EcoService Models Library (ESML)
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Which comparison is best for me?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
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-184 |
EM-422 
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EM-592 |
EM-661
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EM-836 | EM-904 |
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EM Short Name
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ROS (Recreation Opportunity Spectrum), Europe | HexSim v2.4, San Joaquin kit fox, CA, USA | APEX v1501 | Alwife phosphorus flux in lakes, Connecticut, USA | Bird abundance on restored landfills, UK | Drag coefficient Laminaria hyperborea |
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EM Full Name
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ROS (Recreation Opportunity Spectrum), Europe | HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA | APEX (Agricultural Policy/Environmental eXtender Model) v1501 | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK | Drag coefficient Laminaria hyperborea |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | None | None | None |
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EM Source Document ID
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293 |
337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
357 | 383 | 406 | 424 |
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Document Author
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Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips | Steglich, E. M., J. Jeong and J. R. Williams | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Mendez, F. J. and I. J. Losada |
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Document Year
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2014 | 2015 | 2016 | 2010 | 2011 | 2004 |
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Document Title
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Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations | Agricultural Policy/Environmental eXtender Model User's Manual Version 1501 | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | 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 | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
| Not applicable | http://www.hexsim.net/ | https://epicapex.tamu.edu/manuals-and-publications/ | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Maria Luisa Paracchini | Theresa M. Nogeire | E. M. Steglich | Derek C. West | Lutfor Rahman | F. J. Mendez |
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Contact Address
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Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA | Blackland Research and Extension Center, 720 East Blackland Road, Temple, TX 76502 | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Not reported |
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Contact Email
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luisa.paracchini@jrc.ec.europa.eu | tnogeire@gmail.com | epicapex@brc.tamus.edu | derek.west@yale.edu | lutfor.rahman@northampton.ac.uk | mendezf@unican.es |
EM Description
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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Summary Description
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ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]." | ABSTRACT: "APEX is a tool for managing whole farms or small watersheds to obtain sustainable production efficiency and maintain environmental quality. APEX operates on a daily time step and is capable of performing long term simulations (1-4000 years) at the whole farm or small watershed level. The watershed may be divided into many homogeneous (soils, land use, topography, etc.) subareas (<4000). The routing component simulates flow from one subarea to another through channels and flood plains to the watershed outlet and transports sediment, nutrients, and pesticides. This allows evaluation of interactions between fields in respect to surface run-on, sediment deposition and degradation, nutrient and pesticide transport and subsurface flow. Effects of terrace systems, grass waterways, strip cropping, buffer strips/vegetated filter strips, crop rotations, plant competition, plant burning, grazing patterns of multiple herds, fertilizer, irrigation, liming, furrow diking, drainage systems, and manure management (feed yards and dairies with or without lagoons) can be simulated and assessed. Most recent developments in APEX1501 include: • Flexible grazing schedule of multiple owners and herds across landscape and paddocks. • Wind dust distribution from feedlots. • Manure erosion from feedlots and grazing fields. • Optional pipe and crack flow in soil due to tree root growth. • Enhanced filter strip consideration. • Extended lagoon pumping and manure scraping options. • Enhanced burning operation. • Carbon pools and transformation equations similar to those in the Century model with the addition of the Phoenix C/N microbial biomass model. • Enhanced water table monitoring. • Enhanced denitrification methods. • Variable saturation hydraulic conductivity method. • Irrigation using reservoir and well reserves. • Paddy module for use with rice or wetland areas." | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | 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: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." AUTHOR'S DESCRIPTION: "Therefore, a relation between C˜D and some nondimensional flow parameters is desirable to characterize hydrodynamically the L. hyperborea model plants for predictable purposes." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | Restoration and management of diadromous fish runs in coastal New England | None identified | None identified |
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Biophysical Context
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No additional description provided | No additional description provided | No additional description provided | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | 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). | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class | No scenarios presented | current and historical run size | No scenarios presented | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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Method Only, Application of Method or Model Run
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Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
Method Only | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application |
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New or Pre-existing EM?
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Application of existing 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
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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Document ID for related EM
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Doc-290 | Doc-291 | Doc-289 | Doc-328 | Doc-327 | Doc-2 | None | None | None | Doc-424 |
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EM ID for related EM
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None | EM-403 | EM-98 | None | EM-667 | EM-672 | EM-674 | EM-673 | EM-837 | EM-896 | EM-897 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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EM Temporal Extent
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Not reported | 60 yr | Not applicable | 1960"s and early 2000's | Not applicable | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | Not applicable |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Day | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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Bounding Type
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Geopolitical | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Not applicable |
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Spatial Extent Name
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European Union countries | San Joaquin Valley, CA | Not applicable | Bride Lake and Linsley Pond | East Midland | Not applicable |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable | 10-100 ha | 1000-10,000 km^2. | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | 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 |
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Spatial Grain Size
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100 m x 100 m | 14 ha | homogenous subareas | Not applicable | multiple unrelated sites | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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EM Computational Approach
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Analytic | Numeric | Numeric | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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Model Calibration Reported?
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No | Unclear | Not applicable | Yes | Not applicable | Yes |
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Model Goodness of Fit Reported?
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No | No | Not applicable | No | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | Not applicable | No | Not applicable | Unclear |
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Model Uncertainty Analysis Reported?
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No | No | Not applicable | No | Not applicable | No |
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Model Sensitivity Analysis Reported?
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No | Yes | Not applicable | Yes | Not applicable | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | No | Not applicable | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
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| EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
| None | None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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Centroid Latitude
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48.2 | 36.13 | Not applicable | 41.33 | 52.22 | Not applicable |
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Centroid Longitude
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16.35 | -120 | Not applicable | -72.24 | -0.91 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Not applicable | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Lakes and Ponds | Created Greenspace | Grasslands | Near Coastal Marine and Estuarine |
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Specific Environment Type
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Not applicable | Agricultural region (converted desert) and terrestrial perimeter | Terrestrial environment associated with agroecosystems | Coastal lakes and ponds and associated streams | restored landfills and conserved grasslands | Near Coastal Marine and Estuarine |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
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EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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EM Organismal Scale
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Not applicable | Individual or population, within a 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
taxonomyHelp
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| EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
| None Available |
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None Available |
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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)
em.detail.cicesHelp
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| EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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<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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-184 |
EM-422
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EM-592 |
EM-661
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EM-836 | EM-904 |
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None |
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None | None | None |