EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?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.
EM Identity and Description
EM ID
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Soil carbon content, Central French Alps | PATCH, western USA | Landscape importance for crops, Europe | Value of Habitat for Shrimp, Campeche, Mexico | Land-use change and habitat diversity, Europe | Annual profit - carbon plantings, South Australia | Natural attenuation by soil, The Netherlands | ROS (Recreation Opportunity Spectrum), Europe | Redfish and cold water coral (EFH), Norway | Evoland v3.5 (unbounded growth), Eugene, OR, USA | HexSim, tule elk, California, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico | WaSSI, Conterminous USA | Relative wave dissipation, St. Croix, USVI | LUCI, New Zealand | RUM: Valuing fishing quality, Michigan, USA | Mallard recruits, CREP wetlands, Iowa, USA | ESII Tool method | Seed mix and mowing in prairie reconstruction, USA | C sequestration in grassland restoration, England | Wild bees over 26 yrs of restored prairie, IL, USA | Wildflower mix supporting bees, CA, USA | Northern bobwhite abundance, Piedmont region, USA | Horned lark abundance, Piedmont region, USA | Neighborhood greenness and health, FL, USA |
EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Soil carbon content, Central French Alps | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Landscape importance for crop-based production, Europe | Value of Habitat for Shrimp, Campeche, Mexico | Land-use change effects on habitat diversity, Europe | Annual profit from carbon plantings, South Australia | Natural attenuation capacity of the soil, The Netherlands | ROS (Recreation Opportunity Spectrum), Europe | Linkage between redfish and cold water coral, Norway (essential fish habitat model) | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | HexSim, tule elk, California, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | Water Supply Stress Index, Conterminous USA | Relative wave dissipation (by reef), St. Croix, USVI | LUCI (Land Utilisation and Capability Indicator), New Zealand | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Mallard duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | ESII (Ecosystem Services Identification & Inventory) Tool method | Seed mix design and first year management in prairie reconstruction, IA, USA | Carbon sequestration in grassland diversity restoration, England | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA | Northern bobwhite abundance, Piedmont ecoregion, USA | Horned lark abundance, Piedmont ecoregion, USA | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA |
EM Source or Collection
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US EPA | EnviroAtlas | EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | None | EU Biodiversity Action 5 | None | None | EU Biodiversity Action 5 | None | Envision | US EPA | US EPA |
USDA Forest Service ?Comment:While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. |
US EPA | None | None | None | None | None | None | None | None | None | None | None |
EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
260 | 2 | 228 | 227 | 228 | 243 | 287 | 293 | 259 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
328 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
341 | 335 |
380 ?Comment:Document 381 is an additional source for this EM. |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
372 ?Comment:Document 373 is a secondary source for this EM. |
391 ?Comment:Website for online user support |
395 | 396 | 401 | 400 | 405 | 405 | 417 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Haines-Young, R., Potschin, M. and Kienast, F. | Barbier, E. B., and Strand, I. | Haines-Young, R., Potschin, M. and Kienast, F. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | van Wijnen, H.J., Rutgers, M., Schouten, A.J., Mulder, C., de Zwart, D., and Breure, A.M. | Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Huber, P. R., S. E. Greco, N. H. Schumaker, and J. Hobbs | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Trodahl, M. I., B. M. Jackson, J. R. Deslippe, and A. K. Metherell | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | 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 | EcoMetrix Solutions Group (ESG) | Meissen, J. C., A. J. Glidden, M. E. Sherrard, K. J. Elgersma, and L. L. Jackson | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | 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 | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik |
Document Year
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2013 | 2011 | 2006 | 2012 | 1998 | 2012 | 2011 | 2012 | 2014 | 2010 | 2008 | 2014 | 2017 | 2013 | 2014 | 2017 | 2014 | 2010 | 2016 | 2019 | 2011 | 2017 | 2015 | 2008 | 2008 | 2016 |
Document Title
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EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Defining recovery goals and strategies for endangered species: The wolf as a case study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Carbon payments and low-cost conservation | How to calculate the spatial distribution of ecosystem services - Natural attenuation as example from the Netherlands | Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Estimating linkages between redfish and cold water coral on the Norwegian coast | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | A priori assessment of reintroduction strategies for a native ungulate: using HexSim to guide release site selection | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | WaSSI Ecosystem Services Model | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Investigating trade-offs between water quality and agricultural productivity using the Land Utilisation and Capability Indicator (LUCI)-A New Zealand application | Valuing recreational fishing quality at rivers and streams | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | ESII Tool | Seed mix design and first year management influence multifunctionality and cost-effectiveness in prairie reconstruction | Additional carbon sequestration benefits of grassland diversity restoration | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Neighborhood greenness and chronic health conditions in Medicare beneficiaries |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | http://www.hexsim.net/download | Not applicable | http://www.wassiweb.sgcp.ncsu.edu/ | Not applicable |
info@lucitools.org ?Comment:To obtain LUCI, email us your enquiry at info@lucitools.org with information about: The problem you are seeking to solve or your research question. The country and region you wish to apply LUCI in. What data you have with as much detail as possible about the data sources. Your timeframe or deadlines. |
Not applicable | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Carlos Carroll | Marion Potschin | E.B. Barbier | Marion Potschin | Neville D. Crossman | H.J. van Wijnen | Maria Luisa Paracchini | Naomi S. Foley | Michael R. Guzy | P. R. Huber | Susan H. Yee | Ge Sun | Susan H. Yee | Martha I. Trodahl | Richard Melstrom | David Otis | Not reported | Justin Meissen | Gerlinde B. De Deyn | Sean R. Griffin | Neal Williams | Sam Riffell | Sam Riffell | Scott C. Brown |
Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Klamath Center for Conservation Research, Orleans, CA 95556 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Environment Department, University of York, York YO1 5DD, UK | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands | Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | Dept. of Economics and Management, Univeristy of Tromso, Norway | Oregon State University, Dept. of Biological and Ecological Engineering | University of California, Davis, One Shields Ave., Davis, CA 95616, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | School of Geography, Environment & Earth Sciences, Victoria University of Wellington, New Zealand | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | Tallgrass Prairie Center, 2412 West 27th Street, Cedar Falls, IA 50614-0294, USA | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 |
Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | carlos@cklamathconservation.org | marion.potschin@nottingham.ac.uk | Not reported | marion.potschin@nottingham.ac.uk | neville.crossman@csiro.au | harm.van.wijnen@rivm.nl | luisa.paracchini@jrc.ec.europa.eu | naomifoley@gmail.com | Not reported | prhuber@ucdavis.edu | yee.susan@epa.gov | gesun@fs.fed.us | yee.susan@epa.gov | Not reported | melstrom@okstate.edu | dotis@iastate.edu | Not reported | justin.meissen@uni.edu | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | srgriffin108@gmail.com | nmwilliams@ucdavis.edu | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | sbrown@med.miami.edu |
EM ID
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in soil carbon 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…Soil carbon for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on soil carbon. 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." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153) | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes...are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Habitat diversity); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000." AUTHOR'S DESCRIPTION: "The analysis for the regulating service “Habitat diversity” seeks to identify all the areas with potential to support biodiversity…The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers." | 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: "Maps play an important role during the entire process of spatial planning and bring ecosystem services to the attention of stakeholders' negotiation more easily. As example we show the quantification of the ecosystem service ‘natural attenuation of pollutants’, which is a service necessary to keep the soil clean for production of safe food and provision of drinking water, and to provide a healthy habitat for soil organisms to support other ecosystem services. A method was developed to plot the relative measure of the natural attenuation capacity of the soil in a map. Several properties of Dutch soils were related to property-specific reference values and subsequently combined into one proxy for the natural attenuation of pollutants." AUTHOR'S DESCRIPTION: "The natural attenuation capacity that is modeled in this study must be seen as a measure that describes the ‘biodegradation capacity’ of the soil, including biodegradation of all types of contaminants" | ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | ABSTRACT: "…This paper applies the production function approach to estimate the link between cold water corals and redfish in Norway. Both the carrying capacity and growth rate of redfish are found to be functions of cold water coral habitat and thus cold water corals can be considered an essential fish habitat…The essential habitat model shows the best fit to the data…" AUTHOR'S DESCRIPTION: "…the EFH model presented by Barbier and Strand (1998), in which the habitat is considered essential to the stock; i.e., if the habitat declines to zero the fish stock will perish…based on the Gordon-Schaefer model, which is a single-species biomass model, where effort is the control variable and fish stock is the state variable. In the case of habitat-fisheries interactions, such as in our case, a second state variable is introduced, the habitat (CWC)…Scientists have stimated that 30-50% of CWC habitat has been damaged (Fossa, Mortensen, and Furevik 2002. Working within these bounds, we empirically estimate the relationship between CWC as a habitat and a fish stock..." | **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." | AUTHOR'S DESCRIPTION: "HexSim is a simulation framework within which PVA and other models are constructed. HexSim simulations can range from simple and parsimonious, at one extreme, to complex, data intensive, and biologically realistic at the other. Our tule elk simulations were moderately complex, capturing major life history events such as survival, reproduction and movement, while ignoring other details such as impact of environmental stochasticity or the spread of diseases through the population." "One of the features that distinguishes HexSim from its predecessor is the ability to model group, or herd, movement. This is accomplished through use of a ‘‘proto-disperser’’, an imaginary individual that explores the landscape, finds resources, and then serves as a movement target for the other group members who converge on this target. This feature allows for modeling of both individuals and groups. Another useful feature of HexSim is the barriers component. Multiple types of movement barriers can be included in the model, reflecting likely responses to various kinds of blockages to wildlife. Because many of these barriers tend to be human-related, this feature allows for assessing the potential impacts of multiple types of human infrastructure and landscape features on modeled species. This paper examines several reintroduction scenarios for returning an endemic elk subspecies (tule elk; Cervus elaphus nannodes) to a portion of its native range in California, USA." | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | AUTHORS DESCRIPTION: "WaSSI simulates monthly water and carbon dynamics at the Hydrologic Unit Code 8 level in the US. Three modules are integrated within the WaSSI model framework. The water balance module computes ecosystem water use, evapotranspiration and the water yield from each watershed. Water yield is sometimes referred to as runoff and can be thought of as the amount of streamflow at the outlet of each watershed due to hydrologic processes in each watershed in isolation without any flow contribution from upstream watersheds. The ecosystem productivity module simulates carbon gains and losses in each watershed or grid cell as functions of evapotranspiration. The water supply and demand module routes and accumulates the water yield through the river network according to topological relationships between adjacent watersheds, subtracts consumptive water use by humans from river flows, and compares water supply to water demand to compute the water supply stress index, or WaSSI." | 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: "...The Land Utilisation & Capability Indicator (LUCI) is a GIS framework that considers impacts of land use on multiple ecosystem services in a holistic and spatially explicit manner. Due to its fine spatial scale and focus on the rural environment, LUCI is well-placed to help both farm and catchment managers to explore and quantify spatially explicit solutions to improve water quality while also maintaining or enhancing other ecosystem service outcomes. LUCI water quality and agricultural productivity models were applied to a catchment in the Bay of Plenty, New Zealand. Nitrogen (N) and phosphorus (P) sources, sinks and pathways in the landscape were identified and trade-offs and synergies between water quality and agricultural productivity were investigated. Results indicate that interventions to improve water quality are likely to come at the expense of agriculturally productive land. Nonetheless, loss of agriculturally productive land can be minimised by using LUCI to identify, at a fine spatial scale, the most appropriate locations for nutrient intervention. Spatially targeted and strategic nutrient source management and pathway interception can improve water quality, while minimising negative financial impacts on farms. Our results provide spatially explicit solutions to optimize agricultural productivity and water quality, which will inform better farm, land and catchment management as well as national and international policy." AUTHOR'S DESCRIPTION (of OVERSEER submodel): "Water quality models within LUCI use an enhanced, spatially representative export co-efficient (EC) approach to model total nitrogen (TN) and total phosphorus (TP) exports to water… Here, ECs for pastoral land cover are calculated by LUCI using algorithms derived from a large ( > 14 000 samples), pastorally based national dataset. The dataset consists of detailed farm nutrient input and management variables that have been entered and run using OVERSEER® to generate nutrient loss predictions, which are also included in the dataset." NOTE: The LUCI model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-658. https://esml.epa.gov/detail/em/658 | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance." | ABSTRACT: "Agricultural intensification continues to diminish many ecosystem services in the North American Corn Belt. Conservation programs may be able to combat these losses more efficiently by developing initiatives that attempt to balance multiple ecological benefits. In this study, we examine how seed mix design and first year management influence three ecosystem services commonly provided by tallgrass prairie reconstructions (erosion control, weed resistance, and pollinator resources). We established research plots with three seed mixes, with and without first year mowing. The grass-dominated “Economy” mix had 21 species and a 3:1 grass-to-forb seeding ratio. The forb-dominated “Pollinator”mix had 38 species and a 1:3 grass-to-forb seeding ratio. The grass:forb balanced “Diversity” mix, which was designed to resemble regional prairie remnants, had 71 species and a 1:1 grass-to-forb ratio. To assess ecosystem services, we measured native stem density, cover, inflorescence production, and floral richness from 2015 to 2018. The Economy mix had high native cover and stem density, but produced few inflorescences and had low floral richness. The Pollinator mix had high inflorescence production and floral richness, but also had high bare ground and weed cover. The Diversity mix had high inflorescence production and floral richness (comparable to the Pollinator mix) and high native cover and stem density (comparable to the Economy mix). First year mowing accelerated native plant establishment and inflorescence production, enhancing the provisioning of ecosystem services during the early stages of a reconstruction. Our results indicate that prairie reconstructions with thoughtfully designed seed mixes can effectively address multiple conservation challenges." | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | Abstract: " 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:"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." | 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." | ABSTRACT: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." |
Specific Policy or Decision Context Cited
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None Identified | None identified | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | None identified | None identified | None identified | None identified | None identified | None identified | None identified | 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…" | As part of an ongoing restoration program, HexSim was used to evaluate a portion of the former range of tule elk to identify the release scenario producing the most elk and fewest human conflicts. | None provided | WaSSI can be used to project the regional effects of forest land cover change, climate change, and water withdrawals on river flows, water supply stress, and ecosystem productivity (i.e., carbon sequestration).WaSSI can be used to evaluate trade-offs among management strategies that influence multiple ecosystem services | None identified | Land management trade off between agricultural productivity and water quality | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None reported | None identified |
Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | No additional description provided | Gulf of Mexico; mangrove-lagoon system | No additional description provided | 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. | Five soil types including Löss, Fluvial clay, Peat, Sand, and Silty Loam. Five land-use types including Pasture, Arable farming, Semi-natural grassland, Heathland, and Forest. | No additional description provided | Continental slope | No additional description provided | Located in the Central Valley of California. | No additional description provided | Conterminous US | No additional description provided | Groundwater dominated, volcanic caldera catchment, largely comprised of porous allophanic and pumice soils. | stream and river reaches of Michigan | Prairie Pothole Region of Iowa | Not applicable | The site, located at the Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa, is relatively level with slopes not exceeding a 5% grade. Soil composition is primarily poorly drained Clyde clay loams with a minor component of somewhat poorly drained Floyd loams. Sub-surface tile drains exist on site and are spaced approximately 18–24m apart. The land was used for corn and soybean production prior to site establishment in 2015. | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Population growth, road development (density) on public vs private land | No scenarios presented | No scenarios presented | Recent historical land use change from 1990-2000 | 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 | No scenarios presented | No scenarios presented | Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area. | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | Four release sites; Kesterson, Arena Plains, San Luis, and East Bear Creek. | No scenarios presented |
No scenarios presented ?Comment:Model can be run from WaSSI website using a historic data set (1961 - 2010) or projections from various climate models representing different emissions scenarios and time periods from recent past to 2099. |
No scenarios presented | No scenarios presented | targeted sport fish biomass | No scenarios presented | No scenarios presented | Seed mix design | Additional benefits due to biodiversity restoration practices | No scenarios presented | Varied wildflower planting mixes of annuals and perennials | N/A | N/A | No scenarios presented |
EM ID
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Application of existing model |
Application of existing model ?Comment:. |
Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-260 | Doc-269 | Doc-328 | Doc-337 | Doc-231 | Doc-228 | None | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | Doc-245 | Doc-246 | Doc-247 | Doc-243 | Doc-288 | Doc-290 | Doc-291 | Doc-289 | Doc-227 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-327 | Doc-2 | Doc-337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
None | None | None | Doc-379 | Doc-381 | None | Doc-372 | Doc-373 | None | Doc-394 | None | None | Doc-400 | None | Doc-405 | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | EM-65 | EM-66 | EM-68 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-403 | EM-422 | EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-185 | EM-319 | EM-122 | EM-123 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | EM-128 | EM-141 | None | None | EM-106 | EM-12 | EM-369 | EM-98 | EM-422 | None | None | None | EM-658 | None | EM-705 | EM-704 | EM-703 | EM-702 | EM-701 | EM-632 | EM-713 | EM-719 | None | None | EM-784 | EM-793 | EM-838 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | None |
EM Modeling Approach
EM ID
em.detail.idHelp
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
EM Temporal Extent
em.detail.tempExtentHelp
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2006-2010 | 2007-2009 | 2000-2025 | 2000 | 1980-1990 | 1990-2000 | 2009-2050 | 1999-2005 | Not reported | 1986-2002 | 1990-2050 | 25 years | 1978 - 2009 | 1961-2009 | 2006-2007, 2010 | 1930-2013 | 2008-2010 | 1987-2007 | Not applicable | 2015-2018 | 1990-2007 | 1988-2014 | 2011-2012 | 2008 | 2008 | 2010-2011 |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 2 | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Year | Not applicable | Year | Not applicable | Year | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Month | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Physiographic or ecological | Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or Ecological | Geopolitical | Geopolitical | Physiographic or ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Other | Other | Physiographic or ecological |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Physiographic or ecological | Physiographic or ecological | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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counterminous United States | Central French Alps | Western United States | The EU-25 plus Switzerland and Norway | Laguna de Terminos Mangrove system | The EU-25 plus Switzerland and Norway | Agricultural districts of the state of South Australia | The Netherlands | European Union countries | Norwegian Sea (ICES areas I and II) | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Grasslands Ecological Area | Guanica Bay watershed | All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA | Coastal zone surrounding St. Croix | Lake Rotorua catchment | HUCS in Michigan | CREP (Conservation Reserve Enhancement Program | Not applicable | Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Nachusa Grasslands | Agricultural plots | Piedmont Ecoregion | Piedmont Ecoregion | Miami-Dade County |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable | <1 ha | <1 ha | 10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is the HUC-8. A HUC-12 version is under development. Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:map units delineated by user based on project. |
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 lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | 20 m x 20 m | 504 km^2 | 1 km x 1 km | 1 km x 1 km | 1 km x 1 km | 1 ha x 1 ha | 100 m x 100 m | 100 m x 100 m | Not applicable | varies | Not reported | HUC | Computations are at the 8-digit HUC scale. MostHUC-8 watersheds are within a range of 800-8000 km^2 (500-5000 mi^2) in size. | 10 m x 10 m | 5m x 5m | reach in HUC | multiple, individual, irregular sites | map units | 6.1 m x 8.53 m | 3 m x 3 m | Area varies by site | Not applicable | Not applicable | Not applicable | Census block |
EM ID
em.detail.idHelp
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Numeric | Logic- or rule-based | Analytic | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Logic- or rule-based | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Unclear | No | Yes | No | No | No | No | Yes | Unclear | Unclear | No | No | Yes | No | No | Unclear | Not applicable | Not applicable | Not applicable | No | No | No | Yes | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | No | No | Yes | No | No | No | No | Yes | No | Not applicable | No | No | No | No | Yes | No | Not applicable | No | Not applicable | No | No | No | No | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None |
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None | None | None | None |
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None | None | None | None | None | None |
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None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | No | Yes | No | No | No | No | No | No | No | No | No | No | Yes | No | No | No | Not applicable | No | No | No | No | No | No | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
No | Yes | No | No | No | No | Yes | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | Yes | Yes | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Unclear | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
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None |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
None | None | None | None |
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None | None | None | None |
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None | None | None | None |
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None | None | None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
Centroid Latitude
em.detail.ddLatHelp
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39.5 | 45.05 | 39.88 | 50.53 | 18.61 | 50.53 | -34.9 | 52.37 | 48.2 | 70 | 44.11 | 37.25 | 17.96 | 39.83 | 17.73 | -38.14 | 45.12 | 42.62 | Not applicable | 42.93 | 54.2 | 41.89 | 29.4 | 36.23 | 36.23 | 25.64 |
Centroid Longitude
em.detail.ddLongHelp
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-98.35 | 6.4 | -113.81 | 7.6 | -91.55 | 7.6 | 138.7 | 4.88 | 16.35 | 10 | -123.09 | -120.8 | -67.02 | -98.58 | -64.77 | 176.25 | 85.18 | -93.84 | Not applicable | -92.57 | -2.35 | -89.34 | -82.18 | -81.9 | -81.9 | -80.5 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Provided | Provided | Provided | Estimated | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Open Ocean and Seas | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Inland Wetlands | Forests | Agroecosystems | Grasslands | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren |
Lakes and Ponds ?Comment:Watershed model represents all land areas, major streams and rivers. Since leaf area index, LAI, is an important variable, forests, created greenspaces (e.g., urban forests) and scrub/shrub subclasses are included. |
Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Ground Water | Forests | Agroecosystems | Scrubland/Shrubland | Rivers and Streams | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Grasslands | Created Greenspace |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Terrestrial | Subalpine terraces, grasslands, and meadows. | Not reported | Not applicable | Mangrove | Not applicable | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Not applicable | Not applicable | cold water coral reefs | Agricultural-urban interface at river junction | Terrestrial mosaic | Tropical terrestrial | Not applicable | Coral reefs | Largely agricultural, commercial forestry, non-commercial forest and shrubland and urban | stream reaches | Wetlands buffered by grassland within agroecosystems | Not applicable | prairie/grassland reconstruction at demonstration farm site | fertilized grassland (historically hayed) | Restored prairie, prairie remnants, and cropland | Agricultural landscape | grasslands | grasslands | urban neighborhood greenspace |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer 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 | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Ecological scale is coarser than that of the Environmental Sub-class ?Comment:Terrestrial characteristics are aggregated at a broad (HUC-8) scale; different types of aquatic sub-classes are not differentiated. |
Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer 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 | Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community | Species | Not applicable | Guild or Assemblage | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Community | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Community | Community | Species | Species | Species | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
None Available | None Available |
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None Available |
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None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available |
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)
EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-63 | EM-69 |
EM-98 ![]() |
EM-99 | EM-106 | EM-124 |
EM-127 ![]() |
EM-178 | EM-184 | EM-319 |
EM-333 ![]() |
EM-403 ![]() |
EM-432 | EM-439 | EM-445 | EM-659 |
EM-660 ![]() |
EM-700 | EM-712 |
EM-728 ![]() |
EM-735 ![]() |
EM-788 ![]() |
EM-812 ![]() |
EM-831 | EM-842 | EM-876 |
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None |
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None | None | None |
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None |
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None |
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None | None | None |
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