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:

Show Criteria

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Comparing:

EM-65
EM-71
EM-112
EM-193
EM-195
EM-224
EM-260
EM-315
EM-656
EM-718
EM-743
EM-863
EM-964
EM-998

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

EM Short Name

Green biomass production, Central French Alps

Community flowering date, Central French Alps

InVEST nutrient retention, Hood Canal, WA, USA

Cultural ecosystem services, Bilbao, Spain

C Sequestration and De-N, Tampa Bay, FL, USA

FORCLIM v2.9, West Cascades, OR, USA

Coral taxa and land development, St.Croix, VI, USA

ARIES open Space, Puget Sound Region, USA

P8 UCM

WESP: Riparian & stream habitat, ID, USA

WESP: Irrigation water, ID, USA

SLAMM, Tampa Bay, FL, USA

EcoSim II - method

CAESAR landscape evolution model

EM Full Name

Green biomass production, Central French Alps

Community weighted mean flowering date, Central French Alps

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

Cultural ecosystem services, Bilbao, Spain

Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA

FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA

Coral taxa richness and land development, St.Croix, Virgin Islands, USA

ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA

P8 Urban Catchment model method

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

WESP: Irrigation return water treatment, Idaho, USA

SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA

EcoSim II - method

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

EM Source or Collection

EU Biodiversity Action 5

InVEST

None ?

US EPA

ARIES

None

EM Source Document ID

260

205

191

186

23 ?

302

377 ?

393 ?

415 ?

448

468

Document Author

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

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.

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

Russell, M. and Greening, H.

Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.

Oliver, L. M., Lehrter, J. C. and Fisher, W. S.

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

Walker, W. Jr., and J.D. Walker

Murphy, C. and T. Weekley

Sherwood, E. T. and H. S. Greening

Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell

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

Document Year

2011

2013

2007

2011

2014

2015

2012

2014

2000

2007

Document Title

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

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

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

Estimating benefits in a recovering estuary: Tampa Bay, Florida

Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model

Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands

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

P8 Urban Catchment Model Version 3.5

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

Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats

Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II

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

Document Status

Peer reviewed and published

Not peer reviewed but is published (explain in Comment)

Peer reviewed and published

Comments on Status

Published journal manuscript

Published report

Published journal manuscript

Software and Access

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

EM Software Access info Exit

Not applicable

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

Not applicable

http://aries.integratedmodelling.org/

http://www.wwwalker.net/p8/v35/webhelp/splash.htm

Not applicable

http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html

https://ecopath.org/downloads/

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

Contact Name

Sandra Lavorel

J.E. Toft

Izaskun Casado-Arzuaga

M. Russell

Richard T. Busing

Leah Oliver

Ken Bagstad

William Walker Jr., PhD

Chris Murphy

Edward T. Sherwood

Carl Walters

Marco J. Van De Wiel

Contact Address

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

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

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

US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA

U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA

National Health and Environmental Research Effects Laboratory

Geosciences and Environmental Change Science Center, US Geological Survey

Concord, Massachusetts

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

Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA

Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4

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

Contact Email

sandra.lavorel@ujf-grenoble.fr

jetoft@stanford.edu

izaskun.casado@ehu.es

Russell.Marc@epamail.epa.gov

rtbusing@aol.com

leah.oliver@epa.gov

kjbagstad@usgs.gov

bill@wwwalker.net

chris.murphy@idfg.idaho.gov

esherwood@tbep.org

c.walters@oceans.ubc.ca

mvandew3@uwo.ca

EM Description

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

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)."

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)

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."

AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2)

ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire."

AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294)

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: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape."

Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. "

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 Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…"

ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores "

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.

Specific Policy or Decision Context Cited

None identified

Land use change

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

Restoration of seagrass

None Identified

Not applicable

None identified

None

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

No additional description provided

Northern Spain; Bizkaia region

Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove

West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions

nearshore; <1.5 km offshore; <12 m depth

No additional description provided

Urban setting

restored, enhanced and created wetlands

No additional description provided

None, Ocean ecosystems

River Teifi, Lampeter, Wales

EM Scenario Drivers

No scenarios presented

Future land use and land cover; climate change

No scenarios presented

Habitat loss or restoration in Tampa Bay Estuary

Two scenarios modelled, forests with and without fire

Not applicable

No scenarios presented

N/A

Sites, function or habitat focus

Varying sea level rise (baseline - 2m), and two habitat adaption strategies

N/A

Varying flow velocities and durations

EM Relationship to Other EMs or Applications

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

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 + Application

Method Only

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

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-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

Document ID for related EM

Doc-260

Doc-260 | Doc-269

Doc-309 | Doc-338

None

Doc-22 | Doc-23

None

Doc-303 | Doc-305

None

Doc-390

Doc-412 | Doc-413

None

Doc-467

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

EM-363 | EM-438

None

EM-146 | EM-208 | EM-186

None

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

EM-857

None

EM-997

EM Modeling Approach

EM Relationship to Time

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

EM Temporal Extent

2007-2009

2007-2008

2005-7; 2035-45

2000 - 2007

1982-2010

>650 yrs

2006-2007

2000-2011

Not applicable

2010-2011

2010-2012

2002-2100

Not applicable

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

past time

Not applicable

past time

Not applicable

both

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

Not applicable

discrete ?

continuous

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Year

Not applicable

Hour

Not applicable

Day

Not applicable

EM Spatial Extent

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

Bounding Type

Physiographic or Ecological

Watershed/Catchment/HUC

Geopolitical

Physiographic or Ecological

Physiographic or ecological

Physiographic or Ecological

Physiographic or ecological

Not applicable

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

Watershed/Catchment/HUC

Other

Watershed/Catchment/HUC

Spatial Extent Name

Central French Alps

Hood Canal

Bilbao Metropolitan Greenbelt

Tampa Bay Estuary

West Cascades, Oregon

St.Croix, U.S. Virgin Islands

Puget Sound Region

Not applicable

Wetlands in idaho

Tampa Bay estuary watershed

Not applicable

River Teifi

Spatial Extent Area (Magnitude)

10-100 km^2

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

100-1000 km^2

1000-10,000 km^2.

100-1000 km^2

10-100 km^2

10,000-100,000 km^2

Not applicable

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

1000-10,000 km^2.

Not applicable

1000-10,000 km^2.

Spatial Distribution of Computations

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

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)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

Not applicable

area, for pixel or radial feature

Not applicable

area, for pixel or radial feature

Not applicable

Spatial Grain Size

20 m x 20 m

30 m x 30 m

2 m x 2 m

1 ha

0.08 ha

Not applicable

200m x 200m

Not applicable

10 x 10 m

Not applicable

EM Structure and Computation Approach

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

EM Computational Approach

Analytic

Other or unclear (comment)

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

stochastic

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Other or unclear (comment)

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Bayesian statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

Model Calibration Reported?

Yes

Not applicable

Model Goodness of Fit Reported?

Yes

Not applicable

Goodness of Fit (metric| value | unit)

Adjusted R | 69.9 | Not applicable

R squared | 45.2 | Not applicable

None

R squared (taxa richness) | 0.74 | Not applicable
p-value | 0.0020 | unitless

None

Model Operational Validation Reported?

Yes

Not applicable

Model Uncertainty Analysis Reported?

Yes

Not applicable

Model Sensitivity Analysis Reported?

Yes

Not applicable

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-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

Europe
- France

Europe
- France

North America
- United States
  - Washington

Europe
- Spain

None

North America
- United States
  - Oregon

None

North America
- United States
  - Washington

None

North America
- United States
  - Idaho

North America
- United States
  - Idaho

North America
- United States
  - Florida

None

Europe
- United Kingdom

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

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

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 Southwestern Atlantic

Temperate North Pacific
- Northeast Pacific
  - Cold Temperate Northeast Pacific
    - Oregon, Washington, Vancouver Coast and Shelf

None

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

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

Centroid Latitude

45.05

47.8

43.25

27.95

44.24

17.75

Not applicable

44.06

27.76

Not applicable

52.04

Centroid Longitude

6.4

-122.7

-2.92

-82.47

-122.24

-64.75

-123

Not applicable

-114.69

-82.54

Not applicable

-4.39

Centroid Datum

WGS84

NAD83

WGS84

Not applicable

WGS84

Not applicable

WGS84

Centroid Coordinates Status

Provided

Estimated

Provided

Estimated

Not applicable

Estimated

Not applicable

Estimated

Environments and Scales Modeled

EM ID

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

EM Environmental Sub-Class

Agroecosystems | Grasslands

Near Coastal Marine and Estuarine

Near Coastal Marine and Estuarine

Forests

Near Coastal Marine and Estuarine

Terrestrial Environment (sub-classes not fully specified)

Inland Wetlands

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

Open Ocean and Seas

Rivers and Streams

Specific Environment Type

Subalpine terraces, grasslands, and meadows

Subalpine terraces, grasslands, and meadows.

glacier-carved saltwater fjord

none

Subtropical Estuary

Primarily conifer forest

stony coral reef

Terrestrial environment surrounding a large estuary

Urban catchments

created, restored and enhanced wetlands

Esturary and associated urban and terrestrial environment

Pelagic

River

EM Ecological Scale

Not applicable

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-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

EM Organismal Scale

Community

Not applicable

Species

Guild or Assemblage

Not applicable

Other (Comment) ?

Not applicable

Taxonomic level and name of organisms or groups identified

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

None Available

Species
- Pinus ponderosa
- Abies amabilis
- Tsuga mertensiana
- Thuja plicata
- Picea sitchensis
- Abies procera
- Abies grandis
- Chamaecyparis nootkatensis
- Tsuga heterophylla
- Other (trees)
- Pseudotsuga menziesii

Genus
- Acropora
- Porites spp.
- Diploria spp.
Species
- Mycetophylia lamarckiana
- Mycetophylia ferox
- Montastrea cavernosa
- Dichocoenia stokesii
- Meandrina meandrites
- Agaricia tenuifolia
- Agaricia fragilis
- Montastrea annularis
- Agaricia agaricites
- Dendrogyra cylindricus
- Millepora complanata
- Montastrea faveolata
- Porites astreoides
- Montastrea franksii

None Available

other
- Whiting
- shrimp
- Whales
- Seals
- Cod
- Phytoplankton
- Mackerel
- Benthos
- Zooplankton
- Anchovy

None Available

EnviroAtlas URL

EM-65

EM-71

EM-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

GAP Ecological Systems

None Available

Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition

Percent IUCN Status II, Percent GAP Status 1 & 2

Carbon Storage by Tree Biomass

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

None Available

GAP Ecological Systems, Green Space per Capita, Percent GAP Status 1 & 2, Residential Density

Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD), Percent Impervious Area, Water supply from NID reservoirs (million gallons)

Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass

National Hydrography Dataset Plus (NHD PlusV2)

Big game hunting recreation demand

Average Annual Precipitation

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-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

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] 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
  - Atmospheric composition and climate regulation
    - Global climate regulation by reduction of greenhouse gas concentrations
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Bio-remediation by micro-organisms, algae, plants, and animals
    - Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

[Ecosystem] Provisioning
- Materials
  - Biomass
    - Fibres and other materials from plants, algae and animals for direct use or processing
[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes
- Maintenance of physical, chemical, abiotic conditions
  - By natural chemical and physical processes
    - Not applicable

[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
  - Intellectual and representative interactions
    - Aesthetic
    - Entertainment

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

[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
  - 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] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs
[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Regulation & Maintenance
- 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-112

EM-193

EM-195

EM-224

EM-260

EM-315

EM-656

EM-718

EM-743

EM-863

EM-964

EM-998

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

Near Coastal Marine/Estuarine (113)
- Water (3)
  - Liquid water
    - Quality Indicator

None

Near Coastal Marine/Estuarine (113)
- Fauna (4)
  - Invertebrates
    - Quality Indicator

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Rivers and Streams (111)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Forests (21)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Lakes and Ponds (112)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Wetlands (12)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Urban/Suburban (27)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator

None

Open Oceans and Seas (114)
- Fauna (4)
  - Fish
    - Flow Indicator

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

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

EM Modeling Approach

Comment:

EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Comment:

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)