EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

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

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

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

EM Variables by Category

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

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

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

All EM Descriptors

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

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

EM Descriptors by Modeling Concepts

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

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

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

Compare Criteria:

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Comparing:

EM-12
EM-82
EM-127
EM-154
EM-186
EM-333
EM-423
EM-445
EM-457
EM-459
EM-496
EM-604
EM-626
EM-651
EM-653
EM-667
EM-672
EM-709
EM-841

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

EM Short Name

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

Pollination ES, Central French Alps

Annual profit - carbon plantings, South Australia

Mangrove development, Tampa Bay, FL, USA

FORCLIM v2.9, Western OR, USA

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

Air pollutant removal, Guánica Bay, Puerto Rico

Relative wave dissipation, St. Croix, USVI

Visitation to reef dive sites, St. Croix, USVI

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

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

Chinook salmon value (household), Yaquina Bay, OR

SolVES, Shoshone NF, WY

Dickcissel density, CREP, Iowa, USA

Natural amenities and population migration, USA

Alewife derived nutrients, Connecticut, USA

Alewife nutrients in stream food web, CT, USA

Pollinators on landfill sites, United Kingdom

Brown-headed cowbird abundance, Piedmont, USA

EM Full Name

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

Pollination ecosystem service estimated from plant functional traits, Central French Alps

Annual profit from carbon plantings, South Australia

Mangrove wetland development, Tampa Bay, FL, USA

FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA

Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA

Air pollutant removal, Guánica Bay, Puerto Rico, USA

Relative wave dissipation (by reef), St. Croix, USVI

Visitation to dive sites (reef), St. Croix, USVI

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

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

Economic value of Chinook salmon per household method, Yaquina Bay, OR

SolVES, Social Values for Ecosystem Services, Shoshone National Forest, WY

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

Natural amenities and rural population migration, USA

Alewife derived nutrients in stream food web, Connecticut, USA

Pollinating insects on landfill sites, East Midlands, United Kingdon

Brown-headed cowbird abundance, Piedmont ecoregion, USA

EM Source or Collection

Envision

EU Biodiversity Action 5

None

US EPA

Envision

US EPA

None

USDA Forest Service

None

EM Source Document ID

47 ?

260

243

23 ?

47 ?

338 ?

335

333

324

369

372

375

384

389

405

Document Author

Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.

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

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

Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L.

Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.

Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.

Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher

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

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

Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar

Sherrouse, B.C., Semmens, D.J., and J.M. Clement

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

Cordell H. K., V. Heboyan, F. Santos, J. C. Bergstrom

Walters, A. W., R. T. Barnes, and D. M. Post

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

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

Document Year

2008

2011

2012

2007

2008

2017

2014

2012

2014

2010

2011

2009

2013

2008

Document Title

Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests

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

Carbon payments and low-cost conservation

Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence

Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model

Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests

Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico

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

Sediment nitrification and denitrification in a Lake Superior estuary

Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management

An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming

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

Natural amenities and rural population migration

Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs

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

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

Document Status

Peer reviewed and published

Comments on Status

Published journal manuscript

Published report

Published journal manuscript

Software and Access

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

EM Software Access info Exit

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

Not applicable

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

Not applicable

Contact Name

Michael R. Guzy

Sandra Lavorel

Neville D. Crossman

Michael Osland

Richard T. Busing

Michael R. Guzy

Susan H. Yee

Brent J. Bellinger ?

Stephen Jordan

Benson Sherrouse

David Otis

Ken Cordell

Annika W. Walters

Sam Tarrant

Sam Riffell

Contact Address

Oregon State University, Dept. of Biological and Ecological Engineering

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

CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia

U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561

U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA

Oregon State University, Dept. of Biological and Ecological Engineering

U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

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

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

U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA

USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA

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

U.S. Department of Agriculture, Forest Service, Southern Research Station, Athens, GA 30602

Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA

Dept. of Ecology and Evolutionary Biology, Yale University, New Haven CT 06511

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

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

Contact Email

Not reported

sandra.lavorel@ujf-grenoble.fr

neville.crossman@csiro.au

mosland@usgs.gov

rtbusing@aol.com

Not reported

yee.susan@epa.gov

bellinger.brent@epa.gov

jordan.steve@epa.gov

bcsherrouse@usgs.gov

dotis@iastate.edu

Not reported

annika.walters@yale.edu

sam.tarrant@rspb.org.uk

sriffell@cfr.msstate.edu

EM Description

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

Summary Description

**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses."

ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions."

ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially."

ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses."

ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e).

AUTHOR'S DESCRIPTION: "Air pollutant removal, particularly of large dust particles relevant to asthma, was identified as an ecosystem service contributing to the stakeholder objective to improve air quality…Rates of air pollutant removal depend on the downward flux of particles intercepted by the tree canopy…Because atmospheric pollutant concentration can vary widely across space and time, we standardized across watersheds by calculating the removal rate per unit concentration of pollutant, assuming a pollutant concentration of 1 g m^-3. Specifically, the removal rate was calculated per unit concentration of particulate matter greater than…PM<sub>10, applying a typical deposition velocity of 1.25 cm s^-1…"

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). Perhaps the simplest method to estimate shoreline protection is by defining the relative contribution of different habitat types to wave energy attenuation (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to wave energy dissipation as the overall weighted average of the magnitude of wave energy dissipation across habitat types within that grid cell: Relative wave energy dissipation = ΣiciMi 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, Acroporapalmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mi is the relative magnitude of wave energy dissipation associated with each habitat type on a scale of 0–3 (Table3)."

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)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix."

ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(2) density of the queen conch Strombus gigas"

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

ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico.

ABSTRACT: “Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders. With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, social value information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.”

ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Dickcissel (Spiza americana)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: DICK density = 1-1/1+e^(-6.811334 + 1.889878 * bbspath) * e^(-1.831015 + 0.0312571 * hay400)

ABSTRACT: "Research suggests that significant relationships exist between rural population change and natural amenities. Thus, understanding and predicting domestic migration trends as a function of changes in natural amenities is important for effective regional growth and development policies and strategies. In this study, we first estimated an econometric model which showed the effects of natural amenities, such as climate and landscape variables, on rural population migration patterns in the United States between 1990 and 2007. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections. Results suggest that people prefer rural areas with mild winters and cooler summers; thus we can expect a direct impact of climate change on population migration when areas associated with these conditions change. Results also suggest preference for varied landscapes that feature a mix of forest land and open space (e g , pasture and range land). During the projection period from 2010 to 2060 in the United States, changes in natural amenities were predicted to have positive effects on rural population migration trends in most parts of the Intermountain and Pacific Northwest regions, and some parts of the Southeastern, South Central, and Northeastern U S regions (e g , Southern Appalachian Mountains, Ozark Mountains, northern New England). Changes in natural amenities were predicted to have negative effects on rural population migration trends during the projection period in Midwestern regions (e g , Great Plains and North Central regions)." AUTHOR'S DESCRIPTION: "This model was estimated for 2,014 rural counties in the continental United States using various national data bases and sources. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections."

ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year. Natural abundance stable isotope analyses indicate that this influx of marine-derived nitrogen is rapidly incorporated into the stream food web. An enriched d15N signal, indicative of a marine origin, is present at all stream trophic levels with the greatest level of enrichment coincident with the timing of the anadromous alewife spawning migration. There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." AUTHOR'S DESCRIPTION: "Here, we examine the effect of alewife-contributed marine- derived nutrients to coastal stream ecosystems in southern New England. We take a comparative approach examining streams with and without anadromous alewife runs. We use natural abundance stable isotope analyses to assess the incorporation of marine-derived nitrogen and carbon into stream food webs."

ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each

ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. "

Specific Policy or Decision Context Cited

Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…"

None identified

Not applicable

None Identified

None identified

None

None identified

Nutrients and water quality related to anadromous alewife restoration efforts

None identified

None reported

Biophysical Context

No additional description provided

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

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

mangrove forest,Salt marsh, estuary, sea level,

Coastal to montane, Pacific Northwest US (Oregon) forests.

No additional description provided

Yaquina Bay estuary

Rocky mountain conifer forests

Prairie pothole region of north-central Iowa

No additional description provided

Alewife spawning runs typically occur Mid March - May.

No additional description provided

Conservation Reserve Program lands left to go fallow

EM Scenario Drivers

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

No scenarios presented

Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e

Not applicable

Two scenarios modelled, forests with and without fire

Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves.

No scenarios presented

N/A

No scenarios presented

Climate projections based on the CGCM 3 1 general circulation model of moderate warming (IPCC). The A1B scenario assumes a growing world population that peaks in the mid-century and balanced technological growth.

No scenarios presented

N/A

EM Relationship to Other EMs or Applications

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

Method Only, Application of Method or Model Run

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs ?

Method + Application

Method + Application (multiple runs exist) View EM Runs ?

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

New or Pre-existing EM?

New or revised model

Application of existing model

New or revised model

Application of existing model

New or revised model

Application of existing model ?

New or revised model

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

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

Document ID for related EM

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

Doc-260

Doc-245 | Doc-246 | Doc-247 | Doc-243

None

Doc-22 | Doc-23 ?

Doc-183 | Doc-47 | Doc-313 | Doc-314 ?

None

Doc-324

Doc-369

Doc-372

None

Doc-383

Doc-384 | Doc-383

Doc-389

Doc-405

EM ID for related EM

EM-333 | EM-369

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83

EM-128 | EM-141

None

EM-146 | EM-208 | EM-224

EM-12 | EM-369

None

EM-603 | EM-397

EM-629 | EM-628

EM-652 | EM-650 | EM-649 | EM-648

None

EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673

EM-667 | EM-661

EM-697

EM-831 | EM-838 | EM-839 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847

EM Modeling Approach

EM Relationship to Time

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

EM Temporal Extent

1990-2050

Not reported

2009-2050

1990-2010

>650 yrs

1990-2050

2013

2006-2007, 2010

July 2011 to September 2012 ?

2003-2008

2004-2008

1992-2007

1982-2060

1979-2009

2005-2006 (March-July)

2007-2008

2008

EM Time Dependence

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

EM Time Reference (Future/Past)

future time

Not applicable

future time

past time

future time

Not applicable

future time

Not applicable

past time

Not applicable

EM Time Continuity

discrete

Not applicable

discrete

continuous

discrete

Not applicable

discrete

Not applicable

other or unclear (comment) ?

Not applicable

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Year

Not applicable

Year

Not applicable

Year

Not applicable

Year

Not applicable

EM Spatial Extent

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

Bounding Type

Geopolitical

Physiographic or Ecological

Physiographic or ecological

Geopolitical

Watershed/Catchment/HUC

Physiographic or ecological

Geopolitical

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

Geopolitical

Watershed/Catchment/HUC

Geopolitical

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

Physiographic or ecological

Spatial Extent Name

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

Central French Alps

Agricultural districts of the state of South Australia

Tampa Bay

Western Oregon, north of 43.00 N to Washington border

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

Guanica Bay watershed

Coastal zone surrounding St. Croix

St. Louis River Estuary (of western Lake Superior)

Pacific Northwest

National Forest

CREP (Conservation Reserve Enhancement Program) wetland sites

continental United States

Bride Brook

New London County, Connecticut, USA

East Midlands

Piedmont Ecoregion

Spatial Extent Area (Magnitude)

10-100 km^2

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

100-1000 km^2

10,000-100,000 km^2

10-100 km^2

1000-10,000 km^2.

100-1000 km^2

10-100 km^2

>1,000,000 km^2

1000-10,000 km^2.

1-10 km^2

>1,000,000 km^2

1-10 ha

1000-10,000 km^2.

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

Spatial Distribution of Computations

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

EM Spatial Distribution

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)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

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

Not applicable

area, for pixel or radial feature

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

map scale, for cartographic feature

Not applicable

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

Not applicable

Spatial Grain Size

varies

20 m x 20 m

1 ha x 1 ha

m^2

0.08 ha

varies

30 m x 30 m

10 m x 10 m

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

Not applicable

30m2

multiple, individual, irregular shaped sites

varies

Not applicable

variable stream lengths

multiple unrelated locations

Not applicable

EM Structure and Computation Approach

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

EM Computational Approach

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

Not applicable

Analytic

EM Determinism

stochastic

deterministic

stochastic

deterministic

Not applicable

deterministic

Statistical Estimation of EM

Other or unclear (comment)

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Other or unclear (comment)

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

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

Model Calibration Reported?

Unclear

Yes

Unclear

Yes

Yes ?

Not applicable

Yes

Model Goodness of Fit Reported?

Yes

Not applicable

Goodness of Fit (metric| value | unit)

None

r2 | 0.51 | unitless

None

Z score | -.1.8 to -15.9 | NA
R value | 0.577 - 0.91 | NA

None

Model Operational Validation Reported?

Yes

Unclear

Not applicable

Model Uncertainty Analysis Reported?

Yes

Not applicable

Model Sensitivity Analysis Reported?

No ?

Yes

Not applicable

Yes

Model Sensitivity Analysis Include Interactions?

Not applicable

Unclear

EM Locations, Environments, Ecology

Location of EM Application

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

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

North America
- United States
  - Oregon

Europe
- France

Oceania
- Australia

North America
- United States
  - Florida

North America
- United States
  - Oregon

North America
- United States
  - Oregon

North America
- U.S. Caribbean Territories

None

North America
- United States
  - Minnesota
  - Wisconsin

North America
- United States
  - Oregon
  - Washington

North America
- United States
  - Wyoming
  - Colorado

North America
- United States
  - Iowa

North America
- United States

North America
- United States
  - Connecticut

North America
- United States
  - Connecticut

Europe
- United Kingdom

North America
- United States
  - North Carolina
  - Georgia
  - Alabama
  - Maryland
  - New Jersey
  - South Carolina
  - Pennsylvania

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

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Floridian

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Greater Antilles

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

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

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

None

Temperate North Pacific
- Northeast Pacific
  - Cold Temperate Northeast Pacific
    - Oregon, Washington, Vancouver Coast and Shelf

None

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

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

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

Centroid Latitude

44.11

45.05

-34.9

27.8

44.66

44.11

17.96

17.73

46.74

44.62

43.98

42.62

39.8

41.32

41.78

52.22

36.23

Centroid Longitude

-123.09

6.4

138.7

-82.4

-122.56

-123.09

-67.04

-64.77

-96.13

-124.02

109.52

-93.84

-98.55

-72.24

-72.17

-0.91

-81.9

Centroid Datum

WGS84

Centroid Coordinates Status

Estimated

Provided

Estimated

Provided

Estimated

Environments and Scales Modeled

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

EM Environmental Sub-Class

Rivers and Streams | Forests | Agroecosystems | Created Greenspace

Agroecosystems | Grasslands

Agroecosystems

Near Coastal Marine and Estuarine

Forests

Rivers and Streams | Forests | Agroecosystems | Created Greenspace

Near Coastal Marine and Estuarine

Rivers and Streams | Inland Wetlands

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

Forests

Inland Wetlands | Agroecosystems | Grasslands

Rivers and Streams

Created Greenspace | Grasslands

Grasslands

Specific Environment Type

Agricultural-urban interface at river junction

Subalpine terraces, grasslands, and meadows.

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

Created Mangrove wetlands

Primarily conifer forest

Agricultural-urban interface at river junction

Multiple environmental types present

Coral reefs

River and riverine estuary (lake)

Yaquina Bay estuary and ocean

Montain forest

Grassland buffering inland wetlands set in agricultural land

Terrestrial environments including water bodies and coastlines

Coastal stream

Coastal streams

restored landfills and grasslands

grasslands

EM Ecological Scale

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

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

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

Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

EM Organismal Scale

Not applicable

Community

Guild or Assemblage

Not applicable

Species

Not applicable

Community

Not applicable

Species

Not applicable

Other (multiple scales)

Not applicable

Species

Not applicable

Individual or population, within a species

Species

Taxonomic level and name of organisms or groups identified

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

other
- Fish
Species
- Red-Legged Frog
- Osprey
- Western Terrestrial Garter Snake
- Little Brown Myotis
- Belted Kingfisher
- Wood Duck
- Northern River Otter
- Great Blue Heron
- Roughskin Newt
- Pacific Water Shrew
- American Beaver
- Mink
- Western Pond Turtle
- Killdeer
- Northwestern Salamander

None Available

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

Species
- Laguncularia racemosa
- Avicennia germinans
- Rhizophora mangle
- Spartina alterniflora

Species
- Pinus ponderosa
- Abies amabilis
- Tsuga mertensiana
- Thuja plicata
- Picea sitchensis
- Abies procera
- Abies grandis
- Chamaecyparis nootkatensis
- Tsuga heterophylla
- Other (trees)
- Quercus garryana
- Pseudotsuga menziesii

other
- Fish
Species
- Red-Legged Frog
- Osprey
- Western Terrestrial Garter Snake
- Little Brown Myotis
- Belted Kingfisher
- Wood Duck
- Northern River Otter
- Great Blue Heron
- Roughskin Newt
- Pacific Water Shrew
- American Beaver
- Mink
- Western Pond Turtle
- Killdeer
- Northwestern Salamander

None Available

Species
- Panulirus argus

None Available

Species
- Chinook salmon (Onchorhyncus tshawytscha)

None Available

Species
- Dickcissel (Spiza americana)

None Available

Species
- alewife Alosa pseudoharengus

Species
- anadromous alewife (Alosa pseudoharengus)

Genus
- Calliopum
- Lasioglossum
Species
- Apis mellifera
- Bombus lapidarius
- Oedemera nobilis
- Episyrphus balteatus
- Bombus terrestris/lucorum
- Bombus pascuorum

Species
- Brown headed cowbird

EnviroAtlas URL

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

Dasymetric Allocation of Population, GAP Ecological Systems, The National Hydrography Dataset (NHD), Percent Natural Land Cover in Stream Buffer , Hectares of Vegetable Crops, Employment Rate

GAP Ecological Systems

Carbon Storage by Tree Biomass

None Available

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

Dasymetric Allocation of Population, GAP Ecological Systems, The National Hydrography Dataset (NHD), Percent Natural Land Cover in Stream Buffer , Hectares of Vegetable Crops, Employment Rate

The Watershed Boundary Dataset (WBD)

None Available

Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition

Dasymetric Allocation of Population

GAP Ecological Systems, Enabling Conditions

GAP Ecological Systems

Dasymetric Allocation of Population, GAP Ecological Systems, Average Annual Precipitation, Total Employment, Employment Rate

The National Hydrography Dataset (NHD)

None Available

GAP Ecological Systems

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

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

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

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

[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
- 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
  - Soil formation and composition
    - Decomposition and fixing processes
  - Atmospheric composition and climate regulation
    - Global climate regulation by reduction of greenhouse gas concentrations
  - Lifecycle maintenance, habitat and gene pool protection
    - Pollination and seed dispersal

[Ecosystem] Provisioning
- Materials
  - Biomass
    - Fibres and other materials from plants, algae and animals for direct use or processing
[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

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

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

None

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

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

[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Bio-remediation by micro-organisms, algae, plants, and animals

[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

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs
    - Wild plants, algae and their outputs
[Ecosystem] Cultural
- Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Spiritual and/or emblematic
    - Symbolic
- 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
    - Educational

[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
    - 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
    - Entertainment

None

[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

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

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

EM-12

EM-82

EM-127

EM-154

EM-186

EM-333

EM-423

EM-445

EM-457

EM-459

EM-496

EM-604

EM-626

EM-651

EM-653

EM-667

EM-672

EM-709

EM-841

Forests (21)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
Agroecosystems (22)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator

None

Near Coastal Marine/Estuarine (113)
- Flora (5)
  - Trees
    - Stock Indicator
  - Seaweed
    - Stock Indicator
- Composite (8)
  - Regulation of extreme events
    - Quality Indicator

None

Forests (21)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
Agroecosystems (22)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator

None

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator

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

None

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

Forests (21)
- Composite (8)
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Quality Indicator

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

None

Rivers and Streams (111)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator

None

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

EcoService Models Library (ESML)

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EM Locations, Environments, Ecology

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

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

Comment:

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)