EcoService Models Library (ESML)

Compare EMs

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:

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Comparing:

EM-66
EM-87
EM-113
EM-131
EM-438
EM-465
EM-593
EM-784
EM-880

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
EM Short Name em.detail.shortNameHelp ?	Litter biomass production, Central French Alps	Area & hotspots of soil accumulation, South Africa	Wetland conservation for birds, Midwestern USA	InVEST marine water quality, Hood Canal, WA, USA	InVESTv3.0 Nutrient retention, Guánica Bay	Pharmaceutical product potential, St. Croix, USVI	DayCent N2O flux simulation, Ireland	Wildflower mix supporting bees, Florida, USA	Human well-being index, Pensacola Bay, Florida
EM Full Name em.detail.fullNameHelp ?	Litter biomass production, Central French Alps	Area and hotspots of soil accumulation, South Africa	Prioritizing wetland conservation for birds, Midwestern USA	InVEST (Integrated Valuation of Envl. Services and Tradeoffs) marine water quality, Hood Canal, WA, USA	InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA	Relative pharmaceutical product potential (on reef), St. Croix, USVI	DayCent simulation N2O flux and climate change, Ireland	Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA	Human well-being index (HWBI), Pensacola Bay, Florida
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	None	None	InVEST	US EPA \| InVEST	US EPA	None	None	US EPA
EM Source Document ID	260	271	122	205	338	335	358	400	418
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.	Thogmartin, W. A., Potter, B. A. and Soulliere, G. J.	Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M.	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	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	Yee, S.H., Paulukonis, E., Simmons, C., Russell, M., Fullford, R., Harwell, L., and L.M. Smith
Document Year em.detail.documentYearHelp ?	2011	2008	2011	2013	2017	2014	2010	2015	2021
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	Bridging the conservation design and delivery gap for wetland bird habitat maintenance and restoration in the midwestern United States	From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA	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	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	Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States	Projecting effects of land use change on human well being through changes in ecosystem services
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
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	http://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Benis Egoh	Wayne Thogmartin, USGS	J.E. Toft	Susan H. Yee	Susan H. Yee	M. Abdalla	Neal Williams	Susan Yee
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603	Not reported	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	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616	Gulf Ecosystem Measurement and Modeling Division, Center for Environmental Measurement and Modeling, US Environmental Prntection Agency, Gulf Breeze, FL 32561, USA
Contact Email	sandra.lavorel@ujf-grenoble.fr	Not reported	wthogmartin@usgs.gov	jetoft@stanford.edu	yee.susan@epa.gov	yee.susan@epa.gov	abdallm@tcd.ie	nmwilliams@ucdavis.edu	yee.susan@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
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 (e.g., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Litter biomass production for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on litter mass. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use."	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover."	ABSTRACT: "The U.S. Fish and Wildlife Service’s adoption of Strategic Habitat Conservation is intended to increase the effectiveness and efficiency of conservation delivery by targeting effort in areas where biological benefits are greatest. Conservation funding has not often been allocated in accordance with explicit biological endpoints, and the gap between conservation design (the identification of conservation priority areas) and delivery needs to be bridged to better meet conservation goals for multiple species and landscapes. We introduce a regional prioritization scheme for North American Wetlands Conservation Act funding which explicitly addresses Midwest regional goals for wetland-dependent birds. We developed decision-support maps to guide conservation of breeding and non-breeding wetland bird habitat. This exercise suggested ~55% of the Midwest consists of potential wetland bird habitat, and areas suited for maintenance (protection) were distinguished from those most suited to restoration. Areas with greater maintenance focus were identified for central Minnesota, southeastern Wisconsin, the Upper Mississippi and Illinois rivers, and the shore of western Lake Erie and Saginaw Bay. The shores of Lakes Michigan and Superior accommodated fewer waterbird species overall, but were also important for wetland bird habitat maintenance. Abundant areas suited for wetland restoration occurred in agricultural regions of central Illinois, western Iowa, and northern Indiana and Ohio. Use of this prioritization scheme can increase effectiveness, efficiency, transparency, and credibility to land and water conservation efforts for wetland birds in the Midwestern United States."	Marine Water Quality Model. Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We used outputs from the freshwater models as inputs to the marine water quality model.We adapted a box model that has been successfully applied in Puget Sound (Babson et al., 2006; Sutherland et al., 2011) to simulate seasonal and interannual variations in salinity, water temperature, and nitrates in the Canal." (p. 4)	Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model"	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…When data on sponge diversity is unavailable, benthic habitat coverages may be used to estimate relative magnitudes of sponge diversity and abundance as an indicator of potential pharmaceutical production (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to potential pharmaceutical production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Pharmaceutical product potential = ΣiciMi 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 Mi is the relative magnitude of sponge diversity associated with each habitat."	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: " 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: "Changing patterns of land use, temperature, and precipitation are expected to impact ecosystem se1vices, including water quality and quantity, buffering of extreme events, soil quality, and biodiversity. Scenario ana lyses that link such impacts on ecosystem se1vices to human well-being may be valuable in anticipating potential consequences of change that are meaningful to people living in a community. Ecosystem se1vices provide munerous benefits to community well-being, including living standards, health, cultural fulfillment, education, and connection to nature. Yet assessments of impacts of ecosystem se1vices on human well-being have largely focused on human health or moneta1y benefits (e.g. market values). This study applies a human well-being modeling framework to demonsffate the potential impacts of alternative land use scenarios on multi-faceted components of human well-being through changes in ecosystem se1vices (i.e., ecological benefits functions). The modeling framework quantitatively defines these relationships in a way that can be used to project the influence of ecosystem se1vice flows on indicators of human well-being, alongside social se1vice flows and economic se1vice flows. Land use changes are linked to changing indicators of ecosystem se1vices through the application of ecological production functions. The approach is demonstrated for two future land use scenarios in a Florida watershed, representing different degrees of population growth and environmental resource protection. Increasing rates of land development were almost universally associated with declines in ecosystem se1vices indicators and associated indicators of well-being, as natural ecosystems were replaced by impe1vious surfaces that depleted the ability of ecosystems to buffer air pollutants, provide habitat for biodiversity, and retain rainwater. Scenarios with increases in indicators of ecosystem se1vices, however, did not necessarily translate into increases in indicators of well-being, due to cova1ying changes in social and economic se1vices indicators. The approach is broadly ffansferable to other communities or decision scenarios and se1ves to illustrate the potential impacts of changing land use on ecosystem se1vices and human well-being. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Strategic habitat conservation by USFW for Wetland Conservation Act funding	Land use change	Improving water quality	None identified	climate change	None identrified	None identified
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods	No additional description provided	No additional description provided	No additional description provided	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca	N/A
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Conservation efforts for: marsh-wetland breeding birds, regional marsh and open-water for non-breeding birds, mudflat/shallows for birds during non-breeding period.	future land use and land cover; Climate change	No scenarios presented	No scenarios presented	air temperature, precipitation, Atmospheric CO2 concentrations	Varied wildflower planting mixes of annuals and perennials	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist)	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
New or Pre-existing EM? em.detail.newOrExistHelp ?	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	New or revised model	New or revised model

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-271	Doc-169 \| Doc-170 \| Doc-171 \| Doc-172 \| Doc-173 \| Doc-174 \| Doc-175	None	Doc-309 \| Doc-205	None	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-85 \| EM-86 \| EM-88	None	None	EM-363 \| EM-112	None	EM-598	EM-796 \| EM-797 \| EM-804 \| EM-805 \| EM-806 \| EM-812 \| EM-814	EM-882

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	Not reported	2007	varies by run, see runs for values	1980 - 2013	2006-2007, 2010	1961-1990	2011-2012	2010
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent	time-dependent	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	other or unclear (comment)	Not applicable	both	past time	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	discrete	discrete	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	1	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Year	Not applicable	Day	Year	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Physiographic or ecological	Watershed/Catchment/HUC	Physiographic or ecological	Point or points	Point or points ? Comment: This is a guess based on information in the document. 3 field sites were separated by up to 9km	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	South Africa	Upper Mississippi River and Great Lakes Region	Hood Canal	Guanica Bay Study Area	Coastal zone surrounding St. Croix	Oak Park Research centre	Agricultural plots	Pensacola Bay Region
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	>1,000,000 km^2	>1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	100-1000 km^2	1-10 ha	10-100 km^2	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
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 lumped (in all cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)
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	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	Distributed across catchments with average size of 65,000 ha	1 ha	Not reported	30 m x 30 m	10 m x 10 m	Not applicable	Not applicable	county

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic	Numeric	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Other or unclear (comment)	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	No	No	Yes	No	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	No	No	No	Yes ? Comment: for N2O fluxes	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 61.2 \| Not applicable	None	None	None	None	None	r squared \| 0.32 \| uniteless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	No	No	Yes	Yes	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No	No	Yes
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	No	Yes
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Unclear

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
Europe France	Africa South Africa	North America United States Iowa Indiana Illinois Missouri Minnesota Ohio Kansas Wisconsin Michigan Nebraska	North America United States Washington	North America U.S. Caribbean Territories	None	Europe Ireland	North America United States Florida	North America United States Florida

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
None	None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
Centroid Latitude em.detail.ddLatHelp ?	45.05	-30	42.05	47.8	17.97	17.73	52.86	29.4	30.05
Centroid Longitude em.detail.ddLongHelp ?	6.4	25	-88.6	-122.7	-66.93	-64.77	6.54	-82.18	-87.61
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	NAD83	WGS84	WGS84	None provided	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Provided	Provided	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Inland Wetlands \| Near Coastal Marine and Estuarine \| Open Ocean and Seas \| Forests \| Agroecosystems \| Created Greenspace \| Scrubland/Shrubland \| Barren	Near Coastal Marine and Estuarine	Agroecosystems	Agroecosystems	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Not applicable	Not reported	glacier-carver saltwater fjord	13 LULC were used	Coral reefs	farm pasture	Agricultural landscape	Mixed
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Species	Not applicable	Not applicable	Guild or Assemblage	Not applicable	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
None Available	None Available	Species Tundra Swan (eastern) Wilson's Snipe Black Tern Sanderling Black-crowned Night-Heron Wilson's Phalarope American Black Duck Upland Sandpiper Mallard Duck Common Tern Piping Plover Wood Duck Canvasback American Golden Plover Blue-winged Teal Lesser Scaup Yellow Rail King Rail Dunlin American Woodcock Short-billed Dowitcher Killdeer	None Available	None Available	other Sponges	None Available	other Bees Species Lupinus succulentus Trifolium obtusiflorum Helianthus bolaneri Eschscholzia californica Lotus scoparius Rudbeckia hirta Trifolium willdenovii Phacelia tanacetifolia Phacelia californica Trifolium fucatum Helianthus bolanderi Grindelia camporum Lupinus formosus Nemophila menziesii Trichostema lanceolatum Achillea millefolium Clarkia unguinculata lupinus densiflorus	None Available

EnviroAtlas URL

EM-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
GAP Ecological Systems	None Available	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	None Available	Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	None Available	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	None Available	Dasymetric Allocation of Population, GAP Ecological Systems, Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, Total Employment, Average Annual Daily Potential Wind Energy, Hectares of Vegetable Crops, Employment Rate, Enabling Conditions, Carbon Storage by Tree Biomass

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-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of salt waters Mediation of flows Liquid flows Hydrological cycle and water flow maintenance	[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Buffering and attenuation of mass flows Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Provisioning Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings

<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-66	EM-87	EM-113	EM-131	EM-438	EM-465	EM-593	EM-784	EM-880
None	None	Wetlands (12) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator Fauna (4) Birds Stock Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	Agroecosystems (22) Fauna (4) Pollinators Site Indicator	Urban/Surburban (27) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator