EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
EM Short Name
em.detail.shortNameHelp
?
|
Divergence in flowering date, Central French Alps | ACRU, South Africa | Natural attenuation by soil, The Netherlands | ARIES open Space, Puget Sound Region, USA | Coastal protection, Europe | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Decrease in erosion (shoreline), St. Croix, USVI | Ease of reef access, St. Croix, USVI | Reef dive site favorability, St. Croix, USVI | Presence of Euchema sp., St. Croix, USVI | Yasso07 v1.0.1, Switzerland | EnviroAtlas - Restorable wetlands | DayCent N2O flux simulation, Ireland | Waterfowl pairs, CREP wetlands, Iowa, USA | Grasshopper Sparrow density, CREP, Iowa, USA | Savannah Sparrow density, CREP, Iowa, USA | WESP: Marsh and open water, ID, USA | C sequestration in grassland restoration, England | Wildflower mix supporting bees, MI, USA | Wildflower mix supporting bees, CA, USA | ARIES Flood Reg, Santa Fe, NM | Atlantis ecosystem biology submodel |
EM Full Name
em.detail.fullNameHelp
?
|
Functional divergence in flowering date, Central French Alps | ACRU (Agricultural Catchments Research Unit), South Africa | Natural attenuation capacity of the soil, The Netherlands | ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA | Coastal protection, Europe | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Ease of access (to reef), St. Croix, USVI | Dive site favorability (reef), St. Croix, USVI | Relative presence of Euchema sp. (on reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | DayCent simulation N2O flux and climate change, Ireland | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Grasshopper Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | WESP: Deepwater marsh and open Water waterfowl habitat, Idaho, USA | Carbon sequestration in grassland diversity restoration, England | Wildflower planting mix supporting bees in agricultural landscapes, MI, USA | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA | ARIES Flood regulation, Santa Fe, New Mexico | Calibrating process-based marine ecosystem models: An example case using Atlantis |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
EU Biodiversity Action 5 | None | None | ARIES | EU Biodiversity Action 5 | InVEST | US EPA | US EPA | US EPA | US EPA | US EPA | None | US EPA | EnviroAtlas | None | None | None | None | None | None | None | None | None | None |
EM Source Document ID
|
260 | 271 | 287 | 302 | 296 | 315 | 317 | 335 | 335 | 335 | 335 | 343 | 262 | 358 | 372 | 372 | 372 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
396 | 400 | 400 | 411 | 459 |
Document Author
em.detail.documentAuthorHelp
?
|
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. | van Wijnen, H.J., Rutgers, M., Schouten, A.J., Mulder, C., de Zwart, D., and Breure, A.M. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Murphy, C. and T. Weekley | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E. |
Document Year
em.detail.documentYearHelp
?
|
2011 | 2008 | 2012 | 2014 | 2013 | 2015 | 2013 | 2014 | 2014 | 2014 | 2014 | 2014 | 2013 | 2010 | 2010 | 2010 | 2010 | 2012 | 2011 | 2015 | 2015 | 2018 | 2019 |
Document Title
em.detail.sourceIdHelp
?
|
Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | How to calculate the spatial distribution of ecosystem services - Natural attenuation as example from the Netherlands | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Assessment of coastal protection as an ecosystem service in Europe | Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | 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 | 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 | Validating tree litter decomposition in the Yasso07 carbon model | EnviroAtlas - National | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Additional carbon sequestration benefits of grassland diversity restoration | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Towards globally customizable ecosystem service models | Calibrating process-based marine ecosystem models: An example case using Atlantis |
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published report | Published report | Published report | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
Not applicable | Not applicable | Not applicable | http://aries.integratedmodelling.org/ | Not applicable | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
Contact Name
em.detail.contactNameHelp
?
|
Sandra Lavorel | Roland E Schulze | H.J. van Wijnen | Ken Bagstad | Camino Liquete | The Natural Capital Project | Alex Abdelnour | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
EnviroAtlas Team | M. Abdalla | David Otis | David Otis | David Otis | Chris Murphy | Gerlinde B. De Deyn | Neal Williams | Neal Williams | Javier Martinez-Lopez | Heidi R. Pethybridge |
Contact Address
|
Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands | Geosciences and Environmental Change Science Center, US Geological Survey | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, 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 | 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 | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia |
Contact Email
|
sandra.lavorel@ujf-grenoble.fr | schulzeR@nu.ac.za | harm.van.wijnen@rivm.nl | kjbagstad@usgs.gov | camino.liquete@gmail.com | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | enviroatlas@epa.gov | abdallm@tcd.ie | dotis@iastate.edu | dotis@iastate.edu | dotis@iastate.edu | chris.murphy@idfg.idaho.gov | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | nmwilliams@ucdavis.edu | nmwilliams@ucdavis.edu | javier.martinez@bc3research.org | Heidi.Pethybridge@csiro.au |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "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…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | ABSTRACT: "Maps play an important role during the entire process of spatial planning and bring ecosystem services to the attention of stakeholders' negotiation more easily. As example we show the quantification of the ecosystem service ‘natural attenuation of pollutants’, which is a service necessary to keep the soil clean for production of safe food and provision of drinking water, and to provide a healthy habitat for soil organisms to support other ecosystem services. A method was developed to plot the relative measure of the natural attenuation capacity of the soil in a map. Several properties of Dutch soils were related to property-specific reference values and subsequently combined into one proxy for the natural attenuation of pollutants." AUTHOR'S DESCRIPTION: "The natural attenuation capacity that is modeled in this study must be seen as a measure that describes the ‘biodegradation capacity’ of the soil, including biodegradation of all types of contaminants" | ABSTRACT: "...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: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape." | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity 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) ease of access for education" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and | 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:...(4) density of Euchema sp. seaweed," | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Grasshopper Sparrow (Ammodramus savannarum)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: GRSP density = e (-2.554612 + 0.0246975 * grass400 – 0.1032461 * trees400) | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) | 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: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | ABSTRACT: "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. " | Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers. |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | None identified | None identified | Supports global and EU biodiversity policy | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None Identified | climate change | None identified | None identified | None identified | None identified | None identified | None identrified | None identified | None identified | N/A |
Biophysical Context
|
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. | Five soil types including Löss, Fluvial clay, Peat, Sand, and Silty Loam. Five land-use types including Pasture, Arable farming, Semi-natural grassland, Heathland, and Forest. | No additional description provided | No additional description provided | Not applicable | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Prairie pothole region of north-central Iowa | Prairie pothole region of north-central Iowa | Prairie pothole region of north-central Iowa | restored, enhanced and created wetlands | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. | field plots near agricultural fruit and vegetable research farms | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | Marine ecosystem |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented | No scenarios presented | No scenarios presented | Sites, function or habitat focus | Additional benefits due to biodiversity restoration practices | Varied wildflower planting mixes of annuals and perennials | Varied wildflower planting mixes of annuals and perennials | N/A | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | Application of existing 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 | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Application of existing model ?Comment:Models developed by Quamen (2007). |
WESP Deepwater Marsh | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2007-2008 | 1950-1993 | 1999-2005 | 2000-2011 | 1992-2010 | Not applicable | 1969-2008 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 1993-2013 | 2006-2013 | 1961-1990 | 2002-2007 | 2002-2007 | 1992-2007 | 2010-2013 | 1990-2007 | 2010-2011 | 2011-2012 | 1981-2015 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | future time | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | both | Not applicable | Not applicable | Not applicable | past time | Not applicable | past time | past time | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | continuous |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Day | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Geopolitical | Geopolitical | Physiographic or ecological | Geopolitical | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Point or points | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Other |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Watershed/Catchment/HUC | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Central French Alps | South Africa | The Netherlands | Puget Sound Region | Shoreline of the European Union-27 | Not applicable | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | conterminous United States | Oak Park Research centre | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | Wetlands in Idaho | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Agricultural plots | Agricultural plots | Santa Fe Fireshed | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | Not applicable | 10-100 ha | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 1-10 ha | 1-10 km^2 | 1-10 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | <1 ha | 10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
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) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D 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 | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
20 m x 20 m | Distributed by catchments with average size of 65,000 ha | 100 m x 100 m | 200m x 200m | Irregular | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 5 sites | irregular | Not applicable | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | Not applicable | 3 m x 3 m | Not applicable | Not applicable | 30 m | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | No | No | No | Not applicable | No | Yes | Yes | Yes | Yes | No | No | No | Unclear | Unclear | Unclear | No | Not applicable | No | No | Unclear | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Yes | No | No | No | No | Not applicable | No | No | No | No | No | No | No |
Yes ?Comment:for N2O fluxes |
No | No | No | No | Not applicable | No | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
None | None | None | None | None | None | None | None | None | None | None | None |
|
None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | No | No | No | Not applicable | No | Yes | Yes | Yes | Yes | Yes | No | Yes | Unclear | Unclear | Unclear | No | No | No | No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
|
|
|
|
|
None |
|
None | None | None | None |
|
|
|
|
|
|
|
|
|
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
None | None | None |
|
|
None | None |
|
|
|
|
None | None | None | None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | -30 | 52.37 | 48 | 48.2 | -9999 | 44.25 | 17.73 | 17.73 | 17.73 | 17.73 | 46.82 | 39.5 | 52.86 | 42.62 | 42.62 | 42.62 | 44.06 | 54.2 | 43.87 | 29.4 | 35.86 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | 25 | 4.88 | -123 | 16.35 | -9999 | -122.33 | -64.77 | -64.77 | -64.77 | -64.77 | 8.23 | -98.35 | 6.54 | -93.84 | -93.84 | -93.84 | -114.69 | -2.35 | -85.64 | -82.18 | -105.76 | Not applicable |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Provided | Estimated | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Not applicable | Forests | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Agroecosystems | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows | Not reported | Not applicable | Terrestrial environment surrounding a large estuary | Coastal zones | Terrestrial environments, but not specified for methods | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coral reefs | Coral reefs | Coral reefs | Coral reefs | forests | Terrestrial | farm pasture | Wetlands buffered by grassland set in agricultural land | Grassland buffering inland wetlands set in agricultural land | Grassland buffering inland wetlands set in agricultural land | created, restored and enhanced wetlands | fertilized grassland (historically hayed) | Agricultural landscape | Agricultural landscape | watersheds | Multiple |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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 | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | 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
?
|
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Guild or Assemblage | Species | Community | Not applicable | Not applicable | Species | Species | Species | Not applicable | Community | Species | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
|
None Available | None Available | None Available |
|
|
|
None Available | None Available |
|
|
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-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
None |
|
|
|
|
|
None |
|
|
|
|
|
None |
|
|
|
|
|
|
|
|
|
|
<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-79 | EM-84 | EM-178 | EM-315 | EM-320 | EM-374 | EM-379 | EM-449 | EM-451 | EM-456 | EM-461 |
EM-467 ![]() |
EM-492 |
EM-593 ![]() |
EM-632 ![]() |
EM-649 | EM-652 |
EM-734 ![]() |
EM-735 ![]() |
EM-796 ![]() |
EM-812 ![]() |
EM-858 | EM-981 |
None |
|
None |
|
|
None | None |
|
None |
|
|
None | None |
|
|
|
|
None | None |
|
|
None |
|