EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.

This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:

Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
Intermediate (Computed) Variables
Response Variables
- Computed Response Variables
- Measured Response Variables

EM Variables by Category

A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:

Policy Regarding Use or Management of Ecosystem Resources
Land Surface (or Water Body Bed) Cover, Use or Substrate
Human Demographic Data
Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
Monetary Values

Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.

All EM Descriptors

This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:

EM Identity and Description
EM Modeling Approach
EM Locations, Environments, Ecology
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

EM Descriptors by Modeling Concepts

This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:

Conceptual Model
Modeling Objective
Modeling Context
Potential for Model Linkage
Feasibility of Model Use
Model Certainty
Model Structural Information

Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.

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Comparing:

EM-84
EM-143
EM-303
EM-485
EM-541
EM-542
EM-697
EM-702
EM-735

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
EM Short Name em.detail.shortNameHelp ?	ACRU, South Africa	InVEST habitat quality	Biological pest control, Uppland Province, Sweden	Yasso07 v1.0.1, Switzerland, site level	InVEST fisheries, lobster, South Africa	Coastal protection in Belize	Floral resources on landfill sites, United Kingdom	Northern Shoveler recruits, CREP wetlands, IA, USA	C sequestration in grassland restoration, England
EM Full Name em.detail.fullNameHelp ?	ACRU (Agricultural Catchments Research Unit), South Africa	InVEST (Integrated Valuation of Environmental Services and Tradeoffs) Habitat Quality	Biological control of agricultural pests by natural predators, Uppland Province, Sweden	Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level	Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa	Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize:	Floral resources on landfill sites, East Midlands, United Kingdom	Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Carbon sequestration in grassland diversity restoration, England
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	InVEST ? Comment: From the Natural Capital Project website	None	None	InVEST	InVEST	None	None	None
EM Source Document ID	271	278	299	343	349 ? Comment: Supplemented with the InVEST Users Guide fisheries.	350	389	372 ? Comment: Document 373 is a secondary source for this EM.	396
Document Author em.detail.documentAuthorHelp ?	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Natural Capital Project	Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O.	Didion, M., B. Frey, N. Rogiers, and E. Thurig	Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby	Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes	Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever	De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett
Document Year em.detail.documentYearHelp ?	2008	2014	2014	2014	2018	2016	2013	2010	2011
Document Title em.detail.sourceIdHelp ?	Mapping ecosystem services for planning and management	Habitat Quality model - InVEST ver. 3.0	Ecological production functions for biological control services in agricultural landscapes	Validating tree litter decomposition in the Yasso07 carbon model	Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals	The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience	Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Additional carbon sequestration benefits of grassland diversity restoration
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published on Natural Capital Project website	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	http://en.ilmatieteenlaitos.fi/yasso-download-and-support	https://www.naturalcapitalproject.org/invest/	Not identified in paper	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Roland E Schulze	The Natural Capital Project	Mattias Jonsson	Markus Didion	Michelle Ward	Greg Guannel	Sam Tarrant	David Otis	Gerlinde B. De Deyn
Contact Address	School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa	371 Serra Mall Stanford University Stanford, CA 94305-5020 USA	Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden	Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland	ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia	The Nature Conservancy, Coral Gables, FL. USA	RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K.	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands
Contact Email	schulzeR@nu.ac.za	invest@naturalcapitalproject.org	mattias.jonsson@slu.se	markus.didion@wsl.ch	m.ward@uq.edu.au	greg.guannel@gmail.com	sam.tarrant@rspb.org.uk	dotis@iastate.edu	g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature."	Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. AUTHORS DESCRIPTION: "The InVEST habitat quality model combines information on LULC and threats to biodiversity to produce habitat quality maps. This approach generates two key sets of information that are useful in making an initial assessment of conservation needs: the relative extent and degradation of different types of habitat types in a region and changes across time. This approach further allows rapid assessment of the status of and change in a proxy for more detailed measures of biodiversity status. If habitat changes are taken as representative of genetic, species, or ecosystem changes, the user is assuming that areas with high quality habitat will better support all levels of biodiversity and that decreases in habitat extent and quality over time means a decline in biodiversity persistence, resilience, breadth and depth in the area of decline. The habitat rarity model indicates the extent and pattern of natural land cover types on the current or a potential future landscape vis-a-vis the extent of the same natural land cover types in some baseline period. Rarity maps allow users to create a map of the rarest habitats on the landscape relative to the baseline chosen by the user to represent the mix of habitats on the landscape that is most appropriate for the study area’s native biodiversity. The model requires basic data that are available virtually everywhere in the world, making it useful in areas for which species distribution data are poor or lacking altogether. Extensive occurrence (presence/absence) data may be available in many places for current conditions. However, modeling the change in occurrence, persistence, or vulnerability of multiple species under future conditions is often impossible or infeasible. While a habitat approach leaves out the detailed species occurrence data available for current conditions, several of its components represent advances in functionality over many existing biodiversity conservation planning tools. The most significant is the ability to characterize the sensitivity of habitats types to various threats. Not all habitats are affected by all threats in the same way, and the InVEST model accounts for this variability. Further, the model allows users to estimate the relative impact of one threat over another so that threats that are more damaging to biodiversity persistence on the landscape can be represented as such. For example, grassland could be particularly sensitive to threats generated by urban areas yet moderately sensitive to threats generated by roads. In addition, the distance over which a threat will degrade natural systems can be incorporated into the model. Model assessment of the current landscape can be used as an input to a coarse-filter assessment of current conservation needs and opportunities. Model assessment of pote	ABSTRACT: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization."	ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to... (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests; and (iii) evaluate the suitability of Yasso07 for regional and national scale applications in Swiss forests." AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "The decomposition of below- and aboveground litter was studied over 10 years on five forest sites in Switzerland…" "At the time of this study, three parameter sets have been developed and published:... (3): Rantakari et al., 2012 (henceforth P12)… For the development of P12, Rantakari et al. (2012) obtained a subset of the previously used data which was restricted to European sites." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root lit-ter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The r	AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area."	AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems."	ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML)	ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2Rn where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = HZB/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).	ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…"
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified	Future rock lobster fisheries management	Future rock lobster fisheries management	None identified	None identified	None identified
Biophysical Context	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	Not applicable	Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here.	Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa).	No additional description provided	barrier reef and fringing reef in nearshore coastal marine system	No additional description provided	Prairie Pothole Region of Iowa	Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility.
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Potential land Use Land Class (LULC) future and baseline ? Comment: model requires current landuse but can compare to baseline (prior to intensive management of the land) and potential future landuse. These are the two scenarios suggested in the documentation.	No scenarios presented	No scenarios presented	Fisheries exploitation; fishing vulnerability (of age classes)	Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions	No scenarios presented	No scenarios presented	Additional benefits due to biodiversity restoration practices

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method Only	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: Model runs are for different sites (Beatenberg, Vordemwald, Bettlachstock, Schanis, and Novaggio) differentiated by climate and forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa).	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist) View EM Runs
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	New or revised model	Application of existing model	Application of existing model	New or revised model	New or revised model	New or revised model	New or revised model

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-272 ? Comment: Doc ID 272 was also used as a source document for this EM	Doc-309	None	Doc-342 \| Doc-343	None	None	None	Doc-372 \| Doc-373	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-345	None	EM-466 \| EM-467 \| EM-469 \| EM-480	None	None	EM-709	EM-705 \| EM-704 \| EM-703 \| EM-701 \| EM-700 \| EM-632	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
EM Temporal Extent em.detail.tempExtentHelp ?	1950-1993	Not applicable	2009	2000-2010	1986-2115	2005-2013	2007-2008	1987-2007	1990-2007
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	future time	future time	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	discrete	discrete	discrete	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	Not applicable	1	1	1	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Day	Not applicable	Not applicable	Year	Year	Second	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	No location (no locational reference given)	Geopolitical	Geopolitical	Geopolitical	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Multiple unrelated locations (e.g., meta-analysis)	Other
Spatial Extent Name em.detail.extentNameHelp ?	South Africa	Not applicable	Uppland province	Switzerland	Table Mountain National Park Marine Protected Area	Coast of Belize	East Midlands	CREP (Conservation Reserve Enhancement Program	Colt Park meadows, Ingleborough National Nature Reserve, northern England
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	Not applicable	10,000-100,000 km^2	10,000-100,000 km^2	100-1000 km^2	100-1000 km^2	1000-10,000 km^2.	10,000-100,000 km^2	<1 ha

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	Not applicable	length, for linear feature (e.g., stream mile)	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed by catchments with average size of 65,000 ha	LULC pixel size	25 m x 25 m	Not applicable	Not applicable	1 meter	multiple unrelated locations	multiple, individual, irregular sites	3 m x 3 m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Analytic	Numeric	Numeric	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic	deterministic	stochastic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	None	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Model Calibration Reported? em.detail.calibrationHelp ?	No	Not applicable	No	No	No	No	Not applicable	Unclear	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Not applicable	No	No	No	No	Not applicable	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Not applicable	Yes	Yes	Yes ? Comment: A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data.	No ? Comment: Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666.	Not applicable	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Not applicable	No	Yes	No	No	Not applicable	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Not applicable	Yes ? Comment: AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model."	No	No	No	Not applicable	No	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	No	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Africa South Africa	None	Europe Sweden	Europe Switzerland	None	North America Belize	Europe United Kingdom	North America United States Iowa	Europe United Kingdom

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
None	None	None	None	Temperate South Africa South Africa Benguela Namaqua	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Western Caribbean	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Centroid Latitude em.detail.ddLatHelp ?	-30	-9999	59.52	46.82	-34.18	18.63	52.22	42.62	54.2
Centroid Longitude em.detail.ddLongHelp ?	25	-9999	17.9	8.23	18.35	-88.22	-0.91	-93.84	-2.35
Centroid Datum em.detail.datumHelp ?	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Not applicable	Estimated	Estimated	Provided	Estimated	Estimated	Estimated	Provided

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Ground Water \| Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems \| Grasslands	Forests	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Created Greenspace \| Grasslands	Inland Wetlands \| Agroecosystems \| Grasslands	Agroecosystems \| Grasslands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not reported	Not applicable	Spring-sown cereal croplands and surrounding grassland and non-arable land	forests	Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf	coral reefs	restored landfills and grasslands	Wetlands buffered by grassland within agroecosystems	fertilized grassland (historically hayed)
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Individual or population, within a species	Community	Individual or population, within a species	Guild or Assemblage	Individual or population, within a species	Individual or population, within a species	Community

Taxonomic level and name of organisms or groups identified

EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
None Available	None Available	Genus Neuroptera: Chrysopidae Araneae: Linyphiidae Coleoptera: Carabidae Araneae:Lycosidae Coleoptera: Staphylinidae Coleoptera: Coccinellidae Diptera: Syrphidae Species Rhopalosiphum padi L.	None Available	Species West Coast Rock Lobster (Jasus lalandii)	None Available	Species Rhinanthus minor Picris echioides Lotus corniculatus Cardamine flexuosa Ranunculus acris Ranunculus bulbosus Stellaria graminea Trifolium dubium Picris hieracioides Ranunculus repens Trifolium repens Galium verum Cirsium arvense	Species Northern Shoveler (Anas clypeata)	None Available

EnviroAtlas URL

EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Average Annual Precipitation	None Available	GAP Ecological Systems	Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass	Big game hunting recreation demand	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation	GAP Ecological Systems	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	None Available

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
[Ecosystem] Provisioning Nutrition Water Surface water for drinking Materials Water Surface water for non-drinking purposes	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Pest and disease control Pest control	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection Mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

EM-84	EM-143	EM-303	EM-485	EM-541	EM-542	EM-697	EM-702	EM-735
Rivers and Streams (111) Water (3) Liquid water Stock Indicator	None	Grasslands (23) Fauna (4) Pest predators Stock Indicator Agroecosystems (22) Fauna (4) Pest predators Stock Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator Flow Indicator	None	Wetlands (12) Fauna (4) Duck Stock Indicator	None

EcoService Models Library (ESML)

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EM Locations, Environments, Ecology

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)