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Modelling of the Effects of Climate Change on Population Dynamics of a Spiny Lobster, Panulirus Penicillatus, Fishery

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Modelling of the Effects of Climate Change on Population Dynamics of a Spiny Lobster, Panulirus Penicillatus, Fishery Modelling of the effects of climate change on population dynamics of a spiny lobster, Panulirus penicillatus, fishery Yi‐Jay Chang1, Chi‐Lu Sun1, Yong Chen2, Su‐Zan Yeh1 1Institute of Oceanography, National Taiwan University 2School of Marine Science, University of Maine 01 Introduction • Lobsters support some of the largest commercial fisheries in the world and artisanal fisheries. (e.g., Homarus americanus ; Australia rock lobsters) • There are important small‐scale lobster fisheries in the coastal waters off Taiwan. – Highly prized (US$ 70/1kg; high demand in China) – Traditional culture (e.g., wedding banquet) Maine lobster fishery http://www.climateprep.org/wp‐content/uploads/2011/05/Fishing‐Boats.png Panulirus homarus Panulirus ornatus Panulirus penicillatus Panulirus versicolor Kuroshio Current Panulirus japonicus Panulirus longipes Common Lobsters in Taiwan 3 http://140.109.18.74/ImageCache/ImageCache/00%5C0a%5C91%5C3f.jpg 03 The spiny fisheries in Taiwan • Small‐scale lobster fisheries (especially in eastern coast of Taiwan) • Fishing methods: trap, trammel‐nets, diving • Regulation (e.g., legal size 20 cm TL for all lobsters) Bisha fish market, Keelung, Taiwan 04 Life cycle of palinurid Benthic alage lobsters Sea grass, sponges Patch reefs 2m 10m 20m 30m 60m planktonic larvae > 6 months Late stage larvae recruit Pueruli settlement Nomadic Seasonal migration Feeding movement Gravid females 05 Population dynamics and stock productivity Recruitment (Puerulus settlement) (Butler 2003; Fogarty & Gendron, 2004, Caputi et al. 2010) Growth (e.g., intermolt period & molt increment) Environmental (Hartnoll, 2001) Stock productivity factors Reproduction (e.g., spawning season, Can. J. Fish. Aquat. Sci. 67: 85–96 (2010) brood times, incubation duration, egg loss, size-at-maturity) (Chubb, 2000; Caputi et al. 2010) Mortality rate (e.g., predation, diseases, anoxia) Dove (2004), Bartlett et al (2008) 06 Objectives in this study – To develop an individualindividual‐‐basedbased modelmodel (IBM)(IBM) which can incorporate the individual variability and the environmental effect into the model. – To use the developed IBM for the stock assessment of the Taitung spiny lobster (P. penicillatus) fisheries – To explore the possibility of using the developed model to evaluate the impacts of climate change on the estimation of biological reference points and the risk of overexploitation 07 A flowchart of stock assessment framework Growth model Climate change Fishery‐ dependent data Survival model Uncertainty Population Prior knowledge Reproduction dynamic model model (IBM) SR model Data base Other models for Bayesian estimators life histories & fisheries processes Posterior estimates Alternative management Dynamic pool strategies model State of nature Risk analysis Biological Reference Status of the fishery point Optimal management 08 End of day Diagram of individual‐ Recruitment Start of day based model (IBM) for spiny lobster (P. Mortality YES Management module Die naturally NO penicillatus) NO measure YES YES Caught by fishery Legal ? Landing NO ¾ Probabilistic approach Spawning YES Sex −M Temperature Season ? if UeNN(0,1)≤ 1−=− then tt+1 1 NO Male Female NO Model time NO Mature Molt ? Daily‐based YES YES YES Spawning ? (> 4 times) Molt increment NO Super-individual Growth Record size YES Record Hatch ? Temperature Egg production Scheffer et al., 1995 module NO YES NO YES NO Landing Caught by fishery Protect berried ? End of day NO Reproduction Management YES measure Hatch mortality Start of day module 09 IBM’s parameterization: Our field and laboratory data (Chang et al. (2007), (2009); Huang (2009)) Lobster literatures 10 To Incorporate temperature into IBM Degree‐day concept lethal lethal Smith (1997) Wahle & Fogarty (2006) Temperature Spawning Growth pattern C) o ( Temperature Carapace length (mm) length Carapace 0 50 100 150 10 15 20 25 30 0246810 Age Population dynamics & fishery simulation 11 Prior of parameter vector F, M , R & Rsd Individual‐based model Resampling (SIR) to equilibrium ‐ 1‐25 years 26‐50 years Time Natural mortality (M) only Introduce a fishery (F) Bookkeeping last 5 years combined Importance ‐ Predictions 1988 Likelihoods Sampling Rubin, obs pred Posterior CCmm= exp(ε m ) 2006‐2008 observations obs pred Distribution Cpkm,,= Cp km+ ε m 2003‐2004 made in the fishery 12 MLE fit of catch‐composition from the IBM Model Observation (Year 2003‐2004 combined) 13 Projected probabilities of SSBcur larger than x% of SSB0 Negligible risk 5 years ) 10% 20% 0 10 years (a) 20 years (b) 0 20406080100 0 20406080100 0.5 0.7 0.9 1.1 1.3 1.5 0.5 0.7 0.9 1.1 1.3 1.5 High risk >x% of SSB >x% of cur Prob(SSB 30% 40% (c) (d) 0 20406080100 0 20406080100 0.5 0.7 0.9 1.1 1.3 1.5 0.5 0.7 0.9 1.1 1.3 1.5 Percentage of Fcur Climate Change 15 Difference of population dynamics between the base model and the temperature change scenario (+3 oC) Abundance High molt mortality Average size Condensed intermoult period and incubation duration SSB Reach sexual maturity earlier Egg production Increased spawning frequency Extend spawning season Season of simulation 16 Define the simulation scenarios Average Natural Scenario Model description annual mortality SST (°C) (year-1) Current temperature regime (years 2003- CT 26.4 0.46 2004) Increased temperature would effect growth 1T 27.4 0.46 and reproduction only 2T 28.4 0.46 3T 29.4 0.46 Increased temperature would effect growth, 1TM 27.4 0.49 reproduction, and mortality 2TM 28.4 0.52 3TM 29.4 0.55 Increased temperature would effect growth, 3THM 29.4 0.69 reproduction, and introduce extra mortality based on IPCC (2007) 18 Boxplots of the estimated BRPs F20% (a) and F40% (b) under eight different climate change scenarios 0.6 ) 0.6 1 ‐ (a) (b) 0.5 0.5 (year 0.4 0.4 rate 0.3 0.3 0.2 0.2 Mortality 0.1 0.1 0.0 0.0 3THM F 3TM 1T 2TM 3THM CT 2T 3T F 1TM 3TM 1T 2TM CT 2T 3T 1TM cur cur Scenario 20 Summary • The developed IBM can be used for the stock assessment for this small scale and data‐limited fishery. • Decision analysis based on samples from a Bayesian posterior distribution indicated that there is a negligible risk of the stock dropping below 20% of SSB0. • This study demonstrated warming temperature (climate change) can affect the estimation of BRPs. 22 Management implication & Future work… • To explore the likely benefits and feasibility of Can.incorporating J. Fish. Aquat. Sci. 68: environmental 122–136 (2011) factors in management procedures based on the developed IBM (Operating model) – To investigate whether incorporating environmental effects in management procedures (environmental‐based HCRs) would improve management. Thank your for your attention21.
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