EcoService Models Library (ESML)
loading
Compare EMs
Which comparison is best for me?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.
EM Identity and Description
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
EM Short Name
em.detail.shortNameHelp
?
|
Evoland v3.5 (bounded growth), Eugene, OR, USA | EnviroAtlas - Natural biological nitrogen fixation | ACRU, South Africa | Fish species habitat value, Tampa Bay, FL, USA | InVEST habitat quality | FORCLIM v2.9, Santiam watershed, OR, USA | Biological pest control, Uppland Province, Sweden | InVEST crop pollination, California, USA | Wetland shellfish production, Gulf of Mexico, USA | InVESTv3.0 Nutrient retention, Guánica Bay | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 v1.0.1, Switzerland | Yasso07 v1.0.1, Switzerland, site level | InVEST fisheries, lobster, South Africa | SolVES, Bridger-Teton NF, WY | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, United Kingdom | Northern Shoveler recruits, CREP wetlands, IA, USA | WESP Method | C sequestration in grassland restoration, England |
EM Full Name
em.detail.fullNameHelp
?
|
Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | ACRU (Agricultural Catchments Research Unit), South Africa | Fish species habitat value, Tampa Bay, FL, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) Habitat Quality | FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA | Biological control of agricultural pests by natural predators, Uppland Province, Sweden | InVEST crop pollination, California, USA | Wetland shellfish production, Gulf of Mexico, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, East Midlands, United Kingdom | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Carbon sequestration in grassland diversity restoration, England |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
Envision | US EPA | EnviroAtlas | None | US EPA |
InVEST ?Comment:From the Natural Capital Project website |
US EPA | None | InVEST |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
US EPA | InVEST | US EPA | None | None | InVEST | None | US EPA | None | None | None | None |
EM Source Document ID
|
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
271 | 187 | 278 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
299 | 279 | 324 | 338 | 335 | 343 | 343 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
369 | 387 | 389 |
372 ?Comment:Document 373 is a secondary source for this EM. |
390 | 396 |
Document Author
em.detail.documentAuthorHelp
?
|
Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | US EPA Office of Research and Development - National Exposure Research Laboratory | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Natural Capital Project | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Adamus, P. R. | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett |
Document Year
em.detail.documentYearHelp
?
|
2008 | 2013 | 2008 | 2016 | 2014 | 2007 | 2014 | 2009 | 2012 | 2017 | 2014 | 2014 | 2014 | 2018 | 2014 | 2019 | 2013 | 2010 | 2016 | 2011 |
Document Title
em.detail.sourceIdHelp
?
|
Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | EnviroAtlas - National | Mapping ecosystem services for planning and management | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Habitat Quality model - InVEST ver. 3.0 | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Ecological production functions for biological control services in agricultural landscapes | Modelling pollination services across agricultural landscapes | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | Validating tree litter decomposition in the Yasso07 carbon model | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Additional carbon sequestration benefits of grassland diversity restoration |
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | 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 on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published on Natural Capital Project website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published report | Published report | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | Not applicable | http://www.naturalcapitalproject.org/invest/ | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | |
Contact Name
em.detail.contactNameHelp
?
|
Michael R. Guzy |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Roland E Schulze | Richard Fulford | The Natural Capital Project | Richard T. Busing | Mattias Jonsson | Eric Lonsdorf | Stephen J. Jordan | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
Markus Didion | Michelle Ward | Benson Sherrouse | Mulvaney, Kate | Sam Tarrant | David Otis | Paul R. Adamus | Gerlinde B. De Deyn |
Contact Address
|
Oregon State University, Dept. of Biological and Ecological Engineering | Not reported | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands |
Contact Email
|
Not reported | enviroatlas@epa.gov | schulzeR@nu.ac.za | Fulford.Richard@epa.gov | invest@naturalcapitalproject.org | rtbusing@aol.com | mattias.jonsson@slu.se | ericlonsdorf@lpzoo.org | jordan.steve@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | markus.didion@wsl.ch | m.ward@uq.edu.au | bcsherrouse@usgs.gov | Mulvaney.Kate@epa.gov | sam.tarrant@rspb.org.uk | dotis@iastate.edu | adamus7@comcast.net | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
Summary Description
em.detail.summaryDescriptionHelp
?
|
**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "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…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | 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. AUTHORS DESCRIPTION: "The InVEST habitat quality model combines information on LULC and threats to biodiversity to produce habitat quality maps. This approach generates two key sets of information that are useful in making an initial assessment of conservation needs: the relative extent and degradation of different types of habitat types in a region and changes across time. This approach further allows rapid assessment of the status of and change in a proxy for more detailed measures of biodiversity status. If habitat changes are taken as representative of genetic, species, or ecosystem changes, the user is assuming that areas with high quality habitat will better support all levels of biodiversity and that decreases in habitat extent and quality over time means a decline in biodiversity persistence, resilience, breadth and depth in the area of decline. The habitat rarity model indicates the extent and pattern of natural land cover types on the current or a potential future landscape vis-a-vis the extent of the same natural land cover types in some baseline period. Rarity maps allow users to create a map of the rarest habitats on the landscape relative to the baseline chosen by the user to represent the mix of habitats on the landscape that is most appropriate for the study area’s native biodiversity. The model requires basic data that are available virtually everywhere in the world, making it useful in areas for which species distribution data are poor or lacking altogether. Extensive occurrence (presence/absence) data may be available in many places for current conditions. However, modeling the change in occurrence, persistence, or vulnerability of multiple species under future conditions is often impossible or infeasible. While a habitat approach leaves out the detailed species occurrence data available for current conditions, several of its components represent advances in functionality over many existing biodiversity conservation planning tools. The most significant is the ability to characterize the sensitivity of habitats types to various threats. Not all habitats are affected by all threats in the same way, and the InVEST model accounts for this variability. Further, the model allows users to estimate the relative impact of one threat over another so that threats that are more damaging to biodiversity persistence on the landscape can be represented as such. For example, grassland could be particularly sensitive to threats generated by urban areas yet moderately sensitive to threats generated by roads. In addition, the distance over which a threat will degrade natural systems can be incorporated into the model. Model assessment of the current landscape can be used as an input to a coarse-filter assessment of current conservation needs and opportunities. Model assessment of pote | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045. | ABSTRACT: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization." | 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. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery. " | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | 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: "...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: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to... (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests; and (iii) evaluate the suitability of Yasso07 for regional and national scale applications in Swiss forests." AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "The decomposition of below- and aboveground litter was studied over 10 years on five forest sites in Switzerland…" "At the time of this study, three parameter sets have been developed and published:... (3): Rantakari et al., 2012 (henceforth P12)… For the development of P12, Rantakari et al. (2012) obtained a subset of the previously used data which was restricted to European sites." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root lit-ter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The r | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This model focused on the various use by access point type (beaches, landings and way to water, boat use). This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None Identified | None identified | None identifed | None identified | None identified | None identified | None identified | None identified | Improving water quality | None identified | None identified | None identified | Future rock lobster fisheries management | None | None identified | None identified | None identified | None identified | None identified |
Biophysical Context
|
No additional description provided | No additional description provided | 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. | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | Not applicable | No additional description provided | Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here. | No additional description provided | Estuarine environments and marsh-land interfaces | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | Rocky mountain conifer forests | None identified | No additional description provided | Prairie Pothole Region of Iowa | None | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | No scenarios presented |
Potential land Use Land Class (LULC) future and baseline ?Comment:model requires current landuse but can compare to baseline (prior to intensive management of the land) and potential future landuse. These are the two scenarios suggested in the documentation. |
Land Management (3); Climate Change (3) | No scenarios presented | No scenarios presented | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | N/A | N/A | No scenarios presented | No scenarios presented | N/A | Additional benefits due to biodiversity restoration practices |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs differentiated by scenario combination. |
Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application (multiple runs exist) View EM Runs ?Comment:Model runs are for different sites (Beatenberg, Vordemwald, Bettlachstock, Schanis, and Novaggio) differentiated by climate and forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). |
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing 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 | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
None | Doc-309 |
Doc-22 | Doc-23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
None | Doc-279 | None | Doc-309 | Doc-205 | Doc-335 | Doc-342 | Doc-344 | Doc-342 | Doc-343 | None | None | None | None | Doc-372 | Doc-373 | None | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-333 | EM-369 | None | None | None | EM-345 | EM-146 | EM-186 | EM-224 | None | EM-340 | EM-339 | EM-604 | EM-603 | EM-363 | EM-112 | EM-447 | EM-448 | EM-466 | EM-469 | EM-480 | EM-485 | EM-466 | EM-467 | EM-469 | EM-480 | None | EM-629 | EM-626 | EM-682 | EM-684 | EM-685 | EM-709 | EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632 | EM-718 | None |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1990-2050 | 2006-2010 | 1950-1993 | 2006-2011 | Not applicable | 1990-2050 | 2009 | 2001-2002 | 1950 - 2050 | 1980 - 2013 | 2006-2007, 2010 | 1993-2013 | 2000-2010 | 1986-2115 | 2004-2008 | Summer 2017 | 2007-2008 | 1987-2007 | Not applicable | 1990-2007 |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
future time | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | other or unclear (comment) | Not applicable | future time | future time | future time | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
2 | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Varies by Run | 1 | Not applicable | 1 | 1 | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Year | Not applicable | Day | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Year | Not applicable | Year | Year | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Geopolitical | Geopolitical | Physiographic or Ecological | No location (no locational reference given) | Watershed/Catchment/HUC | Geopolitical | Other | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Other |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | counterminous United States | South Africa | Tampa Bay | Not applicable | South Santiam watershed | Uppland province | Agricultural landscape, Yolo County, Central Valley | Gulf of Mexico (estuarine and coastal) | Guanica Bay Study Area | Coastal zone surrounding St. Croix | Switzerland | Switzerland | Table Mountain National Park Marine Protected Area | National Park | Three Bays, Cape Cod | East Midlands | CREP (Conservation Reserve Enhancement Program | Not applicable | Colt Park meadows, Ingleborough National Nature Reserve, northern England |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | Not applicable | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 10,000-100,000 km^2 | Not applicable | <1 ha |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
varies | irregular | Distributed by catchments with average size of 65,000 ha | 1 km^2 | LULC pixel size | 0.08 ha | 25 m x 25 m | 30 m x 30 m | 55.2 km^2 | 30 m x 30 m | 10 m x 10 m | 5 sites | Not applicable | Not applicable | 30m2 | beach length | multiple unrelated locations | multiple, individual, irregular sites | not reported | 3 m x 3 m |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Numeric | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
Comment:Agent based modeling results in response indices. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Unclear | No | No | No | Not applicable | No | No | Unclear | Yes | No | Yes | No | No | No | No | Yes | Not applicable | Unclear | Not applicable | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | Yes | No | Not applicable | No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None | None | None | None | None | None | None | None | None | None | None | None | None |
|
None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | No | No | Not applicable | No | Yes |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
No | No | Yes | Yes | Yes |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
No | No | Not applicable | No | No | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | No | Not applicable | No | No | No | No | No | No | No | Yes | No | No | No | Not applicable | No | Not applicable | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | No | Not applicable | No |
Yes ?Comment:AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model." |
No | No | No | No | No | No | No | No | No | Not applicable | No | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | N/A | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
|
|
|
|
None |
|
|
|
|
|
None |
|
|
None |
|
None |
|
|
None |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
None | None | None |
|
None | None | None | None |
|
None |
|
None | None |
|
None |
|
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
Centroid Latitude
em.detail.ddLatHelp
?
|
44.11 | 39.5 | -30 | 27.74 | -9999 | 44.24 | 59.52 | 38.7 | 30.44 | 17.97 | 17.73 | 46.82 | 46.82 | -34.18 | 43.93 | 41.62 | 52.22 | 42.62 | Not applicable | 54.2 |
Centroid Longitude
em.detail.ddLongHelp
?
|
-123.09 | -98.35 | 25 | -82.57 | -9999 | -122.24 | 17.9 | -121.8 | -87.99 | -66.93 | -64.77 | 8.23 | 8.23 | 18.35 | 110.24 | -70.42 | -0.91 | -93.84 | Not applicable | -2.35 |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | 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 | Near Coastal Marine and Estuarine | Forests | Forests | Near Coastal Marine and Estuarine | Forests | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Agricultural-urban interface at river junction | Terrestrial | Not reported | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Not applicable | primarily Conifer Forest | Spring-sown cereal croplands and surrounding grassland and non-arable land | Cropland and surrounding landscape | Submerged aquatic vegetation in estuaries and coastal lagoons | 13 LULC were used | Coral reefs | forests | forests | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Montain forest | Beaches | restored landfills and grasslands | Wetlands buffered by grassland within agroecosystems | Wetlands | fertilized grassland (historically hayed) |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Zone within an ecosystem | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Not applicable | Species | Not applicable | Species | Individual or population, within a species | Species | Species | Not applicable | Not applicable | Community | Community | Individual or population, within a species | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
|
None Available | None Available |
|
None Available |
|
|
|
|
None Available | None Available | None Available | None Available |
|
None Available | None Available |
|
|
None Available | None Available |
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)
EM-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<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-12 ![]() |
EM-63 | EM-84 |
EM-102 ![]() |
EM-143 |
EM-208 ![]() |
EM-303 |
EM-338 ![]() |
EM-397 ![]() |
EM-438 | EM-449 |
EM-467 ![]() |
EM-485 ![]() |
EM-541 ![]() |
EM-628 |
EM-686 ![]() |
EM-697 ![]() |
EM-702 | EM-706 |
EM-735 ![]() |
|
|
|
|
None | None |
|
|
|
None |
|
None | None |
|
|
|
None |
|
|
None |