Preston, Kristine L. Kus, Barbara E. Perkins, Emily E. 20191101 Raster Digital Data (IMG) Denver, CO U.S. Geological Survey data release https://wildlife.ca.gov/Data/BIOS https://doi.org/10.5066/P90T9WT2 https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/2900_2999/ds2949.zip This habitat model was developed to identify suitable habitat for the federally-endangered least Bell’s vireo (Vireo bellii pusillus) across its current and historic range in California. The vireo disappeared from most of its range by the 1980s, remaining only in small populations in southern California. Habitat protection and management since the mid-1980s has led to an increase in southern California vireo populations with small numbers of birds recently expanding into the historic range. Predictions from this model will be used to focus surveys in the historic range to determine where vireos are recolonizing and to track the status and distribution of populations over time. We used the Partitioned Mahalanobis D2 modeling technique to construct alternative models with different combinations of environmental variables. We developed calibration models for the current range in southern California using vireo locations recorded from 1990 to present. We selected spatially non-redundant observations reflecting average, below average and above average rainfall conditions. For each rainfall condition, we selected three to four years of spatially non-redundant location data from the period 1990-2013. We used this dataset to randomly select 70 percent of the observations for a calibration dataset and used the remaining 30 percent of observations as a validation dataset. We used supplementary validation datasets with observations from 2016, 2017 and 2018 representing average, above average, and below average rainfall conditions, respectively. We cross-walked and merged detailed digital vegetation maps for southern California and utilized the Fire Resource Assessment Program 2015 Vegetation Map as a base map for the rest of California. We used the Klausmeyer et al. (2016) Groundwater Dependent Ecosystems map to capture riparian areas not mapped with other source layers. We selected riparian vegetation types used by vireos to develop a grid of riparian points spaced 150m apart and buffered with 500m of adjacent non-riparian habitats. We calculated environmental variables at each grid point in the center of a 150m x 150m cell for the grid of points in this modeling landscape. Variables reflect various aspects of topography, climate, and land use (percent riparian vegetation and urbanization at 150m, 500m and 1km scales). We developed several Normalized Difference Vegetation Index (NDVI) variables based on means, maximums and percentages of pixels with a minimum specified value at the 150m and 500m spatial scales. We developed alternative calibration models with different combinations of environmental variables reflecting hypotheses about least Bell’s vireo habitat relationships. Due to spatial unevenness in vireo location data, we divided southern California into ten sampling regions and randomly subsampled 70 locations from each region. We repeated this process 1,000 times using a total of 2,270 spatially precise and non-redundant vireo locations in the calibration dataset. We model-averaged the results from sampling iterations to create a calibration model with partitions for each set of variables. We compared among these calibration model-partitions using the randomly selected validation dataset of 972 observations and the 2016, 2017 and 2018 validation datasets of 610, 1,066, and 882 observations, respectively. We created a presence and pseudo-absence dataset for evaluating each model-partition’s performance with the combined 3,530 observations in the validation datasets and 3,566 pseudo-absence points randomly selected from a grid of points encompassing the vireo’s current range in southern California. For every model-partition, we calculated Habitat Similarity Index (HSI) predictions for presence and pseudo-absence points ranging from Very High (0.75-1.00); High (0.50–0.74); Low to Moderate (0-0.49). Suitable habitat is identified as grid cells with HSI equal to or greater than 0.5. We calculated Area Under the Curve (AUC) values from a Receiver Operating Curve (ROC) to determine how well models distinguish between the combined presence and pseudo-absence points. We selected a set of best performing calibration model-partitions based upon median HSI calibration and validation values and AUC results. We then used these models to predict suitable habitat for the riparian grid across California, including the current and historic range. We qualitatively evaluated how well the model-partitions predicted suitable habitat in the historic range across California for historic and recent vireo records in the California Natural Diversity Database. We also used e-Bird observations to qualitatively assess how well the model predicted habitat at recently observed vireo locations in the historic range. Several top-performing model-partitions for southern California did not predict suitable habitat in the historic range. These models included climate variables, elevation, and NDVI variables, which vary widely between the current and historic ranges. Model 30 Partition 1 is the best model-partition for predicting habitat in both the current and historic ranges across California. This model-partition has an AUC of 0.98 and median calibration and random validation HSI values of 0.70. Supplementary validation datasets for 2016, 2017 and 2018 have median HSI values of 0.66, 0.64, and 0.63, respectively. This model includes the following variables: median slope, percent flat land, and percent riparian vegetation at the 150m-scale and distance from water (m). We mapped HSI predictions from this model for each cell in the 150m-scale grid across the California riparian study area to create the habitat suitability map. Predictions from this model will be used to focus surveys in the historic range to determine where vireos are recolonizing and to track the status and distribution of populations over time. An extra table is being included to show the number of sources used for least Bell's vireo location information. The field names correspond with the same information provided for the metadata record in the data quality information, https://www.sciencebase.gov/catalog/item/5dba1199e4b06957974eb763. 19900101 20180101 ground condition Complete None planned -125.072537 -113.248826 42.221379 32.338608 USGS Thesaurus Life sciences, multivariate statistical analysis, statistical analysis, geospatial analysis, wildlife, native species, habitat, endangered species, shrubland ecosystems, partitioned Mahalanobis D2, landscape modeling, species distribution models, niche modeling, Vireo bellii pusillus, Least Bell's vireo ISO 19115 Topic Categories biota environment Getty Thesaurus of Geographic Names California None. Please see 'Distribution Info' for details. The authors of these data require that users direct any questions pertaining to appropriate use or assistance with understanding limitations and interpretation of the data to the individuals/organization listed in the Point of Contact section.Disclaimer: The State makes no claims, promises, or guarantees about the accuracy, completeness, reliability, or adequacy of these data and expressly disclaims liability for errors and omissions in these data. No warranty of any kind, implied, expressed, or statutory, including but not limited to the warranties of non-infringement of third party rights, title, merchantability, fitness for a particular purpose, and freedom from computer virus, is given with respect to these data. U.S. Geological Survey, Western Ecological Research Center Kristine Preston Ecologist mailing and physical

4165 Spruance Rd. Suite 201

San Diego CA 92101 US 619-225-6438 kpreston@usgs.gov Funding support was provided by the National Fish and Wildlife Foundation and Bureau of Land Management. Version 6.2 (Build 9200) ; Esri ArcGIS 10.5.1.7333 The raster model output was visually checked to ensure that the full extent was covered and the values were consistent and logical. Analysis was completed on all areas where variable data was available. Areas outside of the data area were marked with No Data. U.S. Geological Survey National Map 2018 Raster Digital Data n/a U.S Geological Survey National Map https://viewer.nationalmap.gov/basic/ https://www.sciencebase.gov/catalog/item/4f70aa9fe4b058caae3f8de5 https://www.usgs.gov/core-science-systems/ngp/3dep/about-3dep-products-services 2018 Ground conditions DEM Used to calculate topographic variables: elevation, topographic heterogeneity, slope, northness and eastness U.S. Geological Survey 2018 Vector Digital Data (GDB) Reston, VA U.S. Geological Survey https://www.usgs.gov/core-science-systems/ngp/national-hydrography/nhdplus-high-resolution https://viewer.nationalmap.gov/basic/ 20150101 20181231 Ground conditions NHD Use to calculate distance to water and the type of water body Lorraine E Flint Alan L Flint James H Thorne Ryan Boynton 2014 Raster Digital Data n/a Center for Integrated Data Analytics https://cida.usgs.gov/thredds/CA-BCM-Catalog.html?dataset=cida.usgs.gov/CA-BCM-2014/historical http://climate.calcommons.org/ 19810101 20101231 Ground conditions BCM Used to calculate minimum temperature, maximum temperature, and precipitation Western Riverside Regional Conservation Authority 2015 Vector Digital Data (Polygon Shapefile) Riverside, CA Western Riverside Regional Conservation Authority https://www.wildlife.ca.gov/Data/GIS/Vegetation-Data 20150101 20151231 Ground conditions W Riverside Vegetation Used to calculate landcover variables: percent riparian and urban at 150m x 150m and 500m x 500m scales AECOM 2014 Vector Digital Data (Polygon Shapefile) San Diego, CA San Diego Association of Governments http://www.sangis.org/ 20120101 20121231 Ground conditions W San Diego Vegetation Used to calculate landcover variables: percent riparian and urban at 150m x 150m and 500m x 500m scales National Communities Coalition 2013 Vector Digital Data (Polygon Shapefile) Orange County, CA National Communities Coalition 20130101 20131231 Ground conditions OC Vegetation Used to calculate landcover variables: percent riparian and urban at 150m x 150m and 500m x 500m scales Marine Corps Base Camp Pendleton 2003 Vector Digital Data (Polygon Shapefile) Marine Corps Base Camp Pendleton Marine Corps Base Camp Pendleton 20030101 20031231 Ground conditions MCBCP Vegetation Used to calculate landcover variables: percent riparian and urban at 150m x 150m and 500m x 500m scales Naval Weapons Station Fallbrook 2010 Vector Digital Data (Polygon Shapefile) Naval Weapons Station Fallbrook Naval Weapons Station Fallbrook 20100101 20101231 Ground conditions NWS Fallbrook Vegetation Used to calculate landcover variables: percent riparian and urban at 150m x 150m and 500m x 500m scales Fire Resource Assessment Program (FRAP) 2015 Raster Digital Data (File Geodatabase) n/a CALFIRE Fire Resource Assessment Program (FRAP) https://frap.fire.ca.gov/mapping/gis-data/ http://www.frap.cdf.ca.gov/ http://www.fs.fed.us/pnw/fia/ http://www.dfg.ca.gov/biogeodata/vegcamp/ http://www.fs.fed.us/r5/rsl/projects/mapping/ 19900101 20151231 Ground conditions FRAP Vegetation Used to calculate landcover variables: percent riparian and urban at 150m x 150m and 500m x 500m scales Earth Resources Observation and Science (EROS) 2017 Raster Digital Data (GeoTIFF) n/a U.S. Geological Survey Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) On Demand Interface https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance-derived-spectral-indices https://earthexplorer.usgs.gov/ https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con 19900301 20180515 Ground conditions NDVI NDVI values were calculated from LANDSAT level 1 serface reflectance. Used to calculate NDVI values at grid points, percent of area above NDVI thresholds of 0.25, 0.40, and 0.56, mean and maximum NDVI within 500m. California Department of Fish and Wildlife 2004 Vector Digital Data (Point Shapefile) Sacramento, CA California Department of Fish and Wildlife https://www.wildlife.ca.gov/Data/CNDDB 19810101 20041231 Ground conditions CNDDB Model Calibration and Validation SanGIS 2009 Vector Digital Data (Point Shapefile) San Diego, CA SanGIS http://www.sangis.org/ 20000101 20091231 Ground conditions SanBIOS Model Calibration and Validation Klausmeyer, K. Howard, J. 2016 Vector Digital Data (Polygon shapefile) CA The Nature Conservancy 20160101 20161231 Ground conditions NatCon Used to calculate landcover variables: percent riparian and urban at 150m x 150m and 500m x 500m scales ArcGIS was used to extract values of all variables to a 150m grid point. Variable scales are listed in sources. The focal statistics tool was used for variables at 150m x 150m and 500m x 500m. Climate variables were calculated from monthly long term averages. 20190101 Partitioned Mahalanobis D2 niche modeling uses presence only locality data and large-scale environmental variables calculated in Geographic Information Systems to develop a multivariate statistical model identifying suitable habitat for a species over large spatial extents. Model partitioning using principal components analysis identifies limiting habitat requirements for a species and better predicts suitable habitat in changing landscapes. 20190701 Areas within 500m of a riparian vegetation community throughout the state of California. Raster Grid Cell 5588 2625 1 NAD 1983 California Teale Albers 34.0 40.5 -120.0 0.0 0.0 -4000000.0 coordinate pair 0.000000003754707655900803 0.000000003754707655900803 meter D North American 1983 GRS 1980 6378137.0 298.257222101 r30p1_utm10.img Habitat Similarity Index raster Model outputs Value A Habitat Similarity Index value predicting habitat suitability ranging from zero, the least suitable, to one, the most suitable Rotenberry, J.T., K.L. Preston, and S.T. Knick. 2006. GIS-based niche modeling for mapping species’ habitat. Ecology 87:1458-1464 0 1 r30p1_utm11.img Habitat Similarity Index raster Model outputs Value A Habitat Similarity Index value predicting habitat suitability ranging from zero, the least suitable, to one, the most suitable Rotenberry, J.T., K.L. Preston, and S.T. Knick. 2006. GIS-based niche modeling for mapping species’ habitat. Ecology 87:1458-1464 0 1 U.S. Geological Survey GS ScienceBase mailing

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 US 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/P90T9WT2 None 20210916 U.S. Geological Survey, Western Ecological Research Center Kristine Preston Ecologist mailing and physical

4165 Spruance Rd. Suite 200

San Diego CA 92101 US 619-225-6438 kpreston@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time