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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111 
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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EM Short Name
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Litter biomass production, Central French Alps | Area and hotspots of soil retention, South Africa | Stream nitrogen removal, Mississippi R. basin, USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | InVEST water yield, Hood Canal, WA, USA | Land-use change and habitat diversity, Europe | FORCLIM v2.9, Transect in Western OR, USA | N removal by wetlands, Contiguous USA | InVEST crop pollination, California, USA | Visitation to reef dive sites, St. Croix, USVI | Beach visitation, Barnstable, MA, USA | Fish species richness, St. Croix, USVI | Northern bobwhite abundance, Piedmont region, USA | Brown-headed cowbird abundance, Piedmont, USA | InVEST Coastal Vulnerability | ARIES Sediment regulation, Santa Fe, NM | Random wave transformation L. hyperborea field |
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EM Full Name
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Litter biomass production, Central French Alps | Area and hotspots of soil retention, South Africa | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | Land-use change effects on habitat diversity, Europe | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | Nitrogen removal by wetlands as a function of loading, Contiguous USA | InVEST crop pollination, California, USA | Visitation to dive sites (reef), St. Croix, USVI | Beach visitation, Barnstable, Massachusetts, USA | Fish Species Richness, Buck Island, St. Croix , USVI | Northern bobwhite abundance, Piedmont ecoregion, USA | Brown-headed cowbird abundance, Piedmont ecoregion, USA | InVEST Coastal Vulnerability | Artificial Intelligence for Ecosystem Services (ARIES); Sediment regulation, Santa Fe, New Mexico | Random wave transformation on Laminaria hyperboria field |
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EM Source or Collection
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EU Biodiversity Action 5 | None | US EPA | US EPA | InVEST | EU Biodiversity Action 5 | US EPA | US EPA | InVEST | US EPA | US EPA | None | None | None | InVEST | None | None |
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EM Source Document ID
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260 | 271 | 52 | 275 | 205 | 228 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
63 | 279 | 335 | 386 | 355 | 405 | 405 | 408 | 411 | 424 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Hill, B. and Bolgrien, D. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Haines-Young, R., Potschin, M. and Kienast, F. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Jordan, S., Stoffer, J. and Nestlerode, J. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | The Natural Capital Project.org | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Mendez, F. J. and I. J. Losada |
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Document Year
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2011 | 2008 | 2011 | 2014 | 2013 | 2012 | 2007 | 2011 | 2009 | 2014 | 2018 | 2007 | 2008 | 2008 | None | 2018 | 2004 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis | Modelling pollination services across agricultural landscapes | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | InVEST Coastal Vulnerability | Towards globally customizable ecosystem service models | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields |
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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 | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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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 | Published journal manuscript | Published journal manuscript | Website users guide | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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| Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
Not applicable | |
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Contact Name
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Sandra Lavorel | Benis Egoh | Brian Hill |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
J.E. Toft | Marion Potschin | Richard T. Busing | Steve Jordan | Eric Lonsdorf | Susan H. Yee | Kate K, Mulvaney | Simon Pittman | Sam Riffell | Sam Riffell | Not applicable | Javier Martinez-Lopez |
F. J. Mendez ?Comment:Tel.: +34-942-201810 |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561 | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | 1305 East-West Highway, Silver Spring, MD 20910, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not applicable | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Not reported |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | Not reported | hill.brian@epa.gov | frazier@nceas.ucsb.edu | jetoft@stanford.edu | marion.potschin@nottingham.ac.uk | rtbusing@aol.com | steve.jordan@epa.gov | ericlonsdorf@lpzoo.org | yee.susan@epa.gov | Mulvaney.Kate@EPA.gov | simon.pittman@noaa.gov | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | Not applicable | javier.martinez@bc3research.org | mendezf@unican.es |
EM Description
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EM ID
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Litter biomass production 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 litter mass. 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." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | InVEST Water Yield and Scarcity Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub- watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | 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: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA." Author's Description: "The first set of tests involved eight sites on western Oregon transect from west to east… Individual sites were chosen to represent a particular type of potential natural vegetation as described by Franklin and Dyrness (1988)." | ABSTRACT: "We compiled published data from wetland studies worldwide to estimate total Nr removal and to evaluate factors that influence removal rates. Over several orders of magnitude in wetland area and Nr loading rates, there is a positive, near-linear relationship between Nr removal and Nr loading. The linear model (null hypothesis) explains the data better than either a model of declining Nr removal efficiency with increasing Nr loading, or a Michaelis–Menten (saturation) model." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery. " | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | 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. " | Faced with an intensification of human activities and a changing climate, coastal communities need to better understand how modifications of the biological and physical environment (i.e. direct and indirect removal of natural habitats for coastal development) can affect their exposure to storm-induced erosion and flooding (inundation). The InVEST Coastal Vulnerability model produces a qualitative estimate of such exposure in terms of a vulnerability index, which differentiates areas with relatively high or low exposure to erosion and inundation during storms. By coupling these results with global population information, the model can show areas along a given coastline where humans are most vulnerable to storm waves and surge. The model does not take into account coastal processes that are unique to a region, nor does it predict long- or short-term changes in shoreline position or configuration. Model inputs, which serve as proxies for various complex shoreline processes that influence exposure to erosion and inundation, include: a polyline with attributes about local coastal geomorphology along the shoreline, polygons representing the location of natural habitats (e.g., seagrass, kelp, wetlands, etc.), rates of (observed) net sea-level change, a depth contour that can be used as an indicator for surge level (the default contour is the edge of the continental shelf), a digital elevation model (DEM) representing the topography of the coastal area, a point shapefile containing values of observed storm wind speed and wave power, and a raster representing population distribution. Outputs can be used to better understand the relative contributions of these different model variables to coastal exposure and highlight the protective services offered by natural habitats to coastal populations. This information can help coastal managers, planners, landowners and other stakeholders identify regions of greater risk to coastal hazards, which can in turn better inform development strategies and permitting. The results provide a qualitative representation of coastal hazard risks rather than quantifying shoreline retreat or inundation limits. | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " | ASTRACT: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." AUTHOR'S DESCRIPTION: "The theoretical solution for random waves is compared to the experimental results for an artificial kelp field given by Dubi (1995). The experiment was carried out in a 33-m-long, 1-m-wide and 1.6-m-high wave flume...The artificial kelp models were L. hyperborea" |
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Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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None identified | None identified | Not applicable | None identified | Land use change | None identified | None Identified | None identified | None identified | None identified | To assess the number of people who would be impacted by beach closures. | None provided | None reported | None reported | None identified | None identified | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Agricultural landuse , 1st-10th order streams | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Not additional description provided | No additional description provided | Coastal to montane | Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested | No additional description provided | No additional description provided | Four separate beaches within the community of Barnstable | Hard and soft benthic habitat types approximately to the 33m isobath | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | Not applicable | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | No additional description provided |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | Future land use and land cover; climate change | Recent historical land use change from 1990-2000 | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A | Options for future sea level change and population change | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Each of the seven runs represents a different site (ecoregion) along a west to east Oregon transect. An eighth site was not forested and its results were not included. |
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM Modeling Approach
EM Relationship to Time
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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EM Temporal Extent
em.detail.tempExtentHelp
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Not reported | Not reported | 2000-2008 | December 2007 - November 2008 | 2005-7; 2035-45 | 1990-2000 | 1500 yrs | 2004 | 2001-2002 | 2006-2007, 2010 | 2011 - 2016 | 2000-2005 | 2008 | 2008 | Not applicable | 2011 | Not appicable |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | continuous |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Other | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Other |
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Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | South Africa | Upper Mississippi, Ohio and Missouri River sub-basins | Yaquina Estuary (intertidal), Oregon, USA | Hood Canal | The EU-25 plus Switzerland and Norway | Western Oregon transect | Contiguous U.S. | Agricultural landscape, Yolo County, Central Valley | Coastal zone surrounding St. Croix | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | SW Puerto Rico, | Piedmont Ecoregion | Piedmont Ecoregion | Not applicable | Santa Fe Fireshed | wave flume |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 ha | 100-1000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 100-1000 km^2 | <1 ha |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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EM Spatial Distribution
em.detail.distributeLumpHelp
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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) ?Comment:Computations performed at the area size of 0.08 ha. |
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all 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) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | other (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 1 km | 0.87-104.29 ha | 30 m x 30 m | 1 km x 1 km | Not applicable | Not applicable | 30 m x 30 m | 10 m x 10 m | by beach site | not reported | Not applicable | Not applicable | user defined | 30 m | 1 m |
EM Structure and Computation Approach
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Unclear | Yes | No | No | Yes | Unclear | Yes | Yes | No | No | Yes | Not applicable | Unclear | No |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | No | No | No | No | No | No | Yes | No | No | No | Yes | No | No | Not applicable | No | No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None |
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None | None | None |
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None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | No | No | Yes | No | Yes | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
Yes | No | Yes | No | No | Not applicable | No | Yes |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | Yes | No | No | No | No | Yes | No | No | No | No | No | No | Not applicable | No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | Unclear | No | Yes | No | No | Yes | No | No | Yes | Yes | Yes | Yes | Not applicable | No | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | No | Unclear | Unclear | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
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| EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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None |
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None |
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None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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| None | None | None |
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None | None | None | None |
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None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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Centroid Latitude
em.detail.ddLatHelp
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45.05 | -30 | 36.98 | 44.62 | 47.8 | 50.53 | 44.13 | -9999 | 38.7 | 17.73 | 41.64 | 17.79 | 36.23 | 36.23 | Not applicable | 35.86 | 58.1 |
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Centroid Longitude
em.detail.ddLongHelp
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6.4 | 25 | -89.13 | -124.06 | -122.7 | 7.6 | -122.5 | -9999 | -121.8 | -64.77 | -70.29 | -64.62 | -81.9 | -81.9 | Not applicable | -105.76 | -7.1 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Estimated | Provided | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Grasslands | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Not reported | Not applicable | Estuarine intertidal | glacier-carved saltwater fjord | Not applicable | Primarily conifer forest | Wetlands (multiple types) | Cropland and surrounding landscape | Coral reefs | Saltwater beach | shallow coral reefs | grasslands | grasslands | Coastal environments | watersheds | Near coastal marine and estuarine |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | 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 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Species | Not applicable | Species | Not applicable | Not applicable | Guild or Assemblage | Species | Species | Not applicable | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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| None Available | None Available | None Available |
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None Available | None Available |
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None Available |
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None Available | None Available |
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None Available | None Available |
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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.detail.cicesHelp
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| EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-66 | EM-86 | EM-93 | EM-103 |
EM-111
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EM-124 |
EM-146
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EM-196 |
EM-338
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EM-457 | EM-684 | EM-698 | EM-831 | EM-841 | EM-849 | EM-860 |
EM-897
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| None | None |
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None | None |
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None | None | None |