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Ecosystem modelling as a tool for carrying capacity estimations in aquaculture sites Tracadie Bay, PEI (Canada) Ramón Filgueira Ecological modelling. What is a model? Mathematical representations of ecosystems Understand the interactions within complex manipulated ecosystems Problem & Hypothesis New hypothesis Solution Conceptual model Mathematical model Parameterization Boundary conditions Scenario analysis Prediction Optimization Sensitivity analysis Groundtruthing Yes No Good? Yes More results Still good? No Outline Background. Ecological modelling and aquaculture. Why? How? Dynamic simulation of trophic interactions. Carrying Capacity 1. Tracadie Bay, Canada 2. Lysefjord, Norway 3. St. Ann’s Bay, Canada Fluxes of matter and energy in aquaculture sites 4. Rope-scale model, Norway 5. CIMTAN, Canada 6. MUMIHUS, Denmark 7. Spatial connectivity, Spain, Canada & France Environmental impacts and ecological indicators 8. Meat yield project, Canada Observational science 9. Change in estuarine productivity forced by climatic events, Canada 10. Change in phenology triggered by climate change, Canada Ecological modelling in aquaculture sites. Why? Scenario building, prediction and optimization How can science help farmers? Management strategies Growth predictions Disease transmissions Farm location Where and how big? Cultured density Hainan Island, China How can science help regulators? Ecosystem-based management Impacts in the far- and near-field Mitigation alternatives Decision support systems Marine spatial planning Lorbé, Spain Ecological modelling in aquaculture sites. How? Ecosystem models – multiple approaches depending on our needs 0-D models – static in time no predictive power no hydrodynamics no spatial resolution 2-D models – dynamic 3-D models predictive exchange coefficients or hydrodynamic models spatial resolution maps to link with GIS Dame & Prins 1998 X Depth (m) 1-D models – dynamic predictive basic “hydrodynamics” no spatial resolution Tidal turnover vs filtration time Ferreira et al. 2008 Dowd 2003 Ecological modelling in aquaculture sites. How? At which spatial resolution? Ecological modelling in aquaculture sites. How? At which spatial resolution? Bay of Fundy, Canada Ecological modelling in aquaculture sites. How? At which spatial scale? Dynamic Energy Budget Individual Community Ecosystem Outline Background. Ecological modelling and aquaculture. Why? How? Dynamic simulation of trophic interactions. Carrying Capacity 1. Tracadie Bay, Canada 2. Lysefjord, Norway 3. St. Ann’s Bay, Canada Fluxes of matter and energy in aquaculture sites 4. Rope-scale model, Norway 5. CIMTAN, Canada 6. MUMIHUS, Denmark 7. Spatial connectivity, Spain, Canada & France Environmental impacts and ecological indicators 8. Meat yield project, Canada Observational science 9. Change in estuarine productivity forced by climatic events, Canada 10. Change in phenology triggered by climate change, Canada 1. Carrying capacity. Tracadie Bay Mussel culture in a complex manipulated ecosystem Explore the effects of interannual variability on ecosystem performance 1. Carrying capacity. Tracadie Bay Boundary Conditions Time series (chla, seston…) outside the model domain that force the model Huge influence on model performance Different estimations of ecosystem variables Important for management Goal Manage mussel culture in a changing environment based on an ecosystem perspective 1. Carrying capacity. Tracadie Bay Ecosystem-based management Goal Maintain ecosystem functioning to provide services humans want and need McLeod et al. 2005 Resilience The capacity of a system to absorb disturbance and reorganize while undergoing change, that is, to maintain essentially the same function, structure, identity, and feedbacks Walker et al. 2004 Ecosystem State of the system Perturbation Ecological resilience Peterson et al. 1998 1. Carrying capacity. Tracadie Bay EBM + Resilience = Carrying Capacity Carrying Capacity EBM + Resilience alteration of ecosystem functioning within the bounds of natural variation Grant and Filgueira 2011 Ecological resilience Natural Variation Ecosystem State of the system Tipping points Safety margins Sustainability Aquaculture activity 1. Carrying capacity. Tracadie Bay Carrying Capacity in shellfish aquaculture The most relevant interaction in the system: shellfish feeding on phytoplankton Ferreira et al. 2009 Phytoplankton or chlorophyll depletion Zooplankton Mussels Phytoplankton Detrital matter Nutrients Spatial Connections Boundary Conditions 1. Carrying capacity. Tracadie Bay Grant et al. 2007 Depletion Index (D.I.) D.I. (%) = chl-a Box i chl-a Boundary 1 2 3 4 5 Two consecutive years Sustainability criterion x100 Not sustainable (D.I.<73%) Acceptable (73% <D.I.<100%) Sustainable (D.I.>100%) Year 1 Boundary 1998 Depletion index (%) Depletion index (%) 1. Carrying capacity. Tracadie Bay Inner part Year 2 Boundary Inner part -‐ Higher deple4on in second year-‐> less suitable for growing mussels -‐ To obtain the same values both years 40% biomass reduc4on in 1999 -‐ The datasets highlight the importance of inter-‐annual variability and allows us to improve bay management Filgueira & Grant 2009 Filgueira & Grant 2009, Filgueira et al. 2012 2. Carrying capacity. Lysefjord Mussel culture in a pristine area Optimize the system to provide the best management scenario 2. Carrying capacity. Lysefjord Aure et al. 1996 Kristianssan Fjord 20.0 25.0 20.0 25.0 30.0 30.0 32.0 Time (d) 20.0 25.0 30.0 32.0 32.5 Salinity vertical profile through time 15.0 32.0 32.5 32.5 Aure et al. 2007 Salinity vertical profile through fjord 10 Stratification Reduces the exchange between the upper Long, deep glacialdeep valleywaters layers andnarrow, the nutrient-rich 40 km long 0.5 – 2 kminwide Depletion of nutrients the euphotic layer 14 – 460 m depth 9 8 7 6 5 4 3 2 1 Box 4 Box 2 Box 1 2 km Box 3 0 2. Carrying capacity. Lysefjord Artificial pump Hypothesis: Can we use deep nutrients to enhance primary production in the euphotic layer? Artificial pump Chlorophyll vertical distribution (mg m-3) Aure et al. 2007 2. Carrying capacity. Lysefjord Questions? 1.- Where is the optimal location for the pump? 2.- What is the optimal mussel biomass to grow according to CC? And where? CC criterion: Introducing mussels in order to compensate artificial upwelling and maintain chlorophyll level similar to background conditions 2. Carrying capacity. Lysefjord Physical-biogeochemical coupling * *Also for FVCOM and RMA Filgueira et al. 2012 2. Carrying capacity. Lysefjord Where is the optimal location for the pump? (Scenario analysis) Scenario 1 Scenario 2 Scenario 3 Scenario 4 10 0 5 10 Standardized chlorophyll enrichment 0 Scenario 2 is the best one: - Highest averaged chla - Most homogeneous chla distribution in the enriched area Filgueira et al. In review 2. Carrying capacity. Lysefjord What is the optimal mussel biomass to grow according to CC? And where? Optimization tools Estimate the value of a parameter in order to adjust the results of the model to a dataset chosen by the user Zooplankton Zooplankton Phyto. Detrital matter Nutrients Mussels Phyto. Detrital matter Nutrients Forced nutrients CC: maintain chlorophyll level similar to background condi4ons How many mussels and where? 2. Carrying capacity. Lysefjord 0 95 2000 m 190 Optimal mussel density (mg m-3 WW ) What is the optimal mussel biomass to grow according to CC? And where? Optimal biomass according to CC criterion Box model 482 tons wet weight (Filgueira et al. 2011) Fully-spatial model 660 tons wet weight (Filgueira et al. in review) Filgueira et al. 2010 & In review 2. Carrying capacity. Lysefjord Box model Fully-spatial model - Lower spatial resolution - Coarser maps - Simplified hydrodynamics - Higher spatial resolution - Detailed maps and links with GIS - Coupled hydrodynamic model - Computational simplicity - Simple optimization - Cheaper - Computational complexity - Complex optimization - More expensive - Ecosystem-level hypothesis - Ecosystem-level hypothesis Local-level hypothesis 3. Carrying capacity. St. Ann’s Harbour Ecosystem models in data poor environments Explore alternative tools to parameterize and force ecosystem models 3. Carrying capacity. St. Ann’s Harbour Land use maps Diffusive nitrogen inputs Digital elevation maps River discharge 3. Carrying capacity. St. Ann’s Harbour NASA 3. Carrying capacity. St. Ann’s Harbour Depletion index (%) 3. Carrying capacity. St. Ann’s Harbour In the field of applied sciences, researchers must be able to make objective decisions without full knowledge, but by using fully what is known at the time Polasky et al. 2011. Trends in Ecology and Evolution 26(8): 398-404 ADAPTIVE MANAGEMENT Filgueira et al. In review Outline Background. Ecological modelling and aquaculture. Why? How? Dynamic simulation of trophic interactions. Carrying Capacity 1. Tracadie Bay, Canada 2. Lysefjord, Norway 3. St. Ann’s Bay, Canada Fluxes of matter and energy in aquaculture sites 4. Rope-scale model, Norway 5. CIMTAN, Canada 6. MUMIHUS, Denmark 7. Spatial connectivity, Spain, Canada & France Environmental impacts and ecological indicators 8. Meat yield project, Canada Observational science 9. Change in estuarine productivity forced by climatic events, Canada 10. Change in phenology triggered by climate change, Canada 4. Fluxes of matter and energy. Rope-scale modelling Quantify the temporal variation in nutrient fluxes of mussel ropes in relation to mussel biomass, invasive tunicates and organic material Explore the effects of scaling up individual rates 4. Fluxes of matter and energy. Rope-scale modelling 0-D Individual based model Observed Nitrogen Flux (µmol l-1 h-1) 2.5 Individual Chamber r2 = 0.91 b≈1 a≈0 2.0 Pelagic Chamber r2 =0.57 b≈1 a≠0 1.5 1.0 0.5 0.0 0 Community approach 0.5 1 1.5 2 2.5 Modelled Nitrogen Flux (µmol l-1 h-1) - Individual: we understand the physiology - Pelagic: some indications for additional fluxes - Remineralization of organic material (≠intercept) - Associated fauna (red hollow dots) Jansen, Filgueira et al. In review 5. Fluxes Fluxes of ofmatter matterand andenergy. energy. IMTA IMTA Goal: Optimize the recycling of organic matter improve production mitigate ecosystem impact 5. Fluxes of matter and energy. IMTA 5. Fluxes Fluxes of ofmatter matterand andenergy. energy. IMTA Spatial connectivity Filter Feeder Phytoplankton Detrital matter Nutrients Finfish Spatial Connections Boundary Conditions Zooplankton Macroalgae Food Highly influenced by hydrodynamics Engineering concept Finfish Filter-feeder Algae Bay management - Optimal farm location - Implications for Marine Spatial Planning 6. Fluxes of matter and energy. MUMIHUS Production of Mussels – Mitigation and Feed for Husbandry (MUMIHUS) Extract blue mussels to remove the excess of nutrients in coastal waters and recycle them into valuable products Goal: Evaluate mussel production as a source of food for humans a tool to mitigate pollution: N credits 6. Fluxes of matter and energy. MUMIHUS Resuspension model – evaluate the contribution of resuspended material to mussel’s diet Time (d) Quality of resuspended material 6. Fluxes of matter and energy. MUMIHUS 2 Chlorophyll - SCUFA 1m lag -Vy - Wind - rotated 3 2 1 1 0 0 -1 -1 -2 -2 -3 Time (d) -Vy - Wind rotated Chlorophyll (standardized Chla lag averaged mean) 3 -Vy – rotated (standardized) Effect of wind on resuspension 7. Spatial connectivity Farmers asked: Where should we deploy collector ropes? 7. Spatial connectivity Miranda Settlement (indiv col-1) Arnela -QL-lag0 (m2 s-1) Link between mussel settlement, upwelling, longitudinal transport and spatial connectivity Peteiro, Filgueira et al. 2007, 2011 7. Spatial connectivity Release point 1 0 5 Conservative Tracer Concentration Release point 2 10 7. Spatial connectivity Transfer time from one element (white) to the other elements 7. Spatial connectivity Body water divided in 17 specific areas Spatial connectivity but also risk assessment for disease (Infectious salmon anemia virus) or sea lice transmission. Bay management Outline Background. Ecological modelling and aquaculture. Why? How? Dynamic simulation of trophic interactions. Carrying Capacity 1. Tracadie Bay, Canada 2. Lysefjord, Norway 3. St. Ann’s Bay, Canada Fluxes of matter and energy in aquaculture sites 4. Rope-scale model, Norway 5. CIMTAN, Canada 6. MUMIHUS, Denmark 7. Spatial connectivity, Spain, Canada & France Environmental impacts and ecological indicators 8. Meat yield project, Canada Observational science 9. Change in estuarine productivity forced by climatic events, Canada 10. Change in phenology triggered by climate change, Canada 8. Environmental impacts and ecological indicators Ferreira et al. 2009 Phytoplankton or chlorophyll depletion: research tool - - - - Difficult to measure Difficult for farmers Not a good operational indicator Need for other indicators 8. Environmental impacts and ecological indicators Overstocking bivalves leads to increased competition for food resources, phytoplankton, which ultimately could have a significant effect on bivalve growth performance and condition index Condition Index (Arcsin transformed) 8. Environmental impacts and ecological indicators Lease Coverage (Biomass per acre) Overstocking ? Phytoplankton ? Condition index Filgueira et al. 2013 8. Environmental impacts and ecological indicators Can we relate stocking biomass, phytoplankton deple8on and condi8on index and establish thresholds of sustainability based on bivalve measurements? Filgueira et al. In prep. 8. Environmental impacts and ecological indicators Can we correlate phytoplankton deple8on with condi8on index and establish thresholds of sustainability based on bivalve measurements? Depletion index 25 → 75 75 Deple8on Index (%) ↓ 175 0 2000 4000 175 6000 Growth rate (DW) 25 75 → 125 175 225 ↑ 0 2000 4000 6000 8000 0.013 0.012 0.011 0.010 0.009 0.008 0.007 0.006 0.005 0.004 8000 225 26 → 125 125 225 Final CI 25 2000 4000 22 8000 6000 Growth rate (L) 25 75 0.0025 0.0020 → 125 175 225 24 23 ↑ 0 25 0.0010 ↑ 0 2000 4000 0.0015 6000 0.0005 8000 Standing stock biomass (tons) Filgueira et al. In prep. Outline Background. Ecological modelling and aquaculture. Why? How? Dynamic simulation of trophic interactions. Carrying Capacity 1. Tracadie Bay, Canada 2. Lysefjord, Norway 3. St. Ann’s Bay, Canada Fluxes of matter and energy in aquaculture sites 4. Rope-scale model, Norway 5. CIMTAN, Canada 6. MUMIHUS, Denmark 7. Spatial connectivity, Spain, Canada & France Environmental impacts and ecological indicators 8. Meat yield project, Canada Observational science 9. Change in estuarine productivity forced by climatic events, Canada 10. Change in phenology triggered by climate change, Canada 9. Observations. Estuarine productivity & Climate Change Hurricanes can induce severe changes in geomorphology Pine Island, Florida (before 2004) Breach formed after Ivan, 2004. US Geological Survey 9. Observations. Estuarine productivity & Climate Change Tracadie Bay, PEI (Canada) Prior to breach opening (2000) 2 km Present with new breach (2010) 9. Observations. Estuarine productivity & Climate Change Breaching induces changes in hydrodynamics Prior to breach opening (2000) 2 km Present with new breach (2010) 9. Observations. Estuarine productivity & Climate Change Tracadie Bay: Highly manipulated ecosystem Hypothesis: Did the change in coastal geomorphology and the subsequent hydrodynamic alterations affect estuarine productivity, and consequently ecosystem sustainability? Depletion Index 9. Observations. Estuarine productivity & Climate Change B Breach brings phytoplankton inside the bay 1 x 106 kg Wet Weight Year 1 Box 2 Box 3 Box 5 Year 2 Box 2 Box 3 Box 5 20 40 60 80 100 120 Depletion Index (D.I. %) 140 Aquaculture implies: - High pressure on autochthonous phyto - High demand of allochthonous phyto Breach implies: - More dynamic system - Bringing phytoplankton inside the bay - Sustainability increase 2 3 5 9. Observations. Estuarine productivity & Climate Change Observed Aquaculture Production in Tracadie Bay 10. Observations. Change in phenology Farmer: We are seeding earlier and earlier in the year since I can remember! I: Hmmm… how old are you? Farmer: I am 65, all my life in the sea! Why? I: Because you are the best 8me series that I’ve ever had! Weekly larvae survey Size and abundance since 2001 10. Observations. Change in phenology 10. Observations. Change in phenology Filgueira et al. In prep. Summarizing… Ecosystem modeller: - Expertise in aquaculture sites - Farm and bay management using an ecosystem perspective - Carrying capacity - Ecological indicators - Multiple trophic levels - Spatial connectivity - Feed MSP Science-based management Acknowledgements. Why am I a lucky guy? Jon Grant JG’s lab Rune Rosland Cedric Bacher Luc Comeau Thomas Landry Thomas Guyondet Peter Cranford Henrice Jansen Aad Smaal Jens Petersen Bernardino G. Castro Robin Stuart Øivind Strand Jan Aure Lars Asplin Other government agencies Aquaculture companies Funding agencies Thank you! Gracias! [email protected]