These data represent a land facet classification created for the Pacific Northwest Duke Landscape Resilience project.

We developed a dataset categorizing the North American continent into physical habitat types at 100m resolution. The input data used included elevation and soil type. Further details on methodology are available here. The data is provided via a link to a zipfile containing TIFF (.tif) format files that can be imported into ArcGIS or other GIS applications. Projection information is available here.

One potential strategy for protecting biodiversity in a changing climate is based on the idea of protecting the diversity of abiotic conditions that influence patterns of biodiversity. In this strategy, conservation features (the “targets” considered in the conservation planning process) are derived from data on physical features such as topography, soils, and geology. The approach has been described as “conserving the ecological stage” or protecting “land facets” or “enduring features”. Species distributions, communities, ecosystems, and broader patterns of biodiversity are clearly influenced by abiotic drivers such as soils, geology, topography, and climate. Although climates will change relatively rapidly over the coming century, soils, geology, and topography will not. Thus, local, and some regional, climate patterns and gradients influenced by topography will persist (e.g., higher elevations will still be cooler than lower elevations, although both will likely be warmer) as climates change. The hypothesis underlying use of land facets in climate adaptation planning is that by protecting a diversity of land facets, it may be possible to protect areas that will foster a diversity of biota in the future, albeit different biota than those areas would protect today. Although land facets are clearly an imperfect coarse-filter surrogate for biodiversity, physical habitat diversity may still represent a useful additional source of data that can augment biodiversity data in conservation planning processes.

Resistance was calculated as Mahalanobis distance (minimum 0, no theoretical maximum) for land facets, and as the complement of Shannon's evenness (0 to 1) for land facet diversity.1Although the mean resistance in the focal species design was lower than in the land facets design, the resistance profiles were nearly identical except for an additional 7 km segment of low resistance in the focal species profile that reduced its mean value. This was the only pair of profiles where a profile pattern counteracted a large difference in mean resistance.

1Excluding area inside of the wildland blocks.2This design was created by expanding the land facets linkage design so that gaps between breeding patches were no longer than the corresponding gaps in the focal species design. Such expansion was necessary only in the Santa Rita-Tumacacori planning area.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Ecophysiographic facets are unique combinations of climate, lithology, landcover, and landform. This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online. This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all black or white layer. Include this layer in web maps by making it draw 100% transparent. This 2015 map contains updates to the 2014 Ecophysiographic Facets layer in the form of landforms and land cover data, which have greater variety of classes and better spatial coherence (less arbitrary fragmentation). The result is more than twice as many unique facet combinations. Ecophysiographic Facets are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The Ecophysiographic Facets layer was produced by combining the values in four 250-m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). The first three of these inputs (climate, landforms, lithology) represent the primary environmental factors that determine the distribution of living organisms while the fourth (land cover) is vegetation"s response to the physical environment.This layer provides access to a 250-m cell-sized raster of unique combinations of climate, lithology, land cover, and landform known as ecophysiographic facets. The layer was created in 2015 by Esri and the USGS. The following layers were used to create this map: World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010 A simplified classification of the ecological facets is available in the World Ecophysiographic Land Units layer. A layer summarizing the local diversity of ecophysiographic facets is available here. A service is available providing access to the data tables associated with this and other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables. The process used to produce this layer is documented in the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all white layer. To use in pop-ups set the transparency to 100% and configure the pop-up. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Topographic attributes of land facets, as well as location and number of land facets relative to topographic complexity of the three landscapes.

Geology and Soils refer to the variety of soils and bedrock geology that explain basic biodiversity patterns of a region. Geology and soils area component of the Geophysical/Land facet type and used as a stratification template to ensure that we identified resilient examples of all species-relevant physical habitats. Representation of all geophysical settings in conservation plans is critical because these sites provide the “stages” for current and future biodiversity. Because biodiversity-geology relationships vary spatially, each study region developed its own set of appropriate geophysical settings/land facets through testing with known locations of biodiversity features. Here, we integrate these into one map, but we encourage users to read the report for their study region to get details on the species-geophysical setting relationships.

Ecophysiographic facets are unique combinations of climate, lithology, landcover, and landform. This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online.This layer is designed for use as a geoprocessing input and to support pop-ups in ArcGIS Online. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all black or white layer. Include this layer in web maps by making it draw 100% transparent.This 2015 map contains updates to the 2014 Ecophysiographic Facets layer in the form of landforms and land cover data, which have greater variety of classes and better spatial coherence (less arbitrary fragmentation). The result is more than twice as many unique facet combinations.Ecophysiographic Facets are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The Ecophysiographic Facets layer was produced by combining the values in four 250-m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). The first three of these inputs (climate, landforms, lithology) represent the primary environmental factors that determine the distribution of living organisms while the fourth (land cover) is vegetation's response to the physical environment.Dataset SummaryThis layer provides access to a 250-m cell-sized raster of unique combinations of climate, lithology, land cover, and landform known as ecophysiographic facets. The layer was created in 2015 by Esri and the USGS. The following layers were used to create this map:World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010A simplified classification of the ecological facets is available in the World Ecophysiographic Land Units layer. A layer summarizing the local diversity of ecophysiographic facets is available here. A service is available providing access to the data tables associated with this and other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables.The process used to produce this layer is documented in the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. What can you do with this layer?This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all white layer. To use in pop-ups set the transparency to 100% and configure the pop-up.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Details about binning data for outlier identification, sample size for fuzzy c-means cluster analysis, and modifying resistance surfaces or corridor termini to better capture focal land facets.

Further discussion about least-cost modeling, cluster validity indices, subjective decisions, and potential timesaving shortcuts in using land facets to design linkages.

Footnotes in the last 2 columns indicate the metrics that differed most between the two types of linkage designs.1For this species-landscape combination, elevation and topographic position (factors in the land facet models) had a combined weight >50% in the focal species model.2The single 26-km gap between modeled breeding patches in the focal species design was much worse than the 2 gaps of 8.6 and 1.9 km in the land facets design (Table 3).3The resistance profile was much lower in the land facets design (Figure 5A).4The maximum distance between breeding patches was 23% shorter in the land facets design (Table 3), but this was offset by the greater combined length of the two gaps and their higher resistance profiles in the land facets design.5Lengths of largest gaps between modeled breeding patches were much shorter in the land facets design (Table 3).6Resistance profiles were lower in the land facets design than in the focal species design.7Lengths of largest gaps between modeled breeding patches were shorter in the focal species design (Table 3).

Facet is the name given to a grid of 500 metre by 800 metre rectangles covering the entire City as well as UBC and the University Endowment Lands. These boundaries are often used when paper maps are plotted. They are also used for the delivery of orthophotos so that file sizes are manageable.Each facet is defined by the coordinates of its four corners and has a name which is an alpha character followed by two digits, such as M07 or O13. Data currencyThese boundaries never change. Data accuracyThese boundaries are very accurate and have not changed.

This resource contains SWAT-MODFLOW model instances for various land use scenarios for the Santa Fe River of North Central Florida. These land use scenarios were co-developed with stakeholders through a participatory modeling process (PMP) within the Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) project. The FACETS project was funded by the USDA National Institute of Food and Agriculture (Award Number: 2017-68007-26319) to promote the economic sustainability of agriculture and silviculture in North Florida and South Georgia while protecting water quantity, quality, and habitat in the Upper Floridan Aquifer and the springs and rivers it feeds (https://floridanwater.research.ufl.edu/) . SWAT-MODFLOW couples the Soil and Water Assessment Tool (SWAT) to the U.S. Geological Survey modular finite-difference flow model (MODFLOW) to produce an integrated surface-groundwater model (https://swat.tamu.edu/software/swat-modflow/) . Within SWAT-MODFLOW, SWAT handles most surface and soil processes, MODFLOW handles groundwater processes, and both models interact to simulate stream flows.

The PMP land use scenarios are the following:

1) Current Condition (Scenario 1) The base model. This model's land uses and management practices are representative of regional production systems. The simulation period is from January 1st, 1980 to December 31st, 2018. The details of this model and its development can be found in, Reaver, N. G. F., D. Lee, R. De Rooij, D. Kaplan, W. Graham (2025). The Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) project SWAT-MODFLOW model of the Santa Fe River, Florida, HydroShare, https://doi.org/10.4211/hs.b80dae5c7cc7421b80c40f9ce856dbf5.

2) Restoration Forestry-High (Scenario 2) A restoration bookend scenario. All agriculture (row crop, pasture, hay) and production forestry lands are converted to low-density longleaf pine savanna.

3) Restoration Forestry-Low (Scenario 3) A more limited restoration scenario. 50% of non-irrigated agriculture in areas prioritized for spring restoration are converted to low-density longleaf pine savanna.

4) Agricultural Expansion (Scenario 4) All current forest land suitable for agriculture (i.e., those with soil group A) switches to row crops.

5) Sod-based Rotation (Scenario 5) A scenario with widespread implementation of rotational grazing (45% of row crops switch to a rotational production system)

6) High Tech Precision Agriculture (Scenario 6) A scenario representing widespread adoption of advanced best nutrient management practices (e.g., controlled release N fertilizer)

7) Solar Farm Expansion (Scenario 7) A scenario representing the current maximum possible regional solar farm expansion in the region (maximum solar area is limited by transmission line capacity)

8) Urban Expansion (Scenario 8) Urban expansion scenario using estimates from FL 2070 Report (https://1000fof.org/florida2070/)

9) Mix-n-Match (Scenario 9) A scenario implementing land use and management practices changes from Scenario 3, Scenario 6, and Scenario 7.

The details of these nine scenarios can be found in the document "Model_Development_SFRB.pdf" within the "contents" folder of this resource. Additionally, this resource included six Simple Scenarios (i.e., CPMS1, CPMS2, CPMS3, CCPMS1, CCPMS2, and CCPMS3). In these scenarios, all production lands were managed under a single management system level developed by the PMP.

Ecosystem distributions are expected to shift as a result of global warming, raising concerns about the long-term utility of reserve systems based on coarse-filter ecosystem representation. We tested the extent to which proportional ecosystem representation targets would be maintained under a changing climate by projecting the distribution of the major ecosystems of Alberta, Canada, into the future using bioclimatic envelope models and then calculating the composition of reserves in successive periods. We used the Marxan conservation planning software to generate the suite of reserve systems for our test, varying the representation target and degree of reserve clumping. Our climate envelope projections for the 2080s indicate that virtually all reserves will, in time, be comprised of different ecosystem types than today. Nevertheless, our proportional targets for ecosystem representation were maintained across all time periods, with only minor exceptions. We hypothesize that this stability in representation arises because ecosystems may be serving as proxies for land facets, the stable abiotic landscape features that delineate major arenas of biological activity. The implication is that accommodating climate change may not require abandoning the conventional ecosystem-based approach to reserve design in favour of a strictly abiotic approach, since the two approaches may be largely synonymous.

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Aim Mining is increasingly pressuring areas of critical importance for biodiversity conservation, such as the Brazilian Amazon. Biodiversity data are limited in the tropics, restricting the scope for risks to be appropriately estimated before mineral licencing decisions are made. As the distributions and range sizes of other taxa differ markedly from those of vertebrates – the common proxy for analysis of risk to biodiversity from mining – whether mining threatens lesser-studied taxonomic groups differentially at a regional scale is unclear. Location Brazilian Amazon Methods We assess risks to several facets of biodiversity from industrial mining by comparing mining areas (within 70km of an active mining lease) and areas unaffected by mining, employing species richness, species endemism, phylogenetic diversity, and phylogenetic endemism metrics calculated for angiosperms, arthropods, and vertebrates. Results Mining areas contained higher densities of species occurrence records than ...

This study was conducted at the Savannah River Site (SRS; Aiken, SC). Sites that were farmland in 1951 were classified as “post-agricultural woodlands,” and sites that were forested were classified as “nonagricultural woodlands.” The number of fires since 1991 was determined from annual fire records, and sites were characterized as low (five or less burns) or high (more than five burns) fire frequency. Sites were not burned the year of the study. At each of our 24 sites, we deployed an unbaited, motion-activated camera trap between June 8 and July 9 in 2018 for a total trapping period of 33 days. For every photo capturing deer activity during an independent foraging bout, the observer recorded the date and time, the sex of the individual, whether or not it was in a group, group size, and if the individual was foraging (1) or being vigilant (0) as a binomial variable. If the individual’s head was up in a non-feeding posture, then the photo was classified as ...

This layer is a component of Global Ecological Land Units.

This map service contains the global ecological land unit (ELUs) data, produced from a partnership between USGS and Esri to represent an ecophysiographic classification of the Earth’s surface based on the geographic coincidence of climate, landforms, geology, and land cover. The input data (bioclimate region, landform type, surficial lithology, and land cover) were combined into a single 250 meter raster layer, called the Ecological Facets (EFs), which resulted in 106,959 unique combinations. These EFs represent the finest spatial resolution, globally comprehensive biophysical stratification yet attempted, and a detailed geospatial delineation of unique physical environments and their associated land cover. But although very rich in detail, the large number of EFs precluded meaningful cartographic display, therefore a separate generalized product termed Ecological Land Units was aggregated from the EFs by generalizing the number of input attribute classes down to 3,639 global ELUs. Additional information and access to this data is available at http://rmgsc.cr.usgs.gov/ecosystems/.

© USGS Land Change Science

This resource contains the SWAT-MODFLOW model for the Santa Fe River of North Central Florida used in the Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) project. The FACETS project was funded by the USDA National Institute of Food and Agriculture (Award Number: 2017-68007-26319) to promote the economic sustainability of agriculture and silviculture in North Florida and South Georgia while protecting water quantity, quality, and habitat in the Upper Floridan Aquifer and the springs and rivers it feeds (https://floridanwater.research.ufl.edu/). SWAT-MODFLOW couples the Soil and Water Assessment Tool (SWAT) to the U.S. Geological Survey modular finite-difference flow model (MODFLOW) to produce an integrated surface-groundwater model (https://swat.tamu.edu/software/swat-modflow/). Within SWAT-MODFLOW, SWAT handles most surface and soil processes, MODFLOW handles groundwater processes, and both models interact to simulate stream flows.

The SWAT portion of this model was developed using USGS digital elevation models, the 2017 Statewide Land Use / Land Cover map of the Florida Department of Environmental Protection (FDEP), Florida Department of Health septic tank data, STATSGO soil maps, the Public Land Survey System, and NLDAS weather data. Agricultural and silvicultural production land uses and management practices implemented within SWAT were co-developed with stakeholders in a participatory modeling process (PMP) and included row crops (corn-peanut and corn-carrot-peanut rotations) forage crops (bermudagrass hay and pasture), and production forestry (slash pine). Additional land uses implemented in SWAT included urban, low-density residential, septic tanks, rapid infiltration basins, fertilized lawns, natural grass, wetlands, and open water. The MODFLOW portion of the model was developed from the larger North Florida Southeast Georgia (NFSEG) MODFLOW model (version 1.0) as developed by the St John’s River and Suwannee River Water Management Districts. A detailed description of the complete model development process can be found in a document within this resource.

Calibration of the model was conducted using a Bayesian Sample-Importance-Resample method. Data used in the model calibration included: 1) USGS discharge data (Stations 02322500, 02322700, 02322800, and 02321500); 2) USGS operational Simplified Surface Energy Balance (SSEBop) actual evapotranspiration; and 3) Upper Floridan Aquifer potentiometric surfaces from FDEP. The calibration period of the model was 2010-2018 and the validation period was 1980-2009.