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-71
EM-79
EM-91
EM-193
EM-260
EM-359
EM-392
EM-414
EM-446
EM-454
EM-796
EM-836
EM-851
EM-941
EM-990

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

EM Short Name

Green biomass production, 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

CRPI, St. Croix, USVI

Reef snorkeling opportunity, St. Croix, USVI

Wildflower mix supporting bees, MI, USA

Bird abundance on restored landfills, UK

InVEST Coastal Vulnerability, New York, USA

ESTIMAP - Pollination potential, Iran

Atlantis ecosystem economics submodel

EM Full Name

Green biomass production, 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

CRPI (Coral Reef Protection Index, St. Croix, USVI

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

Wildflower planting mix supporting bees in agricultural landscapes, MI, USA

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

InVEST Coastal Vulnerability, Jamaica Bay, New York, USA

ESTIMAP - Pollination potential, Iran

Lessons in modelling and management of marine ecosystems: the Atlantis experience

EM Source or Collection

EU Biodiversity Action 5

US EPA

None ?

US EPA

InVEST

US EPA

None

InVEST

None

EM Source Document ID

260

123

191

309

321

330

335

400

406

410 ?

434

463

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

Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters

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

Hopper T. and M. S. Meixler

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

Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C.

Document Year

2011

2013

2011

2014

2015

2013

2014

2015

2011

2016

2020

2011

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

Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States

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

Modeling coastal vulnerability through space and time

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

Lessons in modelling and management of marine ecosystems: the Atlantis experience

Document Status

Peer reviewed and published

Comments on Status

Published journal manuscript

Published EPA report

Published journal manuscript

Software and Access

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

EM Software Access info Exit

Not applicable

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

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

Not applicable

https://naturalcapitalproject.stanford.edu/software/invest-models/coastal-vulnerability

Not applicable

https://research.csiro.au/atlantis/home/links/

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

Neal Williams

Lutfor Rahman

Thomas Hopper

Ehsan Rahini

Elizabeth Fulton

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

Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616

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

Not reported

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

Department of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia

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

nmwilliams@ucdavis.edu

lutfor.rahman@northampton.ac.uk

Tjhop1123@gmail.com

ehsanrahimi666@gmail.com

beth.fulton@csiro.au

EM Description

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

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: "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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...An alternative index has been developed specifically for coral reefs, the Coral Reef Protection Index (CRPI), that accounts for the continuity of the reef and distance from shore in addition to reef habitat type (Burke et al., 2008): CRPI = ((Reef type + Reef distribution + Reef distance)/10) x 4 where the scaled magnitude of coastal protection due to each factor ranges from 0 (no protection) to 4 (very high protection; Table 2)."

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: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm

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: "Coastal ecosystems experience a wide range of stressors including wave forces, storm surge, sea-level rise, and anthropogenic modification and are thus vulnerable to erosion. Urban coastal ecosystems are especially important due to the large populations these limited ecosystems serve. However, few studies have addressed the issue of urban coastal vulnerability at the landscape scale with spatial data that are finely resolved. The purpose of this study was to model and map coastal vulnerability and the role of natural habitats in reducing vulnerability in Jamaica Bay, New York, in terms of nine coastal vulnerability metrics (relief, wave exposure, geomorphology, natural habitats, exposure, exposure with no habitat, habitat role, erodible shoreline, and surge) under past (1609), current (2015), and future (2080) scenarios using InVEST 3.2.0. We analyzed vulnerability results both spatially and across all time periods, by stakeholder (ownership) and by distance to damage from Hurricane Sandy. We found significant differences in vulnerability metrics between past, current and future scenarios for all nine metrics except relief and wave exposure…"

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."

Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions.

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 identrified

None identified

None reported

None identified

Biophysical Context

Elevation ranges from 1552 to 2442 m, on predominately 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

field plots near agricultural fruit and vegetable research farms

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).

Jamaica Bay, New York, situated on the southern shore of Long Island, and characterized by extensive coastal ecosystems in the central bay juxtaposed with a largely urbanized shoreline containing fragmented and fringing coastal habitat.

None additional

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

Varied wildflower planting mixes of annuals and perennials

No scenarios presented

Past (1609), current (2015), and future (2080) scenarios

N/A

No scenarios presented

EM Relationship to Other EMs or Applications

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

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 (multiple runs exist) View EM Runs

Method + Application

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

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

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

Document ID for related EM

Doc-260

Doc-260 | Doc-269

Doc-123

None

Doc-338

None

Doc-400

None

Doc-408

Doc-432

Doc-456 | Doc-459 | Doc-461

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-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-784

EM-837

EM-849

EM-939

EM-978 | EM-981 | EM-983 | EM-985 | EM-991

EM Modeling Approach

EM Relationship to Time

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

EM Temporal Extent

2007-2009

2007-2008

1987-1997

2000 - 2007

2006-2007

2008-2020

Not applicable

2010 - 2012

2006-2007, 2010

2010-2011

Not applicable

1609-2080

2020

Not applicable

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

past time

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

continuous

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Year

Not applicable

EM Spatial Extent

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

Bounding Type

Physiographic or Ecological

Watershed/Catchment/HUC

Geopolitical

Physiographic or Ecological

Watershed/Catchment/HUC

Geopolitical ?

Physiographic or ecological

Point or points ?

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

Physiographic or ecological

Geopolitical

Not applicable

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

Agricultural plots

East Midland

Jamaica Bay, Long Island, New York

Iran

Not applicable

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

10-100 km^2

1000-10,000 km^2.

10-100 km^2

>1,000,000 km^2

Not applicable

Spatial Distribution of Computations

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

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 lumped (in all cases)

spatially distributed (in at least some cases)

spatially distributed (in at least some cases) ?

Not applicable

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

Not applicable

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

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

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

Not applicable

multiple unrelated sites

80 m

ha^2

Not applicable

EM Structure and Computation Approach

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

Statistical Estimation of EM

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

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-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

Model Calibration Reported?

Yes

Not applicable

Model Goodness of Fit Reported?

Yes

Not applicable

Goodness of Fit (metric| value | unit)

Adjusted R | 69.9 | Not applicable

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

Model Uncertainty Analysis Reported?

Yes

Not applicable

Model Sensitivity Analysis Reported?

No ?

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-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

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

North America
- United States
  - Michigan

Europe
- United Kingdom

North America
- United States
  - New York

Europe

None

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

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Southwestern Atlantic

None

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

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

None

Temperate North Atlantic
- Northwest Atlantic
  - Cold Temperate Northwest Atlantic
    - Virginian

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

Centroid Latitude

45.05

42.5

43.25

17.75

28.05

46.72

17.73

43.87

52.22

40.61

32.29

Not applicable

Centroid Longitude

6.4

-90.63

-2.92

-64.75

102

-82.52

-96.13

-64.77

-85.64

-0.91

-73.84

53.68

Not applicable

Centroid Datum

WGS84

NAD83

WGS84

Not applicable

Centroid Coordinates Status

Provided

Estimated

Provided

Estimated

Provided

Estimated

Provided

Estimated

Provided

Estimated

Not applicable

Environments and Scales Modeled

EM ID

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

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

Agroecosystems

Created Greenspace | Grasslands

Near Coastal Marine and Estuarine

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

Agricultural landscape

restored landfills and conserved grasslands

Coastal

terrestrial land types

Multiple

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-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

EM Organismal Scale

Community

Not applicable

Guild or Assemblage

Community

Not applicable

Community

Guild or Assemblage

Species

Individual or population, within a species

Not applicable

Taxonomic level and name of organisms or groups identified

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

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

other
- Bees
Species
- Silphium perfoliatum
- Monarda fistulosa
- Symnphyotrichum novae-angliae
- Triodanis perfoliata
- Coreopsis tinctoria
- Ratibida pinnata
- Rudbeckia hirta
- Agalinis tenuifolia
- Lupinus perennis
- Lobelia inflata
- Solidago speciosa
- Cleome serrulata
- Dalea purpurea
- Chamaecrista fasciculata
- Linum sulcatum
- Asclepias syriaca
- Symnphyotrichum laeve
- Froelichia floridana

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)

None Available

Species
- Apis mellifera
- Apis florea

None Available

EnviroAtlas URL

EM-65

EM-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

GAP Ecological Systems

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

National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation

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

Big game hunting recreation demand, Total Employment

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-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

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

[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
  - Lifecycle maintenance, habitat and gene pool protection
    - Pollination and seed dispersal

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

[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
    - Pollination and seed dispersal

[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

<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-71

EM-79

EM-91

EM-193

EM-260

EM-359

EM-392

EM-414

EM-446

EM-454

EM-796

EM-836

EM-851

EM-941

EM-990

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

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Regulation of extreme events
    - Extreme Event Indicator

None

Agroecosystems (22)
- Fauna (4)
  - Pollinators
    - Site 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

None

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:

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

EM Modeling Approach

Comment:

Comment:

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)