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Environmental Modelling & Software 89 (2017) 120e130

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Environmental Modelling & Software

journal homepage: www.elsevier .com/locate/envsoft

Standardized ecological indicators to assess aquatic food webs: TheECOIND software plug-in for Ecopath with Ecosim models

M. Coll a, b, c, *, J. Steenbeek a, b, c

a Institut de Recherche pour le D�eveloppement, UMR MARBEC & LMI ICEMASA, University of Cape Town, Private Bag X3, Rondebosch, Cape Town 7701,South Africab Ecopath International Initiative Research Association, Barcelona, Spainc Institut de Ci�encies del Mar (ICM-CSIC), Barcelona, Spain

a r t i c l e i n f o

Article history:Received 30 March 2016Received in revised form6 October 2016Accepted 19 December 2016

Keywords:Software plug-inEcological standardized indicatorsEnvironmental statusFood web modelsEcopath with EcosimEcospace

* Corresponding author. Corresponding author. Inst(ICM-CSIC), Passeig Marítim de la Barceloneta, nº 37-

E-mail address: mcoll@icm.csic.es (M. Coll).

http://dx.doi.org/10.1016/j.envsoft.2016.12.0041364-8152/© 2016 Elsevier Ltd. All rights reserved.

a b s t r a c t

Ecological indicators are useful tools to analyse and communicate historical changes in ecosystems andplausible future scenarios while evaluating environmental status. Here we introduce a new plug-in to theEcopath with Ecosim (EwE) food web modelling approach, which is widely used to quantitativelydescribe aquatic ecosystems. The plug-in (ECOIND) calculates standardized ecological indicators. Wedescribe the primary functionality of ECOIND and provide an example of its application in both static andtemporal-spatial dynamic modelling, while we highlight several related features including a new tax-onomy input database (species traits) and the ability to analyse input uncertainty on output results.ECOIND adds new capabilities to the widely used EwE food web modelling approach and enablesbroadening its applications into biodiversity and conservation-based frameworks to contribute to inte-grated ecosystem analyses.

© 2016 Elsevier Ltd. All rights reserved.

Software and/or data availability

Name of software Ecopath with EcosimDeveloper Ecopath Research and Development ConsortiumContact address Ecopath International Initiative Research

Association, Barcelona, SpainContact phone þ34.685.319.597Contact email ewedevteam@gmail.comYear first available 1991 (ECOIND plug has been released in

July 2016, version 6.5.14040.0)Hardware required PCSoftware required Windows XP service pack 3 or newer,

Microsoft.NET Framework 4 Full ProfileAvailability Public, Open Source (GPLv2), freely available

from www.ecopath.orgProgram language Visual Basic.NET, C# Program size: 16 MB

(basic installation) Website: http://www.ecopath.org

itut de Ci�encies del Mar49, 08003, Barcelona, Spain.

1. Introduction

Historical changes in aquatic resources are documentedworldwide (Lotze et al., 2006) due to changes in multiple anthro-pogenic and climate-related drivers (Halpern et al., 2015). Thesedrivers alter the structure and functioning of ecosystems (Jacksonet al., 2001) and can affect the ecosystem services that humansobtain from healthy oceans (Worm et al., 2006). There is a concernabout the environmental status of aquatic ecosystems and a need toadopt a more integrated view of ecosystemmanagement. This viewshould consider not only the dynamics of target species, but alsonon-target organisms, trophic relationships and flows, and envi-ronmental factors (Pikitch et al., 2004).

To move forward, adaptations and changes to scientific methodsare required, in parallel with changes to the way ecological, socialand economic issues are integrated in management processes(Browman et al., 2005a, 2005b). Within this context, the scientificcommunity has developed new methodological tools such asrobust ecological indicators to track the environmental status ofecosystems and inform management decisions (Cury andChristensen, 2004). In fact, there has been considerable discus-sion about ecological indicators to monitor the pressures on, andstatus of, exploited aquatic ecosystems with emphasis on marineecosystems (Rombouts et al., 2013). Initially, indicators were

M. Coll, J. Steenbeek / Environmental Modelling & Software 89 (2017) 120e130 121

developed to include ecological considerations with the goal ofcapturing the impact of dominant pressures, such as fishing (Fultonet al., 2005). Further progress has included the establishment ofcriteria and frameworks to (i) guide the selection of indicators thatare used to assess the effects of fishing via trend and thresholdanalyses, (ii) define preliminary reference levels and reference di-rections for selected indicators, and (iii) develop and test evaluationframeworks (Blanchard et al., 2010; Kleisner et al., 2013; Large et al.,2013). Recently, the scope of ecosystem indicators has expanded toinclude impact on biodiversity, other socio-economic and gover-nance issues, and the cumulative impacts of multiple human ac-tivities (Boldt et al., 2014; Large et al., 2015; Levin et al., 2009). Suchranges of indicators are needed to fulfil the targets of differentNational and Transnational management plans and conventions,such as the European Marine Strategy Framework Directive (MSFD)seeking to achieve a “Good Environmental Status” (GES) by 2020for all European seas (EC, 2010) or the Aichi Targets of theConvention of Biological Diversity (Tittensor et al., 2014).

Ecological models (Fulton, 2010) provide a variety of results thatcan be used to calculate meaningful ecological indicators andinform about the environmental status of ecosystems (Allesina andBondavalli, 2004; Valentini and Jord�an, 2010). These models cantake into account the dynamics of commercial and non-commercialspecies, their interactions and the main drivers, and are thus usefultools for marine conservation planning and sustainable use man-agement (Piroddi et al., 2015; Smith et al., 2015). One of such tools isthe ecosystemmodelling suite “Ecopath with Ecosim”, or EwE, thatintegrates the original Ecopath food web model with the temporaldynamic and temporal-spatial dynamic modules Ecosim and Eco-space, respectively (Christensen and Walters, 2004a). The EwEapproach was the first user friendly and freely accessibleecosystem-level simulation model, which has contributed to itsglobal uptake and its popularity as a key tool for the ecosystem-approach. Currently, more than 400 EwE ecosystem models havebeen published, mostly in aquatic ecosystems (Coll et al., 2015a)This makes EwE an important approach to explore ecosystemrelated questions and to link with other modelling techniques(Cerco et al., 2010). In acknowledgement of its impact, the US Na-tional Oceanographic and Atmospheric Administration recognizedEcopath as one of the ten biggest scientific breakthroughs in theorganization's 200-year history (http://celebrating200years.noaa.gov/breakthroughs/ecopath/).

A variety of EwE results have been used extensively to calculateecological indicators (e.g., Coll and Libralato, 2012; Heymans et al.,2014). To facilitate this use and enable the standardized calculationof several of the most widely used ecological indicators, wedeveloped the new ECOIND plug-in to be released with EwEversion 6.5 (2016).

2. Methods

2.1. Overview of ECOIND

ECOIND is a compilation of algorithms to quantify ecologicalindicators from EwE food web model results. These indicators arerelated with the environmental status of ecosystems, mostlyassociated with fishing impacts (such as the biomass of commercialspecies or the mean trophic level of the catch), but also withbiodiversity impacts and conservation-based issues (such as thebiomass and catch of predators or species at risk) (Coll et al., 2016a;Shannon et al., 2014) (Table 1). Several of these indicators can bedirectly related with management attributes included in nationaland international management schemes (i.e. MSFD-GES and CBD-Archi Targets). The full description of all the ecological indicatorsincluded in ECOIND and their use and interpretation is beyond the

scope of this paper, but interested readers can find relevant infor-mation in several key papers about ecological indicators (Cury andChristensen, 2004; Fulton et al., 2005; Rochet and Trenkel, 2003;Shin and Shannon, 2010). A table with definitions and referenceinformation about the indicators is provided in Table S1 (Annex).

ECOIND is freely distributed with EwE software version 6.5 andcan be used to calculate indicators from the snapshot Ecopathmodel, the temporal-dynamic module Ecosim, and the temporal-spatial module Ecospace. Ecopath is the mass-balance routinethat allows building spatially and time averaged models of thetrophic web, Ecosim is the time dynamic routine, and Ecospaceallows representing temporal and spatial 2D dynamics of trophicweb components. The ECOIND plug-in integrates with the EwEmodel execution flow, and when enabled, automatically calculatesindicators by aggregating, integrating and interpreting the resultsof different modules of EwE (Fig. 1 and Annex II).

2.2. Data requirements and work flow

The first step to use ECOIND is to develop a food web modelusing the Ecopath module (Christensen and Walters, 2004a)(Fig. 2). Ecopath describes the balance between production offunctional groups and all consumptions within an ecosystem. Eachfunctional group in an Ecopath model can represent a species, asub-group of a species (e.g. juveniles and adults, which are calledmulti-stanza groups) or a group of species that have functional andecological similarities (Fig. 3). The Ecopath model requires, for eachfunctional group or species in the food webmodel, the definition ofbiomass, consumption, and production, in addition to the elementsof the diet matrix in the food web, exports and fishing yields, thefraction of unassimilated food and biomass accumulation terms. Asummary of the main equations and assumptions is provided inAnnex I.

A new feature in Ecopath that has been developed along withECOIND is the ability to provide information about the taxonomiccomposition of each functional group in the food web model. Oncethe taxonomic composition of each functional group is defined,additional information per species as traits within each functionalgroup can be entered (Fig. 2 and Annex II).

A second step to using the plug-in is to calculate indicators fromthe predictions of the temporal-dynamic module Ecosim (Fig. 2 andAnnex II). On the basis of the system of algebraic equations andinput parameters of Ecopath, a set of differential equations isdefined in Ecosim (Walters et al., 2000). The biomass change ateach model time step for each consumer functional groups can beexpressed as changing over time. This change is in relation tochanges in consumption and production of organisms in the foodweb including internal and external drivers (such as fishing andenvironmental changes). Ecosim enables consideration of differentmechanisms of flow control, ecological behaviour of organisms andforcing elements like environmental factors that can become quitecomplicated in the case of multiple uses of resources (multiplepredators on the same prey).

Ecosim is frequently used to fit models to historical data toevaluate the capability of a model to represent past ecosystemconditions (Walters et al., 2005). Once fitted, a model can be used toaddress scenarios of plausible alternative management options. Ifconfidence intervals of initial input parameters to the Ecopathmodel are available (e.g. using the Ecopath “Pedigree” facility ordirectly entering available values), Ecosim can run multiple timesusing a Monte Carlo simulation analysis (Fig. 2 and Annex II). TheMonte Carlo approach searches for, and computes the impact of,alternative Ecopath input parameter combinations. The MonteCarlo routine can be used to test for sensitivity of Ecopath inputparameters to time series outputs, and to improve the fit of the

Table 1Ecological indicators included in ECOIND plug-in (a. present values of the Mediterranean food web model in 1978 and b. in 2010, and b/a is the ratio of values from 2010 inrelation to 1978). A detailed description of these indicators is provided in Table S1 Annex.

Indicator Description Units a.1978 b.2010 b/a

A. Biomass-basedTotal B Total biomass (B) (i) t$km�2 117.53 78.85 0.67Commercial B Biomass (B) of commercial species (i) t$km�2 16.44 13.11 0.80Fish B Biomass (B) of fish species (a) t$km�2 11.22 6.95 0.62Invertebrates B Biomass (B) of invertebrate species (þ) t$km�2 5.22 6.16 1.18Invertebrates/Fish B Biomass (B) of invertebrates over fish (þ) 0.47 0.89 1.90Demersal B Biomass (B) of demersal species (þ) t$km�2 5.22 6.16 1.18Pelagic B Biomass (B) of pelagic species (þ) t$km�2 13.03 8.10 0.62Demersal/Pelagic B Biomass (B) of demersal over pelagic species (a) 0.40 0.76 1.90Predatory B Biomass (B) of predatory organisms with trophic level � 4 (i) (þ) t$km�2 0.67 0.11 0.17Kempton's Q Kempton's biodiversity index (Q) (i) (þ) 4.62 4.02 0.87B. Catch-basedTotal C Total Catch (C) (þ) t$km�2$year�1 3.97 2.52 0.63Fish C Catch (C) of all fish species (þ) t$km�2$year�1 3.75 1.92 0.51Invertebrate C Catch (C) of all invertebrate species (þ) t$km�2$year�1 0.23 0.59 2.63Invertebrates/Fish C Catch (C) of invertebrates over fish (þ) 0.06 0.31 5.12Demersal C Catch (C) of demersal species (þ) t$km�2$year�1 0.23 0.59 2.63Pelagic C Catch (C) of pelagic species (þ) t$km�2$year�1 3.95 2.21 0.56Demersal/pelagic C Catch (C) of demersal over pelagic species (þ) 0.06 0.27 4.71Predatory C Catch (C) of predatory organisms with trophic level � 4 (i) t$km�2$year�1 0.11 0.16 1.42Discards Total discarded catch (i) t$km�2$year�1 0.28 0.27 0.96C. Trophic-basedTL catch Tropic level (TL) of the catch (i) 3.07 3.12 1.02MTI Marine trophic index, trophic level (TL) of the catch (including organisms

with TL � 3.25) (i)3.78 3.91 1.03

TL co. Trophic level (TL) of the community (including all organisms) (i) 1.44 1.39 0.97TL co. 2 Trophic level (TL) of the community (including organisms with TL � 2) (i) 2.42 2.40 0.99TL co. 3.25 Trophic level (TL) of the community (including organisms with TL � 3.25) (i) 3.69 3.91 1.06TL co. 4 Trophic level (TL) of the community (including organisms with TL � 4) (i) 4.12 4.11 1.00D. Size-basedML of fish co. Mean length (ML) of fish in the community (þ) cm 16.74 17.66 1.06ML of fish C Mean length (ML) of fish in the catch (C) (þ) cm 12.09 15.85 1.31MW of fish co. Mean weight (MW) of the fish in the community (þ) kg 170.99 164.19 0.96MW of fish C Mean weight (MW) of fish in the catch (C) (þ) kg 89.11 158.30 1.78MLS of fish co. Mean life span (MLS) of fish in the community (þ) year 4.47 4.61 1.03MLS of fish C Mean life span (MLS) of fish the catch (C) (þ) year 3.34 4.52 1.35E. Species-basedIntrinsic Vul. Index Intrinsic Vulnerability Index of the catch (þ) 49.29 47.97 0.97Endemics B Biomass (B) of endemic species in the community (þ) t$km�2 1.E�04 2.E�04 1.73Endemics C Endemic species in the catch (C) (þ) t$km�2$year�1 2.E�08 6.E�07 33.07IUCN species B Biomass (B) of IUCN-endangered species in the community (þ) t$km�2 1.07 0.72 0.67IUCN species C IUCN-endangered species in the catch (C) (þ) t$km�2$year�1 0.09 0.14 1.61Mammals, birds & reptiles B Biomass (B) of marine mammals, seabirds and reptiles (þ) t$km�2 0.42 0.29 0.71Mammals, birds & reptiles C Catch (C) of marine mammals, seabirds and reptiles (þ) t$km�2$year�1 0.42 0.29 0.70

i: initial information from the Ecopath model, þ: additional information needed to compute this indicator (see Table 2).

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model to historical data (Christensen et al., 2008). In this case, as athird step, ECOIND calculatesmultiple time series for each indicatorfromMonte Carlo-generated alternate initial Ecopath model inputswhen running the Ecosim model a large number of times, enablingfurther analyses of uncertainty in ecological indicators due to inputuncertainty both in Ecopath and in Ecosim indicators’ outputs(Fig. 4A and B).

As a fourth step, ECOIND calculates indicators from spatial-temporal predictions of the Ecospace module (Fig. 2 and AnnexII). Ecospace applies the Ecosim equations to a two-dimensionalspace over time taking species movement, habitat suitability andfisheries dynamics into account (Christensen et al., 2014). In Eco-space, the spatial extent of the ecosystem is represented by a grid ofcells; where each can be assigned a ‘depth’ of 0 or higher. Zero-depth cells, historically referred to as ‘land’ cells, are considereddevoid of Ecosystem dynamics, while non-zero depth cells are‘water’ cells with active ecosystem dynamics. If water, a fixedhabitat type or a habitat suitability value can be assigned to eachcell for each functional group using any combination of environ-mental parameters. Fishing fleets and other human activities can berestricted to specific regions, and map areas can be permanently or

temporarily protected from human exploitation (no-take zones).Moreover, ecological and behavioural parameters including theaverage dispersal rates of organisms can be considered and spatial-temporal simulations can be made more realistic by includingrelative variations of primary productivity, fishing costs (e.g. in casedistance from port is considered a delimiting cost factor), habitat orenvironmental parameters through the spatial-temporal dataframework (Steenbeek et al., 2013). When running Ecospace,ECOIND calculates all ecological indicators by unit of time andspace, and can be set to automatically save these indicators as seriesof geo-referenced maps, which in turn can be visualized and ana-lysed in dedicated spatial data processing software (Figs. 2 and 4C).The ECOIND plug-in can be set to automatically save results fromEcopath, Ecosim, Ecospace and Monte Carlo when these arecomputed, or can save results from its user interface.

2.3. Calculation and visualization of indicators

Ecological indicators of ECOIND plug-in (Table 1 and Table S1Annex) are divided in five groups:

Fig. 1. Integration of ECOIND plug-in in the Ecopath with Ecosim software structure.

Fig. 2. Work flow and main functionalities of ECOIND plug-in. Solid arounds indicate implemented links between each functionality of the plug-in, dotted arrows indicated po-tential links that can be developed in the future.

M. Coll, J. Steenbeek / Environmental Modelling & Software 89 (2017) 120e130 123

Fig. 3. A. Study area in the Mediterranean Sea, and B. Flow diagram of the study food web where functional groups represented by circles are organised by trophic level (y-axis) andhabitat (x-axis). The quantity of trophic flows between functional groups is highlighted from red (largest) to blue (lowest). The size of the circles is proportional to their biomass inthe ecosystem. This figure has been drawn using Ecopath flow diagram drawing tool. (For interpretation of the references to colour in this figure legend, the reader is referred to theweb version of this article.)

M. Coll, J. Steenbeek / Environmental Modelling & Software 89 (2017) 120e130124

i) Biomass-based indicators: these indicators are based on theabundance of organisms in the food web. Biomass data of ma-rine organisms in the ecosystem are basic information to assessspecies population trends and status (Hilborn and Walters,1992).

Indicators include total biomass (Total B) of species in theecosystem, biomass of commercial species (Commercial B),biomass of fish (Fish B), of invertebrates (Invertebrates B) and theratio of the two latter (Invertebrates/Fish B), biomass of demersal(Demersal B) and Pelagic (Pelagic B) organism and their ratio(Demersal/Pelagic B) and the Kempton's biodiversity index (Q). TheQ index is proportional to the inverse slope of the species-abundance curve and is a proxy of ecosystem biodiversity(Ainsworth and Pitcher, 2006).

ii) Catch-based indicators: these indicators are based on the catchand discard species in the food web. Catch data, or the removalof organisms from aquatic ecosystems due to fishing activities,are basic data needed to evaluate the state of fishery resourcesand the fisheries sector to support scientific advice and in somecases can give an idea of the abundance of organisms in theecosystem (Zeller and Pauly, 2007).

Catch-based indicators include total catch (Total C) and thecatch of fish (Fish C), invertebrates (Invertebrates C) and their ratio(Invertebrates/Fish C), demersal (Demersal C) and Pelagic (PelagicC) catch of organisms and their ratio (Demersal/Pelagic C), catch ofpredatory organisms (Predatory C), defined as organisms withtrophic level (TL) �4, and total discards (Discards).

iii) Trophic-level based indicators: six indicators are based onthe trophic level (TL) concept. The TL identifies the positionof organisms within food webs by identifying the source ofenergy for each organism. TL was first defined as an integervalue (Lindeman, 1942), placing species or functional groupsinto a simple scheme. The concept was later modified to be

fractional (Odum and Heald, 1975) to account for omnivory.Following an established convention, fractional trophiclevels are calculated by assigning producers (and often alsodetritus) to trophic level 1 (e.g. phytoplankton), and con-sumers to a trophic level of 1 plus the average trophic level oftheir prey, weighted by their proportion in weight in thepredator's diet (Christensen, 1996). As fishing selectivelyremoves organisms from the food web, the trophic and sizestructure of the ecosystem may be altered and thus trophiclevel-based (TL-based) indicators can be used to capture thiseffect (Shannon et al., 2014).

In ECOIND, the following indicators are included: Tropic level(TL) of the catch (TLcatch) (Christensen,1996; Pauly et al., 1998), theMarine Trophic Index (MTI, or TLc including organisms withTL � 3.25) (Pauly and Watson, 2005), and the TL of the communityincluding all organisms (TL co), TLco including organisms withTL� 2 (TLco2), TLco including organisms with TL� 3.25 (TLco3.25),and TLco including organisms with TL � 4 (TLco 4).

iv) Size-based indicators: these indicators are based on changesin size or weight of organisms in the food web. Size-basedindicators are used to evaluate changes in marine ecosys-tems since fishing tend to capture the largest organisms first(Rochet and Trenkel, 2003).

In ECOIND, the indicators included are Mean length (ML) of fishin the community (ML of fish co) and in the catch (ML of fish C),Mean weight (MW) of the fish in the community (MW of fish co)and in the catch (MWof fish C), and Mean life span (MLS) of fish inthe community (MLS of fish co) and in the catch (MLS of fish C).

However, since EwEmodels aremainly biomass-based (with theexception of multi-stanza modelling capabilities within thebiomass model, Walters et al., 2008), these indicators provide ameans to track the weighted average of length, weight and life spanof organisms in the community and in the catch assuming a con-stant length, weight and life span of organisms in the ecosystem (by

Fig. 4. Biomass-based ecological indicators resulting from the NW Mediterranean food web model. A. Example of the histogram of ecological indicators results for 1978 (300Ecopath models were run). Y-axes represent the frequency of occurrence in the 300 model runs, x-axes represent the values of each indicator. B. Example of temporal resultsshowing time series of biomass-based indicators from 1978 to 2010 (results are based on 300 Ecosim model runs, shaded lines represent single results of each run, blue linerepresents the mean of all results). C. Example of temporal-spatial biomass-based indicators in 2010 using the Ecospace model (colour scale represents relative values: higher, inred, or lower, in blue, relative to the baseline Ecopath model). Additional results for the rest of the indicators are provided in Annex III. (For interpretation of the references to colourin this figure legend, the reader is referred to the web version of this article.)

M. Coll, J. Steenbeek / Environmental Modelling & Software 89 (2017) 120e130 125

default). To vary the length, weight and life span of organisms overtime, extra computations will have to be implemented in the future(this capacity still needs to be developed).

v) Species-based indicators: ECOIND calculates eight indicatorsspecifically based on species traits and conservation status.

The Intrinsic Vulnerability Index of the catch (IVIc) is a weightedmean of the vulnerability of exploited fish species (Cheung et al.,2007) and has been described to decrease with increasing fishingpressure if the larger, longer species that are more vulnerable tocollapse are fished out and effort is then directed at smaller, fastergrowing, less vulnerable species that are usually located lower inthe food web. The biomass (B) of endemic species in the commu-nity (Endemics B) and in the catch (C) (Endemics C) provide ameasure of how abundant endemic species are in the ecosystemandwhich proportion of these important species are exploited (Collet al., 2012, 2016a, 2015b). The biomass of endangered species inthe community using the IUCN (International Union for Conser-vation of Nature (IUCN) Red List of species at risk (IUCN, 2015)(IUCN species B) and in the catch (IUCN species C) provide an ideaonwhich part of the ecosystem is endangered and what proportionof endangered species are exploited. Finally, the biomass and catchof marine mammals, seabirds and reptiles specifically quantify theabundance and exploitation of these key and iconic groups.

Note that the Ecopath Traits table needs to be populated withspecies traits data for the ECOIND plug-in to produce results for allof the indicators (Table 1). However, the new plug-in can be ranwithout this trait information for those indicators for which thenecessary information is already available through the Ecopathmodel initial parameterization.

2.4. Computational aspects and algorithms

ECOIND is a software module that is independently developedof the EwE source code, and ties into themodel execution flow via anumber of plug-in points that are provided by the EwE software(Fig. 1) (Steenbeek et al., 2016). The plug-in system is an integralpart of the EwE software through which programmers can addextensions to the EwE approach without having to change the EwEsource program code. The EwE software contains a wide range ofplug-in points placed at strategic locations throughout its programcode. External plug-in code modules, in turn, can specify interest inconnecting to any number of these plug-in points. When the EwEsoftware encounters a plug-in point during its execution, it checksif there are any external code modules that wish to respond to thatplug-in point, and if so, the external code is activated with access todata relevant to the plug-in point location in the EwE program flow.

To perform its calculations ECOIND connects to EwE plug-inpoints that concern the execution of Ecopath, Ecosim, Ecospace,

Fig. 4. (continued).

M. Coll, J. Steenbeek / Environmental Modelling & Software 89 (2017) 120e130126

and Monte Carlo (Fig. 1). When the EwE execution encounters oneof these plug-in points, ECOIND receives model estimates from theEwE model component that just executed, enabling ECOIND toperform its calculations. Within ECOIND, Ecopath, Ecosim, Eco-space and Monte Carlo indicators are computed via an algorithmthat draws its data from the output of the four different modules.ECOIND Ecopath indicators are delivered as a single value per in-dicator, derived from the Ecopath estimates for a balanced model.Ecosim indicators are computed as time series, with a single valueper indicator per computed Ecosim time step. Ecospace indicatorsare computed for every Ecospace map cell with ecosystem dy-namics for each computed Ecospace time step. Monte Carlo in-dicators are delivered in two formats: (i) as single values for eachalternative mass-balanced Ecopath model produced by MonteCarlo which feed the Monte Carlo histogram (Fig. 4A), and (ii) astime series, with a single value per indicator, per computed timestep for each Monte Carlo-triggered Ecosim run (Fig. 4B).

Display of indicators in the ECOIND user interface is straightforward as it follows standard EwE display principles with excep-tion of the Monte Carlo histogram. In the histogram computedECOIND indicator values are displayed by number of occurrence,grouped in 100 equally sized intervals between the lowest to thehighest value of an indicator (Fig. 4A).

3. Results

To exemplify the functionality of ECOIND we used a previouslydeveloped food web model representing the Mediterranean Sea

marine ecosystem of the South Catalan Sea (in the NW Mediter-ranean Sea, Fig. 3A), which had been previously fitted to time seriesof data from 1978 to 2010 using Ecosim (Coll et al., 2013). The NWMediterranean Sea is a rich area in terms of marine biodiversity(Coll et al., 2010) and is important for several marine predatoryspecies at risk including marine mammals and seabirds (Coll et al.,2015b). The area is of relatively high productivity due to a com-bined effect of the northern current and runoff of the Ebro andRhone Rivers, and it is an important fishing ground for small pelagicfish and demersal meso-predators. The food web model isexpressed in terms of biomass as t$km�2, and production and catchas t$km�2$year�1. It includes 40 functional groups, ranging fromphytoplankton to large predatory species (Fig. 3B), and covers anarea of 5000 km2with depths from 50 to 400m. A spatial version ofthe food web model using the Ecospace habitat capacity model wasrecently developed based on the fitted Ecosim model (Coll et al.,2016b).

We use this case study to showcase how the indicators includedin ECOIND can provide additional information about the historicecosystem dynamics. For example, the ecological indicators ob-tained for 1978 and 2010, the initial and final year of the modelcalibration (Table 1), highlight important changes in the ecosystem.In 1978 the Southern Catalan Sea ecosystemwas dominated by thepelagic flows and biomasses, with an overall low trophic level ofthe community. Fishing catches of organisms were mainly pelagicand the trophic level of these catches was low, suggesting a pre-dominance of small pelagic fish, demersal juvenile fish and in-vertebrates in fishing activities. In 2010 the biomass and catch of

Fig. 4. (continued).

M. Coll, J. Steenbeek / Environmental Modelling & Software 89 (2017) 120e130 127

commercial species had declined, mainly of pelagic fish, while thebiomass of demersal species, mainly invertebrates, increased. Themean length, mean weight and mean life span of fish in the catchincreased due to the decline of pelagic fish. The biomass of endemicspecies, IUCN species and of marinemammals, seabirds andmarineturtles declined as well, while the catch of the first two increased.Results also evidence that the confidence around some of theecological indicators results varies largely due to uncertainty inmodel inputs (Fig. 4A and Annex III).

Time series of ecological indicators show that the ecosystemwent through some important changes from 1978 to 2010 (Fig. 4B).For example, we observe an increase of biomass of demersal or-ganisms, mainly invertebrates, while the biomass of pelagic fishfluctuate and show a declining trend at the end of the time series.The biomass of predators and biodiversity indicator Q’ decline atthe beginning of the time series and mainly fluctuates with time.The rest of indicators also show important ecosystem changes(Annex III) and confirm the degradation state of the ecosystemrecently described using ecological indicators to track the effects offishing (Lockerbie et al., Submitted) and biodiversity analyses (Collet al., 2015b; Navarro et al., 2015). Spatial-temporal results evi-dence that these changes in the ecosystem were heterogenic inspace, and different patterns are seen between the coastal and thecontinental shelf and open sea areas following environmental andexploitation gradients (Fig. 4C and Annex III) (Coll et al., 2015b;

Navarro et al., 2015).

4. Discussion

The development of ECOIND plug-in required the developmentof several features within the EwE software. An important additionis the new capability to define taxa within each functional group ofthe food web model. The taxonomic composition can be defined inthe Ecopathmodel (in the new “Define Taxa” form in Ecopath, Fig. 2and Annex II). This enables better documentation of species infor-mation used to develop ecosystem models and to directly incor-porate this data inmodelling results. Since biodiversity analyses arebecoming necessary to complement ecosystem-based approaches(Browman et al., 2005a; Link, 2011), we foresee that this capabilitycan be interesting to the EwE modelling community. A link be-tween the EwE software and online databases such as FishBase,WoRMS (the World Register for Marine Species), OBIS (OceanBiogeographic Information System) and others will facilitate thetask to populate the new taxonomic data tables. The WoRMS linkwas publicly released with EwE version 6.5; other links are invarious stages of development.

The second development is the capability to associate speciestraits and conservation status informationwith each species withineach functional group (in the new “Traits” form under EcopathTools, Fig. 2 and Table 2, and Annex II). Information about the

Table 2Descriptions of traits included in the new “Traits” form of Ecopath with Ecosim.

Entries Description

Species Name of the speciesOrganism type Type of the organism (Bacteria, Fungi, Algae, Plants, Invertebrates, Fishes, Birds, Mammals, Reptiles, Other)Ecology Ecology of the organism (Bathydemersal, Bathypelagic, Benthic, Benthopelagic, Demersal, Pelagic, Pelagic-neritic, Pelagic-oceanic,

Reef-associated, Land-based)Origin Origin of the organisms (Native, Introduced, Endemic, Questionable)Proportion of biomass Proportion of biomass of the species within the functional group. If there is only one species, then this is ¼ 1Proportion of catch Proportion of catch of the species within the functional group. If there is only one species, then this is ¼ 1IUCN conservation status IUCN category of the species from the Red List (Not evaluated, Data deficient, Least concern, Near threatened, Vulnerable, Endangered,

Critically endangered, Extinct in the wild, Extinct)Exploitation status Exploitation status of the exploited species from regional assessments (Not exploited, Underexploited, Moderately exploited, Fully exploited,

Overexploited, Depleted, Recovering)Vulnerability index Intrinsic extinction vulnerabilities of marine fishes to fishing sensu Cheung et al., 2005 [0, 100]Mean length (cm) Average length (cm) of the species in the populationMax length (cm) Maximum length (cm) of the species in the populationMean weight (g) Average weight (g) of the species in the populationMean life span (year) Mean life span (years) of the species in the population

M. Coll, J. Steenbeek / Environmental Modelling & Software 89 (2017) 120e130128

species ecology (e.g. type of organism, such as invertebrate or fish),species biological traits (e.g. mean length), species conservationstatus (e.g. IUCN status) and species exploitation status (i.e.moderately exploited, fully exploited) can be now included. Thisbroadens the capability of future EwE ecological analyses and in-dicators, needed to contribute to integrated assessments ()(such asthe MSFD and the Intergovernmental Platform on Biodiversity andEcosystem Services, IPBES frameworks, EC, 2010; IPBES, 2012).Trait-based indicators can also contribute to the application of EwEtools in the development of environmental impact analyses (Collet al., 2015a).

A third important new capability is the link between ECOINDand the Monte Carlo simulation routine. This link enables thecalculation of multiple sets of values for a single indicator, bothfrom Ecopath and from Ecosim results, and facilitates to developfurther analyses of uncertainty in ecological indicators within EwE.The ability to assess the impact of input uncertainty on modellingresults is an acknowledged need for the modelling community(Bastin et al., 2013; Coll et al., 2015a; Lassalle et al., 2014). Futuresoftware developments should focus on uncertainty of Ecospaceresults.

Technical limitations of ECOIND include limited capability ofEwE models to represent size-based changes in food webs, andcurrent lack of variation in the proportion of species in catch andbiomass of each functional group if species are not parameterizedas individual groups or size groups. These limitations can currentlybe partially overcome by parametrizing models using individualspecies as functional groups or multi-stanza groups (Walters et al.,2008). Further development could enable variation of size-parameters and proportions of catch and biomass using forcingfunctions. Data shortage in taxonomic resolution of some groups(e.g. invertebrates) can limit the applicability, too. Therefore, theinterpretation of results from the ECOIND plug-in should take theselimitations into account.

Despite these limitations, the new capabilities of ECOIND openthe door to future applications of the EwE software to developautomated analysis that can be used to test indicators in a stan-dardized way and look at the responses of aquatic food webs tosingle or multiple environmental factors and human activities.ECOIND computes standardized valuable information that can beuseful to the application of EwE approach for environmental andbiodiversity analyses. For instance, it can be foreseen that thefishing policy search tool of EwE (Christensen and Walters, 2004b)could be further developed so as to optimize against biodiversityindicator values computed by ECOIND. Currently, this is not yet

feasible as indicators are computed by external code modules andcannot be delivered back to the EwE approach (Fig. 2, dashed lines).We foresee an extension to the EwE system that not only bringsECOIND indicators back to EwE for driving its dynamics, but thatcan also benefit information produced by many other EwE plug-inmodules such as Network Analysis, Value Chain, or EcoTroph(Steenbeek et al., 2016) (Fig. 2). ECOIND indicators can also be usedin external analyses using R packages, for example, expanding thenetwork analysis capabilities (e.g. enaR, Borret and Lau, 2014).

Acknowledgements

MC was partially funded by the European Commission throughtheMarie Curie Career Integration Grant Fellowshipse PCIG10-GA-2011-303534 - to the BIOWEB project. This study is a contributionto the projects ECOTRANS (CTM2011-26333,Ministerio de Economíay Competitividad, Spain), BlueBRIDGE (EC 2020-EINFRA-9-2015-RIA-Proposal number: 675680) and SafeNET (EU-DGMARE MARE/2014/41).

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.envsoft.2016.12.004.

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