Climate change, ocean acidification and individual-based models: Why the little things matter

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1 Climate change, ocean acidification and individual-based models: Why the little things matter Author Richards, Russell, Meynecke, Olaf, Chaloupka, M, Tomlinson, Rodger Benson Published 2012 Conference Title Proceedings of Coast to Coast Conference 2012 Copyright Statement The Author(s) The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference s website or contact the authors. Downloaded from Link to published version Griffith Research Online

2 Climate change, ocean acidification and individual-based models: why the little things matter R.G. Richards, J-O. Meynecke, M. Chaloupka, R. Tomlinson

3 Acknowledgements Australian Rivers Institute Coast and Estuaries, Griffith University Coastal settlements node, ACCARNSI Ecological Modelling Services Pty Ltd, University of Queensland Griffith Centre for Coastal Management, Griffith University Griffith Climate Change Response Program, Griffith University

4 What if.? Why focus on the little things? What are the little things?

5 Individual vs Ecosystem SST Relationship between acidification effects, socio-economic impacts and hierarchical levels of biological complexity (Le Quesne & Pinnegar, 2011) Le Quesne and Pinnegar Fish and Fisheries (DOI: /j x)

6 It s complicated. GHG, CO 2 Increase Ocean Acidification Climate Indicators pco 2 ph CaCO 3 solubility (Ω CaCO3 ) Impacts Calcification Enzyme activity Hypercapnia Membrane permeability Motility Proton pump Toxicant concentrations Organism Response Behavior Condition Development Disease vulnerability Fecundity Fertilization Mating Survival Reproductive cues Settlement cues Physical Effects Ω CaCO3 ph Coastal erosion Mixing Salinity Stratification Upwelling Drivers Long shore current strength Rainfall intensity Sea level Rise Storm intensity SST Wind speed Recruitment Growth Mortality Ecosystem Effects Dispersion (planktonic stage) Nutrient, sediment and toxicant loading patterns Phytoplankton / zooplankton community composition Prey availability Predator-prey interactions Habitat change (GBR, mangroves, sea grass)

7 Individual-based models Explicitly model individual dynamics Individual variability Physiology (E.g. growth, feeding, mortality, reproduction) Behavioural (E.g. habitat selection, movement) Spatially explicit Integrate physical and biogeochemical Individual-level to Ecosystem-level Numerically intensive Difficult to calibrate / validate

8 Case study Mud crabs - Important fishery Lake Coombabah - Marine Protected Area Interested in population dynamics: Efficacy of MPA Impact of environment at different life stages (SST) Heuristic model (investigative) Compliment data collection Framework for integrating data

9 Lake Coombabah, Queensland

10 Mud Crab - life cycle

11 Mud Crab - IBM Physiological: Growth rates Mortality rates Movement: Lagrangian Larvae - planktonic Adults (moulting) - stationary Adults (default) - random Adult males (mating) - searches for females that are softshell, smaller, available, visible Adult females (mated) - leave system to spawn, then return

12 Conceptualisation - IBM

13 Hydrodynamics Hydrodynamic model (larval movement) Depth-averaged Current velocities along transect Full neap-spring cycle 1 hour time steps (696 steps) Jan 1-Feb 6-Feb 11-Feb 16-Feb 21-Feb 26-Feb Time (hours)

14 Movement Rules Larvae planktonic movement (incoming tide) No movement (outgoing tide) Males and females (post-larval) random movement (hard-shell stage) Stationary (soft-shell stage) Mature males seek out available mature females: Visible (field of network) Mature Soft shell state Smaller Not already mated

15 Model Scenario Initial empty population Seed system with larvae Let system run for two years (Test harvest scenarios)

16 Preliminary Model Output

17 Preliminary Model Output

18 Outcomes Individual variability explicitly modelled Coupled processes: physical (larval movement) biological (growth / death) behavioural (mating/migration) processes Management framework: climate (SST) non-climate (harvesting strategies)

19 Findings Uncalibrated Census data required Behavioural data Model expansion: Ocean acidification / offshore effects Habitat Predator-mediated effects Catchment runoff (salinity, ph, nutrients, toxicants)

20 Take home message Management of marine species in the context of climate and non-climate drivers of change require models that can: explicitly account for individual variability, explicitly account for different lifecycle stages, explicitly account for movement (planktonic, motile), integrate physical-biogeochemical processes, and incorporate management scenarios. Individual-based models

21

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