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-105
EM-422
EM-541
EM-592
EM-629
EM-836
EM-843
EM-904

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
EM Short Name em.detail.shortNameHelp ?	Benthic habitat associations, Willapa Bay, OR, USA	HexSim v2.4, San Joaquin kit fox, CA, USA	InVEST fisheries, lobster, South Africa	APEX v1501	SolVES, Pike & San Isabel NF, WY	Bird abundance on restored landfills, UK	Mourning dove abundance, Piedmont region, USA	Drag coefficient Laminaria hyperborea
EM Full Name em.detail.fullNameHelp ?	Benthic macrofaunal habitat associations, Willapa Bay, OR, USA	HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA	Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa	APEX (Agricultural Policy/Environmental eXtender Model) v1501	SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO	Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK	Mourning dove abundance, Piedmont ecoregion, USA	Drag coefficient Laminaria hyperborea
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	US EPA	InVEST	None	None	None	None	None
EM Source Document ID	39	337 ? Comment: The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships.	349 ? Comment: Supplemented with the InVEST Users Guide fisheries.	357	369	406	405	424
Document Author em.detail.documentAuthorHelp ?	Ferraro, S. P. and Cole, F. A.	Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips	Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby	Steglich, E. M., J. Jeong and J. R. Williams	Sherrouse, B.C., Semmens, D.J., and J.M. Clement	Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton	Riffel, S., Scognamillo, D., and L. W. Burger	Mendez, F. J. and I. J. Losada
Document Year em.detail.documentYearHelp ?	2007	2015	2018	2016	2014	2011	2008	2004
Document Title em.detail.sourceIdHelp ?	Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA	Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations	Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals	Agricultural Policy/Environmental eXtender Model User's Manual Version 1501	An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming	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	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields
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
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	http://www.hexsim.net/	https://www.naturalcapitalproject.org/invest/	https://epicapex.tamu.edu/manuals-and-publications/	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Steve Ferraro	Theresa M. Nogeire	Michelle Ward	E. M. Steglich	Benson Sherrouse	Lutfor Rahman	Sam Riffell	F. J. Mendez
Contact Address	U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365	School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA	ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia	Blackland Research and Extension Center, 720 East Blackland Road, Temple, TX 76502	USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA	Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	Not reported
Contact Email	ferraro.steven@epa.gov	tnogeire@gmail.com	m.ward@uq.edu.au	epicapex@brc.tamus.edu	bcsherrouse@usgs.gov	lutfor.rahman@northampton.ac.uk	sriffell@cfr.msstate.edu	mendezf@unican.es

EM Description

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "In this paper we report the results of 2 estuary-wide studies of benthic macrofaunal habitat associations in Willapa Bay, Washington, USA. This research is part of an effort to develop empirical models of biota-habitat associations that can be used to help identify critical habitats, prioritize habitats for environmental protection, index habitat suitability (U.S. Fish and Wildlife Service, 1980; Kapustka, 2003), perform habitat equivalency and compensatory restoration analyses (Fonseca et al., 2002; Kirsch et al., 2005), and as habitat value criteria in ecological risk assessments (Obery and Landis, 2002; Ferraro and Cole, 2004; Landis et al., 2004)." (491)	ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]."	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."	ABSTRACT: "APEX is a tool for managing whole farms or small watersheds to obtain sustainable production efficiency and maintain environmental quality. APEX operates on a daily time step and is capable of performing long term simulations (1-4000 years) at the whole farm or small watershed level. The watershed may be divided into many homogeneous (soils, land use, topography, etc.) subareas (<4000). The routing component simulates flow from one subarea to another through channels and flood plains to the watershed outlet and transports sediment, nutrients, and pesticides. This allows evaluation of interactions between fields in respect to surface run-on, sediment deposition and degradation, nutrient and pesticide transport and subsurface flow. Effects of terrace systems, grass waterways, strip cropping, buffer strips/vegetated filter strips, crop rotations, plant competition, plant burning, grazing patterns of multiple herds, fertilizer, irrigation, liming, furrow diking, drainage systems, and manure management (feed yards and dairies with or without lagoons) can be simulated and assessed. Most recent developments in APEX1501 include: • Flexible grazing schedule of multiple owners and herds across landscape and paddocks. • Wind dust distribution from feedlots. • Manure erosion from feedlots and grazing fields. • Optional pipe and crack flow in soil due to tree root growth. • Enhanced filter strip consideration. • Extended lagoon pumping and manure scraping options. • Enhanced burning operation. • Carbon pools and transformation equations similar to those in the Century model with the addition of the Phoenix C/N microbial biomass model. • Enhanced water table monitoring. • Enhanced denitrification methods. • Variable saturation hydraulic conductivity method. • Irrigation using reservoir and well reserves. • Paddy module for use with rice or wetland areas."	[ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.	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)."	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. "	ABSTRACT: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." AUTHOR'S DESCRIPTION: "Therefore, a relation between C˜D and some nondimensional flow parameters is desirable to characterize hydrodynamically the L. hyperborea model plants for predictable purposes."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Future rock lobster fisheries management	None identified	None	None identified	None reported	None identified
Biophysical Context	benthic estuarine	No additional description provided	No additional description provided	No additional description provided	Rocky mountain conifer forests	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).	Conservation Reserve Program lands left to go fallow	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class	Fisheries exploitation; fishing vulnerability (of age classes)	No scenarios presented	N/A	No scenarios presented	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships.	Method + Application (multiple runs exist) View EM Runs	Method Only	Method + Application	Method Only	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	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	New or revised model

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

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-328 \| Doc-327 \| Doc-2	None	None	Doc-369	None	Doc-405	Doc-424
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-403 \| EM-98	None	None	EM-626 \| EM-628	EM-837	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-844 \| EM-845 \| EM-846 \| EM-847	EM-896 \| EM-897

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
EM Temporal Extent em.detail.tempExtentHelp ?	1996,1998	60 yr	1986-2115	Not applicable	2004-2008	Not applicable	2008	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary	Not applicable
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	future time	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	discrete	discrete	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	1	1	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Year	Day	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or ecological	Geopolitical	Not applicable	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Willapa Bay	San Joaquin Valley, CA	Table Mountain National Park Marine Protected Area	Not applicable	National Park	East Midland	Piedmont Ecoregion	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10,000-100,000 km^2	100-1000 km^2	Not applicable	1000-10,000 km^2.	1000-10,000 km^2.	100,000-1,000,000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially lumped (in all cases)	spatially distributed (in at least some 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 lumped (in all cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	Not applicable	area, for pixel or radial feature	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	Not applicable	14 ha	Not applicable	homogenous subareas	30m2	multiple unrelated sites	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Numeric	Numeric	Numeric	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	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-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Unclear	No	Not applicable	No	Not applicable	Yes	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	Not applicable	Yes	Not applicable	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R^2 \| 0.34-0.82 \| Not applicable R^2 \| 0.29-0.68 \| Not applicable R^2 \| 0.07-0.17 \| Not applicable	None	None	None	Z score \| -3.938 to -21.532 \| NA R value \| 0.412 - 0.811 \| NA	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	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.	Not applicable	No	Not applicable	No	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes	No	No	Not applicable	No	Not applicable	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	No	Not applicable	No	Not applicable	Yes	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	No	Not applicable	Not applicable	Not applicable	Not applicable	Unclear	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
None	North America United States California	None	None	North America United States Colorado	Europe United Kingdom	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	None

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

EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	Temperate South Africa South Africa Benguela Namaqua	None	None	None	None	Temperate North Atlantic Northeast Atlantic Northern European Seas

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Centroid Latitude em.detail.ddLatHelp ?	46.24	36.13	-34.18	Not applicable	38.7	52.22	36.23	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-124.06	-120	18.35	Not applicable	105.89	-0.91	-81.9	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	Not applicable	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Provided	Not applicable	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Agroecosystems	Forests	Created Greenspace \| Grasslands	Grasslands	Near Coastal Marine and Estuarine
Specific Environment Type em.detail.specificEnvTypeHelp ?	Drowned river valley estuary	Agricultural region (converted desert) and terrestrial perimeter	Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf	Terrestrial environment associated with agroecosystems	Montain forest	restored landfills and conserved grasslands	grasslands	Near Coastal Marine and Estuarine
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	Ecological scale corresponds to the Environmental Sub-class	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 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-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
EM Organismal Scale em.detail.orgScaleHelp ?	Species	Individual or population, within a species	Individual or population, within a species	Not applicable	Not applicable	Individual or population, within a species	Species	Species

Taxonomic level and name of organisms or groups identified

EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Species Zostera marina Neotrypaea californiensis Manayunkia aestuarina Upogebia pugettensis Spartina alterniflora	Species Vulpes macrotis mutica	Species West Coast Rock Lobster (Jasus lalandii)	None Available	None Available	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)	Species Mourning dove	Species Laminaria hyporborea

EnviroAtlas URL

EM-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
None Available	Stream Length Impaired by Pesticides	Big game hunting recreation demand	The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Daily Potential Solar Energy, Average Annual Precipitation, Agricultural water use (million gallons/day), Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD), Stream Length Impaired by Pesticides, Water supply from NID reservoirs (million gallons)	GAP Ecological Systems, Enabling Conditions	None Available	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	National Hydrography Dataset Plus (NHD PlusV2)

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-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
[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 Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes Biomass Materials from plants, algae and animals for agricultural use [Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs Wild plants, algae and their outputs [Ecosystem] Cultural Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings] Spiritual and/or emblematic Symbolic Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic Educational	[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 Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection

<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-105	EM-422	EM-541	EM-592	EM-629	EM-836	EM-843	EM-904
Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	Rivers and Streams (111) Water (3) Liquid water Stock Indicator Quality Indicator Agroecosystems (22) Flora (5) Fodder Stock Indicator	Forests (21) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator	None

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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)