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-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
EM Short Name
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Cultural ES and plant traits, Central French Alps | Pollination ES, Central French Alps | RHyME2, Upper Mississippi River basin, USA | AnnAGNPS, Kaskaskia River watershed, IL, USA | Land-use change and recreation, Europe | Mangrove development, Tampa Bay, FL, USA | Cultural ecosystem services, Bilbao, Spain | Coral and land development, St.Croix, VI, USA | C Sequestration and De-N, Tampa Bay, FL, USA | N removal by wetlands, Contiguous USA | SWAT, Aixola watershed, Spain | Biological pest control, Uppland Province, Sweden | Evoland v3.5 (unbounded growth), Eugene, OR, USA | MIMES: For Massachusetts Ocean (v1.0) | Denitrification rates, Guánica Bay, Puerto Rico | State of the reef index, St. Croix, USVI | Yasso 15 - soil carbon model | Yasso07 - SOC, Loess Plateau, China | RUM: Valuing fishing quality, Michigan, USA | WTP for a beach day, Massachusetts, USA | Indigo bunting abund, Piedmont region, USA | Random wave transformation on vegetation fields |
EM Full Name
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Cultural ecosystem service estimated from plant functional traits, Central French Alps | Pollination ecosystem service estimated from plant functional traits, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Land-use change effects on recreation, Europe | Mangrove wetland development, Tampa Bay, FL, USA | Cultural ecosystem services, Bilbao, Spain | Coral colony density and land development, St.Croix, Virgin Islands, USA | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Nitrogen removal by wetlands as a function of loading, Contiguous USA | SWAT (Soil and Water Assessment Tool), Aixola watershed, Spain | Biological control of agricultural pests by natural predators, Uppland Province, Sweden | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Denitrification rates, Guánica Bay, Puerto Rico, USA | State of the reef index, St. Croix, USVI | Yasso 15 - soil carbon | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA | Indigo bunting abundance, Piedmont ecoregion, USA | Random wave transformation on vegetation fields |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | US EPA | EU Biodiversity Action 5 | US EPA |
None ?Comment:EU Mapping Studies |
US EPA | US EPA | US EPA | None | None | Envision | US EPA | US EPA | US EPA | None | None | None | US EPA | None | None |
EM Source Document ID
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260 | 260 | 123 | 137 | 228 | 97 | 191 | 96 | 186 | 63 | 295 | 299 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
316 | 338 | 335 |
342 ?Comment:Webpage pdf users manual for model. |
344 |
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. |
386 | 405 | 424 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Haines-Young, R., Potschin, M. and Kienast, F. | Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Russell, M. and Greening, H. | Jordan, S., Stoffer, J. and Nestlerode, J. | Zabaleta, A., Meaurio, M., Ruiz, E., and Antigüedad, I. | Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Altman, I., R.Boumans, J. Roman, L. Kaufman | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Riffel, S., Scognamillo, D., and L. W. Burger | Mendez, F. J. and I. J. Losada |
Document Year
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2011 | 2011 | 2013 | 2011 | 2012 | 2012 | 2013 | 2011 | 2013 | 2011 | 2014 | 2014 | 2008 | 2012 | 2017 | 2014 | 2016 | 2015 | 2014 | 2018 | 2008 | 2004 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Estimating benefits in a recovering estuary: Tampa Bay, Florida | Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis | Simulation climate change impact on runoff and sediment yield in a small watershed in the Basque Country, Northern Spain | Ecological production functions for biological control services in agricultural landscapes | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Yasso 15 graphical user-interface manual | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Valuing recreational fishing quality at rivers and streams | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields |
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 | Documented, not peer reviewed | 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 |
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 report | Published journal manuscript | Published journal manuscript | Not applicable | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
Not applicable | Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://swat.tamu.edu/software/arcswat/ | Not applicable | http://evoland.bioe.orst.edu/ | http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | Not applicable |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Liem Tran | Yongping Yuan | Marion Potschin | Michael Osland | Izaskun Casado-Arzuaga | Leah Oliver | M. Russell | Steve Jordan | Ane Zabaleta | Mattias Jonsson | Michael R. Guzy | Irit Altman | Susan H. Yee | Susan H. Yee | Jari Liski | Xing Wu | Richard Melstrom | Kate K, Mulvaney | Sam Riffell |
F. J. Mendez ?Comment:Tel.: +34-942-201810 |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | National Health and Environmental Research Effects Laboratory | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561 | Hydrogeology and Environment Group, Science and Technology Faculty, University of the Basque Country, 48940 Leioa, Basque Country (Spain) | Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden | Oregon State University, Dept. of Biological and Ecological Engineering | Boston University, Portland, Maine | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | Chinese Academy of Sciences, Beijing 100085, China | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Not reported | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not reported |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | mosland@usgs.gov | izaskun.casado@ehu.es | leah.oliver@epa.gov | Russell.Marc@epamail.epa.gov | steve.jordan@epa.gov | ane.zabaleta@ehu.es | mattias.jonsson@slu.se | Not reported | iritaltman@bu.edu | yee.susan@epa.gov | yee.susan@epa.gov | jari.liski@ymparisto.fi | xingwu@rceesac.cn | melstrom@okstate.edu | Mulvaney.Kate@EPA.gov | sriffell@cfr.msstate.edu | mendezf@unican.es |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
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." AUTHOR'S DESCRIPTION: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | 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 for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….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…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | 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." | ABSTRACT: "We explored the potential impact of climate change on runoff and sediment yield for the Aixola watershed using the Soil and Water Assessment Tool (SWAT). The model calibration (2007–2010) and validation (2005–2006) results were rated as satisfactory. Subsequently, simulations were run for four climate change model–scenario combinations based on two general circulation models (CGCM2 and ECHAM4) under two emissions scenarios (A2 and B2) from 2011 to 2100." AUTHOR'S DESCRIPTION: "The results were grouped into three consecutive 30-yr periods (2011-2040, 2041-2070, and 2071-2100) and compared with the values simulated for the baseline period (1961-1990)." | ABSTRACT: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization." | **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." | AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. | AUTHOR'S DESCRIPTION: "Improving water quality was an objective of stakeholders in order to improve human health and reduce impacts to coral reef habitats. Four ecosystem services contributing to water quality were identified: denitrification...Denitrification rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature…" | 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 indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000)...for reef ecological integrity (van Beukering and Cesar, 2004) defines the state of the reef as State of the Reef =ΣiwiRi where the Ri are the relative quantity of coral cover, macro-algal cover, fish richness, coral richness, and fish abundance, standardized to reflect the range of conditions at the location being evaluated (in this case, St. Croix). The wi give the weighted contribution of each attribute to reef condition based on expert judgment, originally developed for Hawaii, which were wcoral_cover=0.30, walgae_cover= 0.15, wfish_richness=0.15, wcoral_richness=0.20, and wfish_abundance=0.20 (van Beukering and Cesar, 2004). Ideally, these values would be developed to reflect local knowledge and concerns for the Caribbean or St. Croix. For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models t | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | 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: "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 used existing studies in a meta-analysis to estimate appropriate benefit transfer values of consumer surplus per beach visit for Barnstable. The studies we include in the model are for beaches across the United States, allowing the metaregression model to be more broadly applicable to other beaches and for values to be adjusted based on appropriate site attributes...To identify relevant studies, we selected 25 studies of beach use and swimming from the Recreation Use Values Database (RUVD), where consumer surplus values are presented as value per day in 2016 dollars...We added beach length and history of closures to contextualize the model for our application by proxying water quality and site quality." Equation 1, page 11, provides the meta-regression. | 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." | 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." ENTERER'S COMMENT: Random wave transformation model; equations 31 and 32. |
Specific Policy or Decision Context Cited
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None identified | None identified | Not reported | Not reported | None identified | Not applicable | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | Restoration of seagrass | None identified | Transport of solids for characterizing rivers in the European Water Framework Directive (WFD) | 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…" | None identified | None identified | None identified | None identified | None | None identified | Economic value of protecting coastal beach water quality from contamination caused closures. | None reported | None identified |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | mangrove forest,Salt marsh, estuary, sea level, | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested | The Aixola watershed drains into the Aixola reservoir, which has a cpacity of 2.73 x 10^6 m^3, and is used for water supply. The elevation ranges from 340 m at the outlet of the watershed to 750 m at the highest peak, with a mean elevation of 511 m a.s.l. Most slopes in the watershed are less than 30%. The region is characterized by a humid and temperate climate. The mean annual precipitation is about 1480 mm, distributed fairly evenly throughout the year.; the mean annual temperature is 12 degrees C; and the mean annual discharge is 600 mm (around 0.092 m^3 s^−1). Autochthonus vegetation is limited to small patches, and commercial foresty, mostly evergreen stands composed mainly of Pinus radiata (Monterey pine), occupies more than 80% of the watershed. The lithology is highly homogenous, with most of the bedrock (94%) consisting of impervious Upper Cretaceous Calcareous Flysch. The main types of soils are relatively deep cambisols and regosols, with depths ranging from 0.8 to 10 m and a silt-loam texture. During the 2003-2008 period, mean suspended sediment yield calculated for the watershed was 36 t km^-2. | Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here. | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Not applicable | Agricultural plain, hills, gulleys, forest, grassland, Central China | stream and river reaches of Michigan | Four separate beaches within the community of Barnstable | 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 | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | Not applicable | No scenarios presented | Not applicable | Habitat loss or restoration in Tampa Bay Estuary | No scenarios presented | Four future climate change scenarios combining two IPCC SRES scenarios and two GCMs | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Land use change | targeted sport fish biomass | No scenarios presented | N/A | No scenarios presented |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | 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 | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only |
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 | New or revised 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 | Application of existing model | New or revised model | Application of existing 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 Modeling Approach
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
EM Temporal Extent
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Not reported | Not reported | 1987-1997 | 1980-2006 | 1990-2030 | 1990-2010 | 2000 - 2007 | 2006-2007 | 1982-2010 | 2004 | 1961-2100 | 2009 | 1990-2050 | Not applicable |
1989 - 2011 ?Comment:6/21/16 BH - Rates were assigned from literature, ranging from 1989 - 2006, and the denitrification rate for urban lawns comes from 2011 literature. |
2006-2007, 2010 | Not applicable | 1969-2011 | 2008-2010 | July 1, 2011 to June 31, 2016 | 2008 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | continuous | Not applicable | Not applicable | Not applicable | Not applicable | continuous | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | continuous |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 2 | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Not applicable |
Spatial Extent Name
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Central French Alps | Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | East Fork Kaskaskia River watershed basin | The EU-25 plus Switzerland and Norway | Tampa Bay | Bilbao Metropolitan Greenbelt | St. Croix, U.S. Virgin Islands | Tampa Bay Estuary | Contiguous U.S. | Aixola watershed | Uppland province | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Massachusetts Ocean | Guanica Bay watershed | Coastal zone surrounding St. Croix | Not applicable | Yangjuangou catchment | HUCS in Michigan | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Piedmont Ecoregion | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 1-10 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | Not applicable | 1-10 km^2 | 100,000-1,000,000 km^2 | 10-100 ha | 100,000-1,000,000 km^2 | Not applicable |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
EM Spatial Distribution
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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 distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially 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 lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | NHDplus v1 | length, for linear feature (e.g., stream mile) | 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 | Not applicable | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable | length, for linear feature (e.g., stream mile) |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | NHDplus v1 | 1 km^2 | 1 km x 1 km | m^2 | 2 m x 2 m | Not applicable | 1 ha | Not applicable | Average size 0.2 km^2 | 25 m x 25 m | varies | 1 km x1 km | 30 m x 30 m | 10 m x 10 m | Not applicable | 30m x 30m | reach in HUC | by beach site | Not applicable | 1m |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
Model Calibration Reported?
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No | No | Yes | No | No | No | No | Yes | Yes | Yes | Yes | No | Unclear | No | No | Yes | Not applicable | Yes | No | Yes | Yes | No |
Model Goodness of Fit Reported?
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No | No | Yes | No | No | No | No | Yes | No | Yes | No | No | No | No | No | No | Not applicable |
Yes ?Comment:For the year 2006 and 2011 |
Yes | Yes | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
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None | None | None | None |
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None |
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None | None | None | None | None | None | None |
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None | None |
Model Operational Validation Reported?
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No | No | No | Yes | No | No | Yes | No | No | No | Yes | Yes | No | No | No | Yes | Not applicable | No | No | No | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | Yes | No | Yes | No | Yes | No | Yes | No | No | No | No | No | No | Not applicable | No | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
Unclear | No | Yes | No | No | No | Yes | Yes |
Yes ?Comment:AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model." |
No | No | No | No | Not applicable | No | No |
Yes ?Comment:p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables. |
Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Yes | No | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
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None | None | None |
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None |
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None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
None | None | None | None | None |
Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian |
None |
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None | None | None | None |
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None | None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 42.5 | 38.69 | 50.53 | 27.8 | 43.25 | 17.75 | 27.95 | -9999 | 43 | 59.52 | 44.11 | 41.72 | 17.96 | 17.73 | Not applicable | 36.7 | 45.12 | 41.64 | 36.23 | Not applicable |
Centroid Longitude
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6.4 | 6.4 | -90.63 | -89.1 | 7.6 | -82.4 | -2.92 | -64.75 | -82.47 | -9999 | -1 | 17.9 | -123.09 | -69.87 | -67.02 | -64.77 | Not applicable | 109.52 | 85.18 | -70.29 | -81.9 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Provided | Provided | Estimated | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Agroecosystems | Aquatic 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) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Rivers and Streams | Forests | Barren | Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Forests | Grasslands | Scrubland/Shrubland | Tundra | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine | Grasslands | Near Coastal Marine and Estuarine |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows. | None | Row crop agriculture in Kaskaskia river basin | Not applicable | Created Mangrove wetlands | none | stony coral reef | Subtropical Estuary | Wetlands (multiple types) | Forested watershed used for commercial forestry | Spring-sown cereal croplands and surrounding grassland and non-arable land | Agricultural-urban interface at river junction | None identified | Thirteen land use land cover classes were used | Coral reefs | Not applicable | Loess plain | stream reaches | Saltwater beach | grasslands | Near coastal marine and estuarine |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | 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 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Not applicable | Guild or Assemblage | Species | Not applicable | Not applicable | Not applicable | Species | Species |
Taxonomic level and name of organisms or groups identified
EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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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-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
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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-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-154 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-303 |
EM-333 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 |
EM-660 ![]() |
EM-682 | EM-846 | EM-896 |
None | None | None |
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None |
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None | None |
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None |