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-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
EM Short Name
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Cultural ES and plant traits, Central French Alps | Pollination ES, Central French Alps | Area & hotspots of soil accumulation, South Africa | AnnAGNPS, Kaskaskia River watershed, IL, USA | Land-use change and recreation, Europe | InVEST - Water provision, Francoli River, Spain | 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 | Biological pest control, Uppland Province, Sweden | MIMES: For Massachusetts Ocean (v1.0) | Denitrification rates, Guánica Bay, Puerto Rico | Carbon sequestration, Guánica Bay, Puerto Rico | State of the reef index, St. Croix, USVI | Yasso 15 - soil carbon model | Yasso07 - SOC, Loess Plateau, China | Chinook salmon value (household), Yaquina Bay, OR | RUM: Valuing fishing quality, Michigan, USA | Fish species richness, St. Croix, USVI | Indigo bunting abund, Piedmont region, USA | ARIES: Crop pollination in Rwanda and Burundi | Random wave transformation on vegetation fields | Air pollution removal by green roofs, Chicago, USA |
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 | Area and hotspots of soil accumulation, South Africa | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Land-use change effects on recreation, Europe | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | 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 | Biological control of agricultural pests by natural predators, Uppland Province, Sweden | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Denitrification rates, Guánica Bay, Puerto Rico, USA | Carbon sequestration, 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 | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Fish Species Richness, Buck Island, St. Croix , USVI | Indigo bunting abundance, Piedmont ecoregion, USA | ARIES; Crop pollination in Rwanda and Burundi | Random wave transformation on vegetation fields | Air pollution removal by green roofs, Chigago, USA |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | EU Biodiversity Action 5 | InVEST |
None ?Comment:EU Mapping Studies |
US EPA | US EPA | US EPA | None | US EPA | US EPA | US EPA | US EPA | None | None | US EPA | None | None | None | ARIES | None | None |
EM Source Document ID
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260 | 260 | 271 | 137 | 228 | 280 | 191 | 96 | 186 | 63 | 299 | 316 | 338 | 338 | 335 |
342 ?Comment:Webpage pdf users manual for model. |
344 | 324 |
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. |
355 | 405 | 411 | 424 |
438 ?Comment:Document 439 is an additional source for this EM. |
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. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | 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. | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | 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. | Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O. | Altman, I., R.Boumans, J. Roman, L. Kaufman | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | 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 | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Riffel, S., Scognamillo, D., and L. W. Burger | 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 | Yang, J., Q. Yu and P. Gong |
Document Year
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2011 | 2011 | 2008 | 2011 | 2012 | 2013 | 2013 | 2011 | 2013 | 2011 | 2014 | 2012 | 2017 | 2017 | 2014 | 2016 | 2015 | 2012 | 2014 | 2007 | 2008 | 2018 | 2004 | 2008 |
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 | Mapping ecosystem services for planning and management | 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 | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | 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 | Ecological production functions for biological control services in agricultural landscapes | 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 | 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 | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Valuing recreational fishing quality at rivers and streams | 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 | Towards globally customizable ecosystem service models | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields | Quantifying air pollution removal by green roofs in Chicago |
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 | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Not applicable | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
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 | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | 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 | https://github.com/integratedmodelling/im.aries.global | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Benis Egoh | Yongping Yuan | Marion Potschin | Montse Marquès | Izaskun Casado-Arzuaga | Leah Oliver | M. Russell | Steve Jordan | Mattias Jonsson | Irit Altman | Susan H. Yee | Susan H. Yee | Susan H. Yee | Jari Liski | Xing Wu | Stephen Jordan | Richard Melstrom | Simon Pittman | Sam Riffell | Javier Martinez |
F. J. Mendez ?Comment:Tel.: +34-942-201810 |
Jun Yang |
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 | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | 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 | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | 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 | Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden | Boston University, Portland, Maine | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | 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 | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | 1305 East-West Highway, Silver Spring, MD 20910, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Not reported | Department of Landscape Architecture and Horticulture, Temple University, 580 Meetinghouse Road, Ambler, PA 19002, USA. |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | Not reported | yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | montserrat.marques@fundacio.urv.cat | izaskun.casado@ehu.es | leah.oliver@epa.gov | Russell.Marc@epamail.epa.gov | steve.jordan@epa.gov | mattias.jonsson@slu.se | iritaltman@bu.edu | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | jari.liski@ymparisto.fi | xingwu@rceesac.cn | jordan.steve@epa.gov | melstrom@okstate.edu | simon.pittman@noaa.gov | sriffell@cfr.msstate.edu | javier.martinez@bc3research.org | mendezf@unican.es | juny@temple.edu |
EM ID
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
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." | 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 scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover." | 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." | 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: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | 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 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." | 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…" | AUTHOR'S DESCRIPTION: "In addition to affecting water quality, the ecosystem services of nitrogen retention, phosphorous retention, and sediment retention were also considered to contribute to stakeholder goals of maintaining the productivity of agricultural land and reducing soil loss. Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class, 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:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: " 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: "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: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." ENTERER'S COMMENT: Random wave transformation model; equations 31 and 32. | ABSTRACT: "The level of air pollution removal by green roofs in Chicago was quantified using a dry deposition model. The result showed that a total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs in one year with O3 accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2 (7%). The highest level of air pollution removal occurred in May and the lowest in February. The annual removal per hectare of green roof was 85 kg/ha/yr. The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs. Although costly, the installation of green roofs could be justified in the long run if the environmental benefits were considered. The green roof can be used to supplement the use of urban trees in air pollution control, especially in situations where land and public funds are not readily available." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | Not reported | None identified | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | Restoration of seagrass | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None | None identified | None identified | None provided | None reported | None identified | None identified | 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 | 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. | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | Mediteranean coastal mountains | 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 | 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 | Yaquina Bay estuary | stream and river reaches of Michigan | Hard and soft benthic habitat types approximately to the 33m isobath | Conservation Reserve Program lands left to go fallow | Entire countries of Rwanda and Burundi considered | No additional description provided | 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) | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | No scenarios presented | Not applicable | Habitat loss or restoration in Tampa Bay Estuary | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Land use change | No scenarios presented | targeted sport fish biomass | No scenarios presented | N/A | N/A | No scenarios presented | No scenarios presented |
EM ID
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
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 (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method Only | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing 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 | 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-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
EM Temporal Extent
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Not reported | Not reported | Not reported | 1980-2006 | 1990-2030 | 1971-2100 | 2000 - 2007 | 2006-2007 | 1982-2010 | 2004 | 2009 | 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. |
1978 - 2013 | 2006-2007, 2010 | Not applicable | 1969-2011 | 2003-2008 | 2008-2010 | 2000-2005 | 2008 | 2010 | Not applicable | July 2006 to July 2007 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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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 | future time | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | 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 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | continuous | discrete |
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 | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 |
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 | Year | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Month |
EM ID
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Not applicable | Geopolitical |
Spatial Extent Name
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Central French Alps | Central French Alps | South Africa | East Fork Kaskaskia River watershed basin | The EU-25 plus Switzerland and Norway | Francoli River | Bilbao Metropolitan Greenbelt | St. Croix, U.S. Virgin Islands | Tampa Bay Estuary | Contiguous U.S. | Uppland province | Massachusetts Ocean | Guanica Bay watershed | Guanica Bay watershed | Coastal zone surrounding St. Croix | Not applicable | Yangjuangou catchment | Pacific Northwest | HUCS in Michigan | SW Puerto Rico, | Piedmont Ecoregion | Rwanda and Burndi | Not applicable | Chicago |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | >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 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | Not applicable | 1-10 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable | 100-1000 km^2 |
EM ID
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
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 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 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) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | 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 | Not applicable | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 1 km^2 | 1 km x 1 km | 30m x 30m | 2 m x 2 m | Not applicable | 1 ha | Not applicable | 25 m x 25 m | 1 km x1 km | 30 m x 30 m | 30 m x 30 m | 10 m x 10 m | Not applicable | 30m x 30m | Not applicable | reach in HUC | not reported | Not applicable | 1km | 1m | plot (green roof) size |
EM ID
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | No | No | No | Yes | Yes | Yes | No | No | No | No | Yes | Not applicable | Yes | No | No | No | Yes | Unclear | No | Unclear |
Model Goodness of Fit Reported?
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No | No | No | No | No | No | No | Yes | No | Yes | No | No | No | No | No | Not applicable |
Yes ?Comment:For the year 2006 and 2011 |
No | Yes | Yes | No | No | Not applicable | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None | None |
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None |
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None | None | None | None | None | None |
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None |
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None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | Yes | No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
Yes | No | No | No | Yes | No | No | No | Yes | Not applicable | No | Yes | No | Yes | No | No | Not applicable | No |
Model Uncertainty Analysis Reported?
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No | No | No | Yes | No | No | No | Yes | No | Yes | No | No | No | No | No | Not applicable | No | No | No | No | No | No | Not applicable | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | Unclear | No | No | No | No | No | 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 | No | Yes | Yes | No | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | 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-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
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None | None | None |
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None |
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None | None |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
None | None | None | None | None | None | None |
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None | None |
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None | None |
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None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
Centroid Latitude
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45.05 | 45.05 | -30 | 38.69 | 50.53 | 41.26 | 43.25 | 17.75 | 27.95 | -9999 | 59.52 | 41.72 | 17.96 | 17.96 | 17.73 | Not applicable | 36.7 | 44.62 | 45.12 | 17.79 | 36.23 | -2.59 | Not applicable | 41.88 |
Centroid Longitude
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6.4 | 6.4 | 25 | -89.1 | 7.6 | 1.18 | -2.92 | -64.75 | -82.47 | -9999 | 17.9 | -69.87 | -67.02 | -67.02 | -64.77 | Not applicable | 109.52 | -124.02 | 85.18 | -64.62 | -81.9 | 29.97 | Not applicable | 87.65 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Provided | Provided | Estimated | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided |
EM ID
em.detail.idHelp
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | 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 | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | 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 | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Created Greenspace |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows. | Not applicable | Row crop agriculture in Kaskaskia river basin | Not applicable | Coastal mountains | none | stony coral reef | Subtropical Estuary | Wetlands (multiple types) | Spring-sown cereal croplands and surrounding grassland and non-arable land | None identified | Thirteen land use land cover classes were used | 13 LULC were used | Coral reefs | Not applicable | Loess plain | Yaquina Bay estuary and ocean | stream reaches | shallow coral reefs | grasslands | varied | Near coastal marine and estuarine | urban green roofs |
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 | 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 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 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
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 | Individual or population, within a species | Species | Not applicable | Not applicable | Guild or Assemblage | Species | Not applicable | Other (multiple scales) | Not applicable | Guild or Assemblage | Species | Guild or Assemblage | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-81 | EM-82 | EM-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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None Available | 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-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
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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-87 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 | EM-303 | EM-376 | EM-424 | EM-430 | EM-444 | EM-466 | EM-469 | EM-604 |
EM-660 ![]() |
EM-698 | EM-846 | EM-855 | EM-896 | EM-945 |
None | None | None |
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None | None |
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None | None |
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None | None | None |