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Recent Advances in the Ecopath with Ecosim Food Web Modelling Approach

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Recent Advances in the Ecopath with Ecosim Food Web Modelling Approach Recent advances in the Ecopath with Ecosim food web modelling approach 15 November 2019, Institute for the Oceans and Fisheries Jeroen Steenbeek, EII, Spain Overview My background Brief overview of EwE Challenges to ecosystem modelling New spatial modelling capabilities and applications Other ways to use EwE The EwE ecosystem itself My background Classically trained software developer Corporate years: The Netherlands 1992‐1999 Industrial process software development Media years: Canada 2000‐2004 Multimedia, game development, web RIAs & GIS Academic years: Canada 2004‐2013 SAUP consultancy, EwE development and teaching Best years: Spain 2013‐ EwE development, consultancy, teaching, and software life cycle management International collaborations, GIS, big data, serious gaming Brief overview of EwE Ecopath with Ecosim (EwE) Ecological model that tracks the paths of energy through a food web Functional groups, fleet/gears Requires relatively few input parameters Data often readily available (surveys, stock assessments, fisheries statistics, …) Includes environment and human activities Christensen and Walters 2004, EcoMod; Heymans et al. 2016, EcoMod Ecopath with Ecosim 1984 Ecopath conceived (Polovina 1984) 1990 First desktop version released 1995 Ecosim introduced 1998 Ecospace introduced 2006 Re‐engineered 2011 Ecopath R&D Consortium established 2012 Open source, community‐driven 2019 Thirty‐five years anniversary Freely available from http://www.ecopath.org Ecopath with Ecosim EcoBase: 440 EwE models, 173 for download 8000+ users in 150+ countries (google analytics) 800+ peer‐reviewed publication (ISI Web of Knowledge) Colléter et al. 2015, EcoMod http://ecobase.ecopath.org Ecopath with Ecosim Three major components Ecopath static mass‐balanced model Ecosim temporal dynamics Ecospace spatial‐temporal dynamics Additional modules Ecotracer contaminant tracing ‘Searches’ MCMC, spatial optimizations, MSE, fishing policy, … Plug‐ins extra features, interoperability, … Heymans et al. 2016, EcoMod; Steenbeek et al. 2016, EcoMod Ecopath Main module of EwE, snapshot of the ecosystem Define food web components and energy flows Understand ecosystem structure and functioning Evaluate impact of fisheries Model of entire Mediterranean basin, Piroddi et al. 2018, MEPS Ecosim Temporal‐dynamic module of EwE, initialized from Ecopath Includes biomass and size structure dynamics Introduces behaviour and temporal change Used, among others, to assess Quantify combined effect of species dynamics, fishing impacts, and environmental impacts on a food web over time Replicate past scenarios (time series fitting) Explore future scenarios Explore fishing policy alternatives Test model robustness Walters et al. 1997, RFBF; Heymans et al. 2016, EcoMod Piroddi et al. 2017, SR Ecospace Time dynamic spatially explicit module of EwE, initialized from Ecosim. Introduces concepts of habitats, marine protected areas, and requires additional parameters related to movement and the use of space Used, among others, to explore Distribution of marine species and fishing effort Effectiveness of management options Ecosystem impacts of environmental change, habitat change, fishing Combinations of the above Walters et al. 1999, Ecosystems; Christensen et al. 2014, Ecosystems Challenges to ecosystem modelling Challenges to ecosystem modelling Ecosystem modelling is more than considering biophysical impacts… Physical change Biological / ecological in the ocean change in the ocean Individual Physiology SST increase Growth Body size Population Retreat of sea ice Distribution Abundance Acidification Recruitment Community Species composition Coastal hypoxic & oxygen Invasion/extinction min. zones Ecosystem Productivity Rising sea surface levels Species interaction Ecosystem services After Cheung et al. 2010 Challenges to ecosystem modelling ..it also requires clear objectives, that models can address through rigorous execution of scenarios Christensen 2013, Fisheries; Cury 2013, pers com. Challenges to ecosystem modelling Ecosystems are staggeringly complex Feedback effects throughout entire system Processes cross traditional scientific boundaries Processes and time scales can span orders of magnitude Cumulative impacts are often poorly understood (climate change, anthropogenic) “Essentially, all models are wrong, but some are useful” ‐ George P. Box (1987) Challenges to ecosystem modelling What does this mean for models? Ideas about model scope, abilities and purpose vary greatly One thing is clear Models need to be able to work together Challenges to ecosystem modelling How do models deal with these challenges? Popularity Increase scope and complexity of existing models (“Frankenmodel”, Mackinson et al 2009) Merge existing models Make existing models communicate Make models collaborate Make models exchange components Complexity Challenges to ecosystem modelling Where we need to go… Challenges to ecosystem modelling What does this mean for the EwE approach? EwE all‐over strategy Focus on core food‐web science Link / connect to everything else Facilitate extending EwE with external functionality Facilitate switching hypothesis (modularity) Separate scientific and technical issues Ground‐breaking developments in spatial‐temporal modelling and software capabilities New capabilities and applications Ecospace niche modelling Original Ecospace offered limited capabilities to explain species distributions. Habitat usage was an aggregated assumption implying environmental preferences Since EwE version 6.3+, Ecospace derives cell suitability from species’ responses to local environmental conditions (depth, temperature, salinity, oxygen, pH, …) and/or habitat use. Ecospace is now an integrated food‐web / species envelope model Christensen et al. 2014, Ecosystems Ecospace niche modelling Functional groups respond to (changing) environmental conditions Each group has unique preferences for these conditions Dynamic habitat model predicts how productive individual cells are for each species Adapted from Christensen et al. 2014, Ecosystems Ecospace niche modelling Combining hypotheses Niche priors SDM results Original Ecospace (habitat affinities) New Ecospace (environmental prefs.) Case study 1: protection Models Temporal resolution Vertical integration Drivers Parameter Original unit Annual Monthly Surface Surface Bottom Total 150m Column Phytoplankton biomass mmolN/m2 Yes Yes Yes Yes Yes Zooplankton biomass mmolN/m2 Yes Yes Yes Yes Yes Chlorophyll‐a biomass mmolN/m2 Yes Yes Yes Yes Yes PP mmolN/m2 Yes Yes Yes Yes Yes Oxygen mg/L Yes Yes Yes Yes Yes Salinity PSU Yes Yes Yes Yes Yes Temperature °C Yes Yes Yes Yes Yes Currents m/s Yes Yes Yes Yes Coll et al. 2019, Safenet Case study 1: protection Protection scenarios Many, many simulations Summaries Case study 2: Link BH‐SDM and Ecospace Use Bayesian‐HSDM model and Ecospace‐HFCM to estimate and predict the occurrence and biomass distribution of 5 demersal fish species Explored the complementarity of both approaches, aside from their applicability as independent techniques Explored how to use Bayesian SDM models to incorporate uncertainty into the FWM Merluccius merluccius Lophius piscatorius Lophius budegassa Mullus barbatus Mullus surmuletus Study area: 4,000 km2 Coll et al. 2019. EcoMod Case study 2: Link BH‐SDM and Ecospace Pelagic Demersal Path 1 5 Anglerfish Dolphins Swordfish and Tuna Conger eel Adult hake Atlantic bonito Demersal fishes (3) 4 Fin whale Demersal sharks Mackerel Benthopelagic cephalopods Juvenile hake Benthopelagic fishes Blue whiting Poor cod Horse mackerel Flatfishes Audouins gull Other small pelagic fishes European anchovy Sardine adults Mullets Benthic cephalopods Demersal fishes (1) 3 Jellyfish Shrimps Demersal fishes (2) Crabs Loggerhead turtles Norway lobster Macrozooplankton Polychaetes Other sea birds Suprabenthos 2 Micro- and mesozooplankton Benthic invertebrates Trophic Level 1 Discards1 Phytoplankton Discards2 Detritus Path 2 Correlations with data Coll et al. 2019. EcoMod Spatial temporal data framework The internal data model of Ecospace was hard to access – almost impossible to vary input maps over time Changing environmental conditions could not be included in spatial temporal analysis A new spatial‐temporal framework was developed: Can drive most Ecospace input layers Can build Ecospace maps from external GIS files Produces Ecospace results as GIS files Is connected to the habitat capacity model (!) Steenbeek et al. 2013, EcoMod. Spatial temporal data framework Steenbeek et al. 2013, EcoMod. Spatial temporal data framework Steenbeek et al. 2013, EcoMod. Spatial temporal data framework STDF can provide Ecospace with temporal‐spatial variation in: Environmental drivers (SST, Salinity, Oxygen, …) Primary productivity Currents Species niches Biomass distributions Contaminants Fishing cost Habitats Migration routes MPA placements Case study: Adriatic productivity Steenbeek et al. 2013, EcoMod. Extensions to EwE Plug‐ins and new tools Ecological indicators plug‐in Ecological indicators communicate historical and future changes in ecosystems Functional group taxonomic composition and species traits EcoIND plug‐in calculates standardized ecological indicators from Ecopath, Ecosim, Ecospace, and Monte Carlo Indicators include biomass, catch, trophic level, size, and species Highly extensible (MSFD, others) EcoIND extends EwE into biodiversity and conservation‐based frameworks, and management applications Coll and Steenbeek 2017, EMS Ecological indicators application Coll and Steenbeek 2017, EMS Ecosampler
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