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-68
EM-86
EM-98
EM-127
EM-154
EM-185
EM-369
EM-438
EM-838
EM-985

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
EM Short Name em.detail.shortNameHelp ?	Fodder crude protein content, Central French Alps	Area and hotspots of soil retention, South Africa	PATCH, western USA	Annual profit - carbon plantings, South Australia	Mangrove development, Tampa Bay, FL, USA	Blue crabs and SAV, Chesapeake Bay, USA	Envision, Puget Sound, WA, USA	InVESTv3.0 Nutrient retention, Guánica Bay	Eastern meadowlark abundance, Piedmont region, USA	Atlantis ecosystem assessment submodel
EM Full Name em.detail.fullNameHelp ?	Fodder crude protein content, Central French Alps	Area and hotspots of soil retention, South Africa	PATCH (Program to Assist in Tracking Critical Habitat), western USA	Annual profit from carbon plantings, South Australia	Mangrove wetland development, Tampa Bay, FL, USA	Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA	Envision, Puget Sound, WA, USA	InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA	Eastern meadowlark abundance, Piedmont ecoregion, USA	Lessons in modelling and management of marine ecosystems: the Atlantis experience
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	None	US EPA	None	US EPA	None	Envision	US EPA \| InVEST	None	None
EM Source Document ID	260	271	2	243	97	292 ? Comment: Conference paper	313 ? Comment: Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION.	338	405	463
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H.	Crossman, N. D., Bryan, B. A., and Summers, D. M.	Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L.	Mykoniatis, N. and Ready, R.	Bolte, J. and Vache, K.	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Riffel, S., Scognamillo, D., and L. W. Burger	Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C.
Document Year em.detail.documentYearHelp ?	2011	2008	2006	2011	2012	2013	2010	2017	2008	2011
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Mapping ecosystem services for planning and management	Defining recovery goals and strategies for endangered species: The wolf as a case study	Carbon payments and low-cost conservation	Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence	Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay	Envisioning Puget Sound Alternative Futures: PSNERP Final Report	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Lessons in modelling and management of marine ecosystems: the Atlantis experience
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	Not formally documented	Documentation is 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	Conference proceedings	Published report	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	http://envision.bioe.orst.edu	http://www.naturalcapitalproject.org/invest/	Not applicable	https://noaa-fisheries-integrated-toolbox.github.io/Atlantis
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Benis Egoh	Carlos Carroll	Neville D. Crossman	Michael Osland	Nikolaos Mykoniatis	John Bolte ? Comment: Phone# 541-737-2041	Susan H. Yee	Sam Riffell	Elizabeth Fulton
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Klamath Center for Conservation Research, Orleans, CA 95556	CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia	U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561	Department of Agricultural Economics, Sociology and Education The Pennsylvania State University	Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia
Contact Email	sandra.lavorel@ujf-grenoble.fr	Not reported	carlos@cklamathconservation.org	neville.crossman@csiro.au	mosland@usgs.gov	Not reported	boltej@engr.orst.edu	yee.susan@epa.gov	sriffell@cfr.msstate.edu	beth.fulton@csiro.au

EM Description

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use."	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 retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover."	Note: A more recent version of this model exists. See Related EMs below for links to related models/applications. AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)."	ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially."	ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses."	ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one."	SUMMARY: "...the Puget Sound Nearshore Ecosystem Restoration Project, completed an analysis of alternative future regional trajectories of landscape change for the Puget Sound region. This effort developed three scenarios of change: 1) Status Quo, reflecting a continuation of current trends in the region, 2) Managed Growth, reflecting the adoption of an aggressive set of land use management policies focusing on protecting and restoring ecosystem function and concentrating growth within Urban Growth Areas (UGA) and near regional growth centers, and 3) Unmanaged Growth, reflecting a relaxation of land use restrictions with limited protection of ecosystem functions. Analyses assumed a fixed population growth rate across all three scenarios, defined by the Washington Office of Financial Management county level growth estimates. Scenarios were generated using a spatially- and temporally-explicit alternative futures analysis model, Envision, previously developed by Oregon State University researchers. The model accepts as input a vector-based representation of the landscape and associated datasets describing relevant landscape characteristics, descriptors of various processes influencing landscape change, and a set of policies, or decision alternatives, which reflect scenario-specific land management alternatives. The model generates 1) a set of spatial coverages (maps) reflecting scenario outcomes of a variety of landscape variables, most notably land use/land cover, shoreline modifications, and population projections, and 2) a set of summary statistics describing landscape change variables summarized across spatial reporting units. Analyses were run on each of such sub-basins in the Puget Sound, and aggregated to providing Sound-wide results. This information is being used by PSNERP to project future impairment of ecosystem functions, goods, and services. The Puget Sound Nearshore Ecosystem project data also provide inputs to calculate aspects of future nearshore process degradation. Impairment and degradation are primary factors being used to define future conditions for the PSNERP General Investigation Study." AUTHOR'S DESCRIPTION: "In this report, we document the application of an alternative futures analysis framework that incorporates these capabilities to the analysis of alternative future trajectories in the Puget Sound region. This framework, Envision (Bolte et al, 2007; Hulse et al. 2008) is a spatially and temporally explicit, standards-based, open source toolset specifically designed to facilitate alternative futures analyses. It employs a multiagent-based modeling approach that contains a robust capability for defining alternative management strategies and scenarios, incorporating a variety of landscape change processes, and creating maps of alternative landscape trajectories, expressed though a variety of metrics defined in an application-specific way." ABOUT ENVISION (ENVISION WEBSITE): "Central to Envision, and conceived at the s	Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model"	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. "	Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation	None identified	Not applicable	Not applicable	None identified	Improving water quality	None reported	None identified
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes	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.	Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest	Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm.	mangrove forest,Salt marsh, estuary, sea level,	Submerged Aquatic Vegetation (SAV), eelgrass	No additional description provided	No additional description provided	Conservation Reserve Program lands left to go fallow	N/A
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Population growth, road development (density) on public vs private land	Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e	Not applicable	Essential or Facultative habitat	Alternative future land management strategies (status quo, managed growth, unmanaged growth)	No scenarios presented	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs ? Comment: Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting)	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	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	Application of existing model

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

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	Doc-271 ? Comment: Document 273 used for source information on soil erosion potential variable	Doc-328 \| Doc-337	Doc-245 \| Doc-246 \| Doc-247 \| Doc-243	None	Doc-227	Doc-314 \| Doc-47 ? Comment: Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION.	Doc-309 \| Doc-205	Doc-405	Doc-456 \| Doc-459 \| Doc-461
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-85 \| EM-87 \| EM-88	EM-403 \| EM-422	EM-128 \| EM-141	None	EM-106	EM-12 \| EM-333	EM-363 \| EM-112	EM-831 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-846 \| EM-847	EM-978 \| EM-981 \| EM-983 \| EM-990 \| EM-991

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	Not reported	2000-2025	2009-2050	1990-2010	1993-2011	2000-2060	1980 - 2013	2008	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent	time-dependent	time-dependent	time-dependent	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	future time	future time	future time	past time	future time	other or unclear (comment)	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	discrete	continuous	discrete	discrete	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	1	Not applicable	1	1	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Year	Not applicable	Year	Year	Year	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Physiographic or Ecological	Physiographic or Ecological	Physiographic or ecological	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Physiographic or ecological	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	South Africa	Western United States	Agricultural districts of the state of South Australia	Tampa Bay	Chesapeake Bay	Puget Sound watershed	Guanica Bay Study Area	Piedmont Ecoregion	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	>1,000,000 km^2	>1,000,000 km^2	100,000-1,000,000 km^2	100-1000 km^2	10,000-100,000 km^2	10,000-100,000 km^2	1000-10,000 km^2.	100,000-1,000,000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
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 lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	Not applicable
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	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	Not applicable	Irregular	area, for pixel or radial feature	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	Distributed across catchments with average size of 65,000 ha	504 km^2	1 ha x 1 ha	m^2	Not applicable	Varies	30 m x 30 m	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic	Numeric	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	stochastic	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	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Unclear	No	No	Yes	Unclear	No	Yes	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	No	No	Yes	Not applicable	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 62.4 \|	None	None	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	No	No	Yes	Not applicable	No	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	Yes	Yes	Not applicable	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	Yes ? Comment: No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789.	No	Yes	Yes	Not applicable	No	Yes	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Unclear	Not applicable	No	Yes	Not applicable	Not applicable	Unclear	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
Europe France	Africa South Africa	North America United States Oregon Montana Colorado California Arizona Idaho Nevada	Oceania Australia	North America United States Florida	North America United States Maryland Virgina	North America United States Washington	North America U.S. Caribbean Territories	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	None

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

EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian ? Comment: Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
Centroid Latitude em.detail.ddLatHelp ?	45.05	-30	39.88	-34.9	27.8	36.99	47.58	17.97	36.23	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	25	-113.81	138.7	-82.4	-75.95	-122.32	-66.93	-81.9	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Near Coastal Marine and Estuarine	None	Aquatic Environment (sub-classes not fully specified) \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Inland Wetlands \| Near Coastal Marine and Estuarine \| Open Ocean and Seas \| Forests \| Agroecosystems \| Created Greenspace \| Scrubland/Shrubland \| Barren	Grasslands	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 ?	Subalpine terraces, grasslands, and meadows	Not reported	Not reported	Agricultural land for annual crops, annual legumes, and grazing of sheep and cows	Created Mangrove wetlands	Yes	Pacific NW US region, coastal to montane, urban to rural	13 LULC were used	grasslands	Multiple
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	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	Yes	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Species	Guild or Assemblage	Not applicable	Yes	Not applicable	Not applicable	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
None Available	None Available	Species Canis lupus	Species Acacia implexa Eucalyptus microcarpa Acacia verniciflua Eucalyptus polyanthemos Eucalyptus globulus Eucalyptus sideroxylon Acacia mearnsii Acacia pycnantha Eucalyptus camaldulensis Eucalyptus kochii Acacia dealbata	Species Laguncularia racemosa Avicennia germinans Rhizophora mangle Spartina alterniflora	None Available	None Available	None Available	Species Eastern meadowlark	None Available

EnviroAtlas URL

EM-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
GAP Ecological Systems, Carbon storage by tree biomass (kg/m2)	None Available	Dasymetric Allocation of Population	Carbon Storage by Tree Biomass	None Available	None Available	Dasymetric Allocation of Population, GAP Ecological Systems, Stream Length, Percent GAP Status 1 & 2, Acres of Land Enrolled in the Conservation Reserve Program (CRP), The Watershed Boundary Dataset (WBD), Percent Impervious Area	Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	Big game hunting recreation demand, Percent GAP Status 1 & 2, Total Employment

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-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
[Ecosystem] Provisioning Materials Biomass Materials from plants, algae and animals for agricultural use	[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance 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 Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	None	None	[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Buffering and attenuation of mass flows 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] 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-68	EM-86	EM-98	EM-127	EM-154	EM-185	EM-369	EM-438	EM-838	EM-985
Agroecosystems (22) Flora (5) Fodder quality Quality Indicator	None	Forests (21) Fauna (4) Wolf Stock Indicator Grasslands (23) Fauna (4) Wolf Stock Indicator Scrubland/Shrubland (24) Fauna (4) Wolf Stock Indicator	None	Near Coastal Marine/Estuarine (113) Flora (5) Trees Stock Indicator Seaweed Stock Indicator Composite (8) Regulation of extreme events Quality Indicator	None	None	None	Grasslands (23) Fauna (4) Birds Stock Indicator	None

EcoService Models Library (ESML)

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

Comment:

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)