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-79 |
EM-186 
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EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
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EM-618
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EM-784
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EM Short Name
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Divergence in flowering date, Central French Alps | FORCLIM v2.9, Western OR, USA | InVEST crop pollination, NJ and PA, USA | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Decrease in erosion (shoreline), St. Croix, USVI | DayCent N2O flux simulation, Ireland | WaterWorld v2, Santa Basin, Peru | Wildflower mix supporting bees, Florida, USA |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | InVEST crop pollination, New Jersey and Pennsylvania, USA | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | DayCent simulation N2O flux and climate change, Ireland | WaterWorld v2, Santa Basin, Peru | Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | InVEST | InVEST | US EPA | US EPA | None | None | None |
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EM Source Document ID
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260 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
279 | 315 | 317 | 335 | 358 | 368 | 400 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Van Soesbergen, A. and M. Mulligan | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters |
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Document Year
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2011 | 2007 | 2009 | 2015 | 2013 | 2014 | 2010 | 2018 | 2015 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Modelling pollination services across agricultural landscapes | Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States |
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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 | 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 journal manuscript | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-79 |
EM-186
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EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
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EM-618
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EM-784
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| Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | www.policysupport.org/waterworld | Not applicable | |
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Contact Name
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Sandra Lavorel | Richard T. Busing | Eric Lonsdorf | The Natural Capital Project | Alex Abdelnour | Susan H. Yee | M. Abdalla | Arnout van Soesbergen | Neal Williams |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | rtbusing@aol.com | ericlonsdorf@lpzoo.org | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | yee.susan@epa.gov | abdallm@tcd.ie | arnout.van_soesbergen@kcl.ac.uk | nmwilliams@ucdavis.edu |
EM Description
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EM ID
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EM-79 |
EM-186
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EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
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EM-618
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EM-784
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Summary Description
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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: "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." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e). | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm |
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Specific Policy or Decision Context Cited
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None identified | None Identified | None identified | None identified | None identified | None identified | climate change | None identified | None identrified |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Coastal to montane, Pacific Northwest US (Oregon) forests. | No additional description provided | Not applicable | 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. | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca |
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EM Scenario Drivers
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No scenarios presented | Two scenarios modelled, forests with and without fire | No scenarios presented | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | Varied wildflower planting mixes of annuals and perennials |
EM Relationship to Other EMs or Applications
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EM ID
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EM-79 |
EM-186
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EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
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EM-618
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EM-784
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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:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing 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-79 |
EM-186
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EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
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EM-618
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EM-784
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Document ID for related EM
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Doc-260 | Doc-269 |
Doc-22 | Doc-23 ?Comment:Related document ID 22 provides tree species specific parameters in appendix. |
Doc-279 | Doc-309 | Doc-13 | Doc-317 | Doc-335 | None | None | None |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-146 | EM-208 | EM-224 | EM-340 | EM-338 | EM-349 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-447 | EM-448 | EM-598 | None | EM-796 | EM-797 | EM-804 | EM-805 | EM-806 | EM-812 | EM-814 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-79 |
EM-186
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EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
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EM-618
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EM-784
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EM Temporal Extent
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2007-2008 | >650 yrs | 2000-2002 | Not applicable | 1969-2008 | 2006-2007, 2010 | 1961-1990 | 1950-2071 | 2011-2012 |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | past time | Not applicable | future time | future time | Not applicable | both | both | past time |
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EM Time Continuity
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Not applicable | discrete | Not applicable | discrete | discrete | Not applicable | discrete | discrete | discrete |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | 1 | 1 | Not applicable | 1 | 1 | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Year | Day | Not applicable | Day | Month | Year |
EM Spatial Extent
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EM ID
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EM-79 |
EM-186
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EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
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EM-618
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EM-784
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Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Other | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Point or points | Watershed/Catchment/HUC |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
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Spatial Extent Name
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Central French Alps | Western Oregon, north of 43.00 N to Washington border | Central New Jersey and east-central Pennsylvania | Not applicable | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Oak Park Research centre | Santa Basin | Agricultural plots |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | Not applicable | 10-100 ha | 100-1000 km^2 | 1-10 ha | 10,000-100,000 km^2 | 10-100 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-79 |
EM-186
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EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
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EM-618
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EM-784
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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 distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
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 | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 0.08 ha | 30 m x 30 m | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | Not applicable | 1 km2 | Not applicable |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
|
EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
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Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Numeric |
|
EM Determinism
em.detail.deterStochHelp
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|
deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
|
EM ID
em.detail.idHelp
?
|
EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
|
Model Calibration Reported?
em.detail.calibrationHelp
?
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No | No | Unclear | Not applicable | No | Yes | No | No | No |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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|
Yes | No | No | Not applicable | No | No |
Yes ?Comment:for N2O fluxes |
No | No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
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None | None | None | None | None |
|
None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | Yes |
Yes ?Comment:Aggregate native bee abundance on watermelon flowers was measured at 23 sites in 2005. Species richness was measured using the specimens collected from watermelon flowers at the end of the sampling period. |
Not applicable | No | Yes | Yes | Yes | No |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | Not applicable | No | No | No | No | No |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | Not applicable | No | No | No | No | No |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 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
?
| EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
|
|
|
None |
|
None |
|
|
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
| None | None | None | None | None |
|
None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
|
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | 44.66 | 40.2 | -9999 | 44.25 | 17.73 | 52.86 | -9.05 | 29.4 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | -122.56 | -74.8 | -9999 | -122.33 | -64.77 | 6.54 | -77.81 | -82.18 |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | None provided | WGS84 | WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Estimated | Estimated | Not applicable | Provided | Estimated | Provided | Estimated | Provided |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Forests | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Not applicable | Forests | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows | Primarily conifer forest | Cropland and surrounding landscape | Terrestrial environments, but not specified for methods | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coral reefs | farm pasture | tropical, coastal to montane | Agricultural landscape |
|
EM Ecological Scale
em.detail.ecoScaleHelp
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|
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 | Not applicable | 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 |
Other or unclear (comment) ?Comment:Variable data was derived from multiple climate data stations distrubuted across the study area. The location and distribution of the data stations was not provided. |
Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Species | Species | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
| None Available |
|
|
None Available | None Available | None Available | None Available | None Available |
|
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
?
| EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
| None |
|
|
|
None |
|
|
|
|
<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
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-79 |
EM-186
|
EM-339 | EM-374 | EM-379 | EM-449 |
EM-593
|
EM-618
|
EM-784
|
| None | None |
|
None | None |
|
|
|
|