Fisheries Science: Foundation and Application (BIOL3305) - Past Lecture Notes PDF

Basics of stock assessment School of Biological Sciences Dirk Zeller University of Western Australia Sea Around Us – Indian Ocean www.seaaroundus-io.org [email protected]  Basic...

Basics of stock assessment School of Biological Sciences Dirk Zeller University of Western Australia Sea Around Us – Indian Ocean www.seaaroundus-io.org [email protected]  Basic steps in stock assessment Fisheries exploit populations of wild fish in their natural environments If we want to fish in some form ‘sustainably’ A) What is the meaning of ‘sustainable’? 1. Maintain/do something forever at a given level 2. Sustainable in fisheries? Maintain the stock at a given level of biomass to allow a certain catch level to be taken forever (“consume” only as much as the system “produces”) B) Need to know/understand 2 things 1. The condition of the stock, i.e., its biomass status 2. The effects of fishing on the stock  Basic steps in stock assessment “Theoretically ideal” basic steps in stock assessment 1. Define your unit stock 2. Collect catch data for the whole fishery 3. Collect effort data for the whole fishery 4. Combine 2 and 3 into a time series of CPUE (catch per unit of effort) 5. Collect data on biological characteristics of stock a. Growth b. Mortality c. Stock size d. Recruitment (stock-recruitment relationship) 6. Derive estimates of expected catch (yield) under different conditions of fishing pressure a. Surplus production/surplus yield/biomass dynamics models b. Yield per recruit models c. Age-structured models 7. Provide advice to managers 8. Hope the advice is followed….  Unit stock Determining the spatial extent of a self-contained breeding population is very difficult, mainly because: 1. We cannot see or follow animals in space and time (or extremely difficult/expensive) 2. Dispersive planktonic larval stage, thus stocks can span hundreds or thousands of km Unit stocks often ID on basis of location of their spawning grounds and the potential influence of major land masses and oceanographic features to limit geographic extent (clearly not as clear-cut as island-biogeographic isolation). Mixing between populations can be difficult to assess… genomics may help. Although ‘unit stock ID’ is listed first, it is often done well after the fishery has developed. Many fisheries around the world are managed (or not) in complete ignorance of the actual ‘stock structure’ of the fished species.  Unit stock Different Atlantic herring (Clupea harengus) spawning seasons and sites in northern Europe Political-management based Semi-artificial stock boundaries quite common 5/44  Unit stock Stock definition can be even more complicated : Spawning grounds of autumn versus spring spawning Atlantic herring (Clupea harengus) in North Sea Dickey-Collas et al. (2010) ICES Journal of Marine Science 67(9): 1875-1886  Collect catch & effort data How? Logbooks (fishers) Landings site enumerators (government or port officials etc.) Fishermen co-ops (fishers) On-board observers (in person and/or electronic) Creel surveys (questionnaires) or boat ramp surveys All have data challenges that add uncertainty, bias and error: Completeness: spatial and temporal extent and coverage (all fisheries that catch that species?) Landed versus discarded catch Trust in accurate reporting (incidental misreporting: taxonomic mix-up, wet-weight versus processed weight; deliberate cheating)  Catch per unit of effort (CPUE) How measure effort? Usually by fleet or gear type, e.g., trawl fleet (Northern Prawn Trawl Fleet - NPTF) 1. Nominal effort: e.g., number of boats in a fishery (number of large versus small trawlers in NPTF) 2. Effective effort: takes duration of fishing into account, hours or days fished per year per boat (number of boat-hours trawled per boat per year in NPTF) Effective effort, i.e., the product of nominal effort times the fishing duration provides a more useful and direct indication of the potential fishing mortality imposed on a stock by that fishery Problem: How standardize across fishing gear and different fisheries, and over time? Same species: industrial trawl days/year, recreational hook-line-hours, commercial gillnet km/year How account for improvements in efficiencies and technologies of gears and boats over time? See separate lectures on fishing gears and fishing effort  Catch per unit of effort (CPUE) What can CPUE trends tell you? If catch changes at the same rate as the effort…. CPUE (catch/effort) flat If catch increases while effort decreases or stays flat… CPUE increases If catch decreases (or flat) while effort increases …. CPUE declines Thus, generally CPUE time series trends can give an indication of changes in the relative abundance of the stock over time… all else being equal However, management actions (e.g., effort limits, fishing seasons or catch limits) can impact CPUE  Catch per unit of effort (CPUE) Species by stock area Research trawl survey results, Atlantic cod in North Sea (numbers per trawl hour) Commercial lobster in North/Baltic Sea (numbers per 100 pots per 24 h soak) 10/44  Biological characteristics of stocks Usually via acquiring specific data to determine rates of growth, mortality and recruitment (particularly stock-recruitment relationships) Growth is change in body mass over time (somatic growth). Fisheries biologists use two types of data to estimate growth rates: 1. Size at age (requires aging, generally via otolith analysis… WA Fisheries field trip) 2. Size increase with time (e.g., mark-recapture, length-based methods) Length of a fish is much easier and quicker to measure in the field than mass (~weight), thus most studies of growth measure length at age or length increases with time and use a length- weight relationship to estimate growth rates in terms of weight Jennings et al. (2001) Marine Fisheries Ecology. Blackwell Science, Oxford. 417 p. Hilborn and Walters (1992) Quantitative fisheries stock assessment. Chapman & Hall, New York. 570 p. Walters and Martell (2004) Fisheries ecology and management. Princeton University Press, Princeton. 399 p.  Growth The asymptotic von Bertalanffy Growth Function (VBGF) is the most commonly used model for growth, as it seems to fit all aquatic, water-breathing organisms (but often with variability)  Growth The von Bertalanffy Growth Function (VBGF)  =  (1 −      Where Lt = length at age t t = age Linf or L∞ = asymptotic length, the mean (average) length a fish of a given stock would attain if it were to grow indefinitely (not max. length) K = growth coefficient or curvature parameter, the rate at which the curve approaches Linf e = the base of natural logarithms t0 = age at which length is zero if the fish had grown in the manner described by the VBGF for the available size at age data…. t0 is negative Growth  Mortality Mortality usually thought to be in the form of exponential decay, i.e., younger and/or smaller fish die at a higher rate than bigger/older individuals.  =     Where N0 = the initial number of animals Nt = the number of animals remaining at time t e = the base of natural logarithm Z = the exponential rate of total mortality Jennings et al. (2001) Marine Fisheries Ecology. Blackwell Science, Oxford. 417 p. Hilborn and Walters (1992) Quantitative fisheries stock assessment. Chapman & Hall, New York. 570 p. 15/44 Walters and Martell (2004) Fisheries ecology and management. Princeton University Press, Princeton. 399 p.  Mortality In fisheries, we recognize three types of mortality, all expressed as exponential rates of decay: Z = total mortality M = natural mortality F = fishing mortality Z=M+F Fisheries biologist try to estimate Z and split into its two components, M and F Jennings et al. (2001) Marine Fisheries Ecology. Blackwell Science, Oxford. 417 p. Hilborn and Walters (1992) Quantitative fisheries stock assessment. Chapman & Hall, New York. 570 p. Walters and Martell (2004) Fisheries ecology and management. Princeton University Press, Princeton. 399 p.  Mortality Various methods for estimating mortality rates, all with caveats, biases and limitations: 1. Survival rate estimates 2. Mark-recapture methods (tagging) 3. Age- or length-based catch curves 4. Empirical derivations Overall, directly measuring natural mortality rates (M) is difficult or rare, because such estimates rarely exist before a species is fished, thus indirect method more common, especially catch curves. Many stock assessment models assume M. If interested in more, there is plenty of literature around. Jennings et al. (2001) Marine Fisheries Ecology. Blackwell Science, Oxford. 417 p. Hilborn and Walters (1992) Quantitative fisheries stock assessment. Chapman & Hall, New York. 570 p. Walters and Martell (2004) Fisheries ecology and management. Princeton University Press, Princeton. 399 p.  Stock size Two basic approaches for estimating stock size, most crucial and often very challenging task for fisheries scientists: 1. Estimate absolute stock size (number, biomass) by surveys. Problems due to cost and spatial/temporal coverage… but can provide valuable insights (see lab example) 2. Use an index of stock density (e.g., catch per unit effort) for temporal or spatial comparisons… see later lecture/example on CPUE Various methods for estimating stock size, but all have uncertainty, even for stocks considered “well studied” 1. Mark recapture 2. Areas fished by gears 3. Depletion experiments 4. Catch data and fishing mortality estimates 5. Acoustic surveys 6. Egg surveys 7. Direct enumerations 8. Virtual Population Analysis (Cohort Analysis) 9. Catch per unit effort relationships If interested in more, there is plenty of literature around. Jennings et al. (2001) Marine Fisheries Ecology. Blackwell Science, Oxford. 417 p. Hilborn and Walters (1992) Quantitative fisheries stock assessment. Chapman & Hall, New York. 570 p. Walters and Martell (2004) Fisheries ecology and management. Princeton University Press, Princeton. 399 p.  Stock recruitment “The most important and generally most difficult problem in biological assessment of fisheries is the relationship between stock and recruitment” (Hilborn and Walters 1992). Very difficult to predict the numbers of recruits that will enter the stock in any given year, based simply on knowledge of the size of the spawning stock that gave rise to those particular recruits via reproduction. Plots of abundance of recruits against spawning stock size collected over many years look like shotgun blasts on road signs, i.e., show little evidence of a relationship. Three biological processes contributing to the shape of the stock-recruitment relationship: 1. Density-independent mortality 2. Compensation (reduction in the number of recruits per spawner as stock size increases, including potentially over-compensation) 3. Depensation (recruits per spawner may increase as spawning stock size increases, e.g., due to predator saturation). Jennings et al. (2001) Marine Fisheries Ecology. Blackwell Science, Oxford. 417 p. Hilborn and Walters (1992) Quantitative fisheries stock assessment. Chapman & Hall, New York. 570 p. Walters and Martell (2004) Fisheries ecology and management. Princeton University Press, Princeton. 399 p.  Stock recruitment Various stock-recruitment models, some common ones: Beverton and Holt (Figure b) Ricker (Figure c) Shepherd (Figure d) Important: These are average relationships, with high, non-normal variation Beverton and Holt (2004) On the dynamics of exploited fish populations - 2004 reprint. See Master reading list Ricker (1954) Journal of the Fisheries Research Board of Canada 11: 559-623 20/44 Shepherd (1982) Journal du Conseil International pour l'Exploration de la Mer 40: 67-75  Surplus production Next biomass = last biomass + (R + G) – (M + catch) Without fishing (no “catch”) and combining G + R into P = production Next biomass = last biomass + P – M At B0 (carrying capacity): P = M, as biomass is not growing If P > M then population biomass is growing. If we fish this population down to a lower biomass, and then STOP fishing it… the stock will try and grow larger again, and hence P > M. Surplus production is the amount the population biomass will increase in the absence of fishing and equals the amount of catch that can be taken sustainably while maintaining the biomass at constant size. Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279 Surplus production Fishing effort Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279 Surplus production Fishing effort Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279 Surplus production Fishing effort Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279  Surplus production Fishing effort Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 25/44 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279 Surplus production Fishing effort Maximum Sustainable Yield = MSY Bt = BMSY Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279 Surplus production Fishing effort Bt < BMSY Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279 Surplus production Fishing effort Bt BMSY Fishing effort Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279  Surplus production Overfished Bt > BMSY Bt = BMSY Bt < BMSY Fishing effort Overfished = reduced B to below its maximum production potential Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279  Fishing mortality Same concept applies to fishing mortality, which is a result of fishing effort MSY Overfishing Ft < FMSY Ft > FMSY FMSY Overfishing = applying fishing mortality F above that which supports maximum production potential Jennings et al. (2001) Marine Fisheries Ecology. Blackwell Science, Oxford. 417 p. Hilborn and Walters (1992) Quantitative fisheries stock assessment. Chapman & Hall, New York. 570 p. Walters and Martell (2004) Fisheries ecology and management. Princeton University Press, Princeton. 399 p.  Overfished versus overfishing Overfished related to biomass: Stock is overfished = B < BMSY Overfishing related to fishing mortality: Stock is being overfished = F > FMSY Common convention ratios: Stock biomass at MSY: B/BMSY = 1 Overfished: B/BMSY < 1 Not overfished: B/BMSY > 1 Fishing mortality at MSY: F/FMSY = 1 Overfishing: F/FMSY > 1 Not overfishing: F/FMSY < 1 Can express graphically… Kobe Plots Jennings et al. (2001) Marine Fisheries Ecology. Blackwell Science, Oxford. 417 p. Hilborn and Walters (1992) Quantitative fisheries stock assessment. Chapman & Hall, New York. 570 p. Walters and Martell (2004) Fisheries ecology and management. Princeton University Press, Princeton. 399 p.  Kobe plot Maunder M and Aires-da-Silva A (2011) Evaluation of the Kobe plot and strategy matrix and their application to tuna in the EPO. IATTC Scientific Advisory Committee document SAC-02-11, La Jolla, USA. 14 p. 35/44 https://www.iattc.org/Meetings/Meetings2011/SAC-02/Docs/_English/SAC-02-11_Evaluation-of-Kobe-plot-and-matrix.pdf  Kobe plot Maunder M and Aires-da-Silva A (2011) Evaluation of the Kobe plot and strategy matrix and their application to tuna in the EPO. IATTC Scientific Advisory Committee document SAC-02-11, La Jolla, USA. 14 p. https://www.iattc.org/Meetings/Meetings2011/SAC-02/Docs/_English/SAC-02-11_Evaluation-of-Kobe-plot-and-matrix.pdf  Kobe plot Maunder M and Aires-da-Silva A (2011) Evaluation of the Kobe plot and strategy matrix and their application to tuna in the EPO. IATTC Scientific Advisory Committee document SAC-02-11, La Jolla, USA. 14 p. https://www.iattc.org/Meetings/Meetings2011/SAC-02/Docs/_English/SAC-02-11_Evaluation-of-Kobe-plot-and-matrix.pdf  Kobe plot Maunder M and Aires-da-Silva A (2011) Evaluation of the Kobe plot and strategy matrix and their application to tuna in the EPO. IATTC Scientific Advisory Committee document SAC-02-11, La Jolla, USA. 14 p. https://www.iattc.org/Meetings/Meetings2011/SAC-02/Docs/_English/SAC-02-11_Evaluation-of-Kobe-plot-and-matrix.pdf  Kobe plot Maunder M and Aires-da-Silva A (2011) Evaluation of the Kobe plot and strategy matrix and their application to tuna in the EPO. IATTC Scientific Advisory Committee document SAC-02-11, La Jolla, USA. 14 p. https://www.iattc.org/Meetings/Meetings2011/SAC-02/Docs/_English/SAC-02-11_Evaluation-of-Kobe-plot-and-matrix.pdf  Kobe plot Maunder M and Aires-da-Silva A (2011) Evaluation of the Kobe plot and strategy matrix and their application to tuna in the EPO. IATTC Scientific Advisory Committee document SAC-02-11, La Jolla, USA. 14 p. 40/44 https://www.iattc.org/Meetings/Meetings2011/SAC-02/Docs/_English/SAC-02-11_Evaluation-of-Kobe-plot-and-matrix.pdf  Kobe plot Maunder M and Aires-da-Silva A (2011) Evaluation of the Kobe plot and strategy matrix and their application to tuna in the EPO. IATTC Scientific Advisory Committee document SAC-02-11, La Jolla, USA. 14 p. https://www.iattc.org/Meetings/Meetings2011/SAC-02/Docs/_English/SAC-02-11_Evaluation-of-Kobe-plot-and-matrix.pdf  Conclusion Very basic, simple intro to stock assessment…. (More in week 9, WA Fisheries) Every step in the entire process, from stock definition, catch & effort data collection to final stock assessment derived B and F estimates High variability, data uncertainty Fish populations are extremely dynamic, models only deal with approximate averages MSY should never be a target point, but considered a limit reference point not to be approached/exceeded Curtailing (clawing back) fishing effort often extremely difficult ….  Kobe plot MSY Maunder M and Aires-da-Silva A (2011) Evaluation of the Kobe plot and strategy matrix and their application to tuna in the EPO. IATTC Scientific Advisory Committee document SAC-02-11, La Jolla, USA. 14 p. https://www.iattc.org/Meetings/Meetings2011/SAC-02/Docs/_English/SAC-02-11_Evaluation-of-Kobe-plot-and-matrix.pdf Sea Around Us – Indian Ocean