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:

Show Criteria

Hide Criteria

Comparing:

EM-111
EM-123
EM-127
EM-340
EM-344
EM-422
EM-480
EM-496
EM-655

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
EM Short Name em.detail.shortNameHelp ?	InVEST water yield, Hood Canal, WA, USA	Land-use change and wildlife products, Europe	Annual profit - carbon plantings, South Australia	InVEST crop pollination, Costa Rica	InVEST water yield, Xitiaoxi River basin, China	HexSim v2.4, San Joaquin kit fox, CA, USA	Yasso07 - Land use SOC dynamics, China	Sed. denitrification, St. Louis R., MN/WI, USA	Hunting recreation, Wisconsin, USA
EM Full Name em.detail.fullNameHelp ?	InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA	Land-use change effects on wildlife products, Europe	Annual profit from carbon plantings, South Australia	InVEST crop pollination, Costa Rica	InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) water yield, Xitiaoxi River basin, China	HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA	Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China	Sediment denitrification, St. Louis River, MN/WI, USA	Hunting recreation, Wisconsin, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	InVEST	EU Biodiversity Action 5	None	InVEST	InVEST	US EPA	None	US EPA	None
EM Source Document ID	205	228	243	279	307	337 ? Comment: The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships.	344	333	376
Document Author em.detail.documentAuthorHelp ?	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.	Haines-Young, R., Potschin, M. and Kienast, F.	Crossman, N. D., Bryan, B. A., and Summers, D. M.	Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf	Zhang C., Li, W., Zhang, B., and Liu, M.	Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips	Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu	Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill	Qiu, J. and M. G. Turner
Document Year em.detail.documentYearHelp ?	2013	2012	2011	2009	2012	2015	2015	2014	2013
Document Title em.detail.sourceIdHelp ?	From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Carbon payments and low-cost conservation	Modelling pollination services across agricultural landscapes	Water yield of Xitiaoxi River basin based on InVEST modeling	Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations	Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement	Sediment nitrification and denitrification in a Lake Superior estuary	Spatial interactions among ecosystem services in an urbanizing agricultural watershed
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-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	http://www.naturalcapitalproject.org/models/crop_pollination.html	https://www.naturalcapitalproject.org/invest/	http://www.hexsim.net/	http://en.ilmatieteenlaitos.fi/yasso-download-and-support	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	J.E. Toft	Marion Potschin	Neville D. Crossman	Eric Lonsdorf	Li Wenhua	Theresa M. Nogeire	Xing Wu	Brent J. Bellinger ? Comment: Ph# +1 218 529 5247. Other current address: Superior Water, Light and Power Company, 2915 Hill Ave., Superior, WI 54880, USA.	Monica G. Turner
Contact Address	The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia	Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA	Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China	School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA	Chinese Academy of Sciences, Beijing 100085, China	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	Not reported
Contact Email	jetoft@stanford.edu	marion.potschin@nottingham.ac.uk	neville.crossman@csiro.au	ericlonsdorf@lpzoo.org	liwh@igsnrr.ac.cn	tnogeire@gmail.com	xingwu@rceesac.cn	bellinger.brent@epa.gov	turnermg@wisc.edu

EM Description

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Summary Description em.detail.summaryDescriptionHelp ?	InVEST Water Yield and Scarcity 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 modelled discharge and total nitrogen for the 153 perennial sub- watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2)	ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes…are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Wildlife products); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000." AUTHOR'S DESCRIPTION: "Wildlife products belongs to the service group Biotic Materials in the CICES system; it includes the provisioning of all non-edible raw material products that are gained through non-agricultural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers."	ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially."	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. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..."	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. ABSTRACT: "A water yield model based on InVEST was employed to estimate water runoff in the Xitiaoxi River basin…In order to test model accuracy the natural runoff of Xitiaoxi River was estimated based on linear regression relation of rainfall-runoff in a 'reference period'." AUTHOR'S DESCRIPTION: "The water yield model is based on the Budyko curve (1974) and annual precipitation…Water yield models require land use and land cover, precipitation, average annual potential evapotranspiration, soil depth, plant available water content, watersheds and sub-watersheds as well as a biophysical table reflecting the attributes of each land use and land cover."	ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]."	ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau."	ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature. We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones…Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates." AUTHOR'S DESCRIPTION: "We used different survey designs in 2011 and 2012. Both designs were based on area-weighted probability sampling methods, similar to those developed for EPA's Environmental Monitoring and Assessment Program (EMAP) (Crane et al., 2005; Stevens and Olsen, 2003, 2004). Sampling sites were assigned to spatial zones: “harbor” (river km 0–13), “bay” (river km 13–24), or “river” (river km 24–35) (Fig. 1). Sites were also grouped by depth zones (“shallow,” <1 m; “intermediate,” 1–2 m; and “deep,” >2 m). In 2011 (“vegetated-habitat survey”), the sample frame consisted of areas of emergent and submergent vegetation in the SLRE… The resulting sample frame included 2370 ha of potentially vegetated area out of a total SLRE area of 4378 ha. Sixty sites were distributed across the total vegetated area in each spatial zone using an uneven spatially balanced probabilistic design. Vegetated areas were more prevalent, and thus had greater sampling effort, in the bay (n = 33) and river (n = 17) than harbor (n=10) zones, and in the shallow (n=44) and intermediate (n =14) than deep (n =2) zones. All sampling was done in July. In 2012 a probabilistic sampling design (“estuary-wide survey”) was implemented to determine N-cycling rates for the entire SLRE (not just vegetated areas as in 2011). Thirty sites unevenly distributed across spatial and depth zones were sampled monthly in May–September (Fig. 1). Area weighting for each sampled site reflects the SLRE area attributable to each sample by month, spatial zone, and depth zone." "…we were able to create significant predictive models for NIT and DeNIT rates using linear combinations of physiochemical parameters…" "…Simulations of changes in DeNIT rates in response to altered water depth and surface NOx-N concentration for spring (Fig. 4A) and summer (Fig. 4B) show that for a given season, altering water depths would have a greater influence on DeNIT than rising NO3- concentration."	AUTHOR'S DESCRIPTION (from Supporting Information): "The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility. Similar assumptions were made for this assessment: larger areas and places with more game species would support more hunting, areas closer to large population centers would be used more than remote areas, and proximity to major roads would increase access and use of an area. We first obtained the boundary of public wild areas from Wisconsin DNR and calculated the amount of areas for each management unit. The number of game species (Spe) for each area was derived from Dane County Parks Division (70). We used the same population density (Pop) and road buffer layer (Road) described in the previous forest recreation section. The variables Spe, Pop, and Road were weighted to ranges of 0–40, 0–40, and 0–20, respectively, based on the relative importance of each in determining this service. We estimated overall hunting recreation service for each 30-m grid cell with the following equation: HRSi = Ai Σ(Spei + Popi +Roadi), where HRS is hunting recreation score, A is the area of public wild areas open for hunting/fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28,000) to a range of 0–100, with 0 representing no hunting recreation service and 100 representing highest service.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Land use change	None identified	None identified	None identified	None identified	None identified	None identified	None identified	None identified
Biophysical Context	Not additional description provided	No additional description provided	Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm.	No additional description provided	Mean elevation of 266 m, with southwestern mountainous area. Subtropical monsoon climate. Annual average temperature of 12.2-15.6 °C. Annual mean precipitation is 1500 mm, and over 70% of rainfall occurs in the flood season (Apr-Oct).	No additional description provided	Agricultural plain, hills, gulleys, forest, grassland, Central China	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Future land use and land cover; climate change	Recent historical land-use change from 1990-2000	Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e	No scenarios presented	No scenarios presented	Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class	Land use change	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting)	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships.	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	New or revised model	New or revised model	Application of existing model	Application of existing model	Application of existing model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-280 \| Doc-307 \| Doc-311 \| Doc-338	Doc-228 \| Doc-238 \| Doc-239 \| Doc-240 \| Doc-241 \| Doc-242	Doc-245 \| Doc-246 \| Doc-247 \| Doc-243	Doc-279	Doc-280 \| Doc-311 \| Doc-338 \| Doc-205	Doc-328 \| Doc-327 \| Doc-2	Doc-343 \| Doc-342	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-148 \| EM-344 \| EM-368 \| EM-437	EM-122 \| EM-124 \| EM-125 \| EM-162 \| EM-164 \| EM-165 \| EM-166 \| EM-170 \| EM-171 \| EM-99 \| EM-119 \| EM-120 \| EM-121	EM-128 \| EM-141	EM-338 \| EM-339	EM-148 \| EM-368 \| EM-437 \| EM-111	EM-403 \| EM-98	EM-466 \| EM-467 \| EM-469 \| EM-485	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
EM Temporal Extent em.detail.tempExtentHelp ?	2005-7; 2035-45	1990-2000	2009-2050	2001-2002	2003-2007	60 yr	1969-2011	July 2011 to September 2012 ? Comment: All sampling performed July 2011, and May-September 2012.	2000-2006
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-dependent	time-dependent	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	future time	Not applicable	Not applicable	future time	past time	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	Not applicable	Not applicable	discrete	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	Not applicable	Not applicable	1	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Not applicable	Not applicable	Year	Year	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Bounding Type em.detail.boundingTypeHelp ?	Watershed/Catchment/HUC	Geopolitical	Physiographic or Ecological	Other	Watershed/Catchment/HUC	Physiographic or ecological	Watershed/Catchment/HUC	Physiographic or ecological	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Hood Canal	The EU-25 plus Switzerland and Norway	Agricultural districts of the state of South Australia	Large coffee farm, Valle del General	Xitiaoxi River basin	San Joaquin Valley, CA	Yangjuangou catchment	St. Louis River Estuary (of western Lake Superior)	Yahara Watershed, Wisconsin
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100,000-1,000,000 km^2	>1,000,000 km^2	100,000-1,000,000 km^2	10-100 km^2	1000-10,000 km^2.	10,000-100,000 km^2	1-10 km^2	10-100 km^2	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
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)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	30 m x 30 m	1 km x 1 km	1 ha x 1 ha	30 m x 30 m	Not reported	14 ha	30m x 30m	35 km river estuary reach, 0 to 5 m depth by 1 m increment	30m x 30m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Logic- or rule-based	Analytic	Analytic	Analytic	Numeric	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Other or unclear (comment)	None	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	No	No	Unclear	Yes	Unclear	No	Yes	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	No	No	No	Yes ? Comment: p value: p<0.001	Yes	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	None	None	R squared \| 0.219 \| unitless	r2 \| 0.51 \| unitless	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	Yes	No	No	No	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Yes	No	No	Yes	Yes	Yes	No	No	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	No	Not applicable	Not applicable	No	No	No	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
North America United States Washington	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia France Lithuania Hungary Switzerland Spain Czech Republic Belgium Norway Finland Denmark Italy Germany Estonia	Oceania Australia	North America Costa Rica	Asia China	North America United States California	Asia China	North America United States Minnesota Wisconsin	North America United States Wisconsin

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

EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	None	None	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Centroid Latitude em.detail.ddLatHelp ?	47.8	50.53	-34.9	9.13	30.55	36.13	36.7	46.74	43.1
Centroid Longitude em.detail.ddLongHelp ?	-122.7	7.6	138.7	-83.37	119.5	-120	109.52	-96.13	-89.4
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Provided	Estimated	Provided	Estimated	Provided

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Terrestrial Environment (sub-classes not fully specified) \| Agroecosystems	Rivers and Streams \| Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Rivers and Streams \| Inland Wetlands	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands
Specific Environment Type em.detail.specificEnvTypeHelp ?	glacier-carved saltwater fjord	Not applicable	Agricultural land for annual crops, annual legumes, and grazing of sheep and cows	Cropland and surrounding landscape	Watershed	Agricultural region (converted desert) and terrestrial perimeter	Loess plain	River and riverine estuary (lake)	Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Guild or Assemblage	Species	Not applicable	Individual or population, within a species	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
None Available	None Available	Species Acacia implexa Eucalyptus microcarpa Acacia verniciflua Eucalyptus polyanthemos Eucalyptus globulus Eucalyptus sideroxylon Acacia mearnsii Acacia pycnantha Eucalyptus camaldulensis Eucalyptus kochii Acacia dealbata	other Huge Black 2002 Genus Trigonisca sp. Species Apis mellifera Nannotrigona mellaria Trigona corvina Trigona (Tetragonisca) angustula Trigona fussipennis Trigona fulviventris Trigona dorsalis Plebia frontalis Melipona fasciata Plebeia jatiformis Trigona (Tetragona) clavipies Partamona cupira	None Available	Species Vulpes macrotis mutica	None Available	None Available	None Available

EnviroAtlas URL

EM-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Average Annual Precipitation	Hectares of cotton crops	Carbon Storage by Tree Biomass	GAP Ecological Systems	Average Annual Precipitation, The Watershed Boundary Dataset (WBD)	Stream Length Impaired by Pesticides	Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass	Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	Dasymetric Allocation of Population

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-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Hydrological cycle and water flow maintenance	[Ecosystem] Provisioning Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Provisioning Nutrition Water Surface water for drinking Materials Water Surface water for non-drinking purposes	[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 Atmospheric composition and climate regulation Micro and regional climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by biota Bio-remediation by micro-organisms, algae, plants, and animals	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs

<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-111	EM-123	EM-127	EM-340	EM-344	EM-422	EM-480	EM-496	EM-655
Rivers and Streams (111) Water (3) Liquid water Flow Indicator Quality Indicator	Rivers and Streams (111) Fauna (4) Wildlife Stock Indicator Barren / Rock and Sand (28) Fauna (4) Wildlife Stock Indicator Forests (21) Fauna (4) Wildlife Stock Indicator Tundra (25) Fauna (4) Wildlife Stock Indicator Grasslands (23) Fauna (4) Wildlife Stock Indicator Scrubland/Shrubland (24) Fauna (4) Wildlife Stock Indicator Lakes and Ponds (112) Fauna (4) Wildlife Stock Indicator Wetlands (12) Fauna (4) Wildlife Stock Indicator	None	Agroecosystems (22) Fauna (4) Pollinators Stock Indicator	Rivers and Streams (111) Water (3) Liquid water Stock Indicator	None	None	None	None

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Comment:

Comment:

Comment:

Comment:

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

EM Modeling Approach

Comment:

Comment:

EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

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)

<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)