This habitat model was developed to delineate suitable habitat for coastal cactus wren (Campylorhynchus brunneicapillus) in southern California. A primary purpose of the model is to identify potential restoration sites that may not currently support cactus patches required by wrens, but which are otherwise highly suitable. These are areas that could be planted with cactus to increase wren populations, an important management objective for many land managers. We used the Partitioned Mahalanobis D2 modeling technique to construct alternative models with different combinations of environmental variables. Variables were calculated at each point in the center of a 150 m x 150 m cell in a grid of points across the landscape. Variables reflect various aspects of topography, climate, land use (percent vegetation and urbanization at 150 m and 1 km scales), Normalized Difference Vegetation Index (NDVI), and modeled habitat suitability for coastal prickly pear cactus (Opuntia littoralis) and California sagebrush (Artemisia californica). From compiled cactus wren observation data, we randomly selected a total of 845 spatially precise and non-redundant wren locations to use as a calibration dataset and retained the remaining 338 records to use in validation. We randomly selected 1,000 pseudo-absence points from the study area grid to use with the presence validation points to evaluate model performance. 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 (0.25 - 0.49); and Very Low (0 - 0.24). Suitable habitat is identified as grid cells with HSI greater than or equal to 0.5. We calculated Area Under the Curve (AUC) values from a Receiver Operating Curve (ROC) to determine how well models distinguish between presence and pseudo-absence points. We constructed 11 alternative models and selected a best performing model and partition based upon median HSI calibration and validation values and AUC results. The top performing model (Run 11, Partition 1) has an AUC of 0.95 and a median calibration and validation HSI of 0.72 and 0.75, respectively. This model includes the following variables: average minimum January and maximum July temperatures, annual precipitation, elevation, northness, eastness, slope, topographic heterogeneity (30 m x 30 m neighborhood), percent of urban, coastal sage scrub and chaparral land cover at 150 m scale, and predicted prickly pear and California sagebrush habitat suitability. We mapped HSI predictions for each cell in the 150 m-scale grid across the study area.

This habitat model was developed to delineate a sampling frame for regional monitoring of coastal California gnatcatchers (Polioptila californica californica) to determine: 1) percent area occupied (PAO) in high and very high suitability habitat across conserved lands and participating military lands in the U.S. range in southern California; 2) changes in PAO over time; and 3) extinction and colonization rates. One purpose of the model is to identify areas recovering from disturbance, such as wildfire, that may not currently support coastal sage scrub vegetation used by coastal California gnatcatchers, but are otherwise highly suitable. In this way, we can monitor gnatcatcher occupancy associated with habitat changes over time. We used the Partitioned Mahalanobis D2 modeling technique to construct alternative models with different combinations of environmental variables. Variables were calculated at each point in the center of a 150 m x 150 m cell in a grid of points across the southern California landscape. Variables reflect various aspects of topography, climate, land use (percent vegetation and urbanization at 150 m and 1 km scales), Normalized Difference Vegetation Index, and modeled California sagebrush (Artemisia californica) habitat suitability. Due to spatial unevenness in gnatcatcher location data, we divided southern California into five sampling regions and randomly subsampled 50 locations from each region. We repeated this process 1,000 times using a total of 1,063 spatially precise and non-redundant gnatcatcher locations as a calibration dataset. We model-averaged the results from sampling iterations to create a calibration model and partitions for each set of variables. We compared among calibration model-partitions using a validation dataset of 3,205 presence records independently collected from the calibration dataset and an equivalent number of pseudo-absence points randomly selected from the study area grid. 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 (0.25 - 0.49); and Very Low (0 - 0.24). Suitable habitat is identified as grid cells with HSI greater than or equal to 0.5. We calculated Area Under the Curve (AUC) values from a Receiver Operating Curve (ROC) to determine how well models distinguish between presence and pseudo-absence points. We selected a best performing calibration model and partition based upon median HSI calibration and validation values and AUC results. The top performing model-partition Run 18 Partition 1 of 19 alternative models has an AUC of 0.96 and a median calibration and validation HSI of 0.73 and 0.69, respectively. This model includes the following variables: average minimum January and maximum July temperatures, annual precipitation, elevation, northness, eastness, slope, topographic heterogeneity (30 m x 30 m neighborhood), percent of urban, coastal sage scrub and chaparral land cover at 150 m scale, and predicted California sagebrush habitat suitability. We mapped HSI predictions for each cell in the 150 m-scale grid across the study area.

This habitat model was developed to delineate a sampling frame for regional monitoring of coastal California gnatcatchers (Polioptila californica californica) to determine: 1) percent area occupied (PAO) in high and very high suitability habitat across conserved lands and participating military lands in the U.S. range in southern California; 2) changes in PAO over time; and 3) extinction and colonization rates. One purpose of the model is to identify areas recovering from disturbance, such as wildfire, that may not currently support coastal sage scrub vegetation used by coastal California gnatcatchers, but are otherwise highly suitable. In this way, we can monitor gnatcatcher occupancy associated with habitat changes over time. We used the Partitioned Mahalanobis D2 modeling technique to construct alternative models with different combinations of environmental variables. Variables were calculated at each point in the center of a 150 m x 150 m cell in a grid of points across the southern California landscape. Variables reflect various aspects of topography, climate, land use (percent vegetation and urbanization at 150 m and 1 km scales), Normalized Difference Vegetation Index, and modeled California sagebrush (Artemisia californica) habitat suitability. Due to spatial unevenness in gnatcatcher location data, we divided southern California into five sampling regions and randomly subsampled 50 locations from each region. We repeated this process 1,000 times using a total of 1,063 spatially precise and non-redundant gnatcatcher locations as a calibration dataset. We model-averaged the results from sampling iterations to create a calibration model and partitions for each set of variables. We compared among calibration model-partitions using a validation dataset of 3,205 presence records independently collected from the calibration dataset and an equivalent number of pseudo-absence points randomly selected from the study area grid. 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 (0.25 - 0.49); and Very Low (0 - 0.24). Suitable habitat is identified as grid cells with HSI greater than or equal to 0.5. We calculated Area Under the Curve (AUC) values from a Receiver Operating Curve (ROC) to determine how well models distinguish between presence and pseudo-absence points. We selected a best performing calibration model and partition based upon median HSI calibration and validation values and AUC results. The top performing model-partition Run 18 Partition 1 of 19 alternative models has an AUC of 0.96 and a median calibration and validation HSI of 0.73 and 0.69, respectively. This model includes the following variables: average minimum January and maximum July temperatures, annual precipitation, elevation, northness, eastness, slope, topographic heterogeneity (30 m x 30 m neighborhood), percent of urban, coastal sage scrub and chaparral land cover at 150 m scale, and predicted California sagebrush habitat suitability. We mapped HSI predictions for each cell in the 150 m-scale grid across the study area.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Data presented are 1.) the locations where Coastal Cactus Wren (Campylorhynchus brunneicapillus) genetic samples were collected in southern California, in 2011, 2012, and 2013; 2.) 2012 and 2013 survey results; 3.) the territory locations of all Cactus Wrens detected in 2011, 2012, and 2013 in Orange, Riverside, and San Diego counties; and 4.) dispersal results on a subset of Cactus Wrens color banded in 2011.

Data presented are 1.) the locations where Coastal Cactus Wren (Campylorhynchus brunneicapillus) genetic samples were collected in southern California, in 2011, 2012, and 2013; 2.) 2012 and 2013 survey results; 3.) the territory locations of all Cactus Wrens detected in 2011, 2012, and 2013 in Orange, Riverside, and San Diego counties; and 4.) dispersal results on a subset of Cactus Wrens color banded in 2011.

Data presented are results of surveys and monitoring for San Diego Cactus Wren (Campylorhynchus brunneicapillus) in southern San Diego County. These data were collected to assess Cactus Wren population status to determine the relationships between specific elements of habitat quality and Cactus Wren presence. Surveys were conducted periodically at 507 plots throughout San Diego County. Birds that had been banded in prior years with unique color combinations were identified to individual on subsequent visits to provide data on survival and movement between years.

Data presented are results of surveys and monitoring in 2017 for San Diego Cactus Wren (Campylorhynchus brunneicapillus) in southern San Diego County. Surveys were conducted at 362 plots. Plots were surveyed twice in 2017 and the number, age (adult or juvenile), banding status (color banded or not), breeding status (paired, unpaired, or unknown), and nesting status (active nest detected or not) of all wrens recorded. Habitat covariate data were collected including amount of dead and stressed cactus in the plot, percent cover of bare ground, and the dominant and percent cover of invasive species. During weekly monitoring visits, nests were located and checked to determine the number of eggs laid, the number of eggs that hatched, and the number of chicks that fledged. Nesting data were compiled to present seasonal productivity for each Cactus Wren pair. All nestlings and adults (when possible) were color banded with unique color combinations to identify individuals on subsequent visits to provide data on survival within and between years. During banding, data were collected on age, sex, weight, and reproductive status. Fecal samples and genetic samples (pin feathers) were collected from nestlings when possible. In each territory where vegetation data were collected in 2015 and 2016, a 75 x 75 m grid of 30 points was placed to include as many nests as possible. At each point, data were collected within a 2 m radius circle including presence or absence of native bunch grasses, non-native annual grasses, cactus, elderberry (Sambucus nigra), lemonadeberry (Rhus integrifolia), sagebrush (Artemisia californica), buckwheat (Eriogonum fasciculatum), black mustard (Brassica nigra), and bare ground. These presence/absence data were summarized to present the percent cover of each species or ground cover representing habitat at the Cactus Wren territory.

Data presented are results of surveys and monitoring in 2017 for San Diego Cactus Wren (Campylorhynchus brunneicapillus) in southern San Diego County. Surveys were conducted at 362 plots. Plots were surveyed twice in 2017 and the number, age (adult or juvenile), banding status (color banded or not), breeding status (paired, unpaired, or unknown), and nesting status (active nest detected or not) of all wrens recorded. Habitat covariate data were collected including amount of dead and stressed cactus in the plot, percent cover of bare ground, and the dominant and percent cover of invasive species. During weekly monitoring visits, nests were located and checked to determine the number of eggs laid, the number of eggs that hatched, and the number of chicks that fledged. Nesting data were compiled to present seasonal productivity for each Cactus Wren pair. All nestlings and adults (when possible) were color banded with unique color combinations to identify individuals on subsequent visits to provide data on survival within and between years. During banding, data were collected on age, sex, weight, and reproductive status. Fecal samples and genetic samples (pin feathers) were collected from nestlings when possible. In each territory where vegetation data were collected in 2015 and 2016, a 75 x 75 m grid of 30 points was placed to include as many nests as possible. At each point, data were collected within a 2 m radius circle including presence or absence of native bunch grasses, non-native annual grasses, cactus, elderberry (Sambucus nigra), lemonadeberry (Rhus integrifolia), sagebrush (Artemisia californica), buckwheat (Eriogonum fasciculatum), black mustard (Brassica nigra), and bare ground. These presence/absence data were summarized to present the percent cover of each species or ground cover representing habitat at the Cactus Wren territory.