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-87
EM-184
EM-320
EM-449
EM-480
EM-650
EM-698
EM-963
EM-979

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
EM Short Name em.detail.shortNameHelp ?	Area & hotspots of soil accumulation, South Africa	ROS (Recreation Opportunity Spectrum), Europe	Coastal protection, Europe	Decrease in erosion (shoreline), St. Croix, USVI	Yasso07 - Land use SOC dynamics, China	Sedge Wren density, CREP, Iowa, USA	Fish species richness, St. Croix, USVI	Eastern Meadowlark Abundance	Predicting ecosystem service values, Bangladesh
EM Full Name em.detail.fullNameHelp ?	Area and hotspots of soil accumulation, South Africa	ROS (Recreation Opportunity Spectrum), Europe	Coastal protection, Europe	Decrease in erosion (shoreline) by reef, St. Croix, USVI	Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China	Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Fish Species Richness, Buck Island, St. Croix , USVI	TEST: CRP Impacts on Eastern Meadowlark Abundance	Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	EU Biodiversity Action 5	EU Biodiversity Action 5	US EPA	None	None	None	None ? Comment: Could not find any information pertaining to a model collection.	None
EM Source Document ID	271	293	296	335	344	372	355	405	457
Document Author em.detail.documentAuthorHelp ?	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G.	Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu	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	Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco	Riffel, S., Scognamillo, D., and L. W. Burger	Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y.
Document Year em.detail.documentYearHelp ?	2008	2014	2013	2014	2015	2010	2007	2008	2022
Document Title em.detail.sourceIdHelp ?	Mapping ecosystem services for planning and management	Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU	Assessment of coastal protection as an ecosystem service in Europe	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh
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	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	http://en.ilmatieteenlaitos.fi/yasso-download-and-support	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Benis Egoh	Maria Luisa Paracchini	Camino Liquete	Susan H. Yee	Xing Wu	David Otis	Simon Pittman	L. Wes Burger ? Comment: Lead author, Sam Riffell, pass away. Using last author.	Syed Riad Morshed
Contact Address	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy	European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Chinese Academy of Sciences, Beijing 100085, China	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	1305 East-West Highway, Silver Spring, MD 20910, USA	Mississippi State University, Mississippi State, MS	Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh
Contact Email	Not reported	luisa.paracchini@jrc.ec.europa.eu	camino.liquete@gmail.com	yee.susan@epa.gov	xingwu@rceesac.cn	dotis@iastate.edu	simon.pittman@noaa.gov	w.burger@msstate.edu	riad.kuet.urp16@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "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…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover."	ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU."	ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided."	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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef."	ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau."	ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. 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... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) e^(-0.7982511 + 0.0285891 bbspath + 0.0105094 *grass400)	ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems."	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, comprehen- sive 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. AUTHOR'S DESCRIPTION: For each species, we developed multiple regression models for the entire study area and for each of the seven ecological regions separately. We included only those routes that met quality standards for both bird abundance and land use data, and this left a total of 636 useable routes. The number of routes within individual ecological regions ranged from a low of 55 (central hardwoods) to a high of 154 (Appalachian Mountains). Using our estimates of bird abundance as response variables and landscape variables as explanatory variables, we used a stepwise selection process (retaining only explanatory variables that satisfied α < 0.05) to build models for each of the seven ecological regions and for the study region as a whole.	Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Supports global and EU biodiversity policy	None identified	None identified	None identified	None provided	Food Security Act of 1985	N/A
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.	No additional description provided	No additional description provided	No additional description provided	Agricultural plain, hills, gulleys, forest, grassland, Central China	Prairie pothole region of north-central Iowa	Hard and soft benthic habitat types approximately to the 33m isobath	Bird Conservation Regions ranging from Central to eastern United States and from the Gulf of Mexico to the Great Lakes.	Jashore city, Bangladesh
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	Land use change	No scenarios presented	No scenarios presented	Separate models created for each Bird Conservation Region, including different land use, agriculture, and CRP variable values.	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application (multiple runs exist)	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	New or revised model	Application of existing model	Application of existing model	Application of existing model ? Comment: Models developed by Quamen (2007).	Application of existing 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-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-271	Doc-290 \| Doc-291 \| Doc-289	None	Doc-335	Doc-343 \| Doc-342	Doc-372	Doc-355	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-85 \| EM-86 \| EM-88	None	None	EM-447 \| EM-448	EM-466 \| EM-467 \| EM-469 \| EM-485	EM-652 \| EM-651 \| EM-649 \| EM-648	EM-590 \| EM-699	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	Not reported	1992-2010	2006-2007, 2010	1969-2011	1992-2007	2000-2005	1995-1999	2000-2050
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	past time	Not applicable	Not applicable	Not applicable	both
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	10
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Year	Not applicable	Not applicable	Not applicable	Year

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Geopolitical	Geopolitical	Physiographic or ecological	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	South Africa	European Union countries	Shoreline of the European Union-27	Coastal zone surrounding St. Croix	Yangjuangou catchment	CREP (Conservation Reserve Enhancement Program) wetland sites	SW Puerto Rico,	Bird Conservation Regions comprising the northern bobwhite breeding range.	Jashore city, Bangladesh
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	>1,000,000 km^2	>1,000,000 km^2	100-1000 km^2	1-10 km^2	1-10 km^2	100-1000 km^2	>1,000,000 km^2	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
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 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)	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	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	other (specify), for irregular (e.g., stream reach, lake basin)	map scale, for cartographic feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed across catchments with average size of 65,000 ha	100 m x 100 m	Irregular	10 m x 10 m	30m x 30m	multiple, individual, irregular shaped sites	not reported	1962 km^2	30m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Machine learning

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Yes	No	Unclear	No	Unclear ? Comment: Does accounting for autocorrelation count as validation?	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	Yes ? Comment: p value: p<0.001	No	Yes	Not applicable	Yes
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	R squared \| 0.219 \| unitless	None	R squared \| 0.48 \| unitless Cohen's Kappa statistic \| 0.46 \| unitless AUC \| 0.76 \| unitless	None	Accuracy assessment value \| 93.33-96.67 \| % Kappa statistic \| 0.9143-0.9577 \| N/A
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	Yes	No	Unclear	Yes	No	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	Yes	No	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	No	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-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
Africa South Africa	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia Bulgaria France Lithuania Croatia Romania Hungary United Kingdom Spain Czech Republic Belgium Finland Denmark Italy Germany Estonia	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia Bulgaria France Lithuania Romania Hungary United Kingdom Spain Czech Republic Belgium Finland Denmark Italy Germany Estonia	None	Asia China	North America United States Iowa	None	North America United States	Asia India

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

EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
None	None	Temperate North Atlantic Northeast Atlantic Northern European Seas Celtic Seas Faroe Plateau North Sea Mediterranean Sea Mediterranean Sea Western Mediterranean Aegean Sea Alboran Sea Adriatic Sea Ionian Sea	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
Centroid Latitude em.detail.ddLatHelp ?	-30	48.2	48.2	17.73	36.7	42.62	17.79	36.53	23.95
Centroid Longitude em.detail.ddLongHelp ?	25	16.35	16.35	-64.77	109.52	-93.84	-64.62	-88.45	89.12
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	NAD83	other
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Provided	Estimated	Estimated	Estimated	Provided

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Agroecosystems	Inland Wetlands \| Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified) ? Comment: Is there a way to choose more than one?	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not applicable	Not applicable	Coastal zones	Coral reefs	Loess plain	Grassland buffering inland wetlands set in agricultural land	shallow coral reefs	A mixture of developed and natural environments including cultivated and non-cultivated cropland, pastures, roads / railways, and urban areas as well as grasslands, forest, and freshwater habitats spanning the central to eastern United States.	Urban city
EM Ecological Scale em.detail.ecoScaleHelp ?	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 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-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Species	Guild or Assemblage	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
None Available	None Available	None Available	None Available	None Available	Species Sedge Wren (Cistothorus platensis)	other (fish class)	Genus Sturnella magna	None Available

EnviroAtlas URL

EM-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
None Available	Dasymetric Allocation of Population, GAP Ecological Systems, Ecosystem Markets: Imperiled Species and Habitats	Dasymetric Allocation of Population, National Hydrography Dataset Plus (NHD PlusV2), Percent potentially restorable wetlands on agricultural land	National Hydrography Dataset Plus (NHD PlusV2)	Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass	GAP Ecological Systems	None Available	GAP Ecological Systems	GAP Ecological Systems

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-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[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 Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic Entertainment	[Ecosystem] Regulation & Maintenance Mediation of flows Gaseous / air flows Storm protection	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[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 Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops Energy Biomass-based energy sources Plant-based resources Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems [Ecosystem] Cultural Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings] Spiritual and/or emblematic Sacred and/or religious 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

<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-87	EM-184	EM-320	EM-449	EM-480	EM-650	EM-698	EM-963	EM-979
None	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Ice and Snow (26) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Tundra (25) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Lakes and Ponds (112) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Wetlands (12) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Birds Stock Indicator Rivers and Streams (111) Fauna (4) Wildlife Stock Indicator Forests (21) Fauna (4) Wildlife Stock Indicator Grasslands (23) Fauna (4) Wildlife Stock Indicator Scrubland/Shrubland (24) Fauna (4) Birds Stock Indicator Lakes and Ponds (112) Fauna (4) Birds Stock Indicator Wetlands (12) Fauna (4) Birds Stock Indicator Urban/Surburban (27) Fauna (4) Birds Stock Indicator	Scrubland/Shrubland (24) Composite (8) Regulation of extreme events Site Indicator Urban/Surburban (27) Composite (8) Presence of environmental class/subclass Site Indicator

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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)

Comment:

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)