This means that it is probable that the higher end is closer to the true case than is the lower end.  Fitting P-values The first modelling run to be performed was termed a "P-fit run - fit to end weight" in the bioenergetics model. The purpose o f this run is to determine a P-value for the fish. This value represents the proportion o f the m a x i m u m ration the average individual fish has consumed. M a x i m u m physiological feeding rates are determined by water temperature and the size o f the fish. Thus, a P-value o f less than 1 (i.e. 100% o f the m a x i m u m rate) represents some kind o f ecological constraint on feeding, such as prey availability, competition, predator avoidance, disease, etc. (Hewett and Johnson, 1992). B y e m p l o y i n g the user-defined start and end weights, the computer determines the P-value that fits the observed growth. The model does this by starting with an estimated P-value (also user-defined) and performing a bioenergetics run to determine the resulting growth o f fish feeding at that P-value. It then compares this growth with the observed growth. I f the difference is more than 0.05%, the computer adjusts the P-value, and does a new bioenergetics run. It repeats the process until the P-value produces results that fit the observed growth within  46  0.05%. A s P-values are determined for each cohort, the user is given the option o f whether or not to replace his/her original estimate with the calculated P-value. In all cases, I accepted the calculated P-values as the best estimates.  Bioenergetics run The final step in the bioenergetics modelling process was to do the actual bioenergetics run. The run option chosen assumed a constant P-value. The model was set to simulate growth for the first six months o f ocean life for both species o f smolts. This time interval was chosen to represent the smolt stage o f the life cycle based on feeding regimes. F r o m m y data, by late summer the smolts could be seen to be m o v i n g into an adult feeding regime, where the importance o f fish and larger decapods overshadowed other prey items. Healey (1980) showed that y o u n g chinook were consistently present in the Strait throughout the six months from M a y to October. Presumably, after this period the majority have matured enough to migrate out to deeper water, and to adopt an adult, ocean-going lifestyle. In the same project, Healey studied young coho during their first six months in the Strait. Therefore, it was assumed that modelling the first six months o f marine life realistically simulated the amount o f time young salmon spend in the Georgia Strait. Once the simulation was started, the model continued to track the smolt populations from the time they entered the strait until the end o f October. The main output o f this kind o f bioenergetics run is the calculation o f the amount o f food consumption necessary to produce observed growth rates. G i v e n that the bioenergetics model had time-series estimates o f the proportion o f each prey type in the smolt diets, the calculated total food requirements were apportioned to prey items. Thus, model outputs include both individual and population cumulative consumptions o f all the prey types. In addition to consumption calculations, the model provides daily calculations o f many other interesting and potentially useful parameters. These include rates o f growth, consumption, excretion, egestion and respiration. A l s o included are population parameters, including number, biomass, mortality, and energy density. F r o m this large amount o f information, it is possible to perform simple calculations to extract many other details. However, the main thrust o f the current study was to produce time series estimates o f prey consumptions. Therefore, the cumulative estimates were imported into a spreadsheet program, where they were converted to daily 47  estimates by subtracting each cumulative value from that preceeding it. Hence, the final output o f the bioenergetics modelling process was four sets o f time series data. For both species, a time series o f the daily consumption o f the various prey items was produced for the large and small population estimates. The values for both species were then summed, to produce an overall estimate o f the total impact o f chinook and coho smolts on their prey in the Georgia Strait (Figure 11). This impact is expressed in calories/cubic metre/day, using a value o f 6300 k m as the 2  surface area o f the Strait (Thomson, 1981).  2.4.2. Discussion of Results P-fit runs - fit to end weight A s was stated earlier, the first step in the bioenergetics modelling procedure is to determine P-values for the population being modelled, based on observed growth. These values provide an estimate o f the feeding intensity o f the population. A n estimate o f 1.0 means that the fish are feeding at their physiological m a x i m u m rate, as determined by water temperature and fish size. A n y estimate less than 1.0 suggests that there is some sort o f environmental constraint on the feeding o f the population. Such constraints might take the form o f reduced prey availability, increased competition, increased predator avoidance by the prey, or diseases in the predator population (Hewett and Johnson 1992). For modelled populations o f both species, the P-values were considerably less than 1. In fact, for the chinook population, the computer estimated P-value was 0.40. In other words, the bioenergetics model calculated that the chinook needed to feed at only 4 0 % o f their m a x i m u m rate to achieve the observed growth. C o h o rates were slightly higher, with the model calculating a P-value o f 0.5. This suggests that coho were feeding at h a l f o f their physiological m a x i m u m to attain the observed growth. These results highlight the fact that the observed growth rates are not as high as they could be i f the fish were feeding more intensely. Thus, there appears to be some factor constraining the feeding levels. Since the data used to estimate growth rates were obtained over several years, it is unlikely that a newly developed predator avoidance mechanism by the prey was in operation. Therefore, the most likely constraints producing these lower than m a x i m u m growth rates are either reduced prey availability or increased competition. These mechanisms are by 48  no means mutually exclusive, and the presence o f one w o u l d increase the chances o f the other occurring. The low P-values suggest that both may have been in effect at the time the growth data were collected. However, a possible objection to this conclusion may arise when one examines the dates at w h i c h these data were collected. The fish that Healey used to infer growth rates were collected during several years. The earliest trawls were undertaken in the summers o f 1966-1969. Later collections were made around the Strait in the summer months o f 1972 to 1977 (Healey, 1980). These collection years span the inception and early production years o f S E P hatcheries, w h i c h are thought to be the most likely cause o f increased competition and reduced prey availability for coho and chinook in the Strait. D u r i n g these years, S E P releases o f smolts into the Strait were relatively small, and just beginning to increase to the higher current levels. Therefore, one might suggest that the results o f such competition should not have become manifest in the observed growth rates so early in SEP's lifetime. One w o u l d expect such results to have become apparent more recently, as S E P releases have reached even higher levels. In an attempt to examine P-values as they relate to a more recent investigation, growth rates o f the fish that were used in the stomach content survey were examined. Since these fish were caught in 1993, they were assumed to represent the current growth patterns in the Strait. Unfortunately, they represent a small sample size relative to Healey's data. They also were caught during a shorter period, i n the middle o f the summer. B o t h these facts serve to somewhat reduce their value in inferring growth rates o f smolts over the six months from M a y to October. Nevertheless, P-values were produced for them. In the case o f the chinook, the growth rate calculated for m y samples was somewhat higher than that w h i c h Healey produced. Where he saw an average growth rate o f 0.8 mm/day over the first six months in the ocean, m y samples displayed a growth rate o f 0.96 mm/day over the middle summer months. U s i n g this growth rate instead o f Healey's, the m o d e l produces a P-value o f 0.46, compared to a value o f 0.40. The fact that this is still a l o w proportion o f m a x i m u m physiological feeding rate suggests that, in the case o f chinook, some ecological factor is indeed suppressing the feeding rate. Thus, this may be taken as further evidence o f competition causing reduced prey availability to chinook in the Strait.  49  However, i n the case o f the coho smolts, my growth data do not support the same conclusion. F r o m the fish caught in m y study, the average daily growth appears to be 1.6 mm/day, w h i c h is a 6 0 % increase over Healey's suggested 1 mm/day. U s i n g this increased growth in the bioenergetics model, the calculated P-value becomes 0.8, as opposed to 0.5. This suggests that coho are feeding at 8 0 % o f their physiological m a x i m u m in order to achieve the observed growth. This relatively high value does not support the assertion that ecological impacts are reducing the feeding ability o f smolts in the Strait. Another problem in trying to interpret the observed P-values was one o f scale. W h i l e it was readily apparent that the calculated P-values were low relative to the m a x i m u m feeding rates, it was not readily discernible where they fell in the overall range o f normal P-values at w h i c h the fish w o u l d be feeding. In order to rectify this situation, m i n i m u m P-values were calculated. These were defined as P-values at w h i c h no net gain i n weight was experienced. In other words, they represent maintenance P-values, where the start and end weights for the m o d e l l i n g period are the same. F o r coho, the calculated maintenance P-value was 2 2 % o f the m a x i m u m feeding rate. F o r chinook, the value was 18%. These low values suggest that the fish require relatively little food to maintain the same weight. Thus, there is a large range o f P-values over w h i c h growth can be realized. A t the same time, they suggest a scale for normal ranges o f P-values for growing fish. If the maintenance values are taken to be the m i n i m u m boundary o f this scale, and the physiological m a x i m u m is taken to be the upper boundary, it becomes readily apparent that the P values calculated in the bioenergetics runs do in fact fall in the lower half o f the range o f growing fish. Because o f the variability in the calculated P-values, it is difficult to draw any firm conclusion from them. What does appear to be clear is that chinook are feeding at a low proportion o f their m a x i m u m capability, w h i c h could be due in part to competition. C o h o do not appear to be affected to the same extent, although using growth rates from a large sampling program, there is some evidence o f low feeding rates. Once again, because Healey's values were drawn from a larger data set w h i c h covered a longer period, they were the ones that were used to produce the P-values for the main bioenergetics modelling runs. It should be noted that the assumption o f constant P-values was implicit in the m o d e l l i n g runs performed. O b v i o u s l y , this is an oversimplification. In reality, smolts w o u l d be expected to feed at different proportions o f 50  their m a x i m u m rate every day, in response to changes in the food supply they encountered. In fact, it is possible to model shorter time intervals with different P-values using the Fish Bioenergetics program. H o w e v e r , in order to do this, it is necessary to have weight data for shorter time intervals. Since the weight data used were inferred from growth patterns averaged over six months, I felt it was best to average the P-values over the same period. This simplification probably d i d not have much impact on the final result. In studies that have used both constant P values, and multiple P-values split over shorter time intervals, the final estimates o f total consumption have usually been similar (Hewett and Johnson, 1992).  Total food consumption pattern G i v e n the idealized feeding patterns specified for the bioenergetics run, the patterns o f prey consumption that were produced are not surprising (Figure 11). The individual curves for each prey item are shaped by smolt entries to the Strait, timing o f food preferences, and mortality reducing feeding populations. F o r example, copepods were a main prey item for both species at the beginning o f the modelling runs. The rapid initial increase in copepod consumption is due to increasing numbers o f fish entering the Strait, and their individually increasing consumption demands as they grow. Later, copepod consumption drops off as the smolts switch to larger prey items. Some o f this decrease is also due to mortality reducing smolt numbers. M u c h the same pattern is evident for both terrestrial insects and amphipods. The differences seen are due to the different timing o f prey use suggested by the feeding regime (Figure 6). However, in the cases o f decapod larvae and fish larvae, no major decreases are seen in their consumptions. This is because these items represent the adult feeding habits, in w h i c h other fish, euphausiids, and some crab larvae make up most o f the diet. Therefore the fish continued to eat them beyond the end o f the six month modelling run. Nevertheless, near the end o f the run it can be seen that modelled predator mortality reduced the impact on decapod larvae to a certain degree. F o r both species, early concentrations on the smaller food items (insects and copepods) are replaced by the larger amphipods, decapods, and fish larvae.  51  A s was expected, the main differences between the small and large populations were in the amounts o f each prey item eaten. Obviously, the larger populations ate significantly greater amounts overall than the smaller populations. Other than this, however, the relative patterns o f prey item consumption were almost identical. The fact that the smolts were modelled in separate cohorts is readily apparent from the consumption results. Particularly evident is the entrance o f the large second cohort. This group o f fish was modelled as entering the Strait after the first month, when fish were still concentrating on insects and copepods. Thus, a corresponding increase in the consumption o f these prey items is seen. The entrance o f the third cohort is less apparent. This is due to two factors. A t the time o f their entry, all five o f the diet items are being consumed. Thus the impact o f this new group o f fish is spread out over prey types, and does not show a large impact on any single group. A l s o , this third cohort is a smaller one. It is half the size o f the second cohort, so does not carry the same numerical impact. Quantitatively, the overall results o f the bioenergetics modelling are five sets o f ranges. These ranges define the estimated total amounts consumed by both species o f five separate prey types. The peak consumption o f each prey type falls at a time when its use was specified to be highest by the idealized feeding patterns, b y patterns o f cohort entry into the Strait, and by assumed mortality losses over the summer. For copepods, the highest consumption ocurred in early June, when fish were still concentrating on smaller prey items. The range o f consumption is estimated to be somewhere between 0.3 and 0.49 calories per cubic metre o f the Strait surface per day. Terrestrial insects were the next prey item to peak. The highest consumption rates occurred near the beginning o f July. The estimated range was 3.63 calories per cubic metre per day on the lower end, and 5.76 calories per cubic metre per day on the upper end. A m p h i p o d s , representing groups o f smaller crustaceans, were consumed at the highest level at the beginning o f August. The estimated amount o f this peak is somewhere between 1.73 and 2.77 calories per cubic metre per day.  52  Figure 11. Range of consumption estimates for major prey items (in calories/cubic metre/day). Impacts of coho and chinook have been combined to show total potential exploitation offood supplies. Lower borders indicate small population consumption estimates, and upper borders indicate large population estimates. Copepods  l^i  R  Jjb  Uy  "'  " Afl ' '  Terrestrial Insects  JJy  Sept  Amphipods  ' Wbrth L£t  r  _  "DT"  Decapod Larvae  45 40 35  <u  a E  30  S h°  2 5  0)  '% 1.5 « °1.0 Q5 Q0  IVbnth L£t Total C o n s u m p t i o n  Fish Larvae  Month LB(  Consumption o f decapod crustaceans and fish larvae peaked toward the end o f the modelling period. Decapod consumption was greatest in early October, with rates from 2.19 to 3.48 calories/m /per day. Finally, fish 3  consumption was still increasing at the end o f the modelled period, reflecting continued predator growth, and  53  increasing piscivory. A t the end o f October, estimated fish consumption was between 7.73 and 12.1 calories/m /day. 3  F r o m these results, two important observations can be made. Firstly, the estimated amounts o f prey eaten suggest that the two most energetically important food items are fish and terrestrial insects. F o r fish, this result was due to the fact that coho and chinook were modelled as being intensely piscivorous toward the end o f the m o d e l l i n g run. Because this is also the time when the juvenile fish are at their largest, this means that large amounts o f fish are being eaten. W h e n this is combined with the relatively high energy density o f fish (1900 cal/gram for clupeoids, C u m m i n s and W u y c h e c k 1971) the net result is that, as coho and chinook enter their adult feeding phase, they derive large amounts o f energy from eating other fish. M o r e surprising is the energetic importance o f terrestrial insects in the diets. D u r i n g the early summer, the model estimates that insects are the single most important food source. This is partly a result o f a very high energy density (2300 cal/gram wet weight, C u m m i n s and Wuycheck 1971). However, it is also due to the large amounts o f insects that were seen to be consumed, particularly in m y samples from 1993. This is surprising i n light o f the fact that insects w o u l d be expected to be a relatively ephemeral food source. They are probably supplied only at the surface, and only i n times o f strong offshore winds that blow them out to sea (Brodeur 1989). The rest o f the bioenergetics results were not unexpected, given the idealized feeding pattern that was used. Copepods contributed less in terms o f relative energy supply than they did in terms o f overall weight o f food eaten, due to their low energy density (550 cal/gram wet weight). A m p h i p o d s and decapods, representing small and large classes o f crustaceans, were important sources o f food energy. Large crustaceans in particular become important for adult fish, as euphausiids make up a very large percentage o f their diet.  Model Sensitivity and Limitations H a v i n g estimated the amounts and timing o f prey consumptions, it is important to understand h o w precise they might be. Bionergetics modelling is generally performed to achieve one o f two goals. Estimates o f growth from observed consumptions can be obtained. Alternatively, estimates o f consumption from growth data can be produced, as was done i n this study. The second type o f estimation has been shown to be the more precise o f the 54  two ( K i t c h e l l et al. 1977). Bartell et al. (1986) confirmed this assertion. They found that the bioenergetics model was very sensitive to variations in P-values. Thus constraining P-values to fit observed growth data improved the overall performance o f the model. M o s t applications o f bioenergetics modelling have estimated consumption from k n o w n growth parameters, as this also limits the effects o f errors in temperature cycles, bioenergetics functions, and other necessary parameters (Hewett and Johnson 1992).  Activity U s i n g the Stewart model, activity is calculated as a constant times resting metabolism. The integer multiplier ranges from 1 to 2, depending on the general activity level o f the fish, as w e l l as the actual metabolic level represented by the weight dependant s w i m m i n g speed function (i.e. basal, resting or active metabolism). Since the development o f this activity model by Stewart, more recent work has criticized this approach. Depending on fish species, age, water temperature and other parameters, some studies have shown that the correct integer multiplier probably ranges more broadly, from 1.5 to 4. Typically, the Stewart model underestimates activity (Boisclair et al. 1989). However, more recent work using  137  C s - l a b e l l e d radiotracers has shown that for immature  fish this discrepancy doesn't exist (Rowan et al. 1996). In fact, for immature fish, they found excellent agreement between Hewett and Johnson's activities using the conventional bioenergetics approach and their o w n . This suggests that the integer multipliers I used to estimate activity levels were probably at least i n an appropriate range for m y fish. However, the 6-month life history that was modelled for my fish includes a rapid niche-shift from feeding on small zooplankton and insects to feeding on larger, faster m o v i n g fish. It is probable that, in reality, such a shift entails a significant increase i n the fishes' activity levels. A linear increase in activity w o u l d require an exponential increase in consumption rates to achieve constant growth under constant temperatures ( H i n c h 1996). Therefore, by not including this activity change in m y model, it is possible that I have underestimated the overall consumption levels necessary to achieve the observed growth. This possible underestimation has some interesting implications for m y results. If the m o d e l runs were repeated using higher activity estimates, then the estimates o f overall metabolic rates w o u l d be increased. This 55  means that the fish w o u l d need to eat more in order to achieve the observed growth. Thus, the calculated P-values w o u l d be higher, since the fish w o u l d need to feed at a higher proportion o f their physiologic m a x i m u m ability. This w o u l d serve to bring m y P-values into closer approximation with those found in other studies m o d e l l i n g similar growth trajectories under similar temperatures (Brodeur et al. 1992). However, by increasing activities and, as a result, increasing consumption rates, m y eventual comparison o f overall consumptions to estimated abundances w o u l d be more likely to show a carrying capacity limit. M y policy throughout the bioenergetics modelling procedure was to err on the conservative side when alternative choices o f parameters presented themselves. In this way, variability introduced by parameters with a greater degree o f uncertainty was less likely to overestimate total consumption rates, and end up producing a false positive result for m y carrying capacity estimation. Thus, in this case the lower activities provided by the "canned" s w i m m i n g speed function were used. This is hoped to have produced the most conservative possible estimates o f total consumption.  Growth and Fish Size Because the growth observations and equations were produced using large sample sizes over an extended period (Healey 1980), I am confident that they provide a reasonable assessment o f normal growth patterns. The only possible difficulty with them is that they are not recent data. Therefore, they do not necessarily model the current growth patterns being experienced by coho and chinook in the Strait. Ideally, in order to perform a better bioenergetics analysis, more recent data are necessary. However, the fact that the data used were gathered over several years should help to damp out minor variations in growth rates. Another potential source o f error in the bioenergetics model is related to fish size. The parameters used in the physiological modelling equations were derived using adult fish. However, the fish I attempted to model in the current study were young-of-the-year smolts. M o d e l l i n g younger fish using adult parameters can be a source o f bias, but it is usually only significant for very small fish. A d u l t fish parameters are thought to w o r k w e l l for any fish over 10 grams, and reasonably w e l l for fish between 1 and 10 grams; it is only for fish below 1 gram that parameter modifications become mandatory (Hewett and Johnson 1992). In the current study, the starting weight 56  used for coho was 11.2 grams. Therefore, the adult parameters should have modelled them with sufficient accuracy. In the case o f the chinook, start weight was 4.01 grams. Thus, the early season results for chinook may have some error introduced due to inappropriate physiological parameters. However, this source o f error should have been low by the end o f the first month o f ocean life, after w h i c h chinook sizes were greater than 10 grams.  Population Size Another potential limitation o f the bioenergetics modelling procedure comes from attempting to extrapolate individual fish results to the population level. The modelling procedure is esentially a single fish operation, dealing with physiology at an individual level. Population level results are produced by treating this individual as an average fish, and multiplying the results by the population size, w h i c h is computed using the initial population sizes and the mortality rates which are supplied as external parameters.  However, estimates o f  population sizes and mortalities often have large confidence limits, as high as 5 0 % or more (Hewett and Johnson 1992). I experienced problems in estimating both o f these parameters in the current study. G o o d estimates o f mortality rates proved particularly difficult to find. It was because o f the lack o f this information that a normal V P A procedure was not performed. Instead, m y modified V P A procedure substituted "representation coefficients" in place o f juvenile to adult mortality rates. B y using this modified V P A procedure, estimates o f population sizes were produced. I am fairly confident that the range o f estimates produced is a reasonable representation. This confidence is increased by the knowledge that the estimates are as conservative as possible, so that any error in them is likely to be on the low side o f the true case. Unfortunately, the representation coefficients were o f no use in estimating the seasonal abundance declines over the first six months o f ocean life which were necessary for the bioenergetics model. Therefore, for this period the best estimates from past studies were used. The variation in these estimates is quite high, and there is very little information on the mortality o f chinook and coho during their first six months at sea. The lack o f this information makes it very difficult to produce reliable population level estimates from a bioenergetics model. A n y future attempts at similar procedures should involve a tag and recovery program to estimate early ocean mortality for salmon smolts. Alternatively, it may be possible to employ recently derived allometric relationships o f mortality for 57  Pacific salmon ( M c G u r k 1996). W i t h respect to the current study, I used mortality rates that were among the highest estimated by past studies. However, more recently published information suggests that the chinook estimates should probably have been higher than those used for coho (Bradford 1995). Nevertheless, it is hoped that using relatively high early ocean mortality estimates for both species produced conservative estimates o f overall population consumptions, so that any potential carrying capacity limit that might be seen is not the result o f overestimating smolt numbers in the Strait.  v  Energy density In using the bioenergetics model, I assumed that energy densities o f the prey were consistent for the  duration o f their importance as a food item. A t the same time, variability in the predator energy densities was modelled as a function o f their weight. Obviously, it is more accurate to assume that energy densities o f organisms vary by season and by size o f the organism. However, using average densities tends to give fairly accurate estimates for longer term modelling runs. A l s o , results are less affected by errors in energy density than by errors in population size and mortality rates (Hewett and Johnson 1992). Therefore, the constant energy density assumption for the prey items is assumed to introduce little, i f any, error into the results.  Timing of smolt entries F i n a l l y , there are undoubtedly estimation errors due to assumptions about the timing o f smolt entries to the Strait. After estimating the total numbers o f smolts in the Strait, I realized that all o f the smolts w o u l d not impact the prey resources as a single group. Therefore, in order to diffuse their impact, they were modelled as three separate groups, entering the Strait in three separate months. The largest o f the three groups was timed to enter the Strait closest to the mean entry dates o f the smolts. In reality, the natural situation is much more complex than this. Smolts are constantly entering the Strait from different river systems throughout the early summer. Therefore, instead o f three large groups o f smolts entering, there are actually hundreds or thousands o f smaller groups that combine to produce the overall smolt  58  population. However, it was impossible for the model to handle this degree o f complexity. The number o f cohorts that could be simulated i n a single modelling run was much too limited to model smolt entries as a smooth curve. Thus, it is possible that substantial error in the timing o f predator impacts on prey populations was introduced. Neverthless, the overall numbers o f smolts used is assumed to be reasonably accurate. Therefore, the magnitude o f prey consumptions should represent realistic values that w o u l d be achieved at some point in the summer. A l s o , it is k n o w n that most smolts enter the Strait in the spring and early summer months (Healey 1980). This means that, in the later months, the entire smolt population w o u l d be impacting prey supplies i n unison. Thus, later in the summer, timing o f entries into the Strait is probably not o f much importance.  Confidence in model estimates G i v e n all these limitations, one may question whether or not any confidence can be placed i n m o d e l estimates o f consumption. However, it should be remembered that model and field estimates are both only as good as the data that goes into them. Moreover, both are limited by different sets o f assumptions. In fact, model estimates may actually be more accurate than field estimates when unkown sources o f error exist in field data (Hewett and Johnson 1992). B y producing estimates that are boundaries on a range, I hope to have defined a reasonable "ballpark" o f consumption estimates. Once again, it should be pointed out that, i f anything, these estimates are probably conservative. If substantial errors do exist, the true values o f prey consumptions in the Strait are probably higher than the estimates produced from the bioenergetics model. Overall, I have the most confidence in the magnitude o f the consumption estimates. The main source o f error i n these estimates comes from insufficient mortality data for chinook and coho in their early ocean residence. I place less confidence in the estimated timing o f prey use. These estimates are based on the idealized feeding pattern, and the timing o f smolt entries into the Strait. The idealized feeding pattern depends heavily on m y stomach content data, which, while detailed, covered a relatively short period o f time. The timing o f smolt entries into the Strait, as mentioned above, is admittedly artificial, due to limitations in modelling the complex series o f real smolt entries into the Georgia Strait.  59  2.5. F O O D A V A I L A B I L I T Y S T U D Y Thus far in m y investigation o f potential food limitation, I have produced estimates o f salmon abundances and prey consumption rates. The final step is to compare these consumptions to reasonable estimates o f the prey available to be consumed. Unfortunately, zooplankton samples were not collected concurrently with salmonids during the 1993 sampling process. Therefore, I was dependant on historical data to try and gain knowledge about prey abundances in the Georgia Strait. There have been several attempts to quantify the abundance o f most o f the zooplankton that appear as prey types i n juvenile salmon stomachs in the Georgia Strait ( B r o w n et al. 1987, St. John et al. 1992, and C l i f f o r d et al. 1989 and Clifford et al. 1991). Zooplankton abundances are reported as numbers o f organisms per cubic metre o f water in these studies. C o m b i n i n g data from these sources, I produced estimates o f the average numerical abundances o f the relevant salmonid prey species. However, a difficulty arose when I attempted to convert these numerical abundances to biomass, and thereby caloric abundances. Because the average weights o f the organisms counted were not reported i n the historical collections, it was impossible to derive biomasses from them.. Therefore, it was necessary to refer to studies o f abundances outside o f the Georgia Strait in order to obtain weight data. Brodeur et al. (1992) report average Washington and Oregon Coast zooplankton abundances from 1981 as mean wet weights. These samples were taken in the summer, during the same months as the fish were sampled and modelled i n the current study. Therefore, they were assumed to represent reasonable estimates o f zooplankton sizes for the G e o r g i a Strait, and were used as comparison values for the estimated consumption rates. The only diet item whose abundance I could not estimate in this manner was terrestrial insects. N o n e o f the B . C . coastal zooplankton surveys included count or weight abundance estimates o f terrestrial insects at sea. However, a few authors have attempted to quantify the density o f terrestrial insects found at sea. Brodeur et al. (1987) counted mean abundances o f terrestrial insects found along the Pacific coast, from California to A l a s k a . W h i l e they d i d not include mean weights o f the insects, B o w d e n et al. (1976) estimated that a similar assemblage o f insects found on the decks o f ships i n the North Sea had a mean weight o f about 0.5 grams per individual (wet weight). U s i n g this value, it was possible to calculate that Brodeur's samples represented a mean insect density o f 60  0.034 cal/cubic metre along the Pacific Coast. In a similar study, Cheng (1975) estimated that the insect density i n the central northern Pacific was about 0.145 cal/cubic metre. Interestingly, this value was higher than Brodeur's despite the fact that collections were made in the winter, and farther out to sea. One w o u l d expect this situation to produce relatively l o w insect densities. Another study on the B l a c k Sea showed insect densities o f 1.82 cal/cubic metre (Zaitsev 1970). The generally held view that the abundance o f terrestrial insects at sea is highly variable depending on the season and the distance from land (Bowden et al. 1976) is supported by these three sets o f observations. In the case o f the Georgia Strait, it was assumed that the high abundances observed in the B l a c k Sea w o u l d be the most representative, since both bodies o f water have large areas o f coastline to serve as a source o f terrestrial insects. Once abundance estimates had been produced and converted to calories per cubic metre, it was a simple matter to compare them with consumption estimates and arrive at an assessment o f the daily percent use o f prey. The estimated daily consumption rates were averaged over the modelling period. These were then compared to the average estimated abundances, to provide a representation o f the degree o f exploitation o f available prey. The data are presented in Table 10.  Table 10. Prey consumption (cal/m^/day) vs. abundance estimates (cal/m^). Consumption Estimates  Average Abundance  Percent Exploitation  Upper Bound  Estimates*  Item:  Lower Bound  Lower Bound  Upper Bound  Insects Copepods Fish Amphipods Decapods  1.26 0.06 2.84 0.65 0.64  2.00 0.10 4.52 1.04  1.82 8.64  1.02  63.32 14.68 52.82  69% 1% 4% 4% 1%  110% 1% 7% 7% 2%  A l l Prey Types  5.45  8.68  141.28  4%  6%  *from Brodeur et al. 1992  2.5.1. Discussion  O n examining the percent use estimates, the most striking value is the high estimated exploitation o f insects by the juvenile salmonids. The fact that the upper bound o f this estimate exceeds 100% indicates that the supply o f  61  insects is turning over rapidly within the feeding environment. There are several potential avenues o f insect supply to the sea surface that could be responsible for this high rate o f renewal. Brodeur (1989) found that offshore winds could be responsible for supplying large numbers o f insects to surface waters. Because the Georgia Strait is surrounded by large land masses and contains many chains o f islands, there is no shortage o f land area to supply terrestrial insects v i a such winds. Large numbers o f insects may also be supplied to feeding salmon in the Georgia Strait through other means. The large plume created by the Fraser R i v e r as it empties into the Strait is a dominant oceanographic feature. The plume has been shown to be an important feeding area for juvenile fish. Significantly higher densities o f y o u n g salmon occur in the plume than in the main Strait. The increased numbers are believed to be due to aggregations o f zooplankton, as w e l l as a lower salinity environment which allows salmon smolts to acclimatize more slowly to a marine environment after they leave their freshwater habitat (St. John et al. 1992). This pattern o f high salmon densities in the plume was borne out in the current study. Because o f this, a majority o f the stomach samples used to define the idealized feeding pattern in the bioenergetics model were obtained from fish that had been feeding in the plume. A l o n g with increased zooplankton densities, it is possible that these fish encountered higher than normal terrestrial insect densities. The Fraser R i v e r may act as a sort o f "conveyer belt" o f terrestrial insects, trapping them from the surrounding land as it runs its course, and emptying them into the Georgia Strait. Thus, the Strait, and especially the plume, might w e l l have densities o f terrestrial insects much higher than was found i n the studies used to define the abundance estimate. E v e n i f the standing stock o f insects are being consumed at very h i g h rates, it is possible that the supply is replenished very rapidly, as the Fraser continues to collect insects along its length and deliver them to the congregated fish at its mouth. Despite the existence o f potential errors in the calculation o f insect consumption, the fact remains that, using the best information available, percentage rates o f insect exploitation were estimated to be one to two orders o f magnitude higher than any other prey item. Depending on turnover rates o f insects at the surface, these values must be taken as a suggestion that the supply o f insects to feeding salmonids could be limited. I f such a limitation  62  exists, it probably occurs very early in the ocean residence o f the salmonids, when they are still eating the same types o f prey that they consumed in freshwater. W h i l e the extremely high rates o f insect consumption are worthy o f note, a further species by species comparison o f consumption rates to abundance estimates is probably not a useful exercise. This is true for two reasons. Firstly, the feeding regime used was meant to represent an idealized pattern. In order to create this regime, I included the food items that were the most important in present and past stomach content analyses of juvenile salmon. W h i l e these items are assumed to cover the majority o f the fish diets, they do not include every potential prey item available. That is w h y prey items were represented by energy densities in the bioenergetics model. This allowed them to be used as representative organisms, that could characterize any organism o f a similar size and biochemical makeup containing roughly the same number o f calories per unit weight. F o r example, when I estimate that the fish consumed 1.02 calories o f decapod crustaceans per metre per day, this can be understood to mean that that much energy was derived from moderately large crustaceans, including decapods and other species such as euphausiids. Nevertheless, from the stomach content analyses it seems probable that most o f this energy was indeed derived from decapods (at least early in the summer). The other reason that a species to species comparison is probably not useful is that salmonids are k n o w n to be opportunistic feeders that w i l l shift their predation efforts to the most readily available food items. This means that, as long as one food item is available, it is unlikely that dependence on another rare food item w i l l cause a carrying capacity limit. I f a limit does exist it w o u l d probably be a result o f consumption rates that are high enough to significantly impact all the major food items in the salmonid's diet. Therefore, in investigating a potential carrying capacity limit, the most informative comparison is that o f total energy consumption to total availability. When compared with the extreme values estimated for insect use, the estimate o f total average consumption as a percentage o f the total average biomass (4% to 6% per day) seems low. However, other studies have estimated m u c h lower overall consumption rates, such as 0.05 to 0.10% and 0.2 to 0.4% (Brodeur 1992, Peterson et al. 1982). Relative to these values, the current model suggests that juvenile salmonids are eating a very high proportion o f the available food.  63  Several other factors support this perception o f high overall consumption rates. The first is that juvenile chinook and coho do not represent the only demand on the zooplankton resources in the G e o r g i a Strait. M a n y other fish species, including adult salmonids, occur in pelagic waters, and consume the same types o f prey as juvenile coho and chinook (Brodeur et al. 1987). In fact, Brodeur and Pearcy (1992) showed that juvenile coho and chinook together represented an average o f only 6.5% o f the demand on pelagic nekton found along the Washington and Oregon Coast i n M a y and June, 1981 through 1984. Other juvenile salmonids, including chum, pink and sockeye salmon, as w e l l as steelhead and cutthroat trout compete with coho and chinook for many o f the same prey items. Competition also occurs in the form o f adult salmonids, macrozooplankton, and marine birds and mammals. I f one were to include the consumption o f zooplankton resources by these other predators, the result w o u l d be much higher overall daily rates o f use than was estimated for juvenile coho and chinook alone. Thus, the fact that coho and chinook are estimated to consume between 4 and 6% o f the available prey each day, while only m a k i n g up about 7% o f the total demand, may indicate that much higher levels o f zooplankton consumption are occurring. This provides further evidence for potential food limitation. It is incorrect, however, to consider zooplankton resources as a static supply. In reality, standing stocks do not accurately represent the potentially rapid production and recruitment o f new biomass to the plankton population. This turnover can serve to augment the supply o f zooplankters as they are impacted by predator species. Shannon and Field (1985) found turnover times o f zooplankton in upwelling ecosystems to be on the order o f 5 to 10 days. However, production rates may be much higher during the summer months, when growth and recruitment are at a m a x i m u m (Walters et al. 1978). Nevertheless, a quick calculation using conservative estimates shows that i f juvenile coho and chinook are consuming 4 % o f the standing stock per day, and this represents 10% o f the total demand, the standing stock w o u l d be exhausted in 2 to 3 days. Therefore in order to satisfy the consumption rates estimated by the bioenergetics model, turnover rates w o u l d have to be much less than 5 to 10 days. Thus, these estimates may provide further evidence o f a potential carrying capacity limit. It should be noted that the average daily consumption estimates were compared against zooplankton availability estimates to define prey exploitation rates. I f I had used the maximum estimates o f daily consumption, the exploitation rates w o u l d have been much higher than 4 to 6% per day. However, the t i m i n g o f the m a x i m u m 64  rates o f prey consumption was somewhat suspect. A s already mentioned, the estimated timing o f prey use depended on the idealized feeding pattern, and the timing o f smolt entries into the Strait. Both o f these were admittedly oversimplified. Therefore, it is unclear when during the summer the m a x i m u m rates o f prey use w o u l d actually occur. This makes it difficult to compare these maximums against any specific estimates o f zooplankton stocks. Nevertheless, it is important to realize that, at some point during the summer, prey exploitation rates w o u l d reach a much higher level than represented by the average rates. Thus, i f a carrying capacity food limitation was to occur, it w o u l d probably be around such a time. In considering the evidence supporting a potential carrying capacity limit in the G e o r g i a Strait, it is important to remember that several factors limited the accuracy o f m y estimation procedure. F o r example, a major problem was encountered i n trying to obtain abundance estimates o f zooplankton in the G e o r g i a Strait. Since the 1993 sampling cruises were only intended as a census o f juvenile salmonids in the Strait, there was no concurrent sampling o f zooplankton species. Therefore it was necessary to use historical studies to fill in this gap i n knowledge. Unfortunately, the most applicable historical studies were done outside the Strait, in waters o f f the Washington and Oregon Coasts. O b v i o u s l y these were less than ideal samples, as differences in environmental variables may produce different levels o f zooplankton. In fact, Shenker (1988) found that substantial differences in species compositions may occur over relatively short distances. Therefore the Washington and Oregon coast samples may have been very poor indicators o f prey composition and abundances in the G e o r g i a Strait. Nevertheless, these were the best samples available for the current study. Ideally, i f such a study were to be repeated, zooplankton samples w o u l d be taken in conjunction with fish sampling efforts, and the stomach contents then compared directly to the zooplankton in the same area. However, even this w o u l d not eliminate the sources o f error associated with potential sampling biases due to prey patchiness. M o s t zooplankton species occur in patches o f relatively high density, interspersed with areas relatively free o f that species. Since salmon can focus their feeding in these high density patches, they may in fact see relative densities o f zooplankton that are much higher than the densities sampled in vertical net hauls, or bongo net tows. Harrison et al. (1983) felt that zooplankton densities using vertical net hauls may have been as m u c h as an  65  order o f magnitude underestimation o f patch densities. I f this were to prove true o f the zooplankton densities i n the current study, then the case for a food imposed carrying capacity limit w o u l d be much weaker. The plankton sampling method may have been insufficient in other ways, as w e l l . Brodeur (1989), who produced the neuston samples used in the current study, felt that the sampling gear may have been inadequate in collecting certain o f the prey species. Juvenile fish, for example, may have been mobile enough to avoid being caught i n the nets, and therefore w o u l d not be caught in proportion to their abundance. A l s o , many invertebrate zooplankton species are k n o w n to undergo extensive diel vertical migrations, often c o m i n g to the surface only at night. Therefore, plankton samples taken in the day might largely underestimate the actual abundance o f these prey species. Other potential sources o f error lie in the feeding regime construction and in the V P A estimation o f smolt numbers. However, the problems i n these procedures were anticipated, and any assumptions that had to be made were kept as conservative as possible. F o r example, there is some possibility that major food items exist i n the Strait that were not accounted for in this study. If this were the case, the impact o f salmon smolts on potential food sources might be much less than was estimated. However, in order to minimize this source o f error, nearly 600 stomachs were analyzed in the current study, and this information was combined with major historical data sets to try and get as clear a picture as possible o f juvenile salmon feeding habits. Nevertheless, late in the summer when the smolts are switching from a juvenile to an adult diet, the feeding regime became somewhat unpredictable. T o avoid problems associated with this amibguity, the bioenergetics results were only considered up until the end o f August. A n y estimated limitation due to low zooplankton abundances after'this time w o u l d probably be invalid, since the fish w o u l d be more capable o f supplementing their diets with euphausiids or other fish species. Finally, it should be noted that several assumptions were necessary to perform the V P A procedure, and produce estimates o f smolt numbers entering the Strait. In all cases, I attempted to err on the conservative side, so that m y estimates o f smolt numbers w o u l d be, i f anything, on the low side. Nevertheless, any future attempts at performing a V P A to estimate smolt numbers entering the Strait w o u l d greatly benefit from better early mortality estimates for juvenile salmonids.  66  2.6.  SUMMARY  Overall, the food, V P A and bioenergetics analysis support the hypothesis that food limitation may be the proximate cause o f reduced coho and chinook survival in the Georgia Strait. This evidence is bolstered by the low P-values, indicating reduced rates o f feeding, estimated by the bioenergetics model. I f such a limitation exists, it probably occurs between M a y and September, before euphausiids and fish become the main components o f the salmonid's diet. Unfortunately, several sources o f error serve to limit the degree o f confidence that can be placed in these results. The primary problem lies with uncertainty surrounding the zooplankton abundance estimates. A l s o , there is some possibility that the feeding pattern may be more variable than it appears from this study. T o reduce the influence o f these sources o f error, future research using this approach should focus on replicating feeding studies over several summers, in order to get a more accurate picture o f the important elements in the salmonid diet, and the degree o f temporal variation that exists. A l s o , zooplankton sampling should be done concurrently so that the comparison o f what is being eaten to what is available is more accurate. F i n a l l y , it w o u l d be very useful to incorporate a mark/recapture study o f both w i l d and hatchery smolts that allowed a better estimation o f early ocean mortality rates.  67  C H A P T E R 3: M E T A G A M E - A C O M P U T E R M O D E L T O ASSIST IN D E S I G N I N G A L A R G E S C A L E F I S H E R I E S EXPERIMENT.  3.1. I N T R O D U C T I O N The direct feeding study described in Chapter 1 provides evidence that a food imposed carrying capacity might be limiting the production o f coho and chinook salmon i n the Georgia Strait. Unfortunately, there is much uncertainty about the parameters used to produce estimates o f both salmon consumption and food availability. Nevertheless, having recognized these uncertainties, the results o f Chapter 1 should be more than adequate to highlight the need for further study. The question then becomes, what form should future investigations take? There are two main options that suggest themselves immediately upon considering the results o f the direct feeding study. The first and most obvious is to carry out a more detailed study o f the same k i n d , with improved estimates o f the suspect parameters. Such a project w o u l d have to include mark/recapture w o r k to more clearly estimate mortality rates o f young salmon in the Strait. A l s o , appropriate sampling o f the zooplankton stocks in the Strait w o u l d be necessary to more accurately gauge the availability o f salmon food supplies. Finally, the time frame o f the study w o u l d need to be extended, so that sampling o f both salmon stomachs and zooplankton could be carried out over several summers. This w o u l d provide a better idea o f the variability in resource use and availability. However, ultimately this approach w o u l d not prove anything. It w o u l d provide only a more precise model o f the food limitation hypothesis, not a direct test o f this hypothesis in terms o f the link between food and marine survival rates. It should be possible to carry out a very different k i n d o f study that addresses a more specific question: has hatchery production caused the salmon populations in the Georgia Strait to reach a carrying capacity limit i n v o l v i n g density dependence in marine survival rates? B y phrasing the question i n this manner, we remove the emphasis from the proximate cause o f the limitation. In other words, it becomes unneccessary to investigate w h i c h ecological resource is directly responsible for imposing a constraint on population size. Instead, the focus is shifted to the 68  factor that is almost certainly responsible for failure o f populations to increase in conjunction with enhancement, namely decreases i n marine survival rate. The obvious method o f investigating the impact hatchery releases are having on G e o r g i a Strait salmon populations is to perform an experimental manipulation o f hatchery releases, and examine the impact o f these manipulations on marine survival rate. Obviously, i f such an experiment were to be successful, it w o u l d require large scale cooperation o f hatchery and fisheries managers throughout the Strait. In order to achieve such cooperation, many questions about the details o f the experiment w o u l d have to be answered before it was attempted. For example, h o w long w o u l d such an experiment take to show any conclusive results? A n d to what degree w o u l d releases need to be manipulated in order to measure density-dependant survial effects? A l s o , how definitive w o u l d the results o f such an experiment be? W o u l d they make future management decisions perfectly clear, or w o u l d some uncertainty still remain? A l l o f these questions have far-reaching implications as to the usefulness and viability o f such an experiment. In an attempt to shed some light on these questions, a computer model o f the coho and chinook stocks in the G e o r g i a Strait was developed (Walters 1994). The "Coho and C h i n o o k Hatchery Evaluation G a m e " simulates w i l d and hatchery populations, and the interactions between the two. It also allows the user to simulate different manipulations o f the size o f hatchery releases, every second year (the alternating year treatment structure allows "temporal b l o c k i n g " to provide paired comparisons o f high and low smolt density years). This provides information on what results may be expected i f different stocking reduction protocols were attempted. The model is built around the assumption that one o f the four hypotheses mentioned in the general introduction is responsible for the decline in w i l d stocks and failing salmon catches in the Strait. These hypotheses are: 1. Over fishing. 2. Freshwater rearing habitat limitation. 3. Changing oceanographic conditions. 4. Marine carrying capacity.  69  F o r further explanation o f these hypotheses, and the arguments for and against each o f them, see the general introduction.  '  3.1.1. What the "Game" in the Metagame Program Does W h e n a user starts a gaming session with the program, the first thing it does is to display information from  23 years o f historical data. This information includes catches in the Georgia Strait, hatchery smolt releases, proportion o f catches that were w i l d fish, and marine survival rates from C W T data. After the historical data has been produced, the game player has three m a i n choices. The player can choose to simulate one year at a time, and observe catch and population data on this basis. In simulating a year, the program simultaneously does three important things. First, it has already p i c k e d one o f the four hypotheses at random. The user does not k n o w w h i c h hypothesis has been picked. A t the same time, the computer generates fake data for that year, by simulating the year according to the chosen hypothesis, and adding random variability. W h i l e this is going on, the program simulates all four o f the hypotheses independently, using the current parameter settings and whatever information the player has entered. Then, the program uses a Bayesian assessment method (explained i n section 3.2.1) to compare the observed (fake) data, to the simulated data predicted b y each hypothesis. U s i n g this comparison, the program generates a Bayes posterior probability for each hypothesis. After several years o f play, informative management choices, such as hatchery release manipulations, w i l l cause these probabilities to shift such that the "true" (prechosen) hypothesis becomes progressively more probable. The player can also choose to carry out a simulation for a preset number o f years. This is akin to deciding to apply a single experimental regime for multiple years. The program simulates this regime for multiple years. A t the end o f such a run, the hypothesis that was being used to generate the fake data is revealed to the user. This can then be compared to the Bayesian probability for that hypothesis to see how successful the manager w o u l d have been at deciding w h i c h hypothesis was true given the experiment that was run. The final choice for the user is the most powerful o f the three i n evaluating how successful an experiment w o u l d be i n discovering the true hypothesis. The user can choose to run an experiment for a given number o f years, 70  as outlined above, but to do it multiple times. In other words, the user can simulate a situation i n w h i c h many fisheries managers carried out the same experiment, at the same time, but independent o f each other, as i f they were on separate but identical worlds. O f course, the results o f the experiments are not identical, since the model incorporates random variability in measurements and in survival rates. The user can then observe the outcomes o f each o f these experiments, to get an idea o f how many o f the experiments resulted in strong support (high posterior probability) for the correct hypothesis. This gives the user an estimate o f what the odds o f running a successful experiment might be, given a certain set o f parameters and a certain length o f time. In the parlance o f the program, this procedure is called a multiple run or multitrial. In traditional statistical terms, a multitrial measures the "power" o f a proposed experimental design. It is important to explicitly state the question that the program attempts to answer with a multiple run. Since the program is written with a hatchery manager's interests in mind, the question that is being answered is not " H o w successful w i l l a given experiment be at telling me w h i c h o f the four hypotheses is the true one?" Instead, the question being addressed is " H o w successful w i l l a given experiment be at telling me whether or not hypothesis 4 (marine carrying capacity) is true?" There is a subtle but important difference here. This is because the only hypothesis that suggests that the hatcheries are having an impact on salmon survival in the G e o r g i a Strait is the carrying capacity hypothesis. Thus, this is the only hypothesis the hatcheries are in a position to test through an experimental reduction in stocking rates. In other words, a reduction in stocking rates w i l l not alter the situation for hatchery managers in the Georgia Strait i f one o f the first three hypotheses is true. Therefore, the only insights that can be gained through such an experiment are:  1.  D i d the marine survival situation in the Strait change in response to our experiment? (Hypothesis 4 is true.)  2.  D i d the situation in the Strait not change in response to our experiment? (One o f hypotheses 1 through 3 is true.)  71  This is not a problem for the program from a hatchery manager's perspective. W h i l e it might be nice to k n o w what is really going on in the Strait to satisfy one's curiosity, the only really important question for the hatchery manager should be " A r e m y activities having a negative impact on the w i l d salmon i n the G e o r g i a Strait?" The Metagame is designed to help devise an experiment to answer that question.  3.2. MATERIALS AND METHODS 3.2.1. The Bayesian Assessment Method The Bayesian assessment method is the statistical process that the program uses to calculate probabilities o f each o f the four hypotheses, given the "fake" data that are generated. This section outlines the general ideas behind this method, and how it works. Bayes' theorem provides a way in w h i c h to combine three basic ingredients as a means for inference (Walters 1986). The necessary components are:  1.  A set o f models, or hypotheses about how a managed system w i l l respond to manipulation. In the Metagame program, these are the four hypotheses discussed above, w h i c h are defined by parameters set i n the program.  2.  A set o f historical observations o f the system under consideration, that are incorporated into an historical "database" about the system. F o r the Metagame program, these observations are drawn from the "fake" data, generated by the program by using one o f the four hypotheses chosen at random.  3.  A set o f "prior probabilities" that w o u l d be assigned to the alternative hypotheses in the absence o f any specific data about the system being questioned. These prior probabilities are generated based on experience gained from observations o f other, similar systems. There is often some disagreement about what level o f probability to assign to each alternative hypothesis as a "prior". F o r example, there is evidence to suggest that the habitat limitation and over-fishing hypotheses are less likely than the oceanographic conditions or the marine carrying capacity hypothesis. I f w e could prove that this 72  was the case, it might be possible to arrive at conclusions about what was really going on in the Strait much faster by assigning lower prior probabilities to hypotheses 1 and 2. However, in order to avoid letting any unfair subjective bias colour the results obtained using the Metagame program, all four hypotheses are given equal probabilities at the start o f each run, and the evaluation is made from this initial state. A s s i g n i n g reasonable "uniform priors" in this way also provides the simplest computational option (Walters et al. 1994).  Once these ingredients are supplied, Bayes' theorem provides us with a means to contrast observations generated using each model against the actual data set. This comparison allows us to calculate a "posterior probability" that each hypothesis is true. The calculation involves two steps. First, the program computes the values that w o u l d be expected for each hypothesis i f that hypothesis was true. Then the deviations o f the simulated ("observed") data from the expected values are calculated for each model. These deviations, when combined with the variance in the simulated data, provide a means o f estimating the likelihood o f the "observations" given each alternative hypothesis. Once the likelihood o f the data has been calculated for each model, it is a simple matter to calculate the Bayes' posterior probability for each hypothesis. This calculation is simply the ratio o f each estimated likelihood to the sum o f the likelihoods over all the hypotheses. It is assumed in this calculation that equal prior probabilities were used (Walters 1994). The equation is as follows (Walters et al. 1994):  (Posterior probability o f the data given the hypothesis)  =  ( L i k e l i h o o d o f the data given the hypothesis) (Sum o f the likelihoods over all the hypotheses)  Thus, the Metagame program can assign a probability to each hypothesis after each year o f a simulation. In general, the more information that is available to be compared, the more accurate the posterior probability w i l l be. Therefore, the probabilities in successive years o f a program run tend to point increasingly to one o f the four hypotheses, until eventually its probability o f being true is very high, while the probabilities o f the other three hypotheses are l o w .  73  3.2.2. The Stock Production Model The stock production model used in the program is one in which all the variable parameters are set by the user, and can be changed at any time. Throughout the production model, w i l d and hatchery fish are survived and recruited separately, and are combined only to produce total smolt and total catch numbers. The life history o f the w i l d fish is modelled in two stages: firstly, this year's spawners to next year's smolts and, secondly, smolts to adults. Hatchery fish require only the second step, since the smolt numbers are calculated from hatchery releases. The number o f w i l d smolts is calculated from spawners by an equation o f the Beverton-Holt form. Beverton-Holt type equations predict that recruitment w i l l increase to an asymptote with increased spawning stock sizes (Walters 1986). The equation used in the Metagame is as follows:  Wild Smolts = max smolts where: Smax = the m a x i m u m survival rate experienced by smolts. Fee = an expression o f the fecundity o f the spawners (i.e. how many eggs they lay). N w i l d spawners = the number o f w i l d spawners. max smolts = the m a x i m u m number o f smolts that can exist in the environment. e  w 1  =  a  r a n  d o m normal variability effect.  G i v e n this equation, the slope o f the increasing part o f the stock-recruitment curve is provided by (Smax)(Fec). This expression is manipulated to help define the freshwater habitat deterioration hypothesis. F o r this hypothesis, Smax is lower than the other three; therefore, fewer smolts survive to adulthood. The asymptote o f the curve is defined by the (max smolts) parameter value. Under the marine carrying capacity hypothesis, this value is reduced relative to the other hypotheses. Therefore, the number o f smolts that can exist in the marine environment are limited under hypothesis 4.  74  T o calculate the number o f adult salmon derived from smolt populations the Metagame uses a simple relationship: Adults = (mean smolt to adult survival rate) (number o f smolts) The four hypotheses are differentiated from one another in the expression that is used to express mean survival rate (MSR). For the overfishing and habitat depletion hypotheses, the M S R is expressed as a set base value that is altered yearly by a randomly generated variability effect. F o r the changing oceanographic conditions hypothesis, M S R is produced in the same manner, except that the variability effect is highly auto-correlated. This means that the variation in any given year is tied to that produced for the previous year. Therefore, changes in M S R occur slowly, over several years, as w o u l d be expected i f climatic or other oceanographic conditions were producing the change. For the carrying capacity hypothesis, M S R is calculated using a Beverton-Holt form equation, as follows:  [(MSR max) x e l ^ (total smolts) x (MSR max) w4  MSR =  J  1+  (adult carrying capacity)  where: M S R max =  the user defined m a x i m u m marine survival rate for hypothesis 4.  total smolts =  w i l d + hatchery smolts.  adult carrying capacity = e ^ = w  v  1.5 m i l l i o n fish (as opposed to 10 m i l l i o n for the other hypotheses).  an expression providing variability.  The result o f using this equation is that the number o f adult fish increases with the total number o f smolts provided to the system. This increase reaches an asymptote defined by the adult carrying capacity. A t the same time, as the number o f smolts increases, the M S R decreases, as w o u l d be expected i f a carrying capacity limit was i n effect.  75  3.2.3. Using the Metagame This section describes the Metagame interface, the various features o f the game, and how the features are used. The game is presented as a series offorms, in w h i c h the user can either make changes to the parameters that control the game, or observe simulation results. The following description o f the program is presented as a guided tour through these forms, and an explanation o f what the game player can do in each one.  The Management Learning Game (Main) Form  Figure 12. The Metagame main user interface.  Game  Parameters  Run Control  Options  Print Assessment Methods summarv:  Graph 1  / CatcWIOBB mac .87  / Marine Surv. ma* .05 10  15  20  25  Year (Iran 1071)  Graph 2  p(Hypothesisl habitat fishing  ocean mar.cap.  Year Year Year Year Year Year Year Year Year  0.250 0.250 D.250 0.250 0.135 0.075 0.042 0.177 0.067 [WW  21 22 23 24 25 26 27 28 29  0.250 0.250 0.250 0.250 0.147 0.064 0.010 0.026 0007 IIIIII1C  0.250 0.250 0.250 0.250 0.136 0.062 0.008 0.019 0 004 [IIIIiK  Number of , „ . . „ „ • Time Steps: l a i L J  - . \'*\?  0.250 0.250 0.250 0.250 0.582 0.799 0.940 0.778 0.922  .  |  D save trial results "Run Performance Indicators 10  15  20  25  Veaj (Jron 1971)  Data File Path:  true model was:  |3  Total catch:  | 12.62 |  j  Running Trial tt :  |  |  tt of Trials Svd=  [c:\metagame\metacoho\  The Main form (Figure 12) consists o f four principle components; the Graph display area, the Assessment Methods Summary, the R u n Performance Indicators, and the Control Group. M o s t o f the Main form is taken up by the graph display area. When Metagame is first started, two graphs are displayed there. However, this area can contain up to four graphs, as determined on the Graph Control form.  76  In the upper right hand corner o f the Main form is the "assessment methods summary" list box. O n each line in this box, five items are displayed. First is the simulation year, then comes the likelihood o f each o f the four hypotheses being true. The likelihoods are calculated based on the data produced by the simulation. F o r the historical data, where no experiment took place, these likelihoods are set at equal chances for each hypothesis. Directly below the assessment methods summary is the "control group". This is a set o f three controls, each used for a different aspect o f the program. The first is the "number o f time steps" scroll bar and text box. This scroll bar allows the user to set the number o f years over w h i c h an experiment is run. The number displayed in the text box is the number o f years beyond the end o f the historical data that the experiment w i l l run. In this same group is the "show multitrial plot" check box. C l i c k i n g on this box loads the Multitrial Plots form w h i c h is used to evaluate the outcome o f a multiple run. The final control i n this group is the "save trial results" check box. C l i c k i n g on this box tells the program whether or not the user w i l l be saving the results o f a multitrial. (The program saves multitrial results automatically, unless this check box is clicked off.) In the bottom right hand corner o f the main form is the "run performance indicators" box. In this box are four display areas, w h i c h provide the user with different information about the current simulation. The most important is the "true model was" text box which tells the user w h i c h hypothesis was chosen by the program to be true. The Multitrial Plots Form The two graphs on the Multitrial Plots form (Figure 13) are useful i n evaluating the results o f a multiple run.  The first graph is labelled "Trial Plots". The x-axis shows the number o f years over w h i c h the multiple runs  have been carried out, and does not include the historical data years. In other words, the x-axis shows the length o f the experiments.  77  Figure 13. The multitrial plots form shows the patterns in probabilities placed on the various hypotheses.  Print  Options  IzJLzJI  Dump to File Probability Threihhold Level:! 25  1.0 Cit<» -Trial Prob Level: Rtd» Prop, Trialj Wkh p(e»tr. top.) >TtSrt^kkold  08 0.6 0.4 0.2 0.0 Y o i (Iron end ol historical data)  Probability Count  0  10 20 30 «0 SO 60 70  Fidelity <n pticcnQ Lund I ill: I  I  Click lliiiiiWhi-n Hum- I  O n this graph, one set o f lines are derived from the likelihoods o f hypothesis 4 being true at each year. For example, one line plots the likelihood o f hypothesis 4 being true from the end o f the 23 years o f historical data to the end o f the experiment, for the first trial. The next line plots the probabilities for the next trial and so on, until there are as many lines as there were simulations, or trials, run. It should be noted that the words simulation, run and trial are used interchangeably i n this context. O n the main form, when a multiple run is done, the user is prompted for the number o f simulations to do. This is the same thing as asking for the number o f trials to do. (One simulation, run or trial is not the same as one year, but is the same as one set o f years). A l s o shown on this form is a plot over time o f the proportion o f the trials (i.e. the proportion o f the probability lines) that is above a given probability threshold level. This level can be set in the text box at the top o f the form. F o r example, a user might run 100 trials o f a 10 year experiment in w h i c h the computer chooses hypothesis 4 as the true one. I f the probability threshold level is set at 0.25, then at year 9 the proportion line might be at 0.85 on the y-axis. This means that, by year nine o f the experiment, 85 out o f 100 o f the trials showed the Bayes posterior probability o f hypothesis 4 being true exceeding 2 5 % . Another way to think about this is as 78  follows. I f 100 separate hatchery managers on different worlds carried out the identical experiment at the same time (i.e., using identical hatcheries in identical environments), then, after nine years 85 o f them w o u l d calculate, from the data they had gathered, that the probability o f hypothesis 4 being true was greater than 2 5 % . B y chance, for 15 o f them the data w o u l d show that the probability o f hypothesis 4 being true was less than 2 5 % . A l t h o u g h this graph can take a minute or two to understand, it is the main output o f the program, and is extremely useful in understanding how long it might take, under a given experimental regimen, to achieve a result that can be used with confidence, i.e. a probability that is reliably high or low. The second graph on this page provides another tool for understanding the results o f a multiple run. It is a plot o f how many times each given probability level occurred for each hypothesis during a multiple run. It counts throughout the whole multiple run, not just for one trial. So, i f 100 trials (i.e., experiments) o f 10 years each are carried out, and a likelihood is generated for each hypothesis at each year, then each hypothesis w i l l have had 1000 likelihoods generated for it. The "Probability Count" graph simply shows at what level each o f those likelihoods fell. So, i f a very long set o f experiments is carried out, in w h i c h hypothesis 4 is set to be true, then the user is likely to see more high likelihoods for hypothesis 4 than any other hypothesis. This graph allows the user to see i f that was indeed the case, and in doing so, to check on the power o f the assessment method being used.  The G r a p h Control Form A c r o s s the top o f the Graph Control Form (Figure 14) is a set o f option buttons that allow the user to choose one o f the four graph panes. Directly below these option buttons are 14 check boxes, each one p r o v i d i n g a data series that can be displayed on a graph. The series among w h i c h the user can choose are as follows:  1.  Catch: Graphs the catch o f both hatchery and w i l d fish in the Georgia Strait. This data series is affected to some degree by random variation.  2.  M a r i n e Survival: The survival rate o f the fish in the marine environment.  3.  Smolts: The number o f smolts released by the hatcheries. W h e n an experiment is carried out, this line is reduced every second year by the amount o f the stocking reduction level. 79  Figure 14. The graph control form allows the metagame user to plot up to four graphs, each showing different information.  WMmMP  Graph 2QGiaph 3QjGtaph 4»  |x • ... •• 5 |T Marine Suivival I™ Smolts  r Wild ax Proportion of Catch P Pfcair.cap hyp.) P Wild Escapement  F Survival. Model 1 l"~ Smolts/Cap. r Survival. Model 2 F Smolts/Cap. r Suivival. Model 3 T Smolts/Cap. r 'Jurvival. Model 4 F Smoltz/Cap.  Model 1 Model2 Model 3 Model A  Show: Jx Graph 1 |x Graph 2 Q G r a p h 3fijGiaph 4j F  Thick Lines pc Y Labels pf Ticks Click Here When Done  4.  W i l d as Proportion o f Catch: The proportion o f the catch that is made up o f w i l d , and not hatchery, fish. The general trend i n this line is a reduction throughout the historical data for both chinook and coho.  5.  P(carr. cap. hyp.): The calculated probability that the carrying capacity hypothesis is true. W h e n a multiple run is done, this line is plotted for each trial on the Trial Plots graph on the Multitrial Plots form.  6.  W i l d Escapement: The number o f w i l d fish that return to spawn.  7.  Survival, M o d e l 1-4: Marine survival as predicted by the different hypotheses, 1-4. This is calculated as a function o f the marine survival change as set in the Model Parameters form.  8.  Smolts/Cap, M o d e l 1-4: The number o f smolts relative to the capacity set in the Model Parameters form. This is calculated as a function o f capacity change also set in the Model Parameters form.  80  The Game Parameters Form  Figure 15. The Game Parameters Form shows the various parameters and default values that affect the running of a metagame.  Parameter maximum possible explt. rate  HI  coef.vaf. of marine sur v. e s t  _li±| -25  •  coef.vai. of total catch  .39  suiv. of returned wild fish  u m _ 8 _  base annual exploitation rate  r r  hatchery smolt to ocean surv.  1*11  Reset  Six parameters affect the running o f a game (Figure 15), and can alter the results o f a simulation quite drastically. The default values that appear when the program first starts are best estimates based on currently available data. A description o f the parameters follows:  1.  M a x i m u m possible exploitation rate: Because the exploitation rate is subjected to some random variation in the model, this simply sets an upper limit on that variation.  2.  Coefficient o f variation o f marine survival estimates: This allows the user to change the amount o f variation observed i n the marine survival estimates. In essence, this is akin to changing the level o f stochasticity in the marine environment, or in our ability to estimate marine survival rates.  3.  Coefficient o f variation o f total catch: This allows the user to change the amount o f variation observed in the annual catch. This is akin to changing the level o f variation i n the ability o f fishermen to catch fish, due to some stochastic process.  4.  Survival o f returned fish: This parameter affects the survival rate o f w i l d fish that are released by fishermen. This only occurs if, during a run that is finished by rules, or a multiple run, the w i l d catch retention is set to some level other than 1.  81  5.  Base annual exploitation rate: This is the base rate at w h i c h the fish are harvested every year. There is some random variation around this level.  6.  Hatchery smolt to ocean survival: This is the survival rate o f the hatchery smolts before they proceed out to the ocean.  The Model Parameters Form  Figure 16. The Model Parameters Form shows how the Metagame defines population production under the four different hypotheses. Mod(*l Parameters. • age al recruitment (3=coho, 4=chinook): HYPOTHESIS: Maximum marine survival late  .008  Habitat  Marine carrying capacity (mil) Fry per spawner (fecundity/2)  mar-change  mar-cap.  .008  .02  02  10.  10.  10.  1.5  2250  2250  2250  2250  Maximum fry-smolt survival Wild smolt capacity (millions)  fishing  .2  -15 100  150  100  100  Annual fry cap change (hab hyp | gg Annual marine surv change  Read parameters from  MPD file  |-|  .96 Save parameters in MPD file  Done  The Model Parameters form (Figure 16) allows the user to alter the parameters that the program uses to simulate the population under each o f the four hypotheses. The default parameters are set at best estimates given currently available data. The levels o f these parameters define the four hypotheses. F o r example, under the habitat reduction hypothesis, the annual change in the fry carrying capacity (i.e. available stream habitat) is reduced by four percent every year, while it stays constant under the other hypotheses. In the same way, under the marine carrying capacity hypothesis, the marine carrying capacity is much lower than it is under the other hypotheses, where it is relatively unlimited. The following provides a description o f all the parameters:  1.  A g e at recruitment: This is set at three for coho and four for chinook.  82  2.  M a x i m u m M a r i n e Survival Rate: The survival rate o f fish during their residence in the marine environment.  3.  M a r i n e Carrying Capacity: The m a x i m u m number o f fish that can exist in the marine environment.  4.  F r y Per Spawner (fecundity/2): N u m b e r o f fry produced on a per fish basis, regardless o f the fish's sex.  5.  M a x i m u m fry to smolt survival: The m a x i m u m survival fish can experience at the earliest life history stages, despite stochastic effects.  6.  W i l d smolt capacity: The m a x i m u m number o f w i l d smolts that can survive in freshwater habitats.  7.  A n n u a l fry capacity change: The reduction in survival o f fry to smolts every year. This parameter is important in defining the habitat reduction hypothesis.  8.  A n n u a l marine survival change: The reduction in the marine survival rate every year. This parameter is important in defining the oceanographic conditions hypothesis.  3.2.4. The Questions that Were Addressed In w o r k i n g with the Metagame program, the original question I set out to answer was a simple one. " H o w long w o u l d hatchery managers need to carry out a stocking rate manipulation in order to identify whether or not hatchery released fish are negatively impacting w i l d stocks in the Georgia Strait? I soon found that the question needed to be stated more explicitly. Specifically, the degree o f the manipulation that the managers were w i l l i n g to make needed to be declared. In other words, w o u l d the managers be w i l l i n g to alternate stocking levels by 9 0 % or only 20%? A l s o , what degree o f certainty was necessary before it would be deemed cause for a permanent change in the hatchery release program? I f there was a 10% chance that hatchery releases were reducing w i l d stocks, w o u l d this be enough to convince managers that the release program should be reduced? O r w o u l d there have to be a 9 0 % certainty before any permanent action was taken? After struggling with these difficulties for some time, I realized that the real problem was not in the question, but in m y approach to answering it. In order to fully utilize the abilities o f the Metagame program, the 83  answer that should be sought is not simply " Y o u w i l l need to do an experiment for x number o f years." There is a definable tradeoff in the number o f years an experiment must be run, versus the degree o f the manipulation that is made, and versus the degree o f certainty that is required. Therefore, I turned m y efforts to defining this tradeoff under different parameter settings and the different hypotheses. The following results section describes that tradeoff and helps to answer the original question for different degrees o f certainty and stocking reduction rates. It also helps to define a more specific question to be tested by the model in the future.  3.2.5. Simulations Run In testing the Metagame, I ran over 70 simulation experiments, each one varying a single parameter. Each experiment was run for 55 years, and for 100 trials. In other words, each o f m y single experiments was analogous to 100 different hatchery managers running the same 55 year experiment under identical conditions. The length o f the experiment was intentionally chosen to be very long. This was done to allow me to report on the conditions o f the population at any time during the experiment. In other words, I was able to see what w o u l d have happened had the experiment been run for any time shorter than 55 years, as well as for the entire 55 years. B y running 100 trials o f each experiment, I was able to identify the degree o f variation caused by stochastic effects. The results reported here are based on manipulations o f four o f the Metagame's parameters. W h i l e it is certainly possible to vary more than these four, it was not deemed useful to spend too m u c h time altering other parameters for two reasons. First, the different possible combinations o f parameters rapidly approaches a staggering number (in the hundreds o f trillions) i f they are all taken into account. Second, and more importantly, three o f the four parameters I varied are the only ones that fisheries managers have the ability to alter. These three parameters were:  1.  The degree to w h i c h hatchery releases were reduced every second year.  2.  The percentage o f the w i l d catch that was retained by fishermen.  3.  The exploitation rate o f the entire stock, both hatchery and w i l d .  84  The fourth parameter that I varied was the hypothesis that defined the true situation i n the G e o r g i a Strait. A d m i t t e d l y , this parameter is not within a manager's abilities to alter. However, since we don't k n o w w h i c h hypothesis is actually true, it was important to test the results o f manipulations under all o f them. Table 11 shows a listing o f the experiments on w h i c h I based the results presented below. A l l the manipulations listed in Table 11 were done for both coho and chinook.  Table 11. The parameter settings which defined the manipulations tested. All manipulations were carried out for both chinook and coho over 55 year runs and 100 trials. True Hypothesis 4  Stocking Reduction  W i l d Catch  Levels  Retention  0.25, 0.35, 0.45 ,0.55,  1  0.65,0.75, 0.85,0.95,  Default (Chinook = 0.7  1.0  3.3.  Exploitation Rate  C o h o = 0.8)  4  0.25, 0.65, 0.85, 0.95  0  Default  4  0.25, 0.65, 0.95  1  0.95  4  0.25, 0.65, 0.95  1  0.25 Default  3  0.55,0.75, 0.95  1  3  0.55  1  0.25  2  0.55, 0.95  1  Default  2  0.55  1  0.25  1  0.55, 0.95  1  Default  1  0.55  1  0.25  R E S U L T S A N D DISCUSSION  3.3.1. Manipulation of Stocking Reduction Levels, Hypothesis 4 (Marine Carrying Capacity) True The first question I set out to answer was, " H o w does the amount o f time it takes to detect whether or not hypothesis 4 is true vary with the degree to w h i c h hatchery releases are reduced?" In order to explore this question, I ran simulations at nine different stocking reductions. I set hypothesis 4 to be true for a l l o f them. The stocking reduction ranged from 2 5 % up to 100%. It is important to note that the Metagame program simulates stocking reductions as occurring every second year. This produces paired contrasts between the years when the reduction occurs and the years when stocking levels are normal.  85  Running simulations at different stocking reductions produced a clear pattern. A t high stocking reductions, a greater proportion o f the trials showed high probabilities o f hypothesis 4 being true at earlier years. In essence, this means that i f hatchery releases are reduced by a large amount, an experiment is more likely to correctly identify that hypothesis 4 is true in a short period o f time. This is to be expected, since the more hatchery releases are reduced, the greater is the contrast provided for the experiment to detect. Figure 17 shows this relationship for chinook and coho. Some interesting results o f varying the stocking reduction can be seen in Figure 17. W h i l e the stocking reduction level was increased by a uniform amount for every trial (10%), the ability to detect that hypothesis 4 was true did not increase uniformly. M u c h greater increases are seen among lower stocking reduction levels than higher ones. This is particularly evident in the case o f the coho population, but the pattern holds true for chinook. F o r example, in the coho graph, it can be seen that an increase in the stocking reduction level from 2 5 % to 4 5 % shows a much greater impact than an equivalent increase from 7 5 % to 95%. This suggests that, i f the goal o f the experiment is to learn whether or not hypothesis 4 is true as quickly as possible, much greater gains can be made by increasing stocking reductions when they are at low levels than when they are already at high levels. Therefore, i f a decision is being made whether to increase the amount o f stocking reduction from 25 to 3 5 % , it should be considered much more seriously than a decision to change stocking reductions from 85 to 9 5 % . The same sort o f diminishing return can be seen in the amount o f time over w h i c h an experiment is run. Particularly at higher stocking reduction levels, the greatest amount o f learning goes on in the earliest years o f an experiment. The change in how sure we can be that hypothesis 4 is true is much greater between years 0 to 10 o f an experiment than it is between years 45 and 55. Once again, this suggests that more weight should be given to the decision to lengthen a short experiment than to lengthen an already long experiment. One other policy decision is implied by this pattern o f diminishing returns. A t some point, it w i l l no longer be profitable to increase the stocking reduction level, or to increase the number o f years over w h i c h an experiment is run.  The amount o f information that can be gained by increasing the manipulation w i l l not be worth the investment  that must be made to provide such an increase. Therefore, there should be an upper bound on the amount by w h i c h hatchery releases are reduced, and the number o f years for w h i c h an experiment is run. This upper bound w i l l be 86  set, at least partly, b y economic factors. H o w much does it cost to increase the harshness o f the manipulation, and h o w m u c h does a year's worth o f experimentation cost? H o w much w i l l be gained from the resulting information?  Figure 17. The proportion of trials that showed the probability of hypothesis 4 being true to be greater than 90% when hypothesis 4 was in fact true. Trials were run at 9 different stocking reduction levels, from 25% through 100%.  Chinook  Proportion of Trials  Stocking Reduction  Coho  Proportion of trials  Stocking Reduction  87  Figure 17 gives a qualitative idea o f the changes that occur over a continuum o f different stocking reduction levels. However, it is difficult to see from this figure how to reach quantitative conclusions about the number o f years it takes to reach a desired level o f certainty. This is more easily derived from Figure 18, w h i c h contains some o f the same information as Figure 17, but is presented in a more quantitative manner. U s i n g Figure 18, one can start to answer the question " H o w long w i l l it take to learn whether or not hypothesis 4 is true?" O f course, to begin with, it is still necessary to arrive at a decision as to what level o f stocking reduction is reasonable to undertake. However, in light o f the information provided in Figure 17, it is apparent that, i f an economic limitation exists, it is probably best not to invest in a stocking reduction that is too high. B y the same token, an extremely low stocking reduction is also undesirable, as just a small increase in stocking reductions at low levels can significantly reduce the time to learn. Therefore a stocking reduction level that is high, but not too high, is best. Consider a 7 5 % reduction o f chinook stocking as an example. It is necessary to start by defining what proportion o f the trials one is concerned with. F o r illustrative purposes, this might refer to the number o f trials, out o f 100, that show a probability o f hypothesis 4 being true to be greater than 90%. Another way to look at the proportion o f trials is to think o f it as the probability o f any single experiment w o r k i n g . That is, i f a manager was to run only one experiment, instead o f 100 (which seems likely) what is the probability that it w o u l d show hypothesis 4 to have a likelihood o f greater than 9 0 % at any given year, i f in fact, hypothesis 4 was true. A s an example, I w i l l arbitrarily define 9 5 % as the proportion o f trials I am interested in. This gives an experiment an almost certain chance o f working. Thus, an appropriately specific question has been defined, given the information at hand. " H o w many years w i l l it take for an experiment to show hypothesis 4 to be at leat 9 0 % likely to be true, i f we reduce stocking rates by 7 5 % every second year? I want to have a 9 5 % chance o f this experiment w o r k i n g . " These levels o f certainty are fairly high, and this example could probably be considered as one in w h i c h the manager wants to be as sure as possible that hypothesis 4 is true before he takes any action to permanently alter his hatchery releases. O f course, the hatchery manager w i l l have to run his experiment for a very long time in order to gain this level o f certainty. F r o m the chinook graph in Figure 18, it can be seen that the 7 5 % stocking reduction line reaches a level o f 9 5 % o f the trials at about 46 years. This is probably an unreasonably long experiment. H o w e v e r , before 88  discussing ways in w h i c h a shorter experiment might suffice, I w i l l examine the results for the coho population, as shown i n Figure 18.  Figure 18. The proportion of trials that showed the probability of hypothesis 4 being true to be greater than 90% when hypothesis 4 was true. Trials are shown for 4 different stocking reduction levels,from35% through 95%. Chinook  0  5  10  15  20  25  30  35  40  45  50  55  Year  Coho  0  5  10  15  20  25  30  Year  89  35  40  45  50  55  U s i n g the same requirements we set above, one can see how long it w o u l d take to learn that hypothesis 4 was true i f the experiment was run with the coho population. L o o k i n g at the coho graph i n Figure 18, for the 7 5 % stock reduction line, 9 5 % o f trials show a likelihood o f 9 0 % or greater after about 20 years. W h i l e this is considerably better than the equivalent case with the chinook population, it is quite possible that 20 years is still too long an experiment to justify even starting it. The easiest way to reduce the number o f years required to show that hypothesis 4 is true is to lower the level o f certainty required. F o r example, i f it is decided that a likelihood o f 3 0 % that hypothesis 4 is true is enough to dictate a permanent change in the levels o f hatchery releases, the length o f the experiment can be much shorter. For chinook, at a stocking reduction level o f 75%, 95 out o f 100 experiments w i l l show a likelihood o f 3 0 % or better for hypothesis 4 after only 11 years. This is a substantial improvement over 46 years, as discussed i n the previous example. F o r the coho population, with the same level o f stocking reduction, it only takes about 7 years for 9 5 % i f the trials to show a likelihood o f 3 0 % or better that hypothesis 4 is true. It is not easy to decide on the appropriate level o f certainty to use. This level depends on many factors, chief among them being the relative weight given to the importance o f the hatchery release program versus the w i l d salmon stocks. This is an issue that must be decided by those who wish to halt the decline o f the w i l d stocks, and those w h o stand to benefit from keeping hatchery releases at their current levels. The only general statement that can be drawn from simulations using the Metagame program is that greater levels o f certainty demand longer experiments and higher stocking reduction levels. Figure 19 shows the results o f simulations for several different levels o f certainty. U s i n g these graphs, one can pinpoint how many years an experiment might take given a specific level o f certainty and a specific stock reduction.  90  Figure 19. The proportion of trials that showed a specific likelihood of hypothesis 4 being true. Trials were run for 4 different stocking levels, from 35% through 95%. Graphs are shown for both coho and chinook populations.  3.3.2. Manipulation of Wild Catch Retention, Hypothesis 4 True Once I had experimented with the stocking reduction levels, I wanted to see i f there were other p o l i c y changes fisheries managers could make that w o u l d reduce the time to learn. One such change the m o d e l allows is in  91  the proportion o f w i l d catch that is kept by fishermen. This policy alternative is primarily intended as a method to rebuild w i l d stocks while the experiment is going on, but I wanted to see h o w it w o u l d affect the amount o f time it took to decide whether or not hypothesis 4 was true. The result for both chinook and coho was quite clear. A n y reduction in the proportion o f w i l d catch retained did, in fact, lead to a rebuilding o f the w i l d stock during the experiment. The degree o f rebuilding depended on the proportion o f w i l d stocks that were thrown back, as w o u l d be expected. However, rebuilding the w i l d stocks in this manner also increased the amount o f time it took to learn whether or not hypothesis 4 was true. This was particularly true in the case o f chinook manipulations, w h i c h always took a longer time to produce any level o f certainty about the true hypothesis than the coho did. This result is not surprising when one considers the overall effect o f rebuilding the w i l d stocks during an experiment. This causes the hatchery stock to become a less significant percentage o f the total stock in the Strait, so that any manipulation in the levels o f hatchery stock becomes a smaller manipulation in terms o f the total stock. A smaller manipulation provides less contrast, and therefore the experiment takes a longer time to produce a result. Figure 20 shows the 0 % w i l d catch retention line in a 9 5 % stocking reduction experiment. This can be compared to the 9 5 % stock reduction line with 100% w i l d catch retention, shown on the same graph (as the default line). A 0 % w i l d catch retention means that all w i l d fish that are caught are thrown back. This is the most extreme case possible. It is shown on the graph to clearly illustrate that a reduction in w i l d catch retention leads to increased learning time. The same is true for smaller reductions in w i l d catch retention, but the learning time is not increased to the same degree. From the graphs, it can be seen that, for chinook, a 100% retention o f w i l d catch leads to 9 5 % o f trials showing hypothesis 4 to be greater than 9 0 % likely in about 28 years. However, with a 0 % w i l d catch retention we never reach this level over the entire 55 year experiment. The case with the coho, as always, is not quite as extreme. Under a 100% catch retention, it takes about 11 years to show that hypothesis 4 is true. W i t h 0 % w i l d catch retention, this time is increased to about 20 years. The implication o f these results is clear. A n y attempt to rebuild w i l d stock during an experiment designed to learn whether or not hypothesis 4 is true is likely to increase the amount o f time it takes to reach an acceptable 92  result. Smaller reductions in the w i l d catch retention rate w i l l not impact the time to learn as dramatically, but any change w i l l lengthen the experiment to some extent.  3.3.3. Reducing Exploitation Rates, Hypothesis 4 True The final manipulation I explored i n m y attempt to reduce learning time was the base exploitation rate parameter. Increasing or decreasing this parameter changes the degree to w h i c h fishing impacts the total stock in the G e o r g i a Strait, both w i l d and hatchery. W h e n exploitation rates are reduced, the situation is much the same as reducing the retention o f w i l d fish i n the catch. A larger standing stock o f both hatchery and w i l d fish is left in the Strait every year. This has the effect o f reducing the degree o f contrast that can be obtained by a controlled fluctuation in hatchery releases. A s w o u l d be expected, this increases the time it takes to learn that hypothesis 4 is true. L o o k i n g again at Figure 20, the "learning curve" under a 2 5 % exploitation rate can be compared with the default curve. Once again, for the chinook, a reduction in the exploitation rate leads to a situation in w h i c h even after 55 years o f experiment, a very low proportion o f trials has reached a 9 0 % likelihood that hypothesis 4 is true. F o r the coho population, a 2 5 % exploitation rate produces a curve very similar to that produced by a 0 % w i l d catch retention. Once again, the time to learn that hypothesis 4 is true is increased from 11 to about 20 years. The implication o f this result is very clear. Increasing the standing stock in the Strait by reducing exploitation rates increases the time for a hatchery release experiment to produce an acceptable result. W h i l e it may be desirable to reduce exploitation rates in order to increase standing stock size, the negative impact it w o u l d have on a hatchery release experiment should be considered.  3.3.4. Increasing Exploitation Rates, Hypothesis 4 True The obvious extension to the previous manipulation is to find out what happens when exploitation rates are increased. The result o f this manipulation is not as straightforward as that o f reducing exploitation rates. In the case o f the chinook population, the default exploitation rate is set at 70%. This was compared with an increased exploitation rate o f 9 5 % . Initially, I expected that by increasing the exploitation rate to this extreme 93  level, I w o u l d cause the program to simulate a decrease in the standing stock. Then, any fluctuation o f hatchery releases w o u l d produce a relatively high level o f contrast, and the time to learn that hypothesis 4 was true w o u l d be decreased. However, this did not occur. F o r the chinook, when exploitation rates reach 9 5 % , the w i l d stock is driven almost to extinction, and the standing stock is, indeed, reduced. However, the impact on stocks is so great that the program has a difficult time distinguishing this situation from one in w h i c h overfishing is the cause o f the w i l d stock decline. In fact, by simulating such an extreme exploitation rate, we have produced a situation i n w h i c h both overfishing and a marine carrying capacity are negatively impacting the w i l d stock in the Strait. This means that it takes the program longer to determine that hypothesis 4 is true, and learning time is hence increased. F r o m the first graph in Figure 20, it can be seen that an increase in exploitation rate from the default level (70%) to 9 5 % increases the time to learn that hypothesis 4 is true from 28 years to about 49 years. This result has interesting implications for a chinook hatchery experiment. There is apparently an ideal level o f chinook exploitation (neither too high nor too low) that w i l l produce an experiment that w i l l allow us to determine the situation i n the Strait in the shortest amount o f time. W h i l e I have not run enough simulations to determine the exact level, we can be reasonably sure that it is not higher than current exploitation rates, since a relatively small increase produced an increase in learning time. This implies that, in the case o f the chinook stocks, the best p o l i c y w o u l d probably be to not allow exploitation rates to increase from their current levels. A t the same time, i f a short experiment is o f paramount importance, it might be preferable to reduce exploitation rate to some small degree, but the results argue against any large reduction. W h i l e further simulations w o u l d have to be done to determine the ideal exploitation rate in terms o f a short hatchery experiment, it appears that current exploitation rates seem to be reasonably good at producing short learning times. Increasing exploitation o f the coho population produces a different result. F r o m the coho graph in Figure 20, it can be seen that an increase in exploitation (from 8 0 % to 95%) reduces the amount o f time it takes to learn that hypothesis 4 is true. Under default exploitation rates, it takes about 11 years for 9 5 % o f the trials to agree, while a 9 5 % exploitation rate reduces the time to only about 6 years.  94  Figure 20. The proportion of trials that showed the probability of hypothesis 4 being true to be greater than 90% when hypothesis 4 was true. Trials were done at different exploitation rates and wild catch retentions. Chinook  . 95% stock reduction (default) . 95% exploitation rate  . 25% exploitation rate  . 0 wild catch retention  Coho  . 95% stock reduction (default) . 95% exploitation rate . 25% explotation rate 0 wild catch retention  45  50  55  Year  This result suggests that m y original expectations are true for the coho population. It also indicates that the simulated chinook population is much closer to being over-fished than the coho population. In fact, even at a 9 5 % exploitation rate the coho population did not appear to be recruitment over-fished. However, increased exploitation  95  rates d i d have a negative impact on w i l d stocks, particularly in the hatchery release years. This threat o f extinction for the w i l d stocks argues against allowing exploitation rates to increase, even i f it w o u l d decrease the amount o f time for w h i c h a hatchery experiment w o u l d need to be run. A t the same time, coho exploitation should probably not be decreased, as this w o u l d necessitate a longer hatchery experiment in order to illuminate what the true situation in the Strait is.  3.3.5.  The Other Hypotheses  T o this point, I have only discussed simulation experiments that were carried out when hypothesis 4 was set to be true. This begs the question, "What happens when the other hypotheses are true?" O b v i o u s l y we cannot assume a priori that hypothesis 4 defines the real situation in the Georgia Strait, or there w o u l d be no point in doing a hatchery experiment in the first place. So, i f one o f the other hypotheses is true, how q u i c k l y can we learn that hypothesis 4 is not true? Is the learning time shorter in this situation than when hypothesis 4 is true? C a n we disprove hypothesis 4 faster with some o f the alternative hypotheses than others? T o answer these questions, the obvious first step is to run trials using the alternative hypotheses and compare them to trials run using hypothesis 4. When an alternative hypothesis is run, the probability o f hypothesis 4 being true starts at 2 5 % (as do a l l the other hypotheses) and drops o f f to very low levels, usually to zero. The rate and magnitude o f this decline both depend on the degree o f manipulation o f stocking rates. Eventually, i f the experiment is run long enough and has a significant manipulation o f hatchery releases, the probability o f hypothesis 4 being true drops to 0% for a l l the trials o f a multitrial. The complicating factor is that, sometimes, the probability o f hypothesis 4 being true initially increases, and does not drop off until after a fairly long experiment has been run. Or, the probability o f hypothesis 4 can be low, then increase in the middle o f an experiment, and return to low levels. These apparently anomalous results come from the fact that the data the computer generates for comparison to the hypotheses have a random component to them. So, with some experiments, the other hypotheses can produce data that look like hypothesis 4 is true. Conversely, when a set o f trials is run in which hypothesis 4 is true, the probabilities start at 2 5 % and increase until they are all, eventually, at 100%. However, while some o f the trials increase to 100% probability very 96  rapidly, others stay at low probabilities for a long time, and only increase after a very long experiment. Still others fluctuate up and down before they finally increase to 100% probability. So, the problem becomes one o f distinguishing random effects from the effects seen when hypothesis 4 is true.  Figure 21. Chinook: decline in the estimated probabilities of hypothesis 4 being true when the other hypotheses were true. Error bars show 95% confidence intervals, n=100. (Note: some error bars omittedfor clarity). C h i n o o k , hypothesis 1 true (overfishing)  Chinook, hypothesis 2 true (habitat limitation)  Yo£T cf Bopsrii I u 1  Figures 21 and 22 show time plots o f the decrease in the estimated probability o f hypothesis 4 being true when the other hypotheses are true. These graphs show how efficient the metagame program is, o n average, o f detecting the fact that hypothesis 4 is not true.  97  Figure 22. Coho: decline in the estimated probabilities of hypothesis 4 being true when the other hypotheses were true. Error bars show 95% confidence intervals, n=100 Coho, hypothesis 1 true (over fishing)  Coho, hypothesis 2 true (habitat limitation)  Year of Expert mart  Year cf Experiment  Coho, hypothesis 3 true (oceanographic changes)  Yoarcf Exptrirmt  F o r a l l the hypotheses, it is readily apparent that the intensity o f the manipulation influences learning time in exactly the same w a y as it d i d for the hypothesis 4 experiments. In other words, the greater the degree o f hatchery release fluctuations, the shorter w i l l be the time to learn that hypothesis 4 is not true. In the same w a y , reducing manipulation intensity w i l l slow learning time, no matter w h i c h hypothesis is true. For example, in the case o f chinook, where the overfishing hypothesis (hypothesis 1) was set to be true, it took around 20 years for a l l experiments to assign a 0 % probability to hypothesis 4, when a 7 5 % stocking reduction was used. H o w e v e r , when the stocking reduction was decreased to 2 5 % , even after 55 years the average experiment still assigned a reasonable probability (around 10%) to hypothesis 4. These results are intuitive i f one returns to the idea o f contrast. In m a k i n g a more extreme manipulation, one expects a more extreme response i f hypothesis 4 is true. I f this response  98  does not occur it is more readily apparent than i f only a small manipulation was made to begin with. Therefore, a more extreme manipulation allows the experiment to show that hypothesis 4 is not true more q u i c k l y than a m i l d manipulation does. It is interesting to note that, at least for the chinook population, when hypothesis 3 is true, increasing stocking reductions produced the smallest decreases in learning time. In other words, it appears that an increase i n stocking reduction levels is least beneficial when hypothesis 3 is true. The 9 5 % confidence intervals displayed on Figures 21 and 22 illustrate another interesting effect o f increased stocking reductions. For the high stocking reduction experiments, the variability in results decreases rapidly, as indicated by the shrinking confidence intervals. However, with stocking reductions o f only 2 5 % , there is some degree o f variability in experimental results, even after 55 years. This means that, after a few years, a low stocking reduction experiment is more likely to show erroneously high probabilities o f hypothesis 4 being true than a high stocking reduction experiment. A g a i n , this effect is due to the decreased level o f contrast available to be detected. U s i n g the Metagame program, i f we show that hypothesis 4 is not true, can we say anything about w h i c h o f the other three hypotheses is true? Since the experiment that is simulated by Metagame is only designed to alter the situation in the G e o r g i a Strait i f hypothesis 4 is true, it is much easier to make assertions about it than the other hypotheses. However, using the Probability Count graph on the Multitrial plot form, it is sometimes possible to draw some conclusions about the other three hypotheses, depending on the degree o f the manipulation made. W h e n a trial is run in w h i c h one o f the first three hypotheses is true, this graph usually counts more high probabilities for this hypothesis than the other three. In this situation, hypothesis 4 usually shows a large number o f calculations in w h i c h the estimated probability was 0. F r o m figure 21, it is interesting to note that it is generally easier to distinguish hypothesis 4 from hypotheses 1 and 2 (overfishing and freshwater habitat loss) than it is to distinguish hypothesis 4 from hypothesis 3 (changing oceanographic conditions). This is particularly true in the case o f the chinook population. This result implies that i f hypothesis 1 or 2 is true, a hatchery reduction experiment w i l l start to produce low probabilities o f hypothesis 4 very quickly. I f hypothesis 3 is true, it may take longer to verify that hypothesis 4 is not true.  99  3.3.6. Conclusions E x p l o r i n g different experimental manipulations with the Metagame model, I have been able to draw some general conclusions. Regardless o f w h i c h hypothesis really is true, the following steps outline methods by w h i c h a manager can reduce the time it takes to learn whether or not there is a marine carrying capacity i n the Georgia Strait:  1.  Increase the violence o f fluctuations i n hatchery releases.  2.  Ensure that exploitation rates are not reduced during the experiment. (Exploitation rates should probably not be allowed to deviate from their current levels.)  3.  D o not attempt to rebuild w i l d stocks through some other policy manipulation.  Since the actual number o f years an experiment takes depends on the level o f certainty required, as w e l l as the degree o f hatchery release fluctuations that can be produced, it is not possible to determine exactly h o w long an experiment w o u l d require to discover whether or not there is a carrying capacity limit in the Georgia Strait. However, an example o f a "reasonable" experiment can be given. Suppose it were possible to reduce hatchery releases by 7 5 % o f their current level every second year, that we w o u l d accept the carrying capacity hypothesis as being true i f the experiment produces a 7 0 % or higher likelihood, and that we want to be 9 5 % sure that the likelihood w i l l be at least this high i f the carrying capacity hypothesis is true. F r o m the modelling simulations I have run, the Metagame suggests that such an experiment w o u l d take about 30 years in the case o f the chinook population. F o r coho, the model suggests such an experiment could take as long as 14 years. B y reducing hatchery releases more (say, 9 5 % every second year) we can complete a chinook experiment in about 20 years. A coho experiment can be reduced to 12 years. There is an important point to be made about these estimates o f experiment length. T o produce them, we are looking for the point where 95 out o f 100 theoretical experiments produces the desired result. This is to ensure that no matter what happens in the real experiment, we w i l l achieve the correct result. However, o f these 95 theoretical experiments, it does not take the full amount o f time for all o f them to reach the desired level o f probability. In fact, most o f them take less time, and some o f them are very much faster. In fact, after only 3 or 4 100  years, a small proportion o f them have already reached the desired level o f probability. In other words, the number o f years predicted above are worst case scenarios. There is quite a good chance that any experiment undertaken by the hatcheries w i l l not take the full amount o f time to complete. B y the same token, there is a small (5%) chance that the experiment w i l l take even more years to complete. Therefore, in designing an experiment, the m a x i m u m amount o f time should be allowed for, but it is possible that it w o u l d not take this long to achieve the desired likelihood indicating whether or not hypothesis 4 is true. Finally, it is important to attempt to draw some conclusions regarding practical use o f the Metagame model. In order to gain the m a x i m u m benefit from the model, it should continue to be used w h i l e designing and carrying out a hatchery release manipulation. A s different levels o f manipulation are considered, the program can be used to evaluate them. Estimates o f the time necessary to learn under a given manipulation, and the degree o f certainty that can be achieved should prove useful in deciding whether or not a specific manipulation has a favorable cost/benefit ratio. This should be useful to fisheries managers i n deciding whether or not it w o u l d be worthwhile to even begin a hatchery experiment. Once such an experiment was started, the program could be used to evaluate the probability o f any results obtained, i f the carrying capacity hypothesis is true. F o r example, i f an experiment showed a l o w probability o f the carrying capacity hypothesis being true, Metagame could be used to run a large number o f trials under the same experimental regime, for the same amount o f time. F r o m the results o f these trials, it w o u l d be possible to estimate the likelihood that the low result was produced spuriously, out o f random variability. This process w o u l d be useful in avoiding the early termination o f an experiment based on results that were random, as opposed to results that accurately reflected the situation in the Georgia Strait. Thus, the model could be a useful tool throughout the entire experimental process, from design to completion.  101  C H A P T E R 4: G E N E R A L CONCLUSIONS  D u e to long term increases i n hatchery releases, with little or no corresponding increase i n overall catches o f G e o r g i a Strait coho and chinook, concern has arisen regarding their future. The aims o f this thesis were to examine the possibility that a carrying capacity limit is responsible for the apparent decline i n survivals o f juvenile salmon, and to suggest hatchery manipulation experiments to test for the limit. The results o f a direct feeding study suggest that juvenile salmon may have a m u c h greater impact on available food resources than has previously been suspected. Considering the other species i n the Strait that utilize the same foods, as w e l l as the turnover rate o f the prey species, it appears that the coho and chinook populations may have become large enough that they have reached a carrying capacity limit. This conclusion is reinforced by the l o w feeding rates that were estimated by a bioenergetics model o f salmon growth and feeding. However, the feeding results must be viewed with caution. Several suspect parameters were used in estimations o f smolt diet patterns and food availability. Better estimates o f zooplankton abundances and juvenile mortality rates are necessary to provide more conclusive results. A l s o , the degree to w h i c h salmon are opportunistic feeders needs to be better established. Nevertheless, the results o f m y research emphasize the importance o f further investigation into a carrying capacity. A n opportunity exists to study more fully the impact that juvenile salmon have on their food resources. The use o f a computer program that modelled hatchery release manipulation experiments i n the G e o r g i a Strait provided some insight into the potential outcomes o f such an experiment. The most significant conclusion to be drawn from this gaming is an indication o f the need for very long-term hatchery manipulations. E v e n with 7 5 % reductions i n hatchery releases every second year, the model suggests that an experiment may take as long as 14 years to prove that a carrying capacity limit is impacting coho stocks. F o r chinook, the same result c o u l d take twice as long.  102  However, i f such an experiment is undertaken, the model offers some ways to make it as efficient as possible. Firstly, greater fluctuations in hatchery releases provide more statistical contrast. This makes the pattern o f results easier to interpret. Secondly, it appears that current exploitation rates o f the chinook and coho stock may be close to the ideal levels for providing insight v i a such an experiment. Therefore, i f the results o f the experiment take precedence over rebuilding failing stocks, then fishing levels should be neither increased or decreased during the course o f the trial. The most important implication o f this result is the fact that concurrent attempts to rebuild stocks through other means w i l l prolong the length o f time an experiment takes to show conclusive results. It remains to be seen whether or not the need to understand carrying capacity impacts in the Georgia Strait becomes great enough to overcome the economic factors supporting the operation o f hatchery programs. 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