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:

Show Criteria

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Comparing:

EM-63
EM-337
EM-345
EM-461
EM-542
EM-660
EM-698
EM-709
EM-788
EM-862
EM-892
EM-1020

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

EM Short Name

EnviroAtlas - Natural biological nitrogen fixation

Rate of Fire Spread

InVEST habitat quality, Puli Township, Taiwan

Presence of Euchema sp., St. Croix, USVI

Coastal protection in Belize

RUM: Valuing fishing quality, Michigan, USA

Fish species richness, St. Croix, USVI

Pollinators on landfill sites, United Kingdom

Wild bees over 26 yrs of restored prairie, IL, USA

Recreational fishery index, USA

VELMA v. 2.1 contaminant modeling

EPIC agriculture model, Baden-Wurttemberg, Germany

EM Full Name

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

Rate of Fire Spread

InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan

Relative presence of Euchema sp. (on reef), St. Croix, USVI

Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize:

Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA

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

Pollinating insects on landfill sites, East Midlands, United Kingdon

Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA

Recreational fishery index for streams and rivers, USA

VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling

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

EM Source or Collection

US EPA | EnviroAtlas

None

InVEST

US EPA

InVEST

None

US EPA

None

EM Source Document ID

262 ?

306

308

335

350

382 ?

355

389

401

414

423 ?

482

Document Author

US EPA Office of Research and Development - National Exposure Research Laboratory

Rothermel, Richard C.

Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T.

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

Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes

Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson

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

Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin

Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree

Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold

McKane

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

Document Year

2013

1972

2014

2016

2014

2007

2013

2017

2021

None

2009

Document Title

EnviroAtlas - National

A Mathematical model for predicting fire spread in wildland fuels

Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan

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

The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience

Valuing recreational fishing quality at rivers and streams

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

Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects

Wild bee community change over a 26-year chronosequence of restored tallgrass prairie

Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers.

Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation

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

Documented, not peer reviewed

Peer reviewed and published

Comments on Status

Published on US EPA EnviroAtlas website

Published USDA Forest Service report

Published journal manuscript

Published EPA report

Published journal manuscript

Software and Access

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

EM Software Access info Exit

https://www.epa.gov/enviroatlas

http://firelab.org/project/farsite

https://www.naturalcapitalproject.org/invest/

Not applicable

Not identified in paper

Not applicable

https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355

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

Contact Name

EnviroAtlas Team ?

Charles McHugh

Yu-Pin Lin ?

Susan H. Yee

Greg Guannel

Richard Melstrom

Simon Pittman

Sam Tarrant

Sean R. Griffin

Gregg Lomnicky

Robert B. McKane

Norbert Billen

Contact Address

Not reported

RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808

Not reported

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

The Nature Conservancy, Coral Gables, FL. USA

Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA

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

RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K.

Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A.

200 SW 35th St., Corvallis, OR, 97333

US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333

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

Contact Email

enviroatlas@epa.gov

cmchugh@fs.fed.us

yplin@ntu.edu.tw

yee.susan@epa.gov

greg.guannel@gmail.com

melstrom@okstate.edu

simon.pittman@noaa.gov

sam.tarrant@rspb.org.uk

srgriffin108@gmail.com

lomnicky.gregg@epa.gov

mckane.bob@epa.gov

billen@uni-hohenheim.de

EM Description

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

Summary Description

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."

ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.**

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: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the landscape.

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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(4) density of Euchema sp. seaweed,"

AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems."

ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. "

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: "...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…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each

ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities."

ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.]

ABSTRACT: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters."

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

None Identified

None identified

Environmental effects of Highway 6 construction on Puli Township, Taiwan

None identified

Future rock lobster fisheries management

None identified

None provided

None identified

Impact of different agricultural management strategies

Biophysical Context

No additional description provided

Not applicable

26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere.

No additional description provided

barrier reef and fringing reef in nearshore coastal marine system

stream and river reaches of Michigan

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

No additional description provided

The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season.

None

No additional description provided

Central Europe agricultural sites

EM Scenario Drivers

No scenarios presented

Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas.

No scenarios presented

Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions

targeted sport fish biomass

No scenarios presented

N/A

No scenarios presented

EM Relationship to Other EMs or Applications

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

Method Only, Application of Method or Model Run

Method + Application

Method Only

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

New or Pre-existing EM?

New or revised model

Application of existing model

New or revised 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-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

Document ID for related EM

Doc-346 | Doc-347 ?

None

Doc-278

None

Doc-355

Doc-389

None

Doc-430

Doc-478

EM ID for related EM

None

EM-143

None

EM-590 | EM-699

EM-697

None

EM-883 | EM-884 | EM-887

EM-1012 | EM-1021

EM Modeling Approach

EM Relationship to Time

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

EM Temporal Extent

2006-2010

Not applicable

2010-2025

2006-2007, 2010

2005-2013

2008-2010

2000-2005

2007-2008

1988-2014

2013-2014

Not applicable

4-20 years ?

EM Time Dependence

time-stationary

Not applicable

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

past time

Not applicable

past time

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

other or unclear (comment) ?

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Second

Not applicable

Year

Day

Not applicable

EM Spatial Extent

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

Bounding Type

Geopolitical

Not applicable

Geopolitical

Physiographic or ecological

Geopolitical

Watershed/Catchment/HUC

Physiographic or ecological

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

Physiographic or ecological

Geopolitical

Not applicable

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

Spatial Extent Name

counterminous United States

Not applicable

Puli Township, Nantou County

Coastal zone surrounding St. Croix

Coast of Belize

HUCS in Michigan

SW Puerto Rico,

East Midlands

Nachusa Grasslands

United States

Not applicable

Baden-Wurttemberg

Spatial Extent Area (Magnitude)

>1,000,000 km^2

Not applicable

100-1000 km^2

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

100-1000 km^2

1000-10,000 km^2.

10-100 km^2

>1,000,000 km^2

Not applicable

10,000-100,000 km^2

Spatial Distribution of Computations

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

EM Spatial Distribution

spatially distributed (in at least some cases) ?

Not applicable

spatially distributed (in at least some cases)

spatially lumped (in all cases)

Spatial Grain Type

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

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)

area, for pixel or radial feature

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

length, for linear feature (e.g., stream mile)

volume, for 3-D feature

Not applicable

Spatial Grain Size

irregular

Not applicable

40 m x 40 m

10 m x 10 m

1 meter

reach in HUC

not reported

multiple unrelated locations

Area varies by site

stream reach (site)

user defined

Not applicable

EM Structure and Computation Approach

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

EM Computational Approach

Analytic

Numeric

Analytic

EM Determinism

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

Model Calibration Reported?

Not applicable

Unclear

Yes

Not applicable

Yes

Model Goodness of Fit Reported?

Not applicable

Yes

Not applicable

Yes

Goodness of Fit (metric| value | unit)

None

R squared | 0.552 | unitless

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

None

r-squared | 0.6416 | NA

Model Operational Validation Reported?

Not applicable

Yes

No ?

Yes

Not applicable

Yes

Model Uncertainty Analysis Reported?

Not applicable

Unclear

Model Sensitivity Analysis Reported?

Not applicable

Yes

Not applicable

Unclear

Model Sensitivity Analysis Include Interactions?

Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-63	EM-337	EM-345	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
North America United States	None	Asia China ? Comment: Taiwan	None	North America Belize	North America United States Michigan	None	Europe United Kingdom	North America United States Illinois	North America United States North Carolina Indiana Wyoming Northern Mariana Islands Utah Guam Arizona Montana Kentucky California Kansas Delaware Florida Pennsylvania Iowa Mississippi Illinois Texas Connecticut Georgia Maryland Idaho Oregon Vermont Puerto Rico Maine Oklahoma Tennessee American Samoa Alabama Arkansas South Carolina Washington Nebraska West Virginia Colorado Massachusetts Missouri Alaska U.S. Virgin Islands Washington D.C. North Dakota Wisconsin Nevada New York Rhode Island Hawaii South Dakota Minnesota New Jersey Michigan Virgina New Mexico New Hampshire Louisiana Ohio	None	Europe Germany

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

EM-63	EM-337	EM-345	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Western Caribbean	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

Centroid Latitude

39.5

-9999

23.98

17.73

18.63

45.12

17.79

52.22

41.89

36.21

Not applicable

48.62

Centroid Longitude

-98.35

-9999

120.96

-64.77

-88.22

85.18

-64.62

-0.91

-89.34

-113.76

Not applicable

9.03

Centroid Datum

WGS84

Not applicable

WGS84

Not applicable

WGS84

Centroid Coordinates Status

Estimated

Not applicable

Estimated

Provided

Estimated

Not applicable

Estimated

Environments and Scales Modeled

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

EM Environmental Sub-Class

Terrestrial Environment (sub-classes not fully specified)

Near Coastal Marine and Estuarine

Rivers and Streams

Near Coastal Marine and Estuarine

Created Greenspace | Grasslands

Agroecosystems | Grasslands

Rivers and Streams

Terrestrial Environment (sub-classes not fully specified)

Agroecosystems

Specific Environment Type

Terrestrial

Not applicable

Predominantly an agricultural area with associated forest land

Coral reefs

coral reefs

stream reaches

shallow coral reefs

restored landfills and grasslands

Restored prairie, prairie remnants, and cropland

reach

Terrestrial environment

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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-63

EM-337

EM-345

EM-461

EM-542

EM-660

EM-698

EM-709

EM-788

EM-862

EM-892

EM-1020

EM Organismal Scale

Not applicable

Community

Species

Guild or Assemblage

Not applicable

Guild or Assemblage

Individual or population, within a species

Species

Guild or Assemblage

Not applicable

Taxonomic level and name of organisms or groups identified

EM-63	EM-337	EM-345	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
None Available	None Available	None Available	Genus Euchema	None Available	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye	other (fish class)	Genus Calliopum Lasioglossum Species Apis mellifera Bombus lapidarius Oedemera nobilis Episyrphus balteatus Bombus terrestris/lucorum Bombus pascuorum	other Bees	None Available	None Available	None Available

EnviroAtlas URL

EM-63	EM-337	EM-345	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD)	Average Annual Precipitation	Percent GAP Status 1 & 2	None Available	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate	None Available	GAP Ecological Systems	GAP Ecological Systems	None Available	None Available	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-63	EM-337	EM-345	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Nutrition Biomass Wild plants, algae and their outputs	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection Mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Pollination and seed dispersal	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[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-63	EM-337	EM-345	EM-461	EM-542	EM-660	EM-698	EM-709	EM-788	EM-862	EM-892	EM-1020
Atmosphere Soil (2) Specific types of soil Quality Indicator	None	Forests (21) Composite (8) Presence of environmental class/subclass Stock Indicator Quality Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Stock Indicator Quality Indicator	Near Coastal Marine/Estuarine (113) Flora (5) Seaweed Stock Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator Flow Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	None	None	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	None	Agroecosystems (22) Soil (2) Specific types of soil Stock Indicator

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

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:

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])

Comment:

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

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

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

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

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