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

Hide Criteria

Comparing:

EM-70
EM-91
EM-92
EM-129
EM-326
EM-345
EM-376
EM-458
EM-459
EM-549
EM-703
EM-743
EM-846
EM-992
EM-998

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

EM Short Name

Plant species diversity, Central French Alps

RHyME2, Upper Mississippi River basin, USA

Runoff potential of pesticides, Europe

3-PG, South Australia

ARIES flood regulation, Puget Sound Region, USA

InVEST habitat quality, Puli Township, Taiwan

MIMES: For Massachusetts Ocean (v1.0)

Reef density of P. argus, St. Croix, USVI

Reef density of S. gigas, St. Croix, USVI

Nutrient Tracking Tool (NTT)

Gadwall duck recruits, CREP wetlands, Iowa, USA

WESP: Irrigation water, ID, USA

Indigo bunting abund, Piedmont region, USA

DAISY model, Taastrup, Copenhagen

CAESAR landscape evolution model

EM Full Name

Plant species diversity, Central French Alps

RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA

Runoff potential of pesticides, Europe

3-PG (Physiological Principles Predicting Growth), South Australia

ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA

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

Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0)

Relative density of Panulirus argus (on reef), St. Croix, USVI

Relative density of Strombus gigas (on reef), St. Croix, USVI

Nutrient Tracking Tool (NTT)

Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA

WESP: Irrigation return water treatment, Idaho, USA

Indigo bunting abundance, Piedmont ecoregion, USA

Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark

Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model

EM Source or Collection

EU Biodiversity Action 5

US EPA

None

ARIES

InVEST

US EPA

None

EM Source Document ID

260

123

254

243

302

308

316

335

352

372 ?

393 ?

405

464

468

Document Author

Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.

Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C.

Schriever, C. A., and Liess, M.

Crossman, N. D., Bryan, B. A., and Summers, D. M.

Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.

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

Altman, I., R.Boumans, J. Roman, L. Kaufman

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

Saleh, A. and O. Gallego

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

Murphy, C. and T. Weekley

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

Ghaley, B. B., & Porter, J. R.

Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J.

Document Year

2011

2013

2007

2011

2014

2012

2014

2018

2010

2012

2008

2014

2007

Document Title

Using plant functional traits to understand the landscape distribution of multiple ecosystem services

Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads

Mapping ecological risk of agricultural pesticide runoff

Carbon payments and low-cost conservation

From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments

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

Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0)

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

Nutrient Tracking Tool (NTT) User Manual

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

Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context.

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

Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark

Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model

Document Status

Peer reviewed and published

Documented, not peer reviewed

Peer reviewed and published

Comments on Status

Published journal manuscript

Published report

Published journal manuscript

webpage

Published report

Published journal manuscript

Software and Access

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

EM Software Access info Exit

Not applicable

http://www.csiro.au/products/3PGProductivity#a1

http://aries.integratedmodelling.org/

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

http://www.afordablefutures.com/orientation-to-what-we-do

Not applicable

http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en

Not applicable

http://www.coulthard.org.uk/

Contact Name

Sandra Lavorel

Liem Tran

Carola Alexandra Schriever

Anders Siggins

Ken Bagstad

Yu-Pin Lin ?

Irit Altman

Susan H. Yee

Ali Saleh ?

David Otis

Chris Murphy

Sam Riffell

Bhim Bahadur Ghaley

Marco J. Van De Wiel

Contact Address

Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France

Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA

Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany

Not reported

Geosciences and Environmental Change Science Center, US Geological Survey

Not reported

Boston University, Portland, Maine

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

Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402

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

Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID

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

Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Højbakkegård Allé 30, DK-2630 Taastrup, Denmark.

Department of Geography, University of Western Ontario, London, Ontario, Canada

Contact Email

sandra.lavorel@ujf-grenoble.fr

ltran1@utk.edu

carola.schriever@ufz.de

Anders.Siggins@csiro.au

kjbagstad@usgs.gov

yplin@ntu.edu.tw

iritaltman@bu.edu

yee.susan@epa.gov

saleh@tarleton.edu

dotis@iastate.edu

chris.murphy@idfg.idaho.gov

sriffell@cfr.msstate.edu

bbg@life.ku.dk

mvandew3@uwo.ca

EM Description

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

Summary Description

ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables."

ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin)

ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception."

AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)."

ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures."

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.

AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors.

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: (1) density of the spiny lobster Panulirus argus"

AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)."

ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).

A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level.

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."

With inevitable link between ecosystem function (EF), ecosystem services (ES) and agricultural productivity, there is a need for quantification and valuation of EF and ES in agro-ecosystems. Management practices have significant effects on soil organic matter (SOM), affecting productivity, EF and ES provision. The objective was to quantify two EF: soil water storage and nitrogen mineralization and three ES: food and fodder production and carbon sequestration, in a conventional winter wheat production system at 2.6% SOM compared to 50% lower (1.3%) and 50% higher (3.9%) SOM in Denmark by DAISY model. At 2.6% SOM, the food and fodder production was 6.49 and 6.86 t ha−1 year−1 respectively whereas carbon sequestration and soil water storage was 9.73 t ha−1 year−1 and 684 mm ha−1 year−1 respectively and nitrogen mineralisation was 83.58 kg ha−1 year−1. At 2.6% SOM, the two EF and three ES values were US$ 177 and US$ 2542 ha−1 year−1 respectively equivalent to US$ 96 and US$1370 million year−1 respectively in Denmark. The EF and ES quantities and values were positively correlated with SOM content. Hence, the quantification and valuation of EF and ES provides an empirical tool for optimising the EF and ES provision for agricultural productivity.

We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK.

Specific Policy or Decision Context Cited

None identified

Not reported

European Commission Water Framework Directive (WFD, Directive 2000/60/EC)

None identified

Environmental effects of Highway 6 construction on Puli Township, Taiwan

None identified

None reported

None identified

Biophysical Context

Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes

No additional description provided

Not applicable

Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm.

No additional description provided

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

Prairie Pothole Region of Iowa

restored, enhanced and created wetlands

Conservation Reserve Program lands left to go fallow

Agro-ecosystem test farm, Copenhagen, Denmark.

River Teifi, Lampeter, Wales

EM Scenario Drivers

No scenarios presented

Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings

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

Sites, function or habitat focus

N/A

A soil organic matter value of 1.3% was used for this model run

Varying flow velocities and durations

EM Relationship to Other EMs or Applications

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

Method Only, Application of Method or Model Run

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

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method Only

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

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-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

Document ID for related EM

Doc-260

Doc-123

Doc-255 | Doc-256 | Doc-257

Doc-243 | Doc-246 | Doc-245

Doc-303 | Doc-305

Doc-278

None

Doc-372 | Doc-373

Doc-390

Doc-405

None

Doc-467

EM ID for related EM

EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

None

EM-143

None

EM-584

EM-705 | EM-704 | EM-702 | EM-701 | EM-700 | EM-632

EM-718 | EM-734 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-768

EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847

None

EM-997

EM Modeling Approach

EM Relationship to Time

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

EM Temporal Extent

2007-2009

1987-1997

2000

2009-2050

1971-2006

2010-2025

Not applicable

2006-2007, 2010

35 yr

1987-2007

2010-2012

2008

2003-2013

Not applicable

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

future time

Not applicable

past time

Not applicable

past time

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

Not applicable

discrete

Not applicable

continuous

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Day

Month

Not applicable

Year

Not applicable

Day

Not applicable

EM Spatial Extent

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

Bounding Type

Physiographic or Ecological

Watershed/Catchment/HUC

Geopolitical

Physiographic or Ecological

Physiographic or ecological

Geopolitical

Physiographic or ecological

Not applicable

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

Physiographic or ecological

Watershed/Catchment/HUC

Spatial Extent Name

Central French Alps

Upper Mississippi River basin; St. Croix River Watershed

EU-15

Agricultural districts of the state of South Australia

Puget Sound Region

Puli Township, Nantou County

Massachusetts Ocean

Coastal zone surrounding St. Croix

Not applicable

CREP (Conservation Reserve Enhancement Program

Wetlands in idaho

Piedmont Ecoregion

Taastrup experimental farm

River Teifi

Spatial Extent Area (Magnitude)

10-100 km^2

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

>1,000,000 km^2

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

10,000-100,000 km^2

100-1000 km^2

1000-10,000 km^2.

100-1000 km^2

Not applicable

10,000-100,000 km^2

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

1-10 km^2

1000-10,000 km^2.

Spatial Distribution of Computations

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

EM Spatial Distribution

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

other or unclear (comment)

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

NHDplus v1

area, for pixel or radial feature

Not applicable

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

Not applicable

Spatial Grain Size

20 m x 20 m

NHDplus v1

10 km x 10 km

1 ha x 1 ha

200m x 200m

40 m x 40 m

1 km x1 km

10 m x 10 m

Not applicable

multiple, individual, irregular sites

Not applicable

EM Structure and Computation Approach

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Bayesian 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

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

Model Calibration Reported?

Yes

Unclear

Yes

Not applicable

Unclear

Yes

Unclear

Not applicable

Model Goodness of Fit Reported?

Yes

Not applicable

Unclear

Not applicable

Goodness of Fit (metric| value | unit)

R squared | 31 | Not applicable

R^2 | 0.923 | none
Mean Square Error | 25.1 | %

None

Model Operational Validation Reported?

Not applicable

Yes

Unclear

Yes

Not applicable

Model Uncertainty Analysis Reported?

Yes

Not applicable

Unclear

Not applicable

Model Sensitivity Analysis Reported?

No ?

Yes

Not applicable

Yes

Unclear

Not applicable

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-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

Europe
- France

North America
- United States
  - Indiana
  - Illinois
  - Missouri
  - South Dakota
  - Minnesota
  - Idaho
  - Wisconsin

Europe
- United Kingdom
- Portugal
- Spain
- Greece
- Austria
- Netherlands
- Sweden
- Belgium
- Ireland
- Luxembourg
- Finland
- Denmark
- Italy
- France
- Germany

Oceania
- Australia

North America
- United States

Asia
- China

None

North America
- United States
  - Iowa

North America
- United States
  - Idaho

Oceania

Europe
- Denmark

Europe
- United Kingdom

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

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

None

Temperate North Atlantic
- Northwest Atlantic
  - Cold Temperate Northwest Atlantic
    - Virginian
    - Gulf of Maine/Bay of Fundy

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

Centroid Latitude

45.05

42.5

50.01

-34.9

23.98

41.72

17.73

Not applicable

42.62

44.06

36.23

55.4

52.04

Centroid Longitude

6.4

-90.63

4.67

138.7

-123

120.96

-69.87

-64.77

Not applicable

-93.84

-114.69

-81.9

12.18

-4.39

Centroid Datum

WGS84

Not applicable

WGS84

None provided

WGS84

Centroid Coordinates Status

Provided

Estimated

Not applicable

Estimated

Provided

Estimated

Environments and Scales Modeled

EM ID

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

EM Environmental Sub-Class

Agroecosystems | Grasslands

Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland

Forests | Agroecosystems

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

Near Coastal Marine and Estuarine

Agroecosystems

Inland Wetlands | Agroecosystems | Grasslands

Inland Wetlands

Grasslands

Agroecosystems

Rivers and Streams

Specific Environment Type

Subalpine terraces, grasslands, and meadows

None

Arable lands in near-stream environments

Agricultural land for annual crops, annual legumes, and grazing of sheep and cows

Terrestrial environment surrounding a large estuary

Predominantly an agricultural area with associated forest land

None identified

Coral reefs

Agroecosystems

Wetlands buffered by grassland within agroecosystems

created, restored and enhanced wetlands

grasslands

Agroecosystems

River

EM Ecological Scale

Not applicable

Ecosystem

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-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

EM Organismal Scale

Community

Not applicable

Species

Not applicable

Community

Species

Not applicable

Individual or population, within a species

Not applicable

Species

Guild or Assemblage ?

Not applicable

Taxonomic level and name of organisms or groups identified

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

None Available

Species
- Acacia implexa
- Eucalyptus microcarpa
- Acacia verniciflua
- Eucalyptus polyanthemos
- Eucalyptus globulus
- Eucalyptus sideroxylon
- Acacia mearnsii
- Acacia pycnantha
- Eucalyptus camaldulensis
- Eucalyptus kochii
- Acacia dealbata

None Available

other
- Filter feeders
- Tube builders
- Erect structure builders
Species
- Ocean pout
- Northern sand lance
- Cunner
- Longhorn sculpin
- Bluefish
- American lobster
- Minke whale
- Right whale
- Spiny dogfish
- Atlantic mackeral
- Bluefin tuna
- Atlantic cod (resident)
- Winter flounder
- Harbor seal
- Pollock
- Acadian redfish
- Harbor porpoise
- Blueback herring
- Fin whale
- Atlantic cod (migrant)
- Atlantic menhaden
- Silver hake
- Striped bass
- Humpback whale
- Haddock
- Red hake
- Scup
- Yellowtail flounder
- Witch flounder
- Windowpane flounder
- Atlantic herring
- White sided dolphin
- White hake
- Little skate
- American sand lance
- Goosefish
- Alewives

Species
- Panulirus argus

Species
- Panulirus argus

None Available

Species
- Gadwall (Anas strepera)

None Available

Species
- Indigo bunting

Genus
- Wheat

None Available

EnviroAtlas URL

EM-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

None Available

The National Hydrography Dataset (NHD), National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD)

Average Annual Precipitation, Stream Length Impaired by Pesticides, The Watershed Boundary Dataset (WBD), Hectares of Vegetable Crops

GAP Ecological Systems, Average Annual Precipitation, Acres of Land Enrolled in the Conservation Reserve Program (CRP)

GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Percent Impervious Area

Percent GAP Status 1 & 2

GAP Ecological Systems, Average Annual Daily Potential Solar Energy, Big game hunting recreation demand, Percent GAP Status 1 & 2, Percent potentially restorable wetlands on agricultural land, Average Annual Daily Potential Wind Energy

None Available

GAP Ecological Systems, Agricultural water use (million gallons/day), Water supply from NID reservoirs (million gallons)

Acres of Land Enrolled in the Conservation Reserve Program (CRP)

Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass

GAP Ecological Systems, U.S. EPA (Omernik) ecoregions

Average Annual Daily Potential Solar Energy, Average Annual Precipitation, Agricultural water use (million gallons/day), Stream Length Impaired by Pesticides

Average Annual Precipitation

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-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

None

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Water conditions
    - Chemical condition of freshwaters

None

[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Flood protection

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Provisioning
- 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] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Cultivated crops
- Materials
  - Water
    - Ground water for non-drinking purposes
[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

[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
  - Water conditions
    - Chemical condition of freshwaters
- Mediation of flows
  - Liquid flows
    - Flood protection
    - Hydrological cycle and water flow maintenance
  - Mass flows
    - Mass stabilisation and control of erosion rates
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

[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
    - Cultivated crops
[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Soil formation and composition
    - Decomposition and fixing processes

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Hydrological cycle and water flow maintenance
  - Mass flows
    - Mass stabilisation and control of erosion rates

<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-70

EM-91

EM-92

EM-129

EM-326

EM-345

EM-376

EM-458

EM-459

EM-549

EM-703

EM-743

EM-846

EM-992

EM-998

None

Forests (21)
- Flora (5)
  - Trees
    - Stock Indicator

Rivers and Streams (111)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator
Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator
Forests (21)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator

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)
- Fauna (4)
  - Fish
    - Stock Indicator
  - Mammals
    - Stock Indicator

Near Coastal Marine/Estuarine (113)
- Fauna (4)
  - Fish
    - Stock Indicator

Near Coastal Marine/Estuarine (113)
- Fauna (4)
  - Fish
    - Stock Indicator

Agroecosystems (22)
- Flora (5)
  - food crops
    - Stock Indicator

Wetlands (12)
- Fauna (4)
  - Duck
    - Stock Indicator

None

Grasslands (23)
- Fauna (4)
  - Birds
    - Stock Indicator

Agroecosystems (22)
- Flora (5)
  - food crops
    - Stock Indicator
  - Fodder
    - Stock Indicator
- Soil (2)
  - Specific types of soil
    - Quality Indicator

Rivers and Streams (111)
- Water (3)
  - Liquid water
    - Flow Indicator
- Soil (2)
  - Specific types of soil
    - Flow Indicator

US EPA

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:

Comment:

Comment:

Comment:

Comment:

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

EM Modeling Approach

Comment:

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

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)