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-70
EM-91
EM-92
EM-112
EM-129
EM-154
EM-326
EM-458
EM-459
EM-467
EM-549
EM-672
EM-743
EM-846
EM-998
EM-1020

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

EM Short Name

Plant species diversity, Central French Alps

RHyME2, Upper Mississippi River basin, USA

Runoff potential of pesticides, Europe

InVEST nutrient retention, Hood Canal, WA, USA

3-PG, South Australia

Mangrove development, Tampa Bay, FL, USA

ARIES flood regulation, Puget Sound Region, USA

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

Reef density of S. gigas, St. Croix, USVI

Yasso07 v1.0.1, Switzerland

Nutrient Tracking Tool (NTT)

Alewife nutrients in stream food web, CT, USA

WESP: Irrigation water, ID, USA

Indigo bunting abund, Piedmont region, USA

CAESAR landscape evolution model

EPIC agriculture model, Baden-Wurttemberg, Germany

EM Full Name

Plant species diversity, Central French Alps

RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA

Runoff potential of pesticides, Europe

InVEST (Integrated Valuation of Envl. Services and Tradeoffs) nutrient retention, Hood Canal, WA, USA

3-PG (Physiological Principles Predicting Growth), South Australia

Mangrove wetland development, Tampa Bay, FL, USA

ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA

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

Relative density of Strombus gigas (on reef), St. Croix, USVI

Yasso07 v1.0.1 forest litter decomposition, Switzerland

Nutrient Tracking Tool (NTT)

Alewife derived nutrients in stream food web, Connecticut, USA

WESP: Irrigation return water treatment, Idaho, USA

Indigo bunting abundance, Piedmont ecoregion, USA

Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model

Carbon sequestration in soils of SW-Germany as affected by agricultural management—Calibration of the EPIC model for regional simulations

EM Source or Collection

EU Biodiversity Action 5

US EPA

None

InVEST

None

US EPA

ARIES

US EPA

None

EM Source Document ID

260

123

254

205

243

302

335

343

352

384

393 ?

405

468

482

Document Author

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

Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C.

Schriever, C. A., and Liess, M.

Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M.

Crossman, N. D., Bryan, B. A., and Summers, D. M.

Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L.

Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.

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

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

Saleh, A. and O. Gallego

Walters, A. W., R. T. Barnes, and D. M. Post

Murphy, C. and T. Weekley

Riffel, S., Scognamillo, D., and L. W. Burger

Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J.

Billen, N., Röder, C., Gaiser, T. and Stahr, K.,

Document Year

2011

2013

2007

2013

2011

2012

2014

2018

2009

2012

2008

2007

2009

Document Title

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

Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads

Mapping ecological risk of agricultural pesticide runoff

From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA

Carbon payments and low-cost conservation

Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence

From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments

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

Nutrient Tracking Tool (NTT) User Manual

Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs

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

Effects of the Conservation Reserve Program on northern bobwhite and grassland birds

Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model

Carbon sequestration in soils of SW-Germany as affected by agricultural management—calibration of the EPIC model for regional simulations

Document Status

Peer reviewed and published

Comments on Status

Published journal manuscript

webpage

Published journal manuscript

Published report

Published journal manuscript

Software and Access

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

EM Software Access info Exit

Not applicable

https://www.naturalcapitalproject.org/invest/

http://www.csiro.au/products/3PGProductivity#a1

Not applicable

http://aries.integratedmodelling.org/

Not applicable

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

http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en

Not applicable

http://www.coulthard.org.uk/

https://epicapex.tamu.edu/epic/

Contact Name

Sandra Lavorel

Liem Tran

Carola Alexandra Schriever

J.E. Toft

Anders Siggins

Michael Osland

Ken Bagstad

Susan H. Yee

Markus Didion ?

Ali Saleh ?

Annika W. Walters

Chris Murphy

Sam Riffell

Marco J. Van De Wiel

Norbert Billen

Contact Address

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

Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA

Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany

The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA

Not reported

U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561

Geosciences and Environmental Change Science Center, US Geological Survey

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

Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402

Dept. of Ecology and Evolutionary Biology, Yale University, New Haven CT 06511

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

Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA

Department of Geography, University of Western Ontario, London, Ontario, Canada

University of Hohenheim, Institute of Soil Science and Land Evaluation, Emil Wolff Strasse 27, D-70593 Stuttgart, Germany

Contact Email

sandra.lavorel@ujf-grenoble.fr

ltran1@utk.edu

carola.schriever@ufz.de

jetoft@stanford.edu

Anders.Siggins@csiro.au

mosland@usgs.gov

kjbagstad@usgs.gov

yee.susan@epa.gov

markus.didion@wsl.ch

saleh@tarleton.edu

annika.walters@yale.edu

chris.murphy@idfg.idaho.gov

sriffell@cfr.msstate.edu

mvandew3@uwo.ca

billen@uni-hohenheim.de

EM Description

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

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." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables."

ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin)

ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception."

InVEST Nutrient Retention Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub-watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparameterized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)" (2) "We used the InVEST Nutrient Retention model to quantify the total nitrogen load for each subwatershed. Inputs to the Nutrient Retention model include water yield, land use and land cover, and nutrient loading and filtration rates (Table 1; Conte et al., 2011; Tallis et al., 2011). The nutrient model quantifies natural and anthropogenic sources of total nitrogen within each subwatershed, allowing managers to identify subwatersheds potentially at risk of contributing excessive nitrogen loads given the predicted development and climate future." ( P. 4)

AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)."

ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses."

ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures."

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."

AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)."

ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year. Natural abundance stable isotope analyses indicate that this influx of marine-derived nitrogen is rapidly incorporated into the stream food web. An enriched d15N signal, indicative of a marine origin, is present at all stream trophic levels with the greatest level of enrichment coincident with the timing of the anadromous alewife spawning migration. There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." AUTHOR'S DESCRIPTION: "Here, we examine the effect of alewife-contributed marine- derived nutrients to coastal stream ecosystems in southern New England. We take a comparative approach examining streams with and without anadromous alewife runs. We use natural abundance stable isotope analyses to assess the incorporation of marine-derived nitrogen and carbon into stream food webs."

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:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds."

We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK.

Global emissions trading allows for agricultural measures to be accounted for the carbon sequestration in soils. The Environmental Policy Integrated Climate (EPIC) model was tested for central European site conditions by means of agricultural extensification scenarios. Results of soil and management analyses of different management systems (cultivation with mouldboard plough, reduced tillage, and grassland/fallow establishment) on 13 representative sites in the German State Baden-Württemberg were used to calibrate the EPIC model. Calibration results were compared to those of the Intergovernmental Panel on Climate Change (IPCC) prognosis tool. The first calibration step included adjustments in (a) N depositions, (b) N2-fixation by bacteria during fallow, and (c) nutrient content of organic fertilisers according to regional values. The mixing efficiency of implements used for reduced tillage and four crop parameters were adapted to site conditions as a second step of the iterative calibration process, which should optimise the agreement between measured and simulated humus changes. Thus, general rules were obtained for the calibration of EPIC for different criteria and regions. EPIC simulated an average increase of +0.341 Mg humus-C ha−1 a−1 for on average 11.3 years of reduced tillage compared to land cultivated with mouldboard plough during the same time scale. Field measurements revealed an average increase of +0.343 Mg C ha−1 a−1 and the IPCC prognosis tool +0.345 Mg C ha−1 a−1. EPIC simulated an average increase of +1.253 Mg C ha−1 a−1 for on average 10.6 years of grassland/fallow establishment compared to an average increase of +1.342 Mg humus-C ha−1 a−1 measured by field measurements and +1.254 Mg C ha−1 a−1 according to the IPCC prognosis tool. The comparison of simulated and measured humus C stocks was r2 ≥ 0.825 for all treatments. However, on some sites deviations between simulated and measured results were considerable. The result for the simulation of yields was similar. In 49% of the cases the simulated yields differed from the surveyed ones by more than 20%. Some explanations could be found by qualitative cause analyses. Yet, for quantitative analyses the available information from farmers was not sufficient. Altogether EPIC is able to represent the expected changes by reduced tillage or grassland/fallow establishment acceptably under central European site conditions of south-western Germany.

Specific Policy or Decision Context Cited

None identified

Not reported

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

Land use change

None identified

Not applicable

None identified

Nutrients and water quality related to anadromous alewife restoration efforts

None identified

None reported

None identified

Impact of different agricultural management strategies

Biophysical Context

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

No additional description provided

Not applicable

No additional description provided

Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm.

mangrove forest,Salt marsh, estuary, sea level,

No additional description provided

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

No additional description provided

restored, enhanced and created wetlands

Conservation Reserve Program lands left to go fallow

River Teifi, Lampeter, Wales

Central Europe agricultural sites

EM Scenario Drivers

No scenarios presented

Future land use and land cover; climate change

Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings

Not applicable

No scenarios presented

No scenarios presented ?

No scenarios presented

Sites, function or habitat focus

N/A

Varying flow velocities and durations

EM Relationship to Other EMs or Applications

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

Method Only, Application of Method or Model Run

Method + Application

Method + Application (multiple runs exist) View EM Runs

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

Method + Application

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

Method Only

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method Only

Method + Application

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

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

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

Document ID for related EM

Doc-260

Doc-123

Doc-255 | Doc-256 | Doc-257

Doc-309 | Doc-338

Doc-243 | Doc-246 | Doc-245

None

Doc-303 | Doc-305

None

Doc-342 | Doc-344

None

Doc-384 | Doc-383

Doc-390

Doc-405

Doc-467

Doc-478

EM ID for related EM

EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

None

EM-363 | EM-438

None

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

EM-584

EM-667 | EM-661

EM-718 | EM-734 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-768

EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847

EM-997

EM-1012 | EM-1021

EM Modeling Approach

EM Relationship to Time

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

EM Temporal Extent

2007-2009

1987-1997

2000

2005-7; 2035-45

2009-2050

1990-2010

1971-2006

2006-2007, 2010

1993-2013

35 yr

2005-2006 (March-July)

2010-2012

2008

Not applicable

4-20 years ?

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

future time

Not applicable

future time

Not applicable

past time

Not applicable

past time

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

continuous

Not applicable

discrete

other or unclear (comment) ?

Not applicable

continuous

other or unclear (comment) ?

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Day

Not applicable

Month

Not applicable

Year

Day

Not applicable

EM Spatial Extent

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

Bounding Type

Physiographic or Ecological

Watershed/Catchment/HUC

Geopolitical

Watershed/Catchment/HUC

Physiographic or Ecological

Physiographic or ecological

Geopolitical

Not applicable

Geopolitical

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

Physiographic or ecological

Watershed/Catchment/HUC

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

Spatial Extent Name

Central French Alps

Upper Mississippi River basin; St. Croix River Watershed

EU-15

Hood Canal

Agricultural districts of the state of South Australia

Tampa Bay

Puget Sound Region

Coastal zone surrounding St. Croix

Switzerland

Not applicable

New London County, Connecticut, USA

Wetlands in idaho

Piedmont Ecoregion

River Teifi

Baden-Wurttemberg

Spatial Extent Area (Magnitude)

10-100 km^2

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

>1,000,000 km^2

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

100-1000 km^2

10,000-100,000 km^2

100-1000 km^2

10,000-100,000 km^2

Not applicable

1000-10,000 km^2.

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

1000-10,000 km^2.

10,000-100,000 km^2

Spatial Distribution of Computations

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

EM Spatial Distribution

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

NHDplus v1

area, for pixel or radial feature

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

Not applicable

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

Not applicable

Spatial Grain Size

20 m x 20 m

NHDplus v1

10 km x 10 km

30 m x 30 m

1 ha x 1 ha

m^2

200m x 200m

10 m x 10 m

5 sites

Not applicable

variable stream lengths

Not applicable

EM Structure and Computation Approach

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

EM Computational Approach

Analytic

Numeric

Analytic

Other or unclear (comment)

Numeric

Analytic

Numeric

Not applicable

Numeric

Analytic

EM Determinism

deterministic

stochastic

deterministic

Not applicable

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Other or unclear (comment)

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Bayesian statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Bayesian statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

Model Calibration Reported?

Yes

Not applicable

Yes

Not applicable

Yes

Model Goodness of Fit Reported?

Yes

Not applicable

Yes

Goodness of Fit (metric| value | unit)

R squared | 31 | Not applicable

R^2 | 0.923 | none
Mean Square Error | 25.1 | %

None

r-squared | 0.6416 | NA

Model Operational Validation Reported?

Yes

Unclear

Not applicable

Yes

Model Uncertainty Analysis Reported?

Yes

Not applicable

Unclear

Model Sensitivity Analysis Reported?

No ?

Yes

Not applicable

Yes

Not applicable

Unclear

Model Sensitivity Analysis Include Interactions?

Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

Europe
- France

North America
- United States
  - Indiana
  - Illinois
  - Missouri
  - South Dakota
  - Minnesota
  - Idaho
  - Wisconsin

Europe
- United Kingdom
- Portugal
- Spain
- Greece
- Austria
- Netherlands
- Sweden
- Belgium
- Ireland
- Luxembourg
- Finland
- Denmark
- Italy
- France
- Germany

North America
- United States
  - Washington

Oceania
- Australia

North America
- United States
  - Florida

North America
- United States

None

Europe
- Switzerland

None

North America
- United States
  - Connecticut

North America
- United States
  - Idaho

Oceania

Europe
- United Kingdom

Europe
- Germany

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

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

None

Temperate North Pacific
- Northeast Pacific
  - Cold Temperate Northeast Pacific
    - Puget Trough/Georgia Basin

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Floridian

None

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

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

None

Temperate North Atlantic
- Northwest Atlantic
  - Cold Temperate Northwest Atlantic
    - Virginian

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

Centroid Latitude

45.05

42.5

50.01

47.8

-34.9

27.8

17.73

46.82

Not applicable

41.78

44.06

36.23

52.04

48.62

Centroid Longitude

6.4

-90.63

4.67

-122.7

138.7

-82.4

-123

-64.77

8.23

Not applicable

-72.17

-114.69

-81.9

-4.39

9.03

Centroid Datum

WGS84

Not applicable

WGS84

Centroid Coordinates Status

Provided

Estimated

Not applicable

Estimated

Environments and Scales Modeled

EM ID

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

EM Environmental Sub-Class

Agroecosystems | Grasslands

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

Near Coastal Marine and Estuarine

Forests | Agroecosystems

Near Coastal Marine and Estuarine

Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified)

Near Coastal Marine and Estuarine

Forests

Agroecosystems

Rivers and Streams

Inland Wetlands

Grasslands

Rivers and Streams

Agroecosystems

Specific Environment Type

Subalpine terraces, grasslands, and meadows

None

Arable lands in near-stream environments

glacier-carved saltwater fjord

Agricultural land for annual crops, annual legumes, and grazing of sheep and cows

Created Mangrove wetlands

Terrestrial environment surrounding a large estuary

Coral reefs

forests

Agroecosystems

Coastal streams

created, restored and enhanced wetlands

grasslands

River

Agriculture plots

EM Ecological Scale

Not applicable

Ecosystem

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

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-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

EM Organismal Scale

Community

Not applicable

Species

Not applicable

Species

Community

Not applicable

Individual or population, within a species

Not applicable

Species

Not applicable

Taxonomic level and name of organisms or groups identified

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

None Available

Species
- Acacia implexa
- Eucalyptus microcarpa
- Acacia verniciflua
- Eucalyptus polyanthemos
- Eucalyptus globulus
- Eucalyptus sideroxylon
- Acacia mearnsii
- Acacia pycnantha
- Eucalyptus camaldulensis
- Eucalyptus kochii
- Acacia dealbata

Species
- Laguncularia racemosa
- Avicennia germinans
- Rhizophora mangle
- Spartina alterniflora

None Available

Species
- Panulirus argus

Species
- Panulirus argus

None Available

Species
- anadromous alewife (Alosa pseudoharengus)

None Available

Species
- Indigo bunting

None Available

EnviroAtlas URL

EM-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

None Available

The National Hydrography Dataset (NHD), National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD)

Average Annual Precipitation, Stream Length Impaired by Pesticides, The Watershed Boundary Dataset (WBD), Hectares of Vegetable Crops

Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition

GAP Ecological Systems, Average Annual Precipitation, Acres of Land Enrolled in the Conservation Reserve Program (CRP)

None Available

GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Percent Impervious Area

None Available

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

GAP Ecological Systems, Agricultural water use (million gallons/day), Water supply from NID reservoirs (million gallons)

None Available

Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass

GAP Ecological Systems, U.S. EPA (Omernik) ecoregions

Average Annual Precipitation

Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Agricultural water use (million gallons/day), Acres of Land Enrolled in the Conservation Reserve Program (CRP)

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-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

None

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Water conditions
    - Chemical condition of freshwaters

None

[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

[Ecosystem] Regulation & Maintenance
- 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
  - Soil formation and composition
    - Decomposition and fixing processes
  - Atmospheric composition and climate regulation
    - Global climate regulation by reduction of greenhouse gas concentrations
  - Lifecycle maintenance, habitat and gene pool protection
    - Pollination and seed dispersal

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Flood protection

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs

[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] Provisioning
- Nutrition
  - Biomass
    - Cultivated crops
- Materials
  - Water
    - Ground water for non-drinking purposes
[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

None

[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

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Hydrological cycle and water flow maintenance
  - Mass flows
    - Mass stabilisation and control of erosion rates

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Cultivated crops
[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Soil formation and composition
    - Decomposition and fixing processes

<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-70

EM-91

EM-92

EM-112

EM-129

EM-154

EM-326

EM-458

EM-459

EM-467

EM-549

EM-672

EM-743

EM-846

EM-998

EM-1020

None

Forests (21)
- Flora (5)
  - Trees
    - Stock Indicator

Near Coastal Marine/Estuarine (113)
- Flora (5)
  - Trees
    - Stock Indicator
  - Seaweed
    - Stock Indicator
- Composite (8)
  - Regulation of extreme events
    - Quality Indicator

Rivers and Streams (111)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator
Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator
Forests (21)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator

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

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

None

Agroecosystems (22)
- Flora (5)
  - food crops
    - Stock Indicator

None

Grasslands (23)
- Fauna (4)
  - Birds
    - Stock Indicator

Rivers and Streams (111)
- Water (3)
  - Liquid water
    - Flow Indicator
- Soil (2)
  - Specific types of soil
    - Flow Indicator

Agroecosystems (22)
- Soil (2)
  - Specific types of soil
    - Stock Indicator

US EPA

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Comment:

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

EM Modeling Approach

Comment:

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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)

Comment:

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)