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-321
EM-367
EM-416
EM-418
EM-449
EM-699
EM-712
EM-784
EM-968
EM-981

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
EM Short Name em.detail.shortNameHelp ?	Erosion prevention by vegetation, Portel, Portugal	InVEST Coastal Blue Carbon	Sed. denitrification, St. Louis River, MN/WI, USA	SIRHI, St. Croix, USVI	Decrease in erosion (shoreline), St. Croix, USVI	Fish species richness, St. John, USVI, USA	ESII Tool method	Wildflower mix supporting bees, Florida, USA	EPA Stormwater Manamgement Model	Atlantis ecosystem biology submodel
EM Full Name em.detail.fullNameHelp ?	Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal	InVEST v3.0 Coastal Blue Carbon	Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA	SIRHI (SImplified Reef Health Index), St. Croix, USVI	Decrease in erosion (shoreline) by reef, St. Croix, USVI	Fish species richness, St. John, USVI, USA	ESII (Ecosystem Services Identification & Inventory) Tool method	Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA	Storm Water Management Model User's Manual Version 5.2	Calibrating process-based marine ecosystem models: An example case using Atlantis
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	InVEST	US EPA	US EPA	US EPA	None	None	None	US EPA	None
EM Source Document ID	281	310	333	335	335	355	391 ? Comment: Website for online user support	400	452	459
Document Author em.detail.documentAuthorHelp ?	Guerra, C.A., Pinto-Correia, T., Metzger, M.J.	Natural Capital Project	Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco	EcoMetrix Solutions Group (ESG)	Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters	Rossman, L. A., M., Simon	Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E.
Document Year em.detail.documentYearHelp ?	2014	2014	2014	2014	2014	2007	2016	2015	2022	2019
Document Title em.detail.sourceIdHelp ?	Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy	Blue Carbon model - InVEST (v3.0)	Sediment nitrification and denitrification in a Lake Superior estuary	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean	ESII Tool	Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States	Storm Water Management Model User's Manual Version 5.2	Calibrating process-based marine ecosystem models: An example case using Atlantis
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Documented, not peer reviewed	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Other or unclear (explain in Comment)	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	other	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Website	Published journal manuscript	Published EPA report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model	Not applicable	Not applicable	Not applicable	Not applicable	https://www.esiitool.com/	Not applicable	https://www.epa.gov/water-research/storm-water-management-model-swmm	https://noaa-fisheries-integrated-toolbox.github.io/Atlantis
Contact Name em.detail.contactNameHelp ?	Carlos A. Guerra	Gregg Verutes	Brent J. Bellinger	Susan H. Yee	Susan H. Yee	Simon Pittman	Not reported	Neal Williams	David Burden	Heidi R. Pethybridge
Contact Address	Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal	Stanford University	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	1305 East-West Highway, Silver Spring, MD 20910, USA	Not reported	Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616	U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198	CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia
Contact Email	cguerra@uevora.pt	gverutes@stanford.edu	bellinger.brent@epa.ogv	yee.susan@epa.gov	yee.susan@epa.gov	simon.pittman@noaa.gov	Not reported	nmwilliams@ucdavis.edu	burden.david@epa.gov	Heidi.Pethybridge@csiro.au

EM Description

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)."	Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools.	ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ? Comment: BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model.	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 indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati	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: "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."	AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance."	Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm	EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ? Comment: The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual.	Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified	None identified	None provided	None identified	None identrified	NA	N/A
Biophysical Context	Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure.	Land use land class; habitat type	Estuarine system	No additional description provided	No additional description provided	Hard and soft benthic habitat types approximately to the 33m isobath	Not applicable	field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca	NA	Marine ecosystem
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area	Land use land cover changes; habitat disturbance	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	Varied wildflower planting mixes of annuals and perennials	NA	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method Only	Method + Application	Method + Application	Method + Application	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	Method Only	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	Application of existing model

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

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-282 \| Doc-283 \| Doc-284 \| Doc-285	None	None	None	Doc-335	Doc-355	None	None	None	Doc-456
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None	EM-447 \| EM-448	EM-590 \| EM-698	EM-713	EM-796 \| EM-797 \| EM-804 \| EM-805 \| EM-806 \| EM-812 \| EM-814	EM-971	EM-978 \| EM-983 \| EM-985 \| EM-990 \| EM-991

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
EM Temporal Extent em.detail.tempExtentHelp ?	January to December 2003	Not applicable	2011 - 2012	2006-2007, 2010	2006-2007, 2010	2000-2005	Not applicable	2011-2012	Not applicable	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	past time	both	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	continuous	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	1	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Month	Year	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Year	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Not applicable	Watershed/Catchment/HUC	Physiographic or ecological	Physiographic or ecological	Physiographic or ecological	Not applicable	Point or points ? Comment: This is a guess based on information in the document. 3 field sites were separated by up to 9km	No location (no locational reference given)	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Portel municipality	Not applicable	St. Louis River estuary	Coastal zone surrounding St. Croix	Coastal zone surrounding St. Croix	SW Puerto Rico,	Not applicable	Agricultural plots	Not applicable	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	Not applicable	10-100 km^2	100-1000 km^2	100-1000 km^2	100-1000 km^2	Not applicable	10-100 km^2	Not applicable	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some 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 distributed (in at least some cases) ? Comment: map units delineated by user based on project.	spatially lumped (in all cases)	spatially distributed (in at least some cases)	Not applicable
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	volume, for 3-D feature	Not applicable	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)	Not applicable	area, for pixel or radial feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	250 m x 250 m	user-specified	Not applicable	10 m x 10 m	10 m x 10 m	not reported	map units	Not applicable	mm	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Model Calibration Reported? em.detail.calibrationHelp ?	No	Not applicable	No	Yes	Yes	No	Not applicable	No	Not applicable	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Not applicable	No	No	No	Yes	Not applicable	No	Not applicable	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	R squared \| 0.56 \| unitless Cohen's Kappa statistic \| 0.53 \| unitless AUC \| 0.79 \| unitless	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Not applicable	No	Yes	Yes	Yes	Not applicable	No	Not applicable	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Not applicable	No	No	No	No	Not applicable	No	Not applicable	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Not applicable	No	No	No	Yes	Not applicable	No	Not applicable	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	No	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-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Europe Portugal	None	North America United States Minnesota Wisconsin	None	None	None	None	North America United States Florida	None	None

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

EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Centroid Latitude em.detail.ddLatHelp ?	38.3	-9999	46.75	17.73	17.73	17.79	Not applicable	29.4	Not applicable	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-7.7	-9999	-92.08	-64.77	-64.77	-64.62	Not applicable	-82.18	Not applicable	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84	Not applicable	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Not applicable	Estimated	Estimated	Estimated	Estimated	Not applicable	Provided	Not applicable	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Scrubland/Shrubland	Inland Wetlands \| Near Coastal Marine and Estuarine	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Open Ocean and Seas
Specific Environment Type em.detail.specificEnvTypeHelp ?	Silvo-pastoral system	user specified	Freshwater estuary	Coral reefs	Coral reefs	shallow coral reefs	Not applicable	Agricultural landscape	User-defined catchments	Multiple
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser 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 is finer than that of the Environmental Sub-class	Not applicable	Ecological scale corresponds to the Environmental Sub-class	Other or unclear (comment)	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-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Not applicable	Guild or Assemblage	Not applicable	Guild or Assemblage	Not applicable	Species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
None Available	None Available	None Available	other snapper Family grouper parrotfish surgeonfish	None Available	other (fish class)	None Available	other Bees Species Lupinus succulentus Trifolium obtusiflorum Helianthus bolaneri Eschscholzia californica Lotus scoparius Rudbeckia hirta Trifolium willdenovii Phacelia tanacetifolia Phacelia californica Trifolium fucatum Helianthus bolanderi Grindelia camporum Lupinus formosus Nemophila menziesii Trichostema lanceolatum Achillea millefolium Clarkia unguinculata lupinus densiflorus	None Available	None Available

EnviroAtlas URL

EM-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Average Annual Precipitation	Carbon Storage by Tree Biomass	Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	None Available	National Hydrography Dataset Plus (NHD PlusV2)	None Available	GAP Ecological Systems, The National Hydrography Dataset (NHD), Average Annual Precipitation, Agricultural water use (million gallons/day)	None Available	None Available	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-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Buffering and attenuation of mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[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 Atmospheric composition and climate regulation Micro and regional climate regulation Global climate regulation by reduction of greenhouse gas concentrations Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance Gaseous / air flows Ventilation and transpiration Mass flows Buffering and attenuation of 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 Mediation by ecosystems Mediation of smell/noise/visual impacts Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Provisioning Materials Water Ground water for non-drinking purposes Surface water for non-drinking purposes [Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[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

<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-321	EM-367	EM-416	EM-418	EM-449	EM-699	EM-712	EM-784	EM-968	EM-981
Forests (21) Soil (2) Specific types of soil Flow Indicator Scrubland/Shrubland (24) Soil (2) Specific types of soil Flow Indicator Agroecosystems (22) Soil (2) Specific types of soil Flow Indicator	None	None	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Rivers and Streams (111) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Forests (21) Composite (8) Presence of environmental class/subclass Stock Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Stock Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Stock Indicator Lakes and Ponds (112) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Wetlands (12) Water (3) Liquid water Site Indicator Stock Indicator Flow Indicator Quality Indicator Composite (8) Presence of environmental class/subclass Stock Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Stock Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Stock Indicator Soil (2) Specific types of soil Quality Indicator	Agroecosystems (22) Fauna (4) Pollinators Site Indicator	Rivers and Streams (111) Composite (8) Regulation of extreme events Flow Indicator Groundwater Water (3) Liquid water Flow Indicator Lakes and Ponds (112) Composite (8) Regulation of extreme events Flow Indicator Urban/Surburban (27) Composite (8) Regulation of extreme events Flow Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Stock Indicator Open Oceans and Seas (114) Composite (8) Presence of environmental class/subclass Stock Indicator

EcoService Models Library (ESML)

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EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

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

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

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

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