EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

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

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

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

EM Variables by Category

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

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

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

All EM Descriptors

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

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

EM Descriptors by Modeling Concepts

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

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

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

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Comparing:

EM-79
EM-83
EM-88
EM-125
EM-129
EM-375
EM-701
EM-836
EM-838

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
EM Short Name em.detail.shortNameHelp ?	Divergence in flowering date, Central French Alps	Soil carbon and plant traits, Central French Alps	Area and hotspots of carbon storage, South Africa	Land-use change and recreation, Europe	3-PG, South Australia	VELMA hydro, Oregon, USA	Blue-winged Teal recruits, CREP wetlands, IA, USA	Bird abundance on restored landfills, UK	Eastern meadowlark abundance, Piedmont region, USA
EM Full Name em.detail.fullNameHelp ?	Functional divergence in flowering date, Central French Alps	Soil carbon potential estimated from plant functional traits, Central French Alps	Area and hotspots of carbon storage, South Africa	Land-use change effects on recreation, Europe	3-PG (Physiological Principles Predicting Growth), South Australia	VELMA (visualizing ecosystems for land management assessments) hydro, Oregon, USA	Blue-winged Teal duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK	Eastern meadowlark abundance, Piedmont ecoregion, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	None	EU Biodiversity Action 5	None	US EPA	None	None	None
EM Source Document ID	260	260	271	228	243	13	372 ? Comment: Document 373 is a secondary source for this EM.	406	405
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Haines-Young, R., Potschin, M. and Kienast, F.	Crossman, N. D., Bryan, B. A., and Summers, D. M.	Abdelnour, A., Stieglitz, M., Pan, F. and McKane, R. B.	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever	Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton	Riffel, S., Scognamillo, D., and L. W. Burger
Document Year em.detail.documentYearHelp ?	2011	2011	2008	2012	2011	2011	2010	2011	2008
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Mapping ecosystem services for planning and management	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Carbon payments and low-cost conservation	Catchment hydrological responses to forest harvest amount and spatial pattern	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	http://www.csiro.au/products/3PGProductivity#a1	Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	Benis Egoh	Marion Potschin	Anders Siggins	A. Abdelnour	David Otis	Lutfor Rahman	Sam Riffell
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	Not reported	Dept. of Civil and Environmental Engineering, Goergia Institute of Technology, Atlanta, GA 30332-0335, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	Not reported	marion.potschin@nottingham.ac.uk	Anders.Siggins@csiro.au	abdelnouralex@gmail.com	dotis@iastate.edu	lutfor.rahman@northampton.ac.uk	sriffell@cfr.msstate.edu

EM Description

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables."	ABSTRACT: "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: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions."	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot."	ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios."	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)."	AUTHOR'S DESCRIPTION: "VELMA uses a distributed soil column framework to simulate the movement of water and nutrients (NH4, NO3, DON, DOC) within the soil, between the soil and the vegetation, and between the soil surface and vegetation to the atmosphere. The soil column model consists of three coupled submodels: (1) a hydrological model that simulates vertical and lateral movement of water within soil, losses of water from soil and vegetation to the atmosphere, and the growth and ablation of the seasonal snowpack, (2) a soil temperature model that simulates daily soil layer temperatures from surface air temperature and snow depth, and (3) a plant-soil model that simulates C and N dynamics. (Note: for the purposes of this paper we describe only the hydrologic aspects of the model.) Each soil column consists of n soil layers. Soil water balance is solved for each layer (equations (A1)–(A6)). We employ a simple logistic function that is based on the degree of saturation to capture the breakthrough characteristics of soil water drainage (equations (A7)–(A9)). Evapotranspiration increases exponentially with increasing soil water storage and asymptotically approaches the potential evapotranspiration rate (PET) as water storage reaches saturation [Davies and Allen, 1973; Federer, 1979, 1982; Spittlehouse and Black, 1981] (equation (A12)). PET is estimated using a simple temperature-based method [Hamon, 1963] (equation (A13)). An evapotranspiration recovery function is used to account for the effects of changes in stand-level transpiration rates during succession, e.g., after fire or harvest (equation (B2)). Snowmelt is estimated using the degree-day approach [Rango and Martinec, 1995] and accounts for the effects of rain on snow [Harr, 1981] (equation (A10)). [15] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water (Figures A1 and A2). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow is modeled using a simple logistic function multiplied by a factor to account for the local topographic slope angle (equation (A16))… The model is forced with daily temperature and precipitation. Daily observed streamflow data is used to calibrate and validate simulated discharge." "Model calibration is needed to accurately capture the pre- and postharvest dynamics at WS10. This model calibration consists of two simulations: an old-growth simulation for the period 1969-1974 and a post-harvest simulation for the period 1975-2008." Two additional sets of VELMA simulations examining changes in streamflow are presented in the paper, but not included here. Twenty simulations were conducted varying the location across the watershed of a 20% har	ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2Rn where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = HZB/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).	ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)."	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. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified	None identified	None identified	None identified	None identified	None reported
Biophysical Context	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	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.	Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. Mean annual precipitation is 2300 mm and falls primarily as rain between October and April. Total rainfall during June– September averages 200 mm. Snow rarely persists longer than a couple of weeks and usually melts within 1 to 2 days. Average annual streamflow is 1600 mm, which is approximately 70% of annual precipitation. Soils are of the Frissel series, classified as Typic Dystrochrepts with fine loamy to loamy-skeletal texture that are generally deep and well drained. These soils quickly transmit subsurface water to the stream. Prior to the 1975 100% clearcut, WS10 was a 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). The dominant vegetation of WS10 today is a 35 year old mixed Douglasfir and western hemlock stand.	Prairie Pothole Region of Iowa	The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995).	Conservation Reserve Program lands left to go fallow
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030)	Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings	Stand age; old-growth (pre-harvest), and harvested (postharvest)	No scenarios presented	No scenarios presented	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs ? Comment: Runs are differentiated based on the the average annual biomass flux and carbon sequestration from two types of carbon plantings: 1) Tree-based monocultures of three different species (i.e., monoculture carbon planting) and 2) Diverse plantings of nine different native tree and shrub species (i.e., ecological carbon planting)	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method Only	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	Doc-260	Doc-271	Doc-238 \| Doc-239 \| Doc-240 \| Doc-241 \| Doc-242 \| Doc-228	Doc-243 \| Doc-246 \| Doc-245	Doc-317	Doc-372 \| Doc-373	None	Doc-405
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82	EM-85 \| EM-86 \| EM-87	EM-122 \| EM-123 \| EM-124 \| EM-162 \| EM-164 \| EM-165 \| EM-166 \| EM-170 \| EM-171 \| EM-99 \| EM-119 \| EM-120 \| EM-121	None	EM-379 \| EM-380 \| EM-605 \| EM-884 \| EM-883 \| EM-887	EM-705 \| EM-704 \| EM-703 \| EM-702 \| EM-700 \| EM-632	EM-837	EM-831 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-846 \| EM-847

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2008	Not reported	Not reported	1990-2030	2009-2050	1969-2008	1987-2007	Not applicable	2008
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	future time	future time	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	discrete	discrete	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	1	1	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Month	Day	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Geopolitical	Physiographic or Ecological	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	South Africa	The EU-25 plus Switzerland and Norway	Agricultural districts of the state of South Australia	H. J. Andrews LTER WS10	CREP (Conservation Reserve Enhancement Program	East Midland	Piedmont Ecoregion
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	>1,000,000 km^2	>1,000,000 km^2	100,000-1,000,000 km^2	10-100 ha	10,000-100,000 km^2	1000-10,000 km^2.	100,000-1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	Distributed across catchments with average size of 65,000 ha	1 km x 1 km	1 ha x 1 ha	30 m x 30 m surface pixel and 2-m depth soil column	multiple, individual, irregular sites	multiple unrelated sites	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Logic- or rule-based	Numeric	Numeric	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	No	Yes	Yes	Unclear	Not applicable	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	No	No	Yes	No	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R squared \| 35.5 \| Not applicable	None	None	None	None	Nash-Sutcliffe coefficient \| Varies by Run; view Runs to see values \| unitless Correlation coefficient \| Varies by Run; view Runs to see values \| unitless Index of agreement \| Varies by Run; view Runs to see values \| unitless	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	No	No	No	Not applicable	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	Not applicable	Yes
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Unclear

EM Locations, Environments, Ecology

Location of EM Application

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

EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
Europe France	Europe France	Africa South Africa	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia France Lithuania Hungary Switzerland Spain Czech Republic Belgium Norway Finland Denmark Italy Germany Estonia	Oceania Australia	North America United States Oregon	North America United States Iowa	Europe United Kingdom	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania

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

EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
None	None	None	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	-30	50.53	-34.9	44.15	42.62	52.22	36.23
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	25	7.6	138.7	-122.2	-93.84	-0.91	-81.9
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Estimated	Estimated	Estimated	Provided	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Forests \| Agroecosystems	Rivers and Streams \| Ground Water \| Forests	Inland Wetlands \| Agroecosystems \| Grasslands	Created Greenspace \| Grasslands	Grasslands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	Not applicable	Not applicable	Agricultural land for annual crops, annual legumes, and grazing of sheep and cows	400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata).	Wetlands buffered by grassland within agroecosystems	restored landfills and conserved grasslands	grasslands
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser than that of the Environmental Sub-class	Ecological scale is coarser than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Not applicable	Not applicable	Species	Not applicable	Individual or population, within a species	Individual or population, within a species	Species

Taxonomic level and name of organisms or groups identified

EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
None Available	None Available	None Available	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	None Available	Species Blue-winged teal (Anas discors)	Species Grey partridge (Perdix perdix) Collared dove (Streptopelia decaocto) Woodpidgeon (Columba palumbus) Lapwing (Vanellus vanellus) Magpie (Pica pica) Dunnock (Prunella modularis) Starling (Sturnus vulgaris) Skylark (Alauda arvensis) Pheasant (Phasianus colchicus) Blackbird (Turdus merula) Carrion crow (Corvus corone) Tree sparrow (Passer montanus)	Species Eastern meadowlark

EnviroAtlas URL

EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
None Available	None Available	None Available	None Available	GAP Ecological Systems, Average Annual Precipitation, Acres of Land Enrolled in the Conservation Reserve Program (CRP)	GAP Ecological Systems, Average Annual Precipitation, Average Annual Daily Potential Wind Energy	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	None Available	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions

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

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

EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
None	[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 Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[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 Entertainment	[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] Provisioning Materials Water Surface water for non-drinking purposes	[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 Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats

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

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

EM-79	EM-83	EM-88	EM-125	EM-129	EM-375	EM-701	EM-836	EM-838
None	None	None	Rivers and Streams (111) Composite (8) Presence of environmental class/subclass Quality Indicator Barren / Rock and Sand (28) Composite (8) Presence of environmental class/subclass Quality Indicator Forests (21) Composite (8) Presence of environmental class/subclass Quality Indicator Tundra (25) Composite (8) Presence of environmental class/subclass Quality Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Quality Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Quality Indicator Lakes and Ponds (112) Composite (8) Presence of environmental class/subclass Quality Indicator Wetlands (12) Composite (8) Presence of environmental class/subclass Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	Forests (21) Flora (5) Trees Stock Indicator	None	Wetlands (12) Fauna (4) Duck Stock Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator