EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

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

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

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

EM Variables by Category

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

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

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

All EM Descriptors

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

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

EM Descriptors by Modeling Concepts

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

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

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

Compare Criteria:

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Comparing:

EM-65
EM-68
EM-71
EM-79
EM-91
EM-193
EM-260
EM-359
EM-392
EM-414
EM-454
EM-467
EM-836
EM-941
EM-947
EM-1014

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

EM Short Name

Green biomass production, Central French Alps

Fodder crude protein content, Central French Alps

Community flowering date, Central French Alps

Divergence in flowering date, Central French Alps

RHyME2, Upper Mississippi River basin, USA

Cultural ecosystem services, Bilbao, Spain

Coral taxa and land development, St.Croix, VI, USA

InVEST (v1.004) sediment retention, Indonesia

EPA H2O, Tampa Bay Region, FL,USA

SAV occurrence, St. Louis River, MN/WI, USA

Reef snorkeling opportunity, St. Croix, USVI

Yasso07 v1.0.1, Switzerland

Bird abundance on restored landfills, UK

ESTIMAP - Pollination potential, Iran

C-GEM, Lousiana continental shelf, USA

CommunityViz, Albany county, Wyoming

EM Full Name

Green biomass production, Central French Alps

Fodder crude protein content, Central French Alps

Community weighted mean flowering date, Central French Alps

Functional divergence in flowering date, Central French Alps

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

Cultural ecosystem services, Bilbao, Spain

Coral taxa richness and land development, St.Croix, Virgin Islands, USA

InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia

EPA H2O, Tampa Bay Region, FL, USA

Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA

Relative snorkeling opportunity (in reef), St. Croix, USVI

Yasso07 v1.0.1 forest litter decomposition, Switzerland

Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK

ESTIMAP - Pollination potential, Iran

Carbon Generic Estuary Model (C-GEM), Lousiana Continental Shelf, USA

Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming.

EM Source or Collection

EU Biodiversity Action 5

US EPA

None ?

US EPA

InVEST

US EPA

None

US EPA

None

EM Source Document ID

260

123

191

309

321

330

335

343

406

434

441

479 ?

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.

Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.

Oliver, L. M., Lehrter, J. C. and Fisher, W. S.

Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S.

Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy

Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke

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

Didion, M., B. Frey, N. Rogiers, and E. Thurig

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

Rahimi, E., Barghjelveh, S., and P. Dong

Jarvis, B.M., Lehrter, J.C., Lowe, L.L., Hagy, J.D., Wan, Y., Murrell, M.C., Ko, D.S., Penta, B., and R.W. Gould

Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D.

Document Year

2011

2013

2011

2014

2015

2013

2014

2011

2020

2003

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 recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning

Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands

Ecosystem services reinforce Sumatran tiger conservation in land use plans

EPA H20 User Manual

Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary

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

Validating tree litter decomposition in the Yasso07 carbon model

The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities

Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran)

Modeling spatiotemporal patterns of ecosystem metabolism and organic carbon dynamics affecting hypoxia on the Louisiana continental shelf

Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming

Document Status

Peer reviewed and published

Not peer reviewed but is published (explain in Comment)

Comments on Status

Published journal manuscript

Published EPA report

Published journal manuscript

Published report

Software and Access

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

EM Software Access info Exit

Not applicable

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

http://www.epa.gov/ged/tbes/EPAH2O

Not applicable

http://en.ilmatieteenlaitos.fi/yasso-download-and-support

Not applicable

https://communityviz.com/

Contact Name

Sandra Lavorel

Liem Tran

Izaskun Casado-Arzuaga

Leah Oliver

Nirmal K. Bhagabati

Marc J. Russell, Ph.D.

Ted R. Angradi

Susan H. Yee

Markus Didion ?

Lutfor Rahman

Ehsan Rahini

Brandon Jarvis

Scott Lieske

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

Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain

National Health and Environmental Research Effects Laboratory

The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018

USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561

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

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

Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland

Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK

Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran

1US EPA, Office of Research and Development, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA Office of Research and Development, U.S. EPA, Gulf Breeze, FL, USA

Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071

Contact Email

sandra.lavorel@ujf-grenoble.fr

ltran1@utk.edu

izaskun.casado@ehu.es

leah.oliver@epa.gov

nirmal.bhagabati@wwfus.org

russell.marc@epa.gov

angradi.theodore@epa.gov

yee.susan@epa.gov

markus.didion@wsl.ch

lutfor.rahman@northampton.ac.uk

ehsanrahimi666@gmail.com

jarvis.brandon@epa.gov

lieske@uwyo.edu

EM Description

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."

ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use."

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 "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management."

AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294)

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: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios."

AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . .

ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."

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...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity 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: snorkeling opportunity"

ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data."

ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)."

Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors."

ABSTRACT: "The hypoxic zone on the Louisiana Continental Shelf (LCS) forms each summer due to nutrient‐enhanced primary production and seasonal stratification associated with freshwater discharges from the Mississippi/Atchafalaya River Basin (MARB). Recent field studies have identified highly productive shallow nearshore waters as an important component of shelf‐wide carbon production contributing to hypoxia formation. This study applied a three‐dimensional hydrodynamic‐biogeochemical model named CGEM (Coastal Generalized Ecosystem Model) to quantify the spatial and temporal patterns of hypoxia, carbon production, respiration, and transport between nearshore and middle shelf regions where hypoxia is most prevalent. We first demonstrate that our simulations reproduced spatial and temporal patterns of carbon production, respiration, and bottom‐water oxygen gradients compared to field observations. We used multiyear simulations to quantify transport of articulate organic carbon (POC) from nearshore areas where riverine organic matter and phytoplankton carbon production are greatest. The spatial displacement of carbon production and respiration in our simulations was created by westward and offshore POC flux via phytoplankton carbon flux in the surface layer and POC flux in the bottom layer, supporting heterotrophic respiration on the middle shelf where hypoxia is frequently observed. These results support existing studies suggesting the importance of offshore carbon flux to hypoxia formation, particularly on the west shelf where hypoxic conditions are most variable. "

The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process.

Specific Policy or Decision Context Cited

None identified

Not reported

Land management, ecosystem management, response to EU 2020 Biodiversity Strategy

Not applicable

This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae.

None reported

None identified

None reported

None provided

Biophysical Context

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

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

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

Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes

No additional description provided

Northern Spain; Bizkaia region

nearshore; <1.5 km offshore; <12 m depth

Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east.

Not applicable

submerged aquatic vegetation

No additional description provided

Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa).

The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995).

None additional

Louisiana coastal continental shelf

Groundwater recharge area, City of Laramie

EM Scenario Drivers

No scenarios presented

Not applicable

Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan

Land Use, EGS algorithm values,

No scenarios presented

No scenarios presented ?

No scenarios presented

N/A

Coastal Shelf location

Continuation of trends

EM Relationship to Other EMs or Applications

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

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 Only

Method + Application

Method + Application (multiple runs exist)

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

Application of existing model

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

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

Document ID for related EM

Doc-260

Doc-260 | Doc-269

Doc-123

None

Doc-338

None

Doc-342 | Doc-344

None

Doc-432

None

Doc-473

EM ID for related EM

EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

EM-65 | EM-66 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83

None

EM-435

None

EM-466 | EM-469 | EM-480 | EM-485

EM-837

EM-939

None

EM-1013 | EM-1015 | EM-1016 | EM-1003 | EM-1005

EM Modeling Approach

EM Relationship to Time

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

EM Temporal Extent

2007-2009

2007-2008

1987-1997

2000 - 2007

2006-2007

2008-2020

Not applicable

2010 - 2012

2006-2007, 2010

1993-2013

Not applicable

2020

2003-2007

2050

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

past time

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

Not applicable

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Year

Not applicable

Season

Not applicable

EM Spatial Extent

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

Bounding Type

Physiographic or Ecological

Watershed/Catchment/HUC

Geopolitical

Physiographic or Ecological

Watershed/Catchment/HUC

Geopolitical ?

Physiographic or ecological

Geopolitical

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

Geopolitical

Physiographic or ecological

Watershed/Catchment/HUC

Spatial Extent Name

Central French Alps

Upper Mississippi River basin; St. Croix River Watershed

Bilbao Metropolitan Greenbelt

St.Croix, U.S. Virgin Islands

central Sumatra

Tampa Bay region

St. Louis River Estuary

Coastal zone surrounding St. Croix

Switzerland

East Midland

Iran

Lousiana continental shelf

Laramie City's aquifer protection area

Spatial Extent Area (Magnitude)

10-100 km^2

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

100-1000 km^2

10-100 km^2

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

1000-10,000 km^2.

10-100 km^2

100-1000 km^2

10,000-100,000 km^2

1000-10,000 km^2.

>1,000,000 km^2

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

10-100 km^2

Spatial Distribution of Computations

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

EM Spatial Distribution

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

spatially distributed (in at least some cases) ?

spatially distributed (in at least some cases)

spatially distributed (in at least some cases) ?

spatially distributed (in at least some cases)

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

NHDplus v1

area, for pixel or radial feature

Not applicable

area, for pixel or radial feature

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

area, for pixel or radial feature

Not applicable

Spatial Grain Size

20 m x 20 m

NHDplus v1

2 m x 2 m

Not applicable

30 m x 30 m

30m x 30m

0.07 m^2 to 0.70 m^2

10 m x 10 m

5 sites

multiple unrelated sites

ha^2

regions of shelf

Not applicable

EM Structure and Computation Approach

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

EM Determinism

deterministic

stochastic

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Bayesian statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Other or unclear (comment)

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

Model Calibration Reported?

Yes

Not applicable

Yes

Unclear

Model Goodness of Fit Reported?

Yes

Not applicable

Goodness of Fit (metric| value | unit)

Adjusted R | 69.9 | Not applicable

Adjusted R | 62.4 |

R squared | 45.2 | Not applicable

R squared | 35.5 | Not applicable

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

None

R squared (taxa richness) | 0.74 | Not applicable
p-value | 0.0020 | unitless

None

p-value | <0.001 | unitless
Nagelkerke r2 | 0.30-0.62 | unitless

None

Model Operational Validation Reported?

Yes

Not applicable

Yes

Unclear

Model Uncertainty Analysis Reported?

Yes

Not applicable

Unclear

Model Sensitivity Analysis Reported?

No ?

Not applicable

Yes

Unclear

Model Sensitivity Analysis Include Interactions?

Not applicable

Unclear

Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

Europe
- France

Europe
- France

Europe
- France

Europe
- France

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

Europe
- Spain

None

Asia
- Indonesia

North America
- United States
  - Florida

North America
- United States
  - Minnesota
  - Wisconsin

None

Europe
- Switzerland

Europe
- United Kingdom

Europe

None

North America
- United States
  - Wyoming

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

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Southwestern Atlantic

None

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

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

Centroid Latitude

45.05

42.5

43.25

17.75

28.05

46.72

17.73

46.82

52.22

32.29

30.28

41.31

Centroid Longitude

6.4

-90.63

-2.92

-64.75

102

-82.52

-96.13

-64.77

8.23

-0.91

53.68

-88.39

-105.46

Centroid Datum

WGS84

NAD83

WGS84

Centroid Coordinates Status

Provided

Estimated

Provided

Estimated

Provided

Estimated

Environments and Scales Modeled

EM ID

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

EM Environmental Sub-Class

Agroecosystems | Grasslands

Near Coastal Marine and Estuarine

Terrestrial Environment (sub-classes not fully specified)

Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds

Near Coastal Marine and Estuarine

Forests

Created Greenspace | Grasslands

Terrestrial Environment (sub-classes not fully specified)

Near Coastal Marine and Estuarine

Ground Water | Terrestrial Environment (sub-classes not fully specified)

Specific Environment Type

Subalpine terraces, grasslands, and meadows

Subalpine terraces, grasslands, and meadows.

Subalpine terraces, grasslands, and meadows

None

none

stony coral reef

104 land use land cover classes

All terestrial landcover and waterbodies

Freshwater estuarine system

Coral reefs

forests

restored landfills and conserved grasslands

terrestrial land types

Louisiana continental shelf

watershed

EM Ecological Scale

Not applicable

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

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-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

EM Organismal Scale

Community

Not applicable

Guild or Assemblage

Community

Not applicable

Guild or Assemblage

Community

Individual or population, within a species

Not applicable

Taxonomic level and name of organisms or groups identified

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

None Available

Genus
- Acropora
- Porites spp.
- Diploria spp.
Species
- Mycetophylia lamarckiana
- Mycetophylia ferox
- Montastrea cavernosa
- Dichocoenia stokesii
- Meandrina meandrites
- Agaricia tenuifolia
- Agaricia fragilis
- Montastrea annularis
- Agaricia agaricites
- Dendrogyra cylindricus
- Millepora complanata
- Montastrea faveolata
- Porites astreoides
- Montastrea franksii

None Available

Species
- Grey partridge (Perdix perdix)
- Collared dove (Streptopelia decaocto)
- Woodpidgeon (Columba palumbus)
- Lapwing (Vanellus vanellus)
- Magpie (Pica pica)
- Dunnock (Prunella modularis)
- Starling (Sturnus vulgaris)
- Skylark (Alauda arvensis)
- Pheasant (Phasianus colchicus)
- Blackbird (Turdus merula)
- Carrion crow (Corvus corone)
- Tree sparrow (Passer montanus)

Species
- Apis mellifera
- Apis florea

None Available

EnviroAtlas URL

EM-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

GAP Ecological Systems

GAP Ecological Systems, Carbon storage by tree biomass (kg/m2)

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)

Percent IUCN Status II, Percent GAP Status 1 & 2

None Available

The National Hydrography Dataset (NHD), Average Annual Precipitation

The National Hydrography Dataset (NHD), GAP Ecological Systems, Total Annual Reduced Nitrogen Deposition, Waterbody area, Percent GAP Status 1 & 2, Acres of Land Enrolled in the Conservation Reserve Program (CRP), The Watershed Boundary Dataset (WBD), Carbon Storage by Tree Biomass

Average Annual Precipitation

None Available

GAP Ecological Systems, Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass

None Available

The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Precipitation, Waterbody area

National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, Average Annual Daily Potential Wind Energy, Carbon Storage by Tree Biomass

Dasymetric Allocation of Population, Total Annual Reduced Nitrogen Deposition, Employment Rate

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-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

None

[Ecosystem] Provisioning
- Materials
  - Biomass
    - Materials from plants, algae and animals for agricultural use

None

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

[Ecosystem] Cultural
- Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Physical and experiential interactions
    - Physical use of land-/seascapes in different environmental settings
    - Experiential use of plants, animals and land-/seascapes in different environmental settings
  - Intellectual and representative interactions
    - Aesthetic

[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes
- Maintenance of physical, chemical, abiotic conditions
  - By natural chemical and physical processes
    - Not applicable

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Soil formation and composition
    - Weathering processes

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Atmospheric composition and climate regulation
    - Global climate regulation by reduction of greenhouse gas concentrations
- Mediation of flows
  - Liquid flows
    - Flood protection
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

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

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

[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
  - Water conditions
    - Chemical condition of salt waters

[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes
- Mediation of waste, toxics and other nuisances
  - By natural chemical and physical processes
    - Not applicable
[Ecosystem] Provisioning
- Materials
  - Water
    - Ground water for non-drinking purposes
[Ecosystem] Cultural
- Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Physical and experiential interactions
    - Physical use of land-/seascapes in different environmental settings
  - Intellectual and representative interactions
    - Aesthetic

<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-65

EM-68

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-454

EM-467

EM-836

EM-941

EM-947

EM-1014

Grasslands (23)
- Flora (5)
  - Green biomass
    - Stock Indicator
Agroecosystems (22)
- Flora (5)
  - Green biomass
    - Stock Indicator

Agroecosystems (22)
- Flora (5)
  - Fodder quality
    - Quality Indicator

None

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Forests (21)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Grasslands (23)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Scrubland/Shrubland (24)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator

Near Coastal Marine/Estuarine (113)
- Fauna (4)
  - Invertebrates
    - Quality Indicator

None

Rivers and Streams (111)
- Composite (8)
  - Regulation of extreme events
    - Extreme Event Indicator

None

Forests (21)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Scrubland/Shrubland (24)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Urban/Surburban (27)
- Fauna (4)
  - Wildlife
    - Stock Indicator

Near Coastal Marine/Estuarine (113)
- Water (3)
  - Liquid water
    - Quality Indicator

Groundwater
- Water (3)
  - Liquid water
    - 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:

Comment:

Comment:

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

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)