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.

Compare Criteria:

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Comparing:

EM-106
EM-122
EM-368
EM-432
EM-456
EM-541
EM-760
EM-774
EM-836

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
EM Short Name em.detail.shortNameHelp ?	Value of Habitat for Shrimp, Campeche, Mexico	Land-use change and crop-based production, Europe	InVEST - Water Yield (v3.0)	Nitrogen fixation rates, Guánica Bay, Puerto Rico	Reef dive site favorability, St. Croix, USVI	InVEST fisheries, lobster, South Africa	WESP: Marsh & wet meadow, ID, USA	Plant-pollinator networks at reclaimed mine, USA	Bird abundance on restored landfills, UK
EM Full Name em.detail.fullNameHelp ?	Value of Habitat for Shrimp, Campeche, Mexico	Land-use change effects on crop-based production, Europe	InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield	Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA	Dive site favorability (reef), St. Croix, USVI	Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa	WESP: Seasonally flooded marsh & wet meadow, Idaho, USA	Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA	Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	EU Biodiversity Action 5	InVEST	US EPA	US EPA	InVEST	None	None	None
EM Source Document ID	227	228	311	338 ? Comment: WE received a draft copy prior to journal publication that was agency reviewed.	335	349 ? Comment: Supplemented with the InVEST Users Guide fisheries.	393 ? Comment: Additional data came from electronic appendix provided by author Chris Murphy.	397	406
Document Author em.detail.documentAuthorHelp ?	Barbier, E. B., and Strand, I.	Haines-Young, R., Potschin, M. and Kienast, F.	Natural Capital Project	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby	Murphy, C. and T. Weekley	Cusser, S. and K. Goodell	Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton
Document Year em.detail.documentYearHelp ?	1998	2012	2015	2017	2014	2018	2012	2013	2011
Document Title em.detail.sourceIdHelp ?	Valuing mangrove-fishery linkages: A case study of Campeche, Mexico	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Water Yield: Reservoir Hydropower Production- InVEST (v3.0)	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals	Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context.	Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine	The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities
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 journal manuscript	Web published	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	E.B. Barbier	Marion Potschin	Natural Capital Project	Susan H. Yee	Susan H. Yee	Michelle Ward	Chris Murphy	Sarah Cusser ? Comment: Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A.	Lutfor Rahman
Contact Address	Environment Department, University of York, York YO1 5DD, UK	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	371 Serra Mall, Stanford University, Stanford, Ca 94305	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia	Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID	Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A.	Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK
Contact Email	Not reported	marion.potschin@nottingham.ac.uk	invest@naturalcapitalproject.org	yee.susan@epa.gov	yee.susan@epa.gov	m.ward@uq.edu.au	chris.murphy@idfg.idaho.gov	sarah.cusser@gmail.com	lutfor.rahman@northampton.ac.uk

EM Description

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153)	ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios."	Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind.	AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class"	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and	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."	A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level.	ABSTRACT: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context."	ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None provided	None identified	Future rock lobster fisheries management	None identified	None identified	None identified
Biophysical Context	Gulf of Mexico; mangrove-lagoon system	No additional description provided	None applicable	No additional description provided	No additional description provided	No additional description provided	restored, enhanced and created wetlands	The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils.	The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995).
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030)	N/A	No scenarios presented	No scenarios presented	Fisheries exploitation; fishing vulnerability (of age classes)	Sites, function or habitat focus	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method Only	Method + Application	Method + Application	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 Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	Application of existing model	Application of existing 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-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-238 \| Doc-239 \| Doc-240 \| Doc-241 \| Doc-242 \| Doc-228	Doc-307 \| Doc-280 \| Doc-338 \| Doc-205	None	None	None	Doc-390	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-185 \| EM-319	EM-123 \| EM-124 \| EM-125 \| EM-162 \| EM-164 \| EM-165 \| EM-166 \| EM-170 \| EM-171 \| EM-99 \| EM-119 \| EM-120 \| EM-121	EM-437 \| EM-148 \| EM-344 \| EM-111	None	None	None	EM-718 \| EM-734 \| EM-743	None	EM-837

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
EM Temporal Extent em.detail.tempExtentHelp ?	1980-1990	1990-2030	Not applicable	1978 - 2009	2006-2007, 2010	1986-2115	2010-2012	2009-2010	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-dependent	time-stationary	time-stationary	time-dependent	time-dependent	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	future time	Not applicable	Not applicable	future time	past time	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	discrete	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	6, 10, and 30	1	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Year	Year	Not applicable	Not applicable	Year	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Not applicable	Watershed/Catchment/HUC	Physiographic or ecological	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)
Spatial Extent Name em.detail.extentNameHelp ?	Laguna de Terminos Mangrove system	The EU-25 plus Switzerland and Norway	Not applicable	Guanica Bay watershed	Coastal zone surrounding St. Croix	Table Mountain National Park Marine Protected Area	Wetlands in idaho	The Wilds	East Midland
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	>1,000,000 km^2	Not applicable	100-1000 km^2	100-1000 km^2	100-1000 km^2	100,000-1,000,000 km^2	1-10 km^2	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
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) ? Comment: pixel is likely 30m x 30m	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)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	Not applicable	Not applicable	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 km x 1 km	1 km x 1 km	Not specified	HUC	10 m x 10 m	Not applicable	Not applicable	10 m radius	multiple unrelated sites

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Logic- or rule-based	Numeric	Analytic	Analytic	Numeric	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) 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-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	No	Yes ? Comment: Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones.	No	Yes	No	No	Not applicable	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	Not applicable	No	No	No	No	Not applicable	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R^2 \| 0.745 \| Not applicable F-stat \| 30.216 \| Not applicable SE of regression \| 639745.7 \| Not applicable	None	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	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	Yes	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes	No	No	No	No	No	No	Yes	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Yes	No	Not applicable	No	No	No	No	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Unclear	Not applicable	Not applicable	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-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
North America Mexico	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia France Lithuania Hungary Switzerland Spain Czech Republic Belgium Norway Finland Denmark Italy Germany Estonia	None	North America U.S. Caribbean Territories	None	None	North America United States Idaho	North America United States Ohio	Europe United Kingdom

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

EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Southern Gulf of Mexico	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Temperate South Africa South Africa Benguela Namaqua	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
Centroid Latitude em.detail.ddLatHelp ?	18.61	50.53	-9999	17.96	17.73	-34.18	44.06	39.82	52.22
Centroid Longitude em.detail.ddLongHelp ?	-91.55	7.6	-9999	-67.02	-64.77	18.35	-114.69	-81.75	-0.91
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Not applicable	Estimated	Estimated	Provided	Estimated	Provided	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams	Inland Wetlands \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland \| Barren	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Inland Wetlands	Grasslands	Created Greenspace \| Grasslands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Mangrove	Not applicable	Watershed	Tropical terrestrial	Coral reefs	Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf	created, restored and enhanced wetlands	Grassland	restored landfills and conserved grasslands
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Not applicable	Ecological scale is finer than that of 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 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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
EM Organismal Scale em.detail.orgScaleHelp ?	Guild or Assemblage	Not applicable	Not applicable	Not applicable	Guild or Assemblage	Individual or population, within a species	Not applicable	Species	Individual or population, within a species

Taxonomic level and name of organisms or groups identified

EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
other Shrimp	None Available	None Available	None Available	None Available	Species West Coast Rock Lobster (Jasus lalandii)	None Available	Species Planted plant species Pollinator species	Species Grey partridge (Perdix perdix) Collared dove (Streptopelia decaocto) Woodpidgeon (Columba palumbus) Lapwing (Vanellus vanellus) Magpie (Pica pica) Dunnock (Prunella modularis) Starling (Sturnus vulgaris) Skylark (Alauda arvensis) Pheasant (Phasianus colchicus) Blackbird (Turdus merula) Carrion crow (Corvus corone) Tree sparrow (Passer montanus)

EnviroAtlas URL

EM-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
GAP Ecological Systems, Big game hunting recreation demand	Hectares of Vegetable Crops	Average Annual Precipitation, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD)	Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD), Percent Impervious Area	None Available	Big game hunting recreation demand	Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass	GAP Ecological Systems	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-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance Mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats

<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-106	EM-122	EM-368	EM-432	EM-456	EM-541	EM-760	EM-774	EM-836
Near Coastal Marine/Estuarine (113) Fauna (4) Shrimp Stock Indicator	Agroecosystems (22) Flora (5) food crops Stock Indicator	Rivers and Streams (111) Water (3) Liquid water Stock Indicator	Agroecosystems (22) Soil (2) Specific types of soil Quality Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	None	None	None

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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)