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 plug‐in

Simple engine that facilitates analysing input parameter uncertainty on potentially any EwE output. Record mode Store alternate mass‐balanced parameter sets, ‘samples’, from EwE built‐ in Monte Carlo routines Replay mode Run samples through Ecopath, Ecosim, and Ecospace Defines unique output path for each sample Sample parameter set is written to file All triggered and auto‐saving EwE components and plug‐ins write outputs for a sample Statistical analysis must be performed outside EwE

Steenbeek et al. 2018, SoftwareX Ecosampler plug‐in

Steenbeek et al. 2018, SoftwareX Other ways to use EwE Other ways to use EwE

EwE is available in a number of programming languages to cater to different audiences and modelling environments This story focuses on the .NET version – the EwE mothership

Steenbeek et al. 2016, EcoMod Other ways to use EwE

Modular structure of EwE version 6

Steenbeek et al. 2016, EcoMod Other ways to use EwE

Plug‐ins Independently of EwE Auto‐loaded on start‐up Included in program flow Replace or extend computations and user interface Perform external analysis and do other really neat things!

Steenbeek et al. 2016, EcoMod Other ways to use EwE

A few plug‐ins Aquamaps response fn. import EII, JRC Ecological indicators IRD, ICM, EII Ecotroph analysis Agrocampus Ouest WoRMS taxon search EII Multi‐Sim DFO Network analysis SAMS Result extractor CEFAS Value Chain UBC MPA Dynamics UBC Transect extractions EII, CEFAS CEFAS MSE CEFAS Steenbeek et al. 2016, EcoMod Other ways to use EwE

Standard use: EwE6 desktop interface Other ways to use EwE

Power use through EwE source code. Batch runs, large scale analysis, integrated modelling, sky := limit;

Steenbeek et al. 2016, EcoMod Other ways to use EwE

Distributed computing, model interoperability

Steenbeek et al. 2016, EcoMod Other ways to use EwE

OceanViz – modelling for policy

Steenbeek et al. 2016, EcoMod MSP Challenge 2050

A serious game to educate stakeholders about the MSP process

Up to 80 players, in teams, make spatial plans for their share of a marine region, trying to obtain set goals Plans are designed in a GIS‐like interface Plans can cover shipping, fishing, dredging, recreation, mineral extraction (oil and gas), blue energy (offshore wind, tidal, wave), aquaculture (fish farming), deep sea mining, etc Planning requires information discovery, negotiations with affected neighbours, etc. During simulation phases, plans are put into action, where Ecospace calculates how the ecosystem is affected by plans

http://mspchallenge.info MSP Challenge 2050

http://mspchallenge.info MSP Challenge 2050

1. Game users design plans for a marine area 2. The MSP server aggregates these plans 3. The plans are translated into environmental pressures 4. Pressures influence the ecology of the embedded Ecospace model 5. Indicators (biomass, catch, diversity) are returned to game users Steenbeek et al. (in review) MSP Challenge 2050

Action Pressures Ecological impact

Steenbeek et al. (in review) MSP Challenge 2050

Full‐day test sessions with stakeholders – Firth of Clyde / SIMCELT

User comments: “It has endless potential for all sorts of audiences and is an exciting innovation.” “This would be a great tool for actual consultations and responses.” “Easy to use once I learned commands; useful to non‐ planners and users not familiar with GIS (ARC10.1) programs.” “The balance between realism and entertainment makes it a fantastic educational tool.”

Reprinted from J. Bentley The EwE ecosystem Keeping a scientific software alive and healthy The EwE ecosystem

EwE is a community‐driven software without core funding Ideas and funding from users translate to new features New features are released with EwE once published Software is free and open‐source Projects can purchase a professional license with tech. support

Ecopath R&D Consortium: improving and applying EwE High quality training courses Pursuing and executing projects Release new versions of EwE – EwE 6.6 coming soon! Organizing development, user support, co‐development Open for any institute to join The EwE ecosystem

EwE thus relies on active users to continue! Online user community Support contracts for using EwE Co‐development ideas and funding Training courses Consortium membership

http://ecopath.org/consortium http://facebook.com/eweconsortium http://www.ecopath35.org Thank you [email protected] Ecopath

Respiration

Yield Bi Biomass

Pi/Bi Production Q /B Specific consumption Food i i Production Predation DCij Fraction of prey i in diet of consumption predator j

Net migration BiAi Biomass accumulation

EEi Production used in the system

Other mortality 1‐EEi Unexplained mortality Unassimilated

 Q   P  1.   B i    B i  R i  UN i  B  i  B  i

 P  n  Q   P  2.   B i      B j  DC ij  E i  Y i  BA i    B i 1  EE i  B  i Pred_j  1  B  j  B  i

Polovina, J.J. 1984. Coral Reefs, 3:1‐11; Pauly et al. 2000. ICES J. Mar. Sci., 57: 697‐706; Christensen and Walters. 2004. Ecol. Model., 172(2‐4): 109‐139 Ecosim

Foraging arena: 𝑣 𝐵𝑉 𝑉 𝑎VP 𝑣′

Walters et al 1997 RFBF, Ahrens et al 2012 Fish and Fisheries Ecospace

A spatial, meso‐scale version of Ecosim Executes Ecosim dynamics over spatial grid of ‘homogeneous’ cells (i.e. same size, but different properties) Links cells through movement of organisms, and fishing effort movement/allocation Includes spatial variation in productivity and cost of fishing Groups move through dispersal, advection, and migration Cell suitability originally obtained from habitat affinity Can trace uptake and propagation of contaminants through the food web Ecospace

For each cell, functional groups can move to neighbouring cells if conditions are better

𝑚 𝐵 𝐵

Bi  x,y  mi x,y1  Bi x,y

The instantaneous emigration rates mi from a given cell per functional group depend on cell suitability (niche model), and response of organisms to depredation risk and feeding conditions Walters et al. (1999) Ecosystems 2.6 Ecospace niche modelling

Traditional Ecospace New Ecospace (2014‐) Yes/no habitats Fractional habitats Yes/no habitat preferences Fractional habitat preferences Environmental responses Data exchange framework

Best known Much more powerful Used by the majority of Publications have started Ecospace papers coming

Christensen et al. (2014) Ecosystems, E17, 1397‐1412. Spatial temporal data framework

Current status Connected to most Ecospace layers Reads and writes 20+ GIS data formats, both raster and vector Designed upon EwE plug‐in system. Easy to extend with new capabilities

Not yet publicly released. More R&D needed Needs better support for geospatial projections Needs integration of new data types Needs usability testing to streamline workflow For now, can be applied by involving EwE team into projects MSP Challenge 2050

Three MSP games built to date “I think it’s brilliant: it bridges the gap . North Sea between policy/management and . Firth of Clyde science and generates a whole new . Baltic (in progress) audience for complex ecosystem models. The balance between realism Interest expressed for other systems and entertainment makes it a fantastic . Israel continental shelf educational tool.” – MSP game play . Adriatic + Ionian participant on the MSP – EwE . Western Med integration . Portugal . Perhaps useful for ICES?