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.

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Comparing:

EM-79
EM-374
EM-379
EM-438
EM-449
EM-467
EM-593
EM-603
EM-632
EM-660

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
EM Short Name em.detail.shortNameHelp ?	Divergence in flowering date, Central French Alps	InVEST carbon storage and sequestration (v3.2.0)	VELMA soil temperature, Oregon, USA	InVESTv3.0 Nutrient retention, Guánica Bay	Decrease in erosion (shoreline), St. Croix, USVI	Yasso07 v1.0.1, Switzerland	DayCent N2O flux simulation, Ireland	Chinook salmon value, Yaquina Bay, OR	Waterfowl pairs, CREP wetlands, Iowa, USA	RUM: Valuing fishing quality, Michigan, USA
EM Full Name em.detail.fullNameHelp ?	Functional divergence in flowering date, Central French Alps	InVEST v3.2.0 Carbon storage and sequestration	VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA	InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA	Decrease in erosion (shoreline) by reef, St. Croix, USVI	Yasso07 v1.0.1 forest litter decomposition, Switzerland	DayCent simulation N2O flux and climate change, Ireland	Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR	Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	InVEST	US EPA	US EPA \| InVEST	US EPA	None	None	US EPA	None	None
EM Source Document ID	260	315	317	338	335	343	358	324	372	382 ? Comment: Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010.
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	The Natural Capital Project	Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y.	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Didion, M., B. Frey, N. Rogiers, and E. Thurig	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar	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	Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson
Document Year em.detail.documentYearHelp ?	2011	2015	2013	2017	2014	2014	2010	2012	2010	2014
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Carbon storage and sequestration - InVEST (v3.2.0)	Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment	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	Validating tree litter decomposition in the Yasso07 carbon model	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	Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Valuing recreational fishing quality at rivers and streams
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
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Website	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	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	http://www.naturalcapitalproject.org/invest/	Not applicable	http://en.ilmatieteenlaitos.fi/yasso-download-and-support	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	The Natural Capital Project	Alex Abdelnour	Susan H. Yee	Susan H. Yee	Markus Didion ? Comment: Tel.: +41 44 7392 427	M. Abdalla	Stephen Jordan	David Otis	Richard Melstrom
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	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	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	Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA
Contact Email	sandra.lavorel@ujf-grenoble.fr	invest@naturalcapitalproject.org	abdelnouralex@gmail.com	yee.susan@epa.gov	yee.susan@epa.gov	markus.didion@wsl.ch	abdallm@tcd.ie	jordan.steve@epa.gov	dotis@iastate.edu	melstrom@okstate.edu

EM Description

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
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."	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"	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...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 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."	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:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico.	ABSTRACT: "This 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 paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	Improving water quality	None identified	None identified	climate change	None reported	None identified	None identified
Biophysical Context	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	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	Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa).	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	Yaquina Bay estuary	Prairie pothole region of north-central Iowa	stream and river reaches of Michigan
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Optional future scenarios for changed LULC and wood harvest	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).	air temperature, precipitation, Atmospheric CO2 concentrations	N/A	No scenarios presented	targeted sport fish biomass

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method Only	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 (multiple runs exist) View EM Runs	Method + Application	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	Application of existing 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	New or revised model

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

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	Doc-309	Doc-13 \| Doc-317	Doc-309 \| Doc-205	Doc-335	Doc-342 \| Doc-344	None	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-349	EM-375 \| EM-380 \| EM-884 \| EM-883 \| EM-887	EM-363 \| EM-112	EM-447 \| EM-448	EM-466 \| EM-469 \| EM-480 \| EM-485	EM-598	EM-604 \| EM-397	EM-705 \| EM-703 \| EM-702 \| EM-701 \| EM-700	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2008	Not applicable	1969-2008	1980 - 2013	2006-2007, 2010	1993-2013	1961-1990	2003-2008	2002-2007	2008-2010
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-dependent	time-dependent	time-stationary	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	future time	other or unclear (comment)	Not applicable	future time	both	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	discrete	discrete	Not applicable	discrete	discrete	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	1	1	Not applicable	1	1	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Day	Year	Not applicable	Year	Day	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Not applicable	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Physiographic or ecological	Geopolitical	Point or points	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Not applicable	H. J. Andrews LTER WS10	Guanica Bay Study Area	Coastal zone surrounding St. Croix	Switzerland	Oak Park Research centre	Pacific Northwest	CREP (Conservation Reserve Enhancement Program) wetland sites	HUCS in Michigan
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	Not applicable	10-100 ha	1000-10,000 km^2.	100-1000 km^2	10,000-100,000 km^2	1-10 ha	>1,000,000 km^2	1-10 km^2	100,000-1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
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) ? 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 lumped (in all cases)	spatially lumped (in all 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	volume, for 3-D feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	application specific	30 m x 30 m surface pixel and 2-m depth soil column	30 m x 30 m	10 m x 10 m	5 sites	Not applicable	Not applicable	multiple, individual, irregular shaped sites	reach in HUC

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Numeric	Numeric	Analytic	Numeric	Numeric	Numeric	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
Model Calibration Reported? em.detail.calibrationHelp ?	No	Not applicable	No	No	Yes	No	No	No	Unclear	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Not applicable	No	No	No	No	Yes ? Comment: for N2O fluxes	No	No	Yes
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R squared \| 35.5 \| Not applicable	None	None	None	None	None	r squared \| 0.32 \| uniteless	None	None	R squared \| 0.552 \| unitless
Model Operational Validation Reported? em.detail.validationHelp ?	No	Not applicable	No	No	Yes	Yes	Yes	Yes ? Comment: Compared to a second methodological approach	Unclear	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Not applicable	No	No	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Not applicable	No	No	No	No	No	No	No	No
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

EM Locations, Environments, Ecology

Location of EM Application

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

EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
Europe France	None	North America United States Oregon	North America U.S. Caribbean Territories	None	Europe Switzerland	Europe Ireland	North America United States Oregon Washington	North America United States Iowa	North America United States Michigan

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

EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Oregon, Washington, Vancouver Coast and Shelf	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
Centroid Latitude em.detail.ddLatHelp ?	45.05	-9999	44.25	17.97	17.73	46.82	52.86	44.62	42.62	45.12
Centroid Longitude em.detail.ddLongHelp ?	6.4	-9999	-122.33	-66.93	-64.77	8.23	6.54	-124.02	-93.84	85.18
Centroid Datum em.detail.datumHelp ?	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	None provided	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Not applicable	Provided	Estimated	Estimated	Estimated	Provided	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Not applicable	Forests	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	Forests	Agroecosystems	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Rivers and Streams
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	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).	13 LULC were used	Coral reefs	forests	farm pasture	Yaquina Bay	Wetlands buffered by grassland set in agricultural land	stream reaches
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser 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 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 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-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Not applicable	Not applicable	Not applicable	Community	Not applicable	Individual or population, within a species	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
None Available	None Available	None Available	None Available	None Available	None Available	None Available	Species Chinook salmon	Species Wood Duck (Aix sponsa) Northern Pintail (Anas acuta) Mallard (Anas platyrhynchos) Blue-winged Teal (Anas discors) Northern Shoveler (Anas clypeata) Gadwall (Anas strepera)	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye

EnviroAtlas URL

EM-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
None Available	GAP Ecological Systems, Carbon Storage by Tree Biomass, Hectares of cotton crops	Average Annual Precipitation	Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	National Hydrography Dataset Plus (NHD PlusV2)	GAP Ecological Systems, Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	Big game hunting recreation demand	None Available	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate

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-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	None	[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] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[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 Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	None	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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-79	EM-374	EM-379	EM-438	EM-449	EM-467	EM-593	EM-603	EM-632	EM-660
None	None	None	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	None	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None	Wetlands (12) Fauna (4) Duck Stock Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator

EcoService Models Library (ESML)

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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)