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-340
EM-374
EM-418
EM-449
EM-467
EM-593
EM-962
EM-985
EM-1001

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
EM Short Name em.detail.shortNameHelp ?	Divergence in flowering date, Central French Alps	InVEST crop pollination, Costa Rica	InVEST carbon storage and sequestration (v3.2.0)	SIRHI, St. Croix, USVI	Decrease in erosion (shoreline), St. Croix, USVI	Yasso07 v1.0.1, Switzerland	DayCent N2O flux simulation, Ireland	RZWQM2, Quebec, Canada	Atlantis ecosystem assessment submodel	NBS benefits explorer
EM Full Name em.detail.fullNameHelp ?	Functional divergence in flowering date, Central French Alps	InVEST crop pollination, Costa Rica	InVEST v3.2.0 Carbon storage and sequestration	SIRHI (SImplified Reef Health Index), St. Croix, USVI	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	Root zone water quality model 2 mitigation of greenhouse gases, Quebec, Canada	Lessons in modelling and management of marine ecosystems: the Atlantis experience	Benefit Accounting of Nature-Based Solutions for Watersheds: Guide
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	InVEST	InVEST	US EPA	US EPA	None	None	None	None	None
EM Source Document ID	260	279	315	335	335	343	358	447	463	471
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf	The Natural Capital Project	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.	Jiang, Q., Zhiming, Q., Madramootoo, C.A., and Creze, C.	Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C.	Brill, G., T. Shiao, C. Kammeyer, S. Diringer, K. Vigerstol, N. Ofosu-Amaah, M. Matosich, C. Müller-Zantop, W. Larson and T. Dekker
Document Year em.detail.documentYearHelp ?	2011	2009	2015	2014	2014	2014	2010	2018	2011	2022
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Modelling pollination services across agricultural landscapes	Carbon storage and sequestration - InVEST (v3.2.0)	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	Mitigating greenhouse gas emisssions in subsurface-drained field using RZWQM2	Lessons in modelling and management of marine ecosystems: the Atlantis experience	Benefit Accounting of Nature-Based Solutions for Watersheds: Guide
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	Published journal manuscript	Website	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	http://www.naturalcapitalproject.org/models/crop_pollination.html	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	http://en.ilmatieteenlaitos.fi/yasso-download-and-support	Not applicable	Not applicable	https://noaa-fisheries-integrated-toolbox.github.io/Atlantis	https://nbsbenefitsexplorer.net/tool
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Eric Lonsdorf	The Natural Capital Project	Susan H. Yee	Susan H. Yee	Markus Didion ? Comment: Tel.: +41 44 7392 427	M. Abdalla	Zhiming Qi	Elizabeth Fulton	Gregg Brill
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA	371 Serra Mall Stanford University Stanford, CA 94305-5020 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	Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada	CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia	Not reported
Contact Email	sandra.lavorel@ujf-grenoble.fr	ericlonsdorf@lpzoo.org	invest@naturalcapitalproject.org	yee.susan@epa.gov	yee.susan@epa.gov	markus.didion@wsl.ch	abdallm@tcd.ie	zhiming.qi@mcgill.ca	beth.fulton@csiro.au	Not reported

EM Description

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
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 a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..."	Please note: This ESML entry describes 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 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 indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati	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: "Greenhouse gas (GHG) emissions from agricultural soils are affected by various environmental factors and agronomic practices. The impact of inorganic nitrogen (N) fertilization rates and timing, and water table management practices on N2O and CO2 emissions were investigated to propose mitigation and adaptation efforts based on simulated results founded on field data. Drawing on 2012–2015 data measured on a subsurface-drained corn (Zea mays L.) field in Southern Quebec, the Root Zone Water Quality Model 2 (RZWQM2) was calibrated and validated for the estimation of N2O and CO2 emissions under free drainage (FD) and controlled drainage with sub-irrigation (CD-SI). Long term simulation from 1971 to 2000 suggested that the optimal N fertilization should be in the range of 125 to 175 kg N ha−1 to obtain higher NUE (nitrogen use efficiency, 7–14%) and lower N2O emission (8–22%), compared to 200 kg N ha−1 for corn-soybean rotation (CS). While remaining crop yields, splitting N application would potentially decrease total N2O emissions by 11.0%. Due to higher soil moisture and lower soil O2 under CD-SI, CO2 emissions declined by 6% while N2O emissions increased by 21% compared to FD. The CS system reduced CO2 and N2O emissions by 18.8% and 20.7%, respectively, when compared with continuous corn production. This study concludes that RZWQM2 model is capable of predicting GHG emissions, and GHG emissions from agriculture can be mitigated using agronomic management."	Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions.	Watersheds around the world are in peril and risk further decline from climate change and human impacts, like pollution, degrading landscapes, and unsustainable water use. These impacts can inhibit the ability of ecosystems to regulate water flows, sequester carbon to reduce atmospheric greenhouse gas levels, maintain biodiversity and healthy waterways, promote social well-being, offer economic opportunities, and sustain agricultural productivity. Climate change is exacerbating these impacts by shifting weather and precipitation patterns, degrading habitats, and increasing the recurrence and severity of natural disasters. Urgent action is needed to address these impacts by implementing nature-based solutions (NBS). NBS protect, sustainably manage, and restore natural or modified watersheds, to address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits (IUCN, 2016). Investment in NBS offers a mechanism to restore degraded watersheds and protect intact ones, leading to improved water quality and quantity, improved carbon sequestration and increased biodiversity, among many other social and economic benefits. NBS also support climate mitigation and adaptation efforts and reduce the impacts from other shocks, such as floods, droughts, and extreme weather events. Implementing NBS can also help advance progress toward achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 2 (zero hunger), SDG 6 (water), SDG 11 (sustainable cities and communities), SDG 13 (climate action), and SDG 15 (life on land). NBS therefore support social, economic and environmental objectives, and may be particularly important in supporting vulnerable communities.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified	None identified	None identified	climate change	None	None identified	None identified
Biophysical Context	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	No additional description provided	Not applicable	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	None	N/A	NA
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Optional future scenarios for changed LULC and wood harvest	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	None	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method Only	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	None	Method Only	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	None	Application of existing 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-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	Doc-279	Doc-309	None	Doc-335	Doc-342 \| Doc-344	None	None	Doc-456 \| Doc-459 \| Doc-461	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-338 \| EM-339	EM-349	None	EM-447 \| EM-448	EM-466 \| EM-469 \| EM-480 \| EM-485	EM-598	None	EM-978 \| EM-981 \| EM-983 \| EM-990 \| EM-991	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2008	2001-2002	Not applicable	2006-2007, 2010	2006-2007, 2010	1993-2013	1961-1990	2012-2015	Not applicable	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent	time-dependent	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	future time	Not applicable	Not applicable	future time	both	past time	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	Not applicable	Not applicable	discrete	discrete	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	Not applicable	Not applicable	1	1	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Not applicable	Not applicable	Year	Day	Year	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Other	Not applicable	Physiographic or ecological	Physiographic or ecological	Geopolitical	Point or points	Point or points	Not applicable	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Large coffee farm, Valle del General	Not applicable	Coastal zone surrounding St. Croix	Coastal zone surrounding St. Croix	Switzerland	Oak Park Research centre	Corn field	Not applicable	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	Not applicable	100-1000 km^2	100-1000 km^2	10,000-100,000 km^2	1-10 ha	1-10 ha	Not applicable	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially lumped (in all cases)	Not applicable	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	Not applicable	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	30 m x 30 m	application specific	10 m x 10 m	10 m x 10 m	5 sites	Not applicable	Not applicable	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric	Numeric	*	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic	deterministic	None	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	None	Conventional (frequentist) statistics	Bayesian statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
Model Calibration Reported? em.detail.calibrationHelp ?	No	Unclear	Not applicable	Yes	Yes	No	No	None	Not applicable	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	Not applicable	No	No	No	Yes ? Comment: for N2O fluxes	None	Not applicable	Not applicable
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	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	Not applicable	Yes	Yes	Yes	Yes	None	Not applicable	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	Not applicable	No	No	No	No	None	Not applicable	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	Not applicable	No	No	No	No	None	Not applicable	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	No	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	None	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-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
Europe France	North America Costa Rica	None	None	None	Europe Switzerland	Europe Ireland	North America Canada	None	None

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

EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
Centroid Latitude em.detail.ddLatHelp ?	45.05	9.13	-9999	17.73	17.73	46.82	52.86	45.32	Not applicable	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	-83.37	-9999	-64.77	-64.77	8.23	6.54	74.17	Not applicable	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	Not applicable	WGS84	WGS84	WGS84	None provided	None provided	Not applicable	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Not applicable	Estimated	Estimated	Estimated	Provided	Provided	Not applicable	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified) \| Agroecosystems	Not applicable	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Forests	Agroecosystems	None	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Open Ocean and Seas	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Ground Water \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Cropland and surrounding landscape	Terrestrial environments, but not specified for methods	Coral reefs	Coral reefs	forests	farm pasture	None	Multiple	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 corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	None	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-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Species	Not applicable	Guild or Assemblage	Not applicable	Community	Not applicable	None	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
None Available	other Huge Black 2002 Genus Trigonisca sp. Species Apis mellifera Nannotrigona mellaria Trigona corvina Trigona (Tetragonisca) angustula Trigona fussipennis Trigona fulviventris Trigona dorsalis Plebia frontalis Melipona fasciata Plebeia jatiformis Trigona (Tetragona) clavipies Partamona cupira	None Available	other snapper Family grouper parrotfish surgeonfish	None Available	None Available	None Available	None Available	None Available	None Available

EnviroAtlas URL

EM-79	EM-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
None Available	GAP Ecological Systems	GAP Ecological Systems, Carbon Storage by Tree Biomass, Hectares of cotton crops	None Available	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)	None Available	Big game hunting recreation demand, Percent GAP Status 1 & 2, Total Employment	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions, Residential Density

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-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions 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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[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] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Provisioning Materials Water Ground water for non-drinking purposes Surface water for non-drinking purposes [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Weathering processes Pest and disease control Pest control Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of salt waters Chemical condition of freshwaters Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mass flows Mass stabilisation and control of erosion rates

<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-340	EM-374	EM-418	EM-449	EM-467	EM-593	EM-962	EM-985	EM-1001
None	Agroecosystems (22) Fauna (4) Pollinators Stock Indicator	None	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	None	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None	None	None

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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)