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-154
EM-374
EM-379
EM-449
EM-451
EM-456
EM-467
EM-593
EM-632
EM-1006

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
EM Short Name em.detail.shortNameHelp ?	Divergence in flowering date, Central French Alps	Mangrove development, Tampa Bay, FL, USA	InVEST carbon storage and sequestration (v3.2.0)	VELMA soil temperature, Oregon, USA	Decrease in erosion (shoreline), St. Croix, USVI	Ease of reef access, St. Croix, USVI	Reef dive site favorability, St. Croix, USVI	Yasso07 v1.0.1, Switzerland	DayCent N2O flux simulation, Ireland	Waterfowl pairs, CREP wetlands, Iowa, USA	Vista land-sea planning submodel
EM Full Name em.detail.fullNameHelp ?	Functional divergence in flowering date, Central French Alps	Mangrove wetland development, Tampa Bay, FL, USA	InVEST v3.2.0 Carbon storage and sequestration	VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA	Decrease in erosion (shoreline) by reef, St. Croix, USVI	Ease of access (to reef), St. Croix, USVI	Dive site favorability (reef), St. Croix, USVI	Yasso07 v1.0.1 forest litter decomposition, Switzerland	DayCent simulation N2O flux and climate change, Ireland	Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	A technical guide to the integrated land-sea planning toolkit
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	US EPA	InVEST	US EPA	US EPA	US EPA	US EPA	None	None	None	None
EM Source Document ID	260	97	315	317	335	335	335	343	358	372	473
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L.	The Natural Capital Project	Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Didion, M., B. Frey, N. Rogiers, and E. Thurig	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever	Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K.,
Document Year em.detail.documentYearHelp ?	2011	2012	2015	2013	2014	2014	2014	2014	2010	2010	2009
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence	Carbon storage and sequestration - InVEST (v3.2.0)	Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	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	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit.
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Website	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov	Not applicable	Not applicable	Not applicable	http://en.ilmatieteenlaitos.fi/yasso-download-and-support	Not applicable	Not applicable	https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Michael Osland	The Natural Capital Project	Alex Abdelnour	Susan H. Yee	Susan H. Yee	Susan H. Yee	Markus Didion ? Comment: Tel.: +41 44 7392 427	M. Abdalla	David Otis	Patrick Crist ? Comment: No contact information provided
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561	371 Serra Mall Stanford University Stanford, CA 94305-5020 USA	Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	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. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	None provided
Contact Email	sandra.lavorel@ujf-grenoble.fr	mosland@usgs.gov	invest@naturalcapitalproject.org	abdelnouralex@gmail.com	yee.susan@epa.gov	yee.susan@epa.gov	yee.susan@epa.gov	markus.didion@wsl.ch	abdallm@tcd.ie	dotis@iastate.edu	patrick@planitfwd.com

EM Description

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
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."	ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses."	Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product."	ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation"	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: (1) ease of access for education"	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and	ABSTRACT: "...We 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: "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)"	NatureServe Vista is a broad assessment and planning decision support tool focused on conservation of specific mapped features or “conservation elements.” It facilitates capturing spatial and non-spatial information and conservation requirements for elements, defining scenarios of land use, management, conservation, disturbance, etc., and evaluating the impacts of scenarios on the elements. Vista also contains powerful internal tools and interoperability with outside tools to facilitate mitigating site-level conflicts, offsite mitigation, and development of alternative scenarios. The primary objective (though not exclusive application) of the tool is to develop/mitigate alternative scenarios such that they meet explicit conservation goals for the elements. Vista can also support goal seeking for competing land uses while preventing development of scenarios that attempt to meet goals for conflicting things in the same place. The primary role of NatureServe Vista in this toolkit is to evaluate the impacts of land use scenarios on conservation elements in terrestrial, freshwater, and marine ecosystems. It does this through direct evaluation of land use scenarios from CommunityViz (augmented with other use, management, disturbance data) and interoperating with N-SPECT to evaluate water quality impacts on aquatic/marine elements.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	Not applicable	None identified	None identified	None identified	None identified	None identified	None identified	climate change	None identified	None provided
Biophysical Context	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	mangrove forest,Salt marsh, estuary, sea level,	Not applicable	Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter.	No additional description provided	No additional description provided	No additional description provided	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	Prairie pothole region of north-central Iowa	Not applicable
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Not applicable	Optional future scenarios for changed LULC and wood harvest	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented ? Comment: Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12).	air temperature, precipitation, Atmospheric CO2 concentrations	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method Only	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12).	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method Only
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	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-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	None	Doc-309	Doc-13 \| Doc-317	Doc-335	None	None	Doc-342 \| Doc-344	None	None	Doc-473
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	None	EM-349	EM-375 \| EM-380 \| EM-884 \| EM-883 \| EM-887	EM-447 \| EM-448	None	None	EM-466 \| EM-469 \| EM-480 \| EM-485	EM-598	EM-705 \| EM-703 \| EM-702 \| EM-701 \| EM-700	EM-1003 \| EM-1005 \| EM-1007 \| EM-1008

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2008	1990-2010	Not applicable	1969-2008	2006-2007, 2010	2006-2007, 2010	2006-2007, 2010	1993-2013	1961-1990	2002-2007	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent	time-dependent	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	future time	future time	Not applicable	Not applicable	Not applicable	future time	both	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	continuous	discrete	discrete	Not applicable	Not applicable	Not applicable	discrete	discrete	Not applicable	other or unclear (comment)
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	1	Not applicable	Not applicable	Not applicable	1	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Day	Not applicable	Not applicable	Not applicable	Year	Day	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Not applicable	Watershed/Catchment/HUC	Physiographic or ecological	Physiographic or ecological	Physiographic or ecological	Geopolitical	Point or points	Multiple unrelated locations (e.g., meta-analysis)	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Tampa Bay	Not applicable	H. J. Andrews LTER WS10	Coastal zone surrounding St. Croix	Coastal zone surrounding St. Croix	Coastal zone surrounding St. Croix	Switzerland	Oak Park Research centre	CREP (Conservation Reserve Enhancement Program) wetland sites	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	100-1000 km^2	Not applicable	10-100 ha	100-1000 km^2	100-1000 km^2	100-1000 km^2	10,000-100,000 km^2	1-10 ha	1-10 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
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) ? Comment: See below, grain includes vertical, subsurface dimension.	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)	other or unclear (comment)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	volume, for 3-D feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	m^2	application specific	30 m x 30 m surface pixel and 2-m depth soil column	10 m x 10 m	10 m x 10 m	10 m x 10 m	5 sites	Not applicable	multiple, individual, irregular shaped sites	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic	Numeric	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Not applicable	No	Yes	Yes	Yes	No	No	Unclear	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	Not applicable	No	No	No	No	No	Yes ? Comment: for N2O fluxes	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R squared \| 35.5 \| Not applicable	None	None	None	None	None	None	None	r squared \| 0.32 \| uniteless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Not applicable	No	Yes	Yes	Yes	Yes	Yes	Unclear	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	Not applicable	No	No	No	No	No	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	Not applicable	No	No	No	No	No	No	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	No	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-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
Europe France	North America United States Florida	None	North America United States Oregon	None	None	None	Europe Switzerland	Europe Ireland	North America United States Iowa	None

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

EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian ? Comment: Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
Centroid Latitude em.detail.ddLatHelp ?	45.05	27.8	-9999	44.25	17.73	17.73	17.73	46.82	52.86	42.62	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	-82.4	-9999	-122.33	-64.77	-64.77	-64.77	8.23	6.54	-93.84	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	WGS84	None provided	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Not applicable	Provided	Estimated	Estimated	Estimated	Estimated	Provided	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Not applicable	Forests	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Forests	Agroecosystems	Inland Wetlands \| Agroecosystems \| Grasslands	Not applicable
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Created Mangrove wetlands	Terrestrial environments, but not specified for methods	400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata).	Coral reefs	Coral reefs	Coral reefs	forests	farm pasture	Wetlands buffered by grassland set in agricultural land	None
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Not applicable	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Not applicable	Not applicable	Not applicable	Guild or Assemblage	Guild or Assemblage	Community	Not applicable	Species	Community

Taxonomic level and name of organisms or groups identified

EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
None Available	Species Laguncularia racemosa Avicennia germinans Rhizophora mangle Spartina alterniflora	None Available	None Available	None Available	None Available	None Available	None Available	None Available	Species Wood Duck (Aix sponsa) Northern Pintail (Anas acuta) Mallard (Anas platyrhynchos) Blue-winged Teal (Anas discors) Northern Shoveler (Anas clypeata) Gadwall (Anas strepera)	None Available

EnviroAtlas URL

EM-79	EM-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
None Available	None Available	GAP Ecological Systems, Carbon Storage by Tree Biomass, Hectares of cotton crops	Average Annual Precipitation	National Hydrography Dataset Plus (NHD PlusV2)	None Available	None Available	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)	None Available	Watersheds, Ecosystem Markets: Imperiled Species and Habitats

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-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[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 Liquid flows Flood protection	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Intellectual and representative interactions Educational	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings	[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	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes Weathering processes Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mass flows Buffering and attenuation of mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems Dilution by atmosphere, freshwater and marine ecosystems

<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-154	EM-374	EM-379	EM-449	EM-451	EM-456	EM-467	EM-593	EM-632	EM-1006
None	Near Coastal Marine/Estuarine (113) Flora (5) Trees Stock Indicator Seaweed Stock Indicator Composite (8) Regulation of extreme events Quality Indicator	None	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	None	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Quality Indicator	None	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	Wetlands (12) Fauna (4) Duck Stock Indicator	None

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

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EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

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

EM Modeling Approach

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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)

Comment:

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)