This database receives data from many sources including but not limited to US Fish and Wildlife Service and California Department of Fish and Game. It provides lists and information regarding rare and threatened animals, plants, and ecological communities. It uses scientific classification to identify plants and animals. It also ranks species according to how rare or endangered they are both regionally and worldwide. Lists and reports are available in website, in pdf format. Other CNDDB data is contain in CNDDB data link which is password protected.

This dataset provides basic California Natural Diversity Database (CNDDB) information at the USGS 7.5 minute topographic quad level.

This dataset provides basic California Natural Diversity Database (CNDDB) information at the USGS 7.5 minute topographic quad level.

This database provides life history information and range maps for 675 species of amphibians, birds, mammals, and reptiles of California. A species list of those 675 species is provided. This site also contains fifty-nine wildlife habitat descriptions including the dominant plant taxa of those habitats, their life history information, maps and photographs of each habitat type. This site can be linked to via the California Natural Diversity Database website.

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

CDFW BIOS GIS Dataset, Contact: CNDDB California Natural Diversity Database, Description: To provide the user with a list of all CNDDB-tracked elements (taxa or natural communities) that have been documented by the CNDDB to occur on a particular USGS 7.5 minute topographic quad.

description: The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the dismopackage (Hijmans et al. 2011). Model evaluation was carried out using the PresenceAbsencepackage in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband tifformat with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].; abstract: The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the dismopackage (Hijmans et al. 2011). Model evaluation was carried out using the PresenceAbsencepackage in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband tifformat with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Appendix 1 from Riordan and Rundel (2019) report entitled "Evaluating the Future Role of the University of California Natural Reserve System for Sensitive Plant Protection under Climate Change" provides a list of sensitive vascular plant taxa recorded on University of California Natural Reserve System reserves. Status and taxonomy follow the California Native Plant Society (CNPS) Inventory of Rare and Endangered Plants (as of September 30, 2018). Data was compiled from reserve species lists, floras, and occurrence data downloaded in 2013-2014 from the Consortium of California Herbaria (CCH), California Natural Diversity Database (CNDDB), and CalPhotos. Please note that this list is incomplete and may contain errors stemming from mis-identifications, taxonomic uncertainties, or location uncertainties. Occurrences do not include observations made or digitized after 2013. Sensitive plant status for some taxa may change with periodic revisions to the CNPS rare plant inventory. For the most up-to-date information visit the rare plant inventory's website (http://www.rareplants.cnps.org/). We were unable to find information for sensitive plants on Jenny Pygmy Forest Reserve or the Steele/Burnand Anza-Borrego Desert Research Center. There are no sensitive plants records on Ano Nuevo Island Reserve (due to severe pinniped disturbance), however, several sensitive plants do have historical records on the mainland in Ano Nuevo State Park. Plants for the White Mountains Research Center are based on observations located within 2 miles of the Summit, Barcroft, and Crooked Creek stations. Sensitive plants are included for the associated parks Anza-Borrego Desert State Park and Yosemite National Park, both of which have formal legal agreements with the UCNRS, as well as for the Deep Canyon Transect (associated with Boyd Deep Canyon Desert Research Center) and the 40,000 acre Granite Mountains (associated with Sweeney Granite Mountains Desert Research Center).

CWHR species range datasets represent the maximum current geographic extent of each species within California. Ranges were originally delineated at a scale of 1:5,000,000 by species-level experts more than 30 years ago and have gradually been revised at a scale of 1:1,000,000. Species occurrence data are used in defining species ranges, but range polygons may extend beyond the limits of extant occurrence data for a particular species. When drawing range boundaries, CDFW seeks to err on the side of commission rather than omission. This means that CDFW may include areas within a range based on expert knowledge or other available information, despite an absence of confirmed occurrences, which may be due to a lack of survey effort. The degree to which a range polygon is extended beyond occurrence data will vary among species, depending upon each species’ vagility, dispersal patterns, and other ecological and life history factors. The boundary line of a range polygon is drawn with consideration of these factors and is aligned with standardized boundaries including watersheds (NHD), ecoregions (USDA), or other ecologically meaningful delineations such as elevation contour lines. While CWHR ranges are meant to represent the current range, once an area has been designated as part of a species’ range in CWHR, it will remain part of the range even if there have been no documented occurrences within recent decades. An area is not removed from the range polygon unless experts indicate that it has not been occupied for a number of years after repeated surveys or is deemed no longer suitable and unlikely to be recolonized. It is important to note that range polygons typically contain areas in which a species is not expected to be found due to the patchy configuration of suitable habitat within a species’ range. In this regard, range polygons are coarse generalizations of where a species may be found. This data is available for download from the CDFW website: https://www.wildlife.ca.gov/Data/CWHR. The following data sources were collated for the purposes of range mapping and species habitat modeling by RADMAP. Each focal taxon’s location data was extracted (when applicable) from the following list of sources. BIOS datasets are bracketed with their “ds” numbers and can be located on CDFW’s BIOS viewer: https://wildlife.ca.gov/Data/BIOS. California Natural Diversity Database, Terrestrial Species Monitoring [ds2826], North American Bat Monitoring Data Portal, VertNet, Breeding Bird Survey, Wildlife Insights, eBird, iNaturalist, other available CDFW or partner data.

Vegetation mapping has been conducted at various City of San Diego Park and Recreation Open Space lands in support of natural resource management objectives and the City's MSCP. In 2004, vegetation mapping and sensitive species surveys were conducted at the Tecolote Canyon Natural Park by HELIX Environmental Planning, Inc. (HELIX) to establish baseline data for the park's Natural Resource Management Plan. RECON Environmental conducted vegetation mapping at Mission Trails Regional Park (MTRP) in 2008 in support of the MSCP. This dataset was compiled as part of the Southern California Data Integration Project. Vegetation mapping (including mapping of exotic species) of Tecolote Canyon was conducted in the spring of 2004 on georeferenced aerial photos (flown January 2003; scale 1"=200'; Andrea Bitterling, pers. comm.). Vernal pools and rare plants were mapped with a GPS, with accuracy of approximately 1 meter. RECON biologists mapped the vegetation in MTRP between March 18 and April 24, 2008. A three-fold approach to vegetation mapping at MTRP was taken: reviewing historical biological information, mapping potential vegetation communities using aerial photographs, and conducting field mapping. Prior to conducting the field work, existing GIS data from the California Natural Diversity Database (CNDDB), vegetation data from SanGIS, and vernal pool data from the City of San Diego were all examined. In addition, aerial photographs from 2000, 2002, 2003, and 2007 were reviewed to assist with vegetation identification. Digital orthorectified aerial images with 1-foot resolution, taken in 2007, were carefully reviewed on a computer monitor and on printed maps prior to field surveys to identify unique color and shape patterns of exotic species and to distinguish stands (polygons) to be mapped in the field. These photographs, with preliminary stand overlays, were used to create field maps for MTRP. The field maps were used to confirm and map vegetation communities identified in the field. Field mapping consisted of driving or hiking to all areas in the MTRP. Areas identified as a homogeneous vegetation community stand in the field were mapped as individual polygons (CNPS 2004). Homogenous stands ranged in size from 0.25 acre to hundreds of acres. Most of the homogeneous stands were large, making it difficult to summarize the species composition, cover, and structure of an entire stand. Sampling of representative portions of large stands was performed using the California Native Plant Society (CNPS) Rapid Assessment Method to determine the species composition and cover class for characteristic plant species within each stand. Sampling consisted of selecting a representative area, walking through the area, and identifying characteristic species present, qualitatively estimating cover for each characteristic species and using binoculars to scan the entire stand to confirm that it was homogenous. Occasionally, stands were inaccessible, precluding direct observations. In these cases, estimations were made using aerial photographs, binoculars, and best judgment of the biologists. Following field mapping, all collected data was reviewed in the office to determine a vegetation series classification, vegetation alliance and where applicable, an association (Sawyer and Keeler-Wolf classification). The series, alliance, and association were used to name all vegetation communities mapped at MTRP. Several factors limited data collection at MTRP. Primarily these included species phenology and some areas with difficult access. Native or invasive plant species apparent at the time of the surveys were noted and classified. Due to differences in phenology of many species, plants apparent or obvious outside of the early spring survey period may not have been identified. References Andrea Bitterling, HELIX. 2009. Personal Communication, 2009. RECON. 2009. Report for Phase I of the Resource Management Plan for Mission Trails Regional Park, City of San Diego. Prepared for City of San Diego Parks & Recreation Department, January 7, 2009.

Groundwater monitoring data. Survey of San José Creek for aquatic fauna and habitat, including red-legged frogs (Rana aurora) and steelhead (Oncorhynchus mykiss). Map of pitch canker infection areas as of 12/1997. California DFG Natural Diversity Database printouts for species and ecosystems of special status. Color-coded maps of geologic features and plant succession. Unlabeled computer printouts of old aerial photos. Photos and list of mounds in grassy area by shore at Point Lobos SR. Handwritten notes with names of plants (presumably species found at various sites). Maps of Hickman’s onion (Allium hickmanii) distribution. Number-coded 1997 map of vegetation communities.

CWHR species range datasets represent the maximum current geographic extent of each species within California. Ranges were originally delineated at a scale of 1:5,000,000 by species-level experts more than 30 years ago and have gradually been revised at a scale of 1:1,000,000. Species occurrence data are used in defining species ranges, but range polygons may extend beyond the limits of extant occurrence data for a particular species. When drawing range boundaries, CDFW seeks to err on the side of commission rather than omission. This means that CDFW may include areas within a range based on expert knowledge or other available information, despite an absence of confirmed occurrences, which may be due to a lack of survey effort. The degree to which a range polygon is extended beyond occurrence data will vary among species, depending upon each species’ vagility, dispersal patterns, and other ecological and life history factors. The boundary line of a range polygon is drawn with consideration of these factors and is aligned with standardized boundaries including watersheds (NHD), ecoregions (USDA), or other ecologically meaningful delineations such as elevation contour lines. While CWHR ranges are meant to represent the current range, once an area has been designated as part of a species’ range in CWHR, it will remain part of the range even if there have been no documented occurrences within recent decades. An area is not removed from the range polygon unless experts indicate that it has not been occupied for a number of years after repeated surveys or is deemed no longer suitable and unlikely to be recolonized. It is important to note that range polygons typically contain areas in which a species is not expected to be found due to the patchy configuration of suitable habitat within a species’ range. In this regard, range polygons are coarse generalizations of where a species may be found. This data is available for download from the CDFW website: https://www.wildlife.ca.gov/Data/CWHR. The following data sources were collated for the purposes of range mapping and species habitat modeling by RADMAP. Each focal taxon’s location data was extracted (when applicable) from the following list of sources. BIOS datasets are bracketed with their “ds” numbers and can be located on CDFW’s BIOS viewer: https://wildlife.ca.gov/Data/BIOS. California Natural Diversity Database, Terrestrial Species Monitoring [ds2826], North American Bat Monitoring Data Portal, VertNet, Breeding Bird Survey, Wildlife Insights, eBird, iNaturalist, other available CDFW or partner data.

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

This raster dataset depicts a habitat suitability analysis using the California Department of Fish and Wildlife (CDFW) California Wildlife Habitat Relationships (CHWR) range and habitat suitability values conducted by the Conservation Lands Network (CLN) Team. CWHR provides habitat suitability values by vegetation type for wildlife species. The first step was to correlate, or crosswalk, the CWHR vegetation types with the San Francisco Bay Area Upland Habitat Goals Project Coarse Filter vegetation types so that CWHR habitat suitability values (the average suitability across all stages of each vegetation type) could be assigned to Upland Habitat Goals vegetation types. CWHR habitat suitability values range from 0 to 1, with higher values representing vegetation types of higher suitability for the target species. The Project Team created maps for twelve target species showing habitat suitability for the CWHR range, and CDFW California Natural Diversity Database and the University of California Berkeley Museum of Vertebrate Zoology records. A second map was derived by overlaying the Coarse Filter CLN on the first map to expose gaps in suitable habitat coverage. These maps helped the Project Team determine if the CLN was providing adequate coverage for the species that needed further review.

CWHR species range datasets represent the maximum current geographic extent of each species within California. Ranges were originally delineated at a scale of 1:5,000,000 by species-level experts more than 30 years ago and have gradually been revised at a scale of 1:1,000,000. Species occurrence data are used in defining species ranges, but range polygons may extend beyond the limits of extant occurrence data for a particular species. When drawing range boundaries, CDFW seeks to err on the side of commission rather than omission. This means that CDFW may include areas within a range based on expert knowledge or other available information, despite an absence of confirmed occurrences, which may be due to a lack of survey effort. The degree to which a range polygon is extended beyond occurrence data will vary among species, depending upon each species’ vagility, dispersal patterns, and other ecological and life history factors. The boundary line of a range polygon is drawn with consideration of these factors and is aligned with standardized boundaries including watersheds (NHD), ecoregions (USDA), or other ecologically meaningful delineations such as elevation contour lines. While CWHR ranges are meant to represent the current range, once an area has been designated as part of a species’ range in CWHR, it will remain part of the range even if there have been no documented occurrences within recent decades. An area is not removed from the range polygon unless experts indicate that it has not been occupied for a number of years after repeated surveys or is deemed no longer suitable and unlikely to be recolonized. It is important to note that range polygons typically contain areas in which a species is not expected to be found due to the patchy configuration of suitable habitat within a species’ range. In this regard, range polygons are coarse generalizations of where a species may be found. This data is available for download from the CDFW website: https://www.wildlife.ca.gov/Data/CWHR. The following data sources were collated for the purposes of range mapping and species habitat modeling by RADMAP. Each focal taxon’s location data was extracted (when applicable) from the following list of sources. BIOS datasets are bracketed with their “ds” numbers and can be located on CDFW’s BIOS viewer: https://wildlife.ca.gov/Data/BIOS. California Natural Diversity Database, Terrestrial Species Monitoring [ds2826], North American Bat Monitoring Data Portal, VertNet, Breeding Bird Survey, Wildlife Insights, eBird, iNaturalist, other available CDFW or partner data.

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo’package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence’package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif’format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].