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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-70 | EM-91 | EM-92 |
EM-129 
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
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EM-846 | EM-998 |
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EM Short Name
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Plant species diversity, Central French Alps | RHyME2, Upper Mississippi River basin, USA | Runoff potential of pesticides, Europe | 3-PG, South Australia | ARIES flood regulation, Puget Sound Region, USA | Retained rainwater, Guánica Bay, Puerto Rico | Reef density of P. argus, St. Croix, USVI | Reef density of S. gigas, St. Croix, USVI | Nutrient Tracking Tool (NTT) | Alewife derived nutrients, Connecticut, USA | Fish species richness, St. Croix, USVI | WESP: Irrigation water, ID, USA | Indigo bunting abund, Piedmont region, USA | CAESAR landscape evolution model |
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EM Full Name
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Plant species diversity, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Runoff potential of pesticides, Europe | 3-PG (Physiological Principles Predicting Growth), South Australia | ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | Retained rainwater, Guánica Bay, Puerto Rico, USA | Relative density of Panulirus argus (on reef), St. Croix, USVI | Relative density of Strombus gigas (on reef), St. Croix, USVI | Nutrient Tracking Tool (NTT) | Alewife derived nutrients in stream food web, Connecticut, USA | Fish Species Richness, Buck Island, St. Croix , USVI | WESP: Irrigation return water treatment, Idaho, USA | Indigo bunting abundance, Piedmont ecoregion, USA | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | None | ARIES | US EPA | US EPA | US EPA | None | None | None | None | None | None |
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EM Source Document ID
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260 | 123 | 254 | 243 | 302 | 338 | 335 | 335 | 352 | 384 | 355 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 | 468 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Schriever, C. A., and Liess, M. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Saleh, A. and O. Gallego | Walters, A. W., R. T. Barnes, and D. M. Post | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J. |
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Document Year
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2011 | 2013 | 2007 | 2011 | 2014 | 2017 | 2014 | 2014 | 2018 | 2009 | 2007 | 2012 | 2008 | 2007 |
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Document Title
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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 | Mapping ecological risk of agricultural pesticide runoff | Carbon payments and low-cost conservation | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | 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 | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Nutrient Tracking Tool (NTT) User Manual | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | webpage | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
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EM-846 | EM-998 |
| Not applicable | Not applicable | Not applicable | http://www.csiro.au/products/3PGProductivity#a1 | http://aries.integratedmodelling.org/ | Not applicable | Not applicable | Not applicable | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | Not applicable | Not applicable | Not applicable | http://www.coulthard.org.uk/ | |
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Contact Name
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Sandra Lavorel | Liem Tran | Carola Alexandra Schriever | Anders Siggins | Ken Bagstad | Susan H. Yee | Susan H. Yee | Susan H. Yee |
Ali Saleh ?Comment:Phone # 254-968-9079 |
Annika W. Walters | Simon Pittman | Chris Murphy | Sam Riffell | Marco J. Van De Wiel |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | Not reported | Geosciences and Environmental Change Science Center, US Geological Survey | 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 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA | 1305 East-West Highway, Silver Spring, MD 20910, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Geography, University of Western Ontario, London, Ontario, Canada |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | carola.schriever@ufz.de | Anders.Siggins@csiro.au | kjbagstad@usgs.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | saleh@tarleton.edu | annika.walters@yale.edu | simon.pittman@noaa.gov | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | mvandew3@uwo.ca |
EM Description
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EM ID
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EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
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EM-846 | EM-998 |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | 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) | ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures." | AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j: (1) density of the spiny lobster Panulirus argus" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(2) density of the queen conch Strombus gigas" | AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)." | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." | 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." | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | 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." | We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK. |
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Specific Policy or Decision Context Cited
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None identified | Not reported | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | None identified | Meeting water demands for agriculture and domestic purposes. | None identified | None identified | None identified | None identified | None provided | None identified | None reported | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | No additional description provided | Not applicable | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | No additional description provided | No additional descriptions provided | No additional description provided | No additional description provided | No additional description provided | Alewife spawning runs typically occur Mid March - May. | Hard and soft benthic habitat types approximately to the 33m isobath | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | River Teifi, Lampeter, Wales |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | No scenarios presented | No scenarios presented | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Sites, function or habitat focus | N/A | Varying flow velocities and durations |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
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EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
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EM-846 | EM-998 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the average annual biomass flux and carbon sequestration from two types of carbon plantings: 1) Tree-based monocultures of three different species (i.e., monoculture carbon planting) and 2) Diverse plantings of nine different native tree and shrub species (i.e., ecological carbon planting) |
Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | Application of existing 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
em.detail.idHelp
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EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
|
EM-846 | EM-998 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-260 | Doc-123 | Doc-255 | Doc-256 | Doc-257 | Doc-243 | Doc-246 | Doc-245 | Doc-303 | Doc-305 | None | None | None | None | Doc-383 | Doc-355 | Doc-390 | Doc-405 | Doc-467 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | None | None | None | None | EM-584 | EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673 | EM-590 | EM-699 | EM-718 | EM-734 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-768 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847 | EM-997 |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
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EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
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EM-846 | EM-998 |
|
EM Temporal Extent
em.detail.tempExtentHelp
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2007-2009 | 1987-1997 | 2000 | 2009-2050 | 1971-2006 | 2006 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | 35 yr | 1979-2009 | 2000-2005 | 2010-2012 | 2008 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | continuous |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Day | Month | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
|
EM-698 |
EM-743
|
EM-846 | EM-998 |
|
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Watershed/Catchment/HUC |
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Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | EU-15 | Agricultural districts of the state of South Australia | Puget Sound Region | Guanica Bay watershed | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Not applicable | Bride Brook | SW Puerto Rico, | Wetlands in idaho | Piedmont Ecoregion | River Teifi |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | Not applicable | 1-10 ha | 100-1000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
|
EM-846 | EM-998 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | NHDplus v1 | 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 | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | NHDplus v1 | 10 km x 10 km | 1 ha x 1 ha | 200m x 200m | 30 m x 30 m | 10 m x 10 m | 10 m x 10 m | Not applicable | Not applicable | not reported | Not applicable | Not applicable | Not applicable |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
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EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
|
EM-846 | EM-998 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
|
EM-846 | EM-998 |
|
Model Calibration Reported?
em.detail.calibrationHelp
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No | Yes | No | Yes | No | No | Yes | Yes | Not applicable |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
No | No | Yes | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | Yes | No | No | No | No | No | No | Not applicable | No | Yes | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None |
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None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | No | No | Yes | Yes | Unclear | No | Yes | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | Yes | No | No | No | No | No | Not applicable | No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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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. |
Yes | No | No | No | No | No | Not applicable | No | Yes | No | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
|
EM-698 |
EM-743
|
EM-846 | EM-998 |
|
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None | None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
|
EM-698 |
EM-743
|
EM-846 | EM-998 |
| None | None | None | None | None | None |
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None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
|
EM-846 | EM-998 |
|
Centroid Latitude
em.detail.ddLatHelp
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|
45.05 | 42.5 | 50.01 | -34.9 | 48 | 17.96 | 17.73 | 17.73 | Not applicable | 41.32 | 17.79 | 44.06 | 36.23 | 52.04 |
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Centroid Longitude
em.detail.ddLongHelp
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6.4 | -90.63 | 4.67 | 138.7 | -123 | -67.02 | -64.77 | -64.77 | Not applicable | -72.24 | -64.62 | -114.69 | -81.9 | -4.39 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
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EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
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EM-698 |
EM-743
|
EM-846 | EM-998 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Forests | Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine | Inland Wetlands | Grasslands | Rivers and Streams |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | None | Arable lands in near-stream environments | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Terrestrial environment surrounding a large estuary | 13 LULC were used | Coral reefs | Coral reefs | Agroecosystems | Coastal stream | shallow coral reefs | created, restored and enhanced wetlands | grasslands | River |
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EM Ecological Scale
em.detail.ecoScaleHelp
?
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Not applicable | Ecosystem | 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 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
|
EM-698 |
EM-743
|
EM-846 | EM-998 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Not applicable | Not applicable | Species | Not applicable | Not applicable | Species | Species | Not applicable | Individual or population, within a species | Guild or Assemblage | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
|
EM-698 |
EM-743
|
EM-846 | EM-998 |
| None Available | None Available | None Available |
|
None Available | None Available |
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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.detail.cicesHelp
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
|
EM-698 |
EM-743
|
EM-846 | EM-998 |
| None |
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None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 | EM-428 | EM-458 | EM-459 | EM-549 |
EM-667
|
EM-698 |
EM-743
|
EM-846 | EM-998 |
| None | None | None |
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None |
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None |
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