EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.

This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:

Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
Intermediate (Computed) Variables
Response Variables
- Computed Response Variables
- Measured Response Variables

EM Variables by Category

A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:

Policy Regarding Use or Management of Ecosystem Resources
Land Surface (or Water Body Bed) Cover, Use or Substrate
Human Demographic Data
Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
Monetary Values

Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.

All EM Descriptors

This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:

EM Identity and Description
EM Modeling Approach
EM Locations, Environments, Ecology
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

EM Descriptors by Modeling Concepts

This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:

Conceptual Model
Modeling Objective
Modeling Context
Potential for Model Linkage
Feasibility of Model Use
Model Certainty
Model Structural Information

Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.

Compare Criteria:

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Comparing:

EM-12
EM-63
EM-87
EM-177
EM-449
EM-496
EM-650
EM-698
EM-838
EM-849
EM-948
EM-1020

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

EM Short Name

Evoland v3.5 (bounded growth), Eugene, OR, USA

EnviroAtlas - Natural biological nitrogen fixation

Area & hotspots of soil accumulation, South Africa

Salmon habitat values, west coast of Canada

Decrease in erosion (shoreline), St. Croix, USVI

Sed. denitrification, St. Louis R., MN/WI, USA

Sedge Wren density, CREP, Iowa, USA

Fish species richness, St. Croix, USVI

Eastern meadowlark abundance, Piedmont region, USA

InVEST Coastal Vulnerability

Global forest stock, biomass and carbon downscaled

EPIC agriculture model, Baden-Wurttemberg, Germany

EM Full Name

Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA

US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA

Area and hotspots of soil accumulation, South Africa

Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada

Decrease in erosion (shoreline) by reef, St. Croix, USVI

Sediment denitrification, St. Louis River, MN/WI, USA

Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA

Fish Species Richness, Buck Island, St. Croix , USVI

Eastern meadowlark abundance, Piedmont ecoregion, USA

InVEST Coastal Vulnerability

Global forest growing stock, biomass and carbon downscaled map

Carbon sequestration in soils of SW-Germany as affected by agricultural management—Calibration of the EPIC model for regional simulations

EM Source or Collection

Envision

US EPA | EnviroAtlas

None

US EPA

None

InVEST

None

EM Source Document ID

47 ?

262 ?

271

286

335

333

372

355

405

408

442

482

Document Author

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.

Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M

Yee, S. H., Dittmar, J. A., and L. M. Oliver

Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill

Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever

Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco

Riffel, S., Scognamillo, D., and L. W. Burger

The Natural Capital Project.org

Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner

Billen, N., Röder, C., Gaiser, T. and Stahr, K.,

Document Year

2008

2013

2008

2003

2014

2010

2007

2008

None

2008

2009

Document Title

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

Valuing freshwater salmon habitat on the west coast of Canada

Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI

Sediment nitrification and denitrification in a Lake Superior estuary

Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt

Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean

Effects of the Conservation Reserve Program on northern bobwhite and grassland birds

InVEST Coastal Vulnerability

A global forest growing stock, biomass and carbon map based on FAO statistics

Carbon sequestration in soils of SW-Germany as affected by agricultural management—calibration of the EPIC model for regional simulations

Document Status

Peer reviewed and published

Comments on Status

Published journal manuscript

Published on US EPA EnviroAtlas website

Published journal manuscript

Published report

Published journal manuscript

Website users guide

Published journal manuscript

Software and Access

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

EM Software Access info Exit

http://evoland.bioe.orst.edu/ ?

https://www.epa.gov/enviroatlas

Not applicable

https://naturalcapitalproject.stanford.edu/software/invest

Not applicable

https://epicapex.tamu.edu/epic/

Contact Name

Michael R. Guzy

EnviroAtlas Team ?

Benis Egoh

Duncan Knowler

Susan H. Yee

Brent J. Bellinger ?

David Otis

Simon Pittman

Sam Riffell

Not applicable

Georg Kindermann

Norbert Billen

Contact Address

Oregon State University, Dept. of Biological and Ecological Engineering

Not reported

Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy

School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6

US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA

U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University

1305 East-West Highway, Silver Spring, MD 20910, USA

Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA

Not applicable

International Institute for Applied Systems Analysis, Laxenburg, Austria

University of Hohenheim, Institute of Soil Science and Land Evaluation, Emil Wolff Strasse 27, D-70593 Stuttgart, Germany

Contact Email

Not reported

enviroatlas@epa.gov

Not reported

djk@sfu.ca

yee.susan@epa.gov

bellinger.brent@epa.gov

dotis@iastate.edu

simon.pittman@noaa.gov

sriffell@cfr.msstate.edu

Not applicable

kinder(at)iiasa.ac.at

billen@uni-hohenheim.de

EM Description

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

Summary Description

**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: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover."

ABSTRACT: "In this paper, we present a framework for valuing benefits for fisheries from protecting areas from degradation, using the example of the Strait of Georgia coho salmon fishery in southern British Columbia, Canada. Our study improves upon previous methods used to value fish habitat in two major respects. First, we use a bioeconomic model of the coho fishery to derive estimates of value that are consistent with economic theory. Second, we estimate the value of changing the quality of fish habitat by using empirical analyses to link fish population dynamics with indices of land use in surrounding watersheds."

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: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature. We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones…Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates." AUTHOR'S DESCRIPTION: "We used different survey designs in 2011 and 2012. Both designs were based on area-weighted probability sampling methods, similar to those developed for EPA's Environmental Monitoring and Assessment Program (EMAP) (Crane et al., 2005; Stevens and Olsen, 2003, 2004). Sampling sites were assigned to spatial zones: “harbor” (river km 0–13), “bay” (river km 13–24), or “river” (river km 24–35) (Fig. 1). Sites were also grouped by depth zones (“shallow,” <1 m; “intermediate,” 1–2 m; and “deep,” >2 m). In 2011 (“vegetated-habitat survey”), the sample frame consisted of areas of emergent and submergent vegetation in the SLRE… The resulting sample frame included 2370 ha of potentially vegetated area out of a total SLRE area of 4378 ha. Sixty sites were distributed across the total vegetated area in each spatial zone using an uneven spatially balanced probabilistic design. Vegetated areas were more prevalent, and thus had greater sampling effort, in the bay (n = 33) and river (n = 17) than harbor (n=10) zones, and in the shallow (n=44) and intermediate (n =14) than deep (n =2) zones. All sampling was done in July. In 2012 a probabilistic sampling design (“estuary-wide survey”) was implemented to determine N-cycling rates for the entire SLRE (not just vegetated areas as in 2011). Thirty sites unevenly distributed across spatial and depth zones were sampled monthly in May–September (Fig. 1). Area weighting for each sampled site reflects the SLRE area attributable to each sample by month, spatial zone, and depth zone." "…we were able to create significant predictive models for NIT and DeNIT rates using linear combinations of physiochemical parameters…" "…Simulations of changes in DeNIT rates in response to altered water depth and surface NOx-N concentration for spring (Fig. 4A) and summer (Fig. 4B) show that for a given season, altering water depths would have a greater influence on DeNIT than rising NO3- concentration."

ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400)

ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems."

ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, 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. "

Faced with an intensification of human activities and a changing climate, coastal communities need to better understand how modifications of the biological and physical environment (i.e. direct and indirect removal of natural habitats for coastal development) can affect their exposure to storm-induced erosion and flooding (inundation). The InVEST Coastal Vulnerability model produces a qualitative estimate of such exposure in terms of a vulnerability index, which differentiates areas with relatively high or low exposure to erosion and inundation during storms. By coupling these results with global population information, the model can show areas along a given coastline where humans are most vulnerable to storm waves and surge. The model does not take into account coastal processes that are unique to a region, nor does it predict long- or short-term changes in shoreline position or configuration. Model inputs, which serve as proxies for various complex shoreline processes that influence exposure to erosion and inundation, include: a polyline with attributes about local coastal geomorphology along the shoreline, polygons representing the location of natural habitats (e.g., seagrass, kelp, wetlands, etc.), rates of (observed) net sea-level change, a depth contour that can be used as an indicator for surge level (the default contour is the edge of the continental shelf), a digital elevation model (DEM) representing the topography of the coastal area, a point shapefile containing values of observed storm wind speed and wave power, and a raster representing population distribution. Outputs can be used to better understand the relative contributions of these different model variables to coastal exposure and highlight the protective services offered by natural habitats to coastal populations. This information can help coastal managers, planners, landowners and other stakeholders identify regions of greater risk to coastal hazards, which can in turn better inform development strategies and permitting. The results provide a qualitative representation of coastal hazard risks rather than quantifying shoreline retreat or inundation limits.

ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. "

Global emissions trading allows for agricultural measures to be accounted for the carbon sequestration in soils. The Environmental Policy Integrated Climate (EPIC) model was tested for central European site conditions by means of agricultural extensification scenarios. Results of soil and management analyses of different management systems (cultivation with mouldboard plough, reduced tillage, and grassland/fallow establishment) on 13 representative sites in the German State Baden-Württemberg were used to calibrate the EPIC model. Calibration results were compared to those of the Intergovernmental Panel on Climate Change (IPCC) prognosis tool. The first calibration step included adjustments in (a) N depositions, (b) N2-fixation by bacteria during fallow, and (c) nutrient content of organic fertilisers according to regional values. The mixing efficiency of implements used for reduced tillage and four crop parameters were adapted to site conditions as a second step of the iterative calibration process, which should optimise the agreement between measured and simulated humus changes. Thus, general rules were obtained for the calibration of EPIC for different criteria and regions. EPIC simulated an average increase of +0.341 Mg humus-C ha−1 a−1 for on average 11.3 years of reduced tillage compared to land cultivated with mouldboard plough during the same time scale. Field measurements revealed an average increase of +0.343 Mg C ha−1 a−1 and the IPCC prognosis tool +0.345 Mg C ha−1 a−1. EPIC simulated an average increase of +1.253 Mg C ha−1 a−1 for on average 10.6 years of grassland/fallow establishment compared to an average increase of +1.342 Mg humus-C ha−1 a−1 measured by field measurements and +1.254 Mg C ha−1 a−1 according to the IPCC prognosis tool. The comparison of simulated and measured humus C stocks was r2 ≥ 0.825 for all treatments. However, on some sites deviations between simulated and measured results were considerable. The result for the simulation of yields was similar. In 49% of the cases the simulated yields differed from the surveyed ones by more than 20%. Some explanations could be found by qualitative cause analyses. Yet, for quantitative analyses the available information from farmers was not sufficient. Altogether EPIC is able to represent the expected changes by reduced tillage or grassland/fallow establishment acceptably under central European site conditions of south-western Germany.

Specific Policy or Decision Context Cited

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 provided

None reported

None identified

Impact of different agricultural management strategies

Biophysical Context

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.

No additional description provided

Prairie pothole region of north-central Iowa

Hard and soft benthic habitat types approximately to the 33m isobath

Conservation Reserve Program lands left to go fallow

Not applicable

No additional description provided

Central Europe agricultural sites

EM Scenario Drivers

Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?

No scenarios presented

Habitat quality

No scenarios presented

N/A

Options for future sea level change and population change

No scenarios presented

EM Relationship to Other EMs or Applications

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

Method Only, Application of Method or Model Run

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method Only

Method + Application (multiple runs exist) View EM Runs

Method + Application

New or Pre-existing EM?

New or revised model

Application of existing model

New or revised model

Application of existing model ?

Application of existing model

New or revised model

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

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

Document ID for related EM

Doc-47 | Doc-313 | Doc-314 ?

Doc-346 | Doc-347 ?

Doc-271

None

Doc-335

None

Doc-372

Doc-355

Doc-405

Doc-410

None

Doc-478

EM ID for related EM

EM-333 | EM-369

None

EM-85 | EM-86 | EM-88

EM-179 | EM-183 | EM-180 | EM-181

EM-447 | EM-448

None

EM-652 | EM-651 | EM-649 | EM-648

EM-590 | EM-699

EM-831 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847

EM-851

None

EM-1012 | EM-1021

EM Modeling Approach

EM Relationship to Time

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

EM Temporal Extent

1990-2050

2006-2010

Not reported

1989-1999

2006-2007, 2010

July 2011 to September 2012 ?

1992-2007

2000-2005

2008

Not applicable

1999-2005

4-20 years ?

EM Time Dependence

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

future time

Not applicable

past time

EM Time Continuity

discrete

Not applicable

other or unclear (comment) ?

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Year

Not applicable

EM Spatial Extent

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

Bounding Type

Geopolitical

Physiographic or ecological

Multiple unrelated locations (e.g., meta-analysis)

Physiographic or ecological

Not applicable

No location (no locational reference given)

Multiple unrelated locations (e.g., meta-analysis)

Spatial Extent Name

Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA

counterminous United States

South Africa

South Thompson watershed

Coastal zone surrounding St. Croix

St. Louis River Estuary (of western Lake Superior)

CREP (Conservation Reserve Enhancement Program) wetland sites

SW Puerto Rico,

Piedmont Ecoregion

Not applicable

Global

Baden-Wurttemberg

Spatial Extent Area (Magnitude)

10-100 km^2

>1,000,000 km^2

1000-10,000 km^2.

100-1000 km^2

10-100 km^2

1-10 km^2

100-1000 km^2

100,000-1,000,000 km^2

Not applicable

>1,000,000 km^2

10,000-100,000 km^2

Spatial Distribution of Computations

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

EM Spatial Distribution

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 lumped (in all cases)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

Not applicable

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

area, for pixel or radial feature

Not applicable

area, for pixel or radial feature

Not applicable

Spatial Grain Size

varies

irregular

Distributed across catchments with average size of 65,000 ha

Not applicable

10 m x 10 m

35 km river estuary reach, 0 to 5 m depth by 1 m increment

multiple, individual, irregular shaped sites

not reported

Not applicable

user defined

0.5 x 0.5 degrees

Not applicable

EM Structure and Computation Approach

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

EM Computational Approach

Numeric

Analytic

EM Determinism

stochastic

deterministic

Statistical Estimation of EM

Other or unclear (comment)

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

Model Calibration Reported?

Unclear

Yes

Unclear

Yes

Not applicable

Yes

Model Goodness of Fit Reported?

Yes

Not applicable

Yes ?

Yes

Goodness of Fit (metric| value | unit)

None

r2 | 0.51 | unitless

None

R squared | 0.48 | unitless
Cohen's Kappa statistic | 0.46 | unitless
AUC | 0.76 | unitless

None

r2 | Varies by run; view runs to see values | unitless

r-squared | 0.6416 | NA

Model Operational Validation Reported?

Yes

Unclear

Yes

Not applicable

Yes

Model Uncertainty Analysis Reported?

Not applicable

Unclear

Model Sensitivity Analysis Reported?

No ?

Yes

Not applicable

Unclear

Model Sensitivity Analysis Include Interactions?

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-12	EM-63	EM-87	EM-177	EM-449	EM-496	EM-650	EM-698	EM-838	EM-849	EM-948	EM-1020
North America United States Oregon	North America United States	Africa South Africa	North America Canada	None	North America United States Minnesota Wisconsin	North America United States Iowa	None	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	None	None	Europe Germany

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

EM-12	EM-63	EM-87	EM-177	EM-449	EM-496	EM-650	EM-698	EM-838	EM-849	EM-948	EM-1020
None	None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

Centroid Latitude

44.11

39.5

-30

49.29

17.73

46.74

42.62

17.79

36.23

Not applicable

44.51

48.62

Centroid Longitude

-123.09

-98.35

-123.8

-64.77

-96.13

-93.84

-64.62

-81.9

Not applicable

-123.51

9.03

Centroid Datum

WGS84

Not applicable

WGS84

Centroid Coordinates Status

Estimated

Not applicable

Estimated

Environments and Scales Modeled

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

EM Environmental Sub-Class

Rivers and Streams | Forests | Agroecosystems | Created Greenspace

Terrestrial Environment (sub-classes not fully specified)

Rivers and Streams | Near Coastal Marine and Estuarine

Near Coastal Marine and Estuarine

Rivers and Streams | Inland Wetlands

Inland Wetlands | Agroecosystems | Grasslands

Near Coastal Marine and Estuarine

Grasslands

Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified)

Forests

Agroecosystems

Specific Environment Type

Agricultural-urban interface at river junction

Terrestrial

Not applicable

Rivers and streams

Coral reefs

River and riverine estuary (lake)

Grassland buffering inland wetlands set in agricultural land

shallow coral reefs

grasslands

Coastal environments

Forests

Agriculture plots

EM Ecological Scale

Ecological scale is finer than that of the Environmental Sub-class

Ecological scale corresponds to the Environmental Sub-class

Ecological scale is finer than that of the Environmental Sub-class

Ecological scale corresponds to the Environmental Sub-class

Ecological scale is finer than that of the Environmental Sub-class

Ecological scale corresponds to the Environmental Sub-class

Ecological scale is finer than that of the Environmental Sub-class

Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-12

EM-63

EM-87

EM-177

EM-449

EM-496

EM-650

EM-698

EM-838

EM-849

EM-948

EM-1020

EM Organismal Scale

Not applicable

Other (Comment) ?

Not applicable

Species

Guild or Assemblage

Species

Not applicable

Taxonomic level and name of organisms or groups identified

EM-12	EM-63	EM-87	EM-177	EM-449	EM-496	EM-650	EM-698	EM-838	EM-849	EM-948	EM-1020
other Fish Species Red-Legged Frog Osprey Western Terrestrial Garter Snake Little Brown Myotis Belted Kingfisher Wood Duck Northern River Otter Great Blue Heron Roughskin Newt Pacific Water Shrew American Beaver Mink Western Pond Turtle Killdeer Northwestern Salamander	None Available	None Available	Species Oncorhynchus kisutch	None Available	None Available	Species Sedge Wren (Cistothorus platensis)	other (fish class)	Species Eastern meadowlark	None Available	None Available	None Available

EnviroAtlas URL

EM-12	EM-63	EM-87	EM-177	EM-449	EM-496	EM-650	EM-698	EM-838	EM-849	EM-948	EM-1020
Dasymetric Allocation of Population, GAP Ecological Systems, The National Hydrography Dataset (NHD), Percent Natural Land Cover in Stream Buffer , Hectares of Vegetable Crops, Employment Rate	Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD)	None Available	The National Hydrography Dataset (NHD), GAP Ecological Systems, Ecosystem Markets: Imperiled Species and Habitats, Big game hunting recreation demand	National Hydrography Dataset Plus (NHD PlusV2)	Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	GAP Ecological Systems	None Available	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation	GAP Ecological Systems	Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Agricultural water use (million gallons/day), Acres of Land Enrolled in the Conservation Reserve Program (CRP)

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-87	EM-177	EM-449	EM-496	EM-650	EM-698	EM-838	EM-849	EM-948	EM-1020
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by biota Bio-remediation by micro-organisms, algae, plants, and animals	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	None	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes

<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-87	EM-177	EM-449	EM-496	EM-650	EM-698	EM-838	EM-849	EM-948	EM-1020
Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Atmosphere Soil (2) Specific types of soil Quality Indicator	None	Rivers and Streams (111) Fauna (4) Fish Stock Indicator Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Grasslands (23) Fauna (4) Birds Stock Indicator	None	None	Agroecosystems (22) Soil (2) Specific types of soil Stock Indicator

US EPA

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

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All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Comment:

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

Comment:

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EM Modeling Approach

Comment:

Comment:

Comment:

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EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Comment:

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)