EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.

This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:

Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
Intermediate (Computed) Variables
Response Variables
- Computed Response Variables
- Measured Response Variables

EM Variables by Category

A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:

Policy Regarding Use or Management of Ecosystem Resources
Land Surface (or Water Body Bed) Cover, Use or Substrate
Human Demographic Data
Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
Monetary Values

Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.

All EM Descriptors

This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:

EM Identity and Description
EM Modeling Approach
EM Locations, Environments, Ecology
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

EM Descriptors by Modeling Concepts

This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:

Conceptual Model
Modeling Objective
Modeling Context
Potential for Model Linkage
Feasibility of Model Use
Model Certainty
Model Structural Information

Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.

Compare Criteria:

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Comparing:

EM-65
EM-71
EM-94
EM-193
EM-340
EM-414
EM-417
EM-455
EM-458
EM-467
EM-590
EM-718
EM-820
EM-890
EM-891
EM-984
EM-1001

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

EM Short Name

Green biomass production, Central French Alps

Community flowering date, Central French Alps

Reduction in pesticide runoff risk, Europe

Cultural ecosystem services, Bilbao, Spain

InVEST crop pollination, Costa Rica

SAV occurrence, St. Louis River, MN/WI, USA

SWAT, Guanica Bay, Puerto Rico, USA

Value of a reef dive site, St. Croix, USVI

Reef density of P. argus, St. Croix, USVI

Yasso07 v1.0.1, Switzerland

Fish species richness, Puerto Rico, USA

WESP: Riparian & stream habitat, ID, USA

MMI method for aquatic surveys

HWB Blood pressure, Great Lakes waterfront, USA

Home ownership, Great Lakes, USA

Salmonid toxicity to heavy metals, USA

NBS benefits explorer

EM Full Name

Green biomass production, Central French Alps

Community weighted mean flowering date, Central French Alps

Reduction in pesticide runoff risk, Europe

Cultural ecosystem services, Bilbao, Spain

InVEST crop pollination, Costa Rica

Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA

SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA

Value of a dive site (reef), St. Croix, USVI

Relative density of Panulirus argus (on reef), St. Croix, USVI

Yasso07 v1.0.1 forest litter decomposition, Switzerland

Fish species richness, Puerto Rico, USA

WESP: Riparian and stream habitat focus projects, ID, USA

Multimetric Indice (MMI) method for large scale aquatic surveys

Human well being indicator- Blood pressure, Great Lakes waterfront, USA

Human well being indicator - home ownership, Great Lakes waterfront, USA

Chinook salmon and steelhead toxicity to heavy metals, USA

Benefit Accounting of Nature-Based Solutions for Watersheds: Guide

EM Source or Collection

EU Biodiversity Action 5

None

None ?

InVEST

US EPA

None

US EPA

None

US EPA

None

EM Source Document ID

260

255

191

279

330

334

335

343

355

393 ?

403

422 ?

462

471

Document Author

Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.

Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C.

Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.

Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf

Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke

Hu, W. and Y. Yuan

Yee, S. H., Dittmar, J. A., and L. M. Oliver

Didion, M., B. Frey, N. Rogiers, and E. Thurig

Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco

Murphy, C. and T. Weekley

Stoddard, J.L., Herlihy, A.T., Peck, D.V., Hughes, R.M., Whittier, T.R., and E. Tarquinio

Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick

Chapman, G.

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

2011

2012

2013

2009

2013

2014

2007

2012

2008

None

1978

2022

Document Title

Using plant functional traits to understand the landscape distribution of multiple ecosystem services

Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction

Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning

Modelling pollination services across agricultural landscapes

Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary

Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model

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

Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean

Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context.

A process for creating multimetric indices for large-scale A process for creating multimetic indices for large-scale aquatic surveys

Human well-being and natural capital indictors for Great Lakes waterfront revitalization

Toxicities of Cadmium, Copper, and Zinc to Four Juvenile Toxicities of Cadmium, Copper, and Zinc to Four Juvenile Stages of Chinook Salmon and Steelhead

Benefit Accounting of Nature-Based Solutions for Watersheds: Guide

Document Status

Peer reviewed and published

Peer reviewed but unpublished (explain in Comment)

Peer reviewed and published

Comments on Status

Published journal manuscript

Published EPA report

Published journal manuscript

Published report

Published journal manuscript

Journal manuscript submitted or in review

Published journal manuscript

Published report

Software and Access

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

EM Software Access info Exit

Not applicable

http://www.naturalcapitalproject.org/models/crop_pollination.html

Not applicable

http://en.ilmatieteenlaitos.fi/yasso-download-and-support

Not applicable

https://nbsbenefitsexplorer.net/tool

Contact Name

Sandra Lavorel

Sven Lautenbach

Izaskun Casado-Arzuaga

Eric Lonsdorf

Ted R. Angradi

Yongping Yuan

Susan H. Yee

Markus Didion ?

Simon Pittman

Chris Murphy

John Stoddard

Ted Angradi

Gary Chapman

Gregg Brill

Contact Address

Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France

Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain

Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA

U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA

USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada

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

1305 East-West Highway, Silver Spring, MD 20910, USA

Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID

200 SW 35th St., Corvallis, OR 97333

USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804

Corvallis Environmental Research Laboratory, Western Fish Toxicology Station U.S. Environmental Protection Agency, Corvallis, Oregon 97330

Not reported

Contact Email

sandra.lavorel@ujf-grenoble.fr

sven.lautenbach@ufz.de

izaskun.casado@ehu.es

ericlonsdorf@lpzoo.org

angradi.theodore@epa.gov

Yuan.Yongping@epa.gov

yee.susan@epa.gov

markus.didion@wsl.ch

simon.pittman@noaa.gov

chris.murphy@idfg.idaho.gov

stoddard.john@epa.gov

tedangradi@gmail.com

N/A

Not reported

EM Description

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

Summary Description

ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."

AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality."

ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management."

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..."

ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."

AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008).

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)…Another method to quantify recreational opportunities is to use survey data of tourists and recreational visitors to the reefs to generate statistical models to quantify the link between reef condition and production of recreation-related ecosystem services. Wielgus et al. (2003) used interviews with SCUBA divers in Israel to derive coefficients for a choice model in which willingness to pay for higher quality dive sites was determined in part by a weighted combination of factors identified with dive quality: Relative value of dive site = 0.1227(Scoral+Sfish+Acoral+Afish)+0.0565V where Scoral, Sfish are coral and fish richness, Acoral, Afish are abundances of fish and coral per square meter, and V is water visibility (meters)."

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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production 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) density of the spiny lobster Panulirus argus"

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."

ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems."

A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level.

Abstract: "Differences in sampling and laboratory protocols, differences in techniques used to evaluate metrics, and differing scales of calibration and application prohibit the use of many existing multimetric indices (MMIs) in large-scale bioassessments. We describe an approach to developing MMIs of ecological condition that is applicable to a variety of biological assemblage types and to spatially extensive (regional, national) aquatic resource surveys. The process involves testing the performance characteristics of candidate metrics in several categories that correspond to key dimensions of biotic condition. The performance characteristics include: information content (range), reproducibility, calibration for natural gradients, responsiveness to stressor gradients, and independence from other metrics. The best-performing metric from each category is included in the final MMI. The consistency of the process enables development of separate MMIs in different regions that can be combined in a national assessment and that are more comparable across regions and taxonomic groups than a set of independently developed MMIs would be. Range: Generally eliminate metrics if their range is <4 or if > 1/3 of samples have values = 0. Very few macroinvertebrate metrics are eliminated by this test. It does eliminate a large number of potentially poor metrics for assemblages with fewer taxa (e.g., fish). Reproducibility: We quantify metric reproducibility with a variant of the signal:noise ratio (S/N). S/N is the ratio of the variance among all sites (signal) to the variance of repeated visits to the same site (noise). S/N values 1 indicate that visiting a single site twice yields as much metric variability as visiting 2 different sites. Natural gradient calibration: Focusing solely on reference-site data and to quantify the remaining correspondence between the metric value and the natural gradient. Responsiveness: The ability of a metric to distinguish least-disturbed (reference) from most-disturbed sites. We identify the metrics that have the highest responsiveness (t-scores) within each class and aggregated ecoregion. Redundancy: We often consider metrics as too strongly correlated when their Pearson correlation coefficients at least-disturbed sites are > |0.71| (R2 = 0.5).

ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context."

ABSTRACT: "Continuous-flow toxicity tests were conducted to determine the relative tolerances of newly hatched alevins, swim-up alevins, parr, and smolts of chinook salmon (Oncorhynchus tshawytscha) and steelhead (Salmo gairdneri) to cadmium, copper, and zinc. Newly hatched alevins were much more tolerant to cadmium and, to a lesser extent, to zinc than were later juvenile forms. However, the later progression from swim-up alevin, through parr, to smolt was accompanied by a slight increase in metal tolerance. The 96-h LC50 values for all four life stages ranged from 1.0 to >27ug Cd/liter, 17 to 38ug Cu/liter, and 93 to 815ug Zn/liter. Steelhead were consistently more sensitive to these metals than were chinook salmon. When a sensitive life stage for acute toxicity tests with metals is sought, the more resistant newly hatched alevins should be avoided. Although tolerance may increase with age, all later juvenile life stages are more sensitive and should give similar results.

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

None identified

European Commission Water Framework Directive (WFD, Directive 2000/60/EC)

Land management, ecosystem management, response to EU 2020 Biodiversity Strategy

None identified

None Identified

None identified

None provided

None identified

Biophysical Context

Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes

Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes

Not applicable

Northern Spain; Bizkaia region

No additional description provided

submerged aquatic vegetation

Need to fill in

No additional description provided

Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa).

Hard and soft benthic habitat types approximately to the 33m isobath

restored, enhanced and created wetlands

Aquatic systems

Waterfront districts on south Lake Michigan and south lake Erie

Microcosms

EM Scenario Drivers

No scenarios presented

Planting type, fertilizing rate, harvest rate

No scenarios presented

No scenarios presented ?

No scenarios presented

Sites, function or habitat focus

Not applicable

N/A

Life stage

No scenarios presented

EM Relationship to Other EMs or Applications

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

Method Only, Application of Method or Model Run

Method + Application

Method + Application (multiple runs exist) View EM Runs ?

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method Only

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method Only

New or Pre-existing EM?

New or revised model

Application of existing model

New or revised model

Application of existing model

New or revised model

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-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

Document ID for related EM

Doc-260

Doc-260 | Doc-269

Doc-254 | Doc-256 ?

None

Doc-279

None

Doc-342 | Doc-344

Doc-355

Doc-390

None

Doc-422

None

EM ID for related EM

EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

None

EM-338 | EM-339

None

EM-466 | EM-469 | EM-480 | EM-485

EM-698 | EM-699

EM-706 | EM-729 | EM-730 | EM-734 | EM-743 | EM-749 | EM-750 | EM-756 | EM-757 | EM-758 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-732 | EM-737 | EM-738 | EM-739 | EM-741 | EM-742 | EM-751 | EM-768

EM-821

EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895

EM-886 | EM-888 | EM-889 | EM-890 | EM-893 | EM-894 | EM-895

None

EM Modeling Approach

EM Relationship to Time

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

EM Temporal Extent

2007-2009

2007-2008

2000

2000 - 2007

2001-2002

2010 - 2012

1981-2004

2006-2007, 2010

1993-2013

2000-2005

2010-2011

Not applicable

2022

1978

Not applicable

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

future time

Not applicable

past time

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

Not applicable

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Day

Not applicable

Year

Not applicable

EM Spatial Extent

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

Bounding Type

Physiographic or Ecological

Geopolitical

Other

Physiographic or ecological

Watershed/Catchment/HUC

Physiographic or ecological

Geopolitical

Physiographic or ecological

Multiple unrelated locations (e.g., meta-analysis)

Not applicable

Geopolitical

Not applicable

Spatial Extent Name

Central French Alps

EU-27

Bilbao Metropolitan Greenbelt

Large coffee farm, Valle del General

St. Louis River Estuary

Guanica Bay, Puerto Rico watersheds

Coastal zone surrounding St. Croix

Switzerland

SW Puerto Rico,

Wetlands in idaho

Not applicable

Great Lakes waterfront

Northwest

Not applicable

Spatial Extent Area (Magnitude)

10-100 km^2

>1,000,000 km^2

100-1000 km^2

10-100 km^2

100-1000 km^2

10,000-100,000 km^2

100-1000 km^2

100,000-1,000,000 km^2

Not applicable

1000-10,000 km^2.

100,000-1,000,000 km^2

Not applicable

Spatial Distribution of Computations

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

EM Spatial Distribution

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)

Not applicable

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

area, for pixel or radial feature

Not applicable

Spatial Grain Size

20 m x 20 m

10 km x 10 km

2 m x 2 m

30 m x 30 m

0.07 m^2 to 0.70 m^2

30m x 30m

10 m x 10 m

5 sites

not reported

Not applicable

EM Structure and Computation Approach

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

stochastic

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

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

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Bayesian statistics

Model Checking Procedures Used

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

Model Calibration Reported?

Unclear

Yes

Yes ?

Yes

Not applicable

Model Goodness of Fit Reported?

Yes

No ?

Yes

Not applicable

Goodness of Fit (metric| value | unit)

Adjusted R | 69.9 | Not applicable

R squared | 45.2 | Not applicable

None

p-value | <0.001 | unitless
Nagelkerke r2 | 0.30-0.62 | unitless

Nash-Sutcliffe | 0.82 | unitless
Rsquared | 0.89 | unitless
Rsquared for sed | 0.15 | unitless
NSE for sed | -0.02 | unitless
RME for sed | 0.15 | unitless

None

R squared | 0.55 | unitless
Cohen's Kappa statistic | 0.54 | unitless
AUC | 0.79 | unitless

None

Model Operational Validation Reported?

Yes

Yes ?

Yes

Not applicable

Unclear

Model Uncertainty Analysis Reported?

Unclear

Not applicable

Model Sensitivity Analysis Reported?

Yes

Yes ?

Yes

Not applicable

Model Sensitivity Analysis Include Interactions?

Not applicable

Yes

Not applicable

Yes

Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

Europe
- France

Europe
- France

Europe
- Cyprus
- Portugal
- Malta
- Greece
- Austria
- Latvia
- Netherlands
- Sweden
- Ireland
- Luxembourg
- Poland
- Slovakia
- Slovenia
- Bulgaria
- France
- Lithuania
- Romania
- Hungary
- United Kingdom
- Spain
- Czech Republic
- Belgium
- Finland
- Denmark
- Italy
- Germany
- Estonia

Europe
- Spain

North America
- Costa Rica

North America
- United States
  - Minnesota
  - Wisconsin

North America
- United States
  - Puerto Rico
- U.S. Caribbean Territories

None

Europe
- Switzerland

None

North America
- United States
  - Idaho

North America
- United States

North America
- United States
  - Illinois
  - Minnesota
  - Ohio
  - Wisconsin

North America
- United States
  - Illinois
  - Minnesota
  - Ohio
  - Wisconsin

North America
- United States
  - Oregon

None

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

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Greater Antilles

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

Centroid Latitude

45.05

50.53

43.25

9.13

46.72

18.19

17.73

46.82

17.9

44.06

Not applicable

42.26

44.53

Not applicable

Centroid Longitude

6.4

7.6

-2.92

-83.37

-96.13

-66.76

-64.77

8.23

67.11

-114.69

Not applicable

-87.84

123.25

Not applicable

Centroid Datum

WGS84

Not applicable

WGS84

Not applicable

Centroid Coordinates Status

Provided

Estimated

Provided

Estimated

Not applicable

Estimated

Not applicable

Environments and Scales Modeled

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

EM Environmental Sub-Class

Agroecosystems | Grasslands

Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland

Terrestrial Environment (sub-classes not fully specified) | Agroecosystems

Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds

Terrestrial Environment (sub-classes not fully specified)

Near Coastal Marine and Estuarine

Forests

Near Coastal Marine and Estuarine

Inland Wetlands

Aquatic Environment (sub-classes not fully specified)

Terrestrial Environment (sub-classes not fully specified)

Rivers and Streams

Specific Environment Type

Subalpine terraces, grasslands, and meadows

Subalpine terraces, grasslands, and meadows.

Streams and near upstream environments

none

Cropland and surrounding landscape

Freshwater estuarine system

watershed

Coral reefs

forests

shallow coral reefs

created, restored and enhanced wetlands

Multiple

Lake Michigan & Lake Erie waterfront

Modeling stream exposure

None

EM Ecological Scale

Not applicable

Ecological scale is coarser 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 is finer than that of the Environmental Sub-class

Other or unclear (comment) ?

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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

EM Organismal Scale

Community

Not applicable

Species

Not applicable

Guild or Assemblage

Species

Community

Guild or Assemblage

Not applicable

Guild or Assemblage

Not applicable

Species

Not applicable

Taxonomic level and name of organisms or groups identified

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

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

Species
- Panulirus argus

None Available

other
- (fish class)

None Available

Species
- Chinook salmon
- Steelhead trout

None Available

EnviroAtlas URL

EM-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

GAP Ecological Systems

None Available

Stream Length Impaired by Pesticides

Percent IUCN Status II, Percent GAP Status 1 & 2

GAP Ecological Systems

Average Annual Precipitation

National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD)

None Available

GAP Ecological Systems, Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass

None Available

Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass

None Available

GAP Ecological Systems, Enabling Conditions

None Available

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-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

None

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Cultural
- Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Physical and experiential interactions
    - Physical use of land-/seascapes in different environmental settings
    - Experiential use of plants, animals and land-/seascapes in different environmental settings
  - Intellectual and representative interactions
    - Aesthetic

[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
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

[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] 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

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats
  - Water conditions
    - Chemical condition of freshwaters
- Mediation of flows
  - Liquid flows
    - Flood protection
    - Hydrological cycle and water flow maintenance
  - Mass flows
    - Mass stabilisation and control of erosion rates
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

None

[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-65

EM-71

EM-94

EM-193

EM-340

EM-414

EM-417

EM-455

EM-458

EM-467

EM-590

EM-718

EM-820

EM-890

EM-891

EM-984

EM-1001

Grasslands (23)
- Flora (5)
  - Green biomass
    - Stock Indicator
Agroecosystems (22)
- Flora (5)
  - Green biomass
    - Stock Indicator

None

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Forests (21)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Grasslands (23)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Scrubland/Shrubland (24)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator

Agroecosystems (22)
- Fauna (4)
  - Pollinators
    - Stock Indicator

None

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator

Near Coastal Marine/Estuarine (113)
- Fauna (4)
  - Fish
    - Stock Indicator

None

Near Coastal Marine/Estuarine (113)
- Fauna (4)
  - Fish
    - Stock Indicator

None

Rivers and Streams (111)
- Fauna (4)
  - Fish
    - Quality Indicator
Lakes and Ponds (112)
- Fauna (4)
  - Fish
    - Quality Indicator

None

US EPA

EcoService Models Library (ESML)

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EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

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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)