Bogdan Chivoiu Erin L. Koen Michael J. Osland Christopher A. Gabler Jerald T. Garrett Ernesto Reyes Stephanie A. Bilodeau Mitch A. Sternberg Miguel L. Villarreal Eric K. Waller Samuel N. Chambers Jude A. Benavides Robert S. Lawson James Martinez 20260409 raster digital data https://doi.org/10.5066/P16WUBDY Bogdan Chivoiu Erin L. Koen Michael J. Osland Christopher A. Gabler Jerald T. Garrett Ernesto Reyes Stephanie A. Bilodeau Mitch A. Sternberg Miguel L. Villarreal Eric K. Waller Samuel N. Chambers Jude A. Benavides Robert S. Lawson James Martinez 20260508 publication Global Change Biology vol. 32, issue 5 n/a Wiley https://doi.org/10.1111/gcb.70888 We used a landscape connectivity model to investigate the effects of barriers on wildlife access to water within the Lower Rio Grande Valley in southern Texas (USA). We used a modified omnidirectional connectivity model to compare wildlife access to the river across three border barrier scenarios: 1) a landscape without border barriers; 2) a landscape with the existing barrier system as of 2022; and 3) a potential future landscape with a continuous barrier system. These data represent the probability of movement across the landscape for three focal species: javelina (Pecari tajacu), coyote (Canis latrans), and white-tailed deer (Odocoileus virginianus). Areas with high, per-pixel cumulative current density represent paths taken most often by random walkers in the model, given the structure of landscape costs and barriers, as they cross the study area to access the Rio Grande. The effects of physical barriers on animal movement and landscape connectivity can be exacerbated in dryland environments where access to water is a limiting factor. In recent decades, border barrier construction has accelerated along the international boundary between U.S. and Mexico. This is a dryland environment, where wildlife access to water is especially important during hot and dry summer months. This data release has three components that can be used to model animal movement in the Lower Rio Grande Valley: 1) cost surface raster datasets that represent the relative cost of movement for three focal species; 2) raster datasets that represent the probability of movement across the landscape for three focal species under three barrier scenarios; and 3) a shapefile that represents the mean cost and the mean connectivity within the gaps between barrier segments. The information from these data could help managers to better understand and manage possible ecological impacts of transboundary barriers. Author ORCIDs: Chivoiu, B.(0000-0002-4568-3496);Koen, E.L.(0000-0001-9481-7692);Osland, M.J.(0000-0001-9902-8692);Gabler, C.A.(0000-0001-9311-7248);Bilodeau, S.A.(0009-0008-0881-059X);Sternberg, M.A.(0009-0003-0028-2669);Villarreal, M.L.(0000-0003-0720-1422);Waller, E.K.(0000-0002-9169-9210);Chambers, S.N.(0000-0002-4734-2855);Lawson, R.S.(0009-0001-2812-5310) 2008 2022 ground condition Complete None planned -99.6768 -96.6255 27.0114 25.3755 ISO 19115 Topic Category biota boundaries None Transboundary conservation border barrier international border landscape connectivity landscape conservation wildlife movement drylands water access javelina coyote white-tailed deer USGS Metadata Identifier USGS:681e119ad4be022199d04f42 None Lower Rio Grande Valley Texas Mexico Lower Rio Grande Valley National Wildlife Refuge Santa Ana National Wildlife Refuge Cameron County Hidalgo County Starr County Tamaulipas State Nuevo León State None. Please see 'Distribution Info' for details. Acknowledgement of the U.S. Geological Survey (USGS) Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data. Sharing new data layers developed directly from the data would be appreciated by the WARC staff. Users should be aware that comparison with other datasets for the same area from other time periods may be inaccurate because of inconsistencies resulting from changes in mapping conventions, data collection procedures, and computer processes over time. These data have been approved for release by the USGS. Although these data have been subjected to rigorous review and are substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, these data are released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use. Bogdan Chivoiu U.S. Geological Survey, Southeast Region mailing

700 Cajundome Blvd

Lafayette LA 70506 US 337-266-8669 chivoiub@contractor.usgs.gov Esri ArcGIS Pro 3.3 Erin L. Koen Jeff Bowman Carrie Sadowski Aaron A. Walpole 20140510 publication Methods in Ecology and Evolution vol. 5, issue 7 n/a Wiley ppg. 626-633 https://doi.org/10.1111/2041-210X.12197 No formal attribute accuracy tests were conducted. The Map Topology enforcing setting was used when filling the small gaps in the existing barrier layer. Random checks were done to verify the correctness of various raster operations that combined several layers (land cover, border barrier, roads, canals, and resacas) for the final cost surface per species. Dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. Miguel L. Villarreal Eric K. Waller 2023 raster digital data Digital and/or Hardcopy 2023 publication date Land cover The land cover data are the basis of the cost surface definition for the Circuitscape simulations, by using expert-defined relative costs for animal movement through specific land cover classes. U.S. Department of Transportation Bureau of Transportation Statistics 2020 vector digital data USDOT BTS (2020). North American Roads. https://geodata.bts.gov/datasets/usdot::north-american-roads/about, U.S. Department of Transportation Bureau of Transportation Statistics. https://geodata.bts.gov/datasets/usdot::north-american-roads/about 100000 Digital and/or Hardcopy 2020 publication date North American roads We obtained spatial data for major roads (more than four lanes) from this dataset. Texas Department of Transportation 2024 vector digital data TxDOT (2024). Roadways. https://gis-txdot.opendata.arcgis.com/datasets/TXDOT::txdot-roadways/about, Texas Department of Transportation. https://gis-txdot.opendata.arcgis.com/datasets/TXDOT::txdot-roadways/about Digital and/or Hardcopy 2024 publication date TxDOT roads We obtained minor road data (four lanes or less) from the Texas roadways dataset. Instituto Nacional de Estadística y Geografía 2023 vector digital data INEGI (2023). Mexico Red Nacional de Caminos. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=794551067307. Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=794551067307 Digital and/or Hardcopy 2023 publication date Mexico roads We obtained minor road data (four lanes or less) from the Mexico roads dataset. Jude A. Benavides John Karges Kevin B. Mayes Hanadi S. Rifai Cyndi V. Castro 2023 vector digital data Benavides J. A., Karges J., Mayes K. B., Rifai H. S., & Castro C. V. (2023). Gulf coast rivers of the Southwestern United States. In D. D. Delong, T. D. Jardine, A. C. Benke, & C. E. Cushing (Eds.), Rivers of North America. (pp. 176-224). Academic Press. Digital and/or Hardcopy 2023 publication date Resacas Resacas dataset was used to alter the cost surface derived from the base land cover. U.S. Geological Survey 2019 vector digital data USGS (2019). National Hydrography Dataset. https://www.usgs.gov/national-hydrography/access-national-hydrography-products, U.S. Geological Survey. https://www.usgs.gov/national-hydrography/access-national-hydrography-products Digital and/or Hardcopy 2019 publication date Streams Texas streams layer was used to alter the cost surface derived from the base land cover. Instituto Nacional de Estadística y Geografía 2006 vector digital data INEGI (2006). Red Hidrográfica Digital de México Escala 1:250 000. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=889463598428, Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=889463598428 250000 Digital and/or Hardcopy 2006 publication date Mexico streams Mexico streams dataset was used to alter the cost surface derived from the base land cover. U.S. Geological Survey 2019 vector digital data USGS (2019). National Hydrography Dataset. https://www.usgs.gov/national-hydrography/access-national-hydrography-products, U.S. Geological Survey. https://www.usgs.gov/national-hydrography/access-national-hydrography-products Digital and/or Hardcopy 2019 publication date Canals Texas canals dataset was used to alter the cost surface derived from the base land cover. U.S. Customs and Border Protection 2022 vector digital data Digital and/or Hardcopy 2008 2022 publication date Border barrier The existing border barrier and the potential future border barrier layers were used to modify the cost surface for each species for the two corresponding border barrier scenarios. Bogdan Chivoiu Erin L. Koen Michael J. Osland 2024 raster digital data Digital and/or Hardcopy 2024 publication date Cost surface The cost surface layers are input files for landscape connectivity modeling with Circuitscape. Bogdan Chivoiu Erin L. Koen Michael J. Osland 2024 raster digital data Digital and/or Hardcopy 2024 publication date Current density The cumulative current density layers are used to quantify the effects of the border barrier scenarios in areas adjacent to the barrier and in the gaps between existing barrier segments. We delineated the study’s modeling domain using a 40-km radius buffer around the segment of the Rio Grande from the Falcon International Reservoir to the ocean. We added a 10-km wide buffer around the modeling domain to reduce bias near source nodes for Circuitscape modeling. We used expert opinion to parameterize cost surfaces for each of three focal species (javelina, coyote, and white-tailed deer) based on a 30-m resolution land cover dataset, a road dataset, and datasets representing canals and resacas. Relative cost values ranged from 1 to 250, where forested land cover was typically assigned low-cost values close to one, and major highways were assigned the highest cost of 250. We considered the border barrier to be an obstruction to movement for our focal species, regardless of the type of barrier, meaning that no individual could cross over or through the barrier. Thus, we assigned a “no data” value to pixels in the cost surface that contained the border barrier. After assigning costs to each raster (i.e., rasters representing land cover, border barrier, roads, canals, and resacas), we combined them into one 30-m resolution cost surface per species by retaining the maximum (i.e., roads present but no canals or resacas) or minimum (i.e., resacas or canals present) cost value among the rasters when cells from different rasters overlapped. We obtained spatial data of the existing border barrier system (as of 2022) from the U.S. Customs and Border Protection. We digitally filled in small gaps in the border barrier polyline that were less than 30 m across (i.e., less than one raster pixel) by connecting the adjacent polylines before converting to raster. To assess the effects of the border barrier on landscape connectivity, we compared three scenarios: (1) the landscape when there was no border barrier (i.e., prior to construction that began in 2008; (2) the existing border barrier (as of 2022); and (3) a potential future border barrier. To represent a potential future continuous barrier, we digitally connected the ends of the existing border barrier sections with straight-line segments; the result was a 278-km long continuous barrier that spanned the study area with no gaps. Land cover North American roads TxDOT roads Mexico roads Resacas Streams Mexico streams Canals Border barrier 2024 Cost surface To model the most likely pathways for wildlife to access the Rio Grande from any point in the landscape, we modified the point-based, omnidirectional, multi-species landscape connectivity model in Koen et al. (2014). Specifically, we placed 25 source nodes around the buffered modeling domain and 500 ground nodes equally spaced (~ 908 m apart) along the centerline of the Rio Grande. We used Circuitscape.jl in Julia to model pairwise cumulative current density between source and ground nodes, with 8 neighbors, using the pairwise mode. We used the USGS Hovenweep Supercomputer to run Circuitscape models across 9 cost surfaces (i.e., 3 border barrier scenarios by 3 species). The source and ground nodes remained the same in each run. We removed the 10-km wide buffer from each raster after running Circuitscape but before conducting further analyses, to reduce biased current density near the source nodes. Cost surface 2024 Current density We developed polygon shapefiles to represent areas within 300 m north or south of existing barrier segments and polygons that straddle the gaps between barrier segments. For gaps between border barrier segments, we compared the gap-specific mean cost surface value to connectivity through the gap (mean current density). Cost surface Current density 2025 Raster Grid Cell 5970 10093 1 Universal Transverse Mercator 14 0.9996 -99.0 0.0 500000.0 0.0 row and column 30.0 30.0 meters North_American_Datum_1983 GRS 1980 6378137.0 298.257222101 NoBarrier_Javelina_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for javelina under the no barrier scenario. Producer Defined 0.0 90.320 current units (ampere in the electrical circuit theory sense) NoBarrier_Coyote_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for coyote under the no barrier scenario. Producer Defined 0.0 76.119 NoBarrier_Deer_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for deer under the no barrier scenario. Producer Defined 0.0 76.093 ExistingBarrier_Javelina_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for javelina under the existing barrier scenario. Producer Defined 0.0 98.837 ExistingBarrier_Coyote_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for coyote under the existing barrier scenario. Producer Defined 0.0 76.123 ExistingBarrier_Deer_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for deer under the existing barrier scenario. Producer Defined 0.0 76.095 FutureBarrier_Javelina_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for javelina under the potential future barrier scenario. Producer Defined 0.0 145.443 FutureBarrier_Coyote_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for coyote under the potential future barrier scenario. Producer Defined 0.0 101.656 FutureBarrier_Deer_CurrentDensity.tif Raster geospatial data file. Producer Defined Value Cumulative current output from Circuitscape simulation for deer under the potential future barrier scenario. Producer Defined 0.0 87.826 NoBarrier_Javelina_Cost.tif Raster geospatial data file. Producer Defined Value Pixel values represent the relative cost of the landscape to javelina movement, as defined by expert opinion. They are derived from land cover, road, resaca, and canal data. The forested land cover was typically assigned low-cost values close to one, and major highways were assigned the highest cost of 250. Producer Defined 65535 Value reserved for the barrier footprint, to model an impermeable region of the landscape. Producer defined 1.0 250.0 NoBarrier_Coyote_Cost.tif Raster geospatial data file. Producer Defined Value Pixel values represent the relative cost of the landscape to coyote movement, as defined by expert opinion. They are derived from land cover, road, resaca, and canal data. The forested land cover was typically assigned low-cost values close to one, and major highways were assigned the highest cost of 200. Producer Defined 65535 Value reserved for the barrier footprint, to model an impermeable region of the landscape. Producer defined 1.0 200.0 NoBarrier_Deer_Cost.tif Raster geospatial data file. Producer Defined Value Pixel values represent the relative cost of the landscape to deer movement, as defined by expert opinion. They are derived from land cover, road, resaca, and canal data. The forested land cover was typically assigned low-cost values close to one, and major highways were assigned the highest cost of 250. Producer Defined 65535 Value reserved for the barrier footprint, to model an impermeable region of the landscape. Producer defined 1.0 250.0 BarrierGaps_Cost_CurrentDensity_javelina.shp Attribute Table Table containing attribute information associated with the data set. Producer Defined FID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Shape Feature geometry. ESRI Coordinates defining the features. MeanCost The mean cost for each gap polygon. Producer Defined 6.23529411765 145.284946237 MeanCumCur The mean current density for javelina by gap polygon. Producer Defined 1.90152936945 29.2424975999 U.S. Geological Survey - ScienceBase mailing and physical

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Denver CO 80225 USA 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P16WUBDY None 20260513 Bogdan Chivoiu U.S. Geological Survey, Southeast Region mailing

700 Cajundome Blvd

Lafayette LA 70506 US 337-266-8669 chivoiub@contractor.usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998