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.

This study measured the population dynamics of coyotes in the grasslands and creosote shrublands of McKenzie Flats, Sevilleta National Wildlife Refuge. The study was begun in January, 1992, and continued quarterly each year. Coyotes were sampled via scat counts along the roads of McKenzie Flats during winter, spring, summer, and fall of each year. The entire road transect was 21.5 miles in length. Scat counts over a week period (number of scats/mile/day) in each season along the roads were used to calculate the densities of coyotes (number of coyotes per square kilometer). Results from 1992 to 2002 indicated that autumn was the peak density period of the year, with generally steady declines through the year until the following autumn. Coyote populations appeared to fluctuate seasonally, but remained relatively stable at 0.27 +/- 0.03 (SE) coyotes per km2 during summer periods (this likely represents the "breeding pair" density, during which coyote pairs have set up territories and are raising young, but the pups have not as yet joined the parents in foraging activities).

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).

Eigenvalues, eigenvectors, and factor loadings of environmental factors assessed within home ranges of coyotes in Alabama, Georgia, and South Carolina of the United States.

Predator-prey interactions are important to regulating populations and structuring communities but are affected by many dynamic, complex factors, across larges-scales, making them difficult to study. Integrated population models (IPMs) offer a potential solution to understanding predator-prey relationships by providing a framework for leveraging many different datasets and testing hypotheses about interactive factors. Here, we evaluate the coyote-deer (Canis latrans – Odocoileus virginianus) predator-prey relationship across the state of North Carolina (NC). Because both species have similar habitat requirements and may respond to human disturbance, we considered net primary productivity (NPP) and urbanization as key mediating factors. We estimated deer survival and fecundity by integrating camera trap, harvest, biological and hunter observation datasets into a two-stage, two-sex Lefkovich population projection matrix. We allowed survival and fecundity to vary as functions of urbanization, NPP and coyote density and projected abundance forward to test eight hypothetical scenarios. We estimated initial average deer and coyote densities to be 11.83 (95% CI: 5.64, 20.80) and 0.46 (95% CI: 0.02, 1.45) individuals/km2, respectively. We found a negative relationship between current levels of coyote density and deer fecundity in most areas which became more negative under hypothetical conditions of lower NPP or higher urbanization, leading to lower projected deer abundances. These results suggest that coyotes could have stronger effects on deer populations in NC if their densities rise, but primarily in less productive and/or more suburban habitats. Our case study provides an example of how IPMs can be used to better understand the complex relationships between predator and prey under changing environmental conditions. Methods Survival and harvest rates: We used the dynamic N-mixture model of Zipkin et al. (2014) to estimate stage and sex-based survival and harvest rates from stage-at-harvest data collected statewide from 2012-2017 over all 100 counties of North Carolina. The stage-at-harvest data were collected by county each year for two stages for male deer (adults and fawns about to transition to adulthood (i.e., button bucks)) and does. We assumed that all button bucks were fawns and all females were adults. The census took place right before fawns transitioned to adulthood and we considered all fawns to reach adulthood at one year of age. Fawn:doe ratio: To represent hunted populations, we used 2017 hunter observation data from each county of NC. Hunters documented what species they observed on their hunts, given the number of hours they spent hunting, to get an index of abundance. The location of these observations was known only to the county level. Hunters were instructed to report their hunting activity even if no wildlife was observed (Fuller et al. 2018). For use in our model, we removed all observations made over bait and averaged observations of hunters that remained in the same hunting stand for multiple days instead of treating those days as independent samples. Litter size: To provide explicit information about litter size we used fertility data collected by the NCWRC. Fertility data (number of fetuses/doe) are recorded by a subset of hunters each year as part of biological data collection.

Global change is increasing the frequency and severity of human-wildlife interactions by pushing people and wildlife into increasingly resource-limited shared spaces. To understand the dynamics of human-wildlife interactions, and what may constitute human-wildlife coexistence in the Anthropocene, there is a critical need to explore the spatial, temporal, sociocultural, and ecological variables that contribute to human-wildlife conflicts in urban areas. Due to their opportunistic foraging and behavioral flexibility, coyotes (Canis latrans) frequently interact with people in urban environments. San Francisco, California, USA hosts a very high density of coyotes, making it an excellent region for analyzing urban human-coyote interactions and attitudes toward coyotes over time and space. We used a community-curated long-term data source from San Francisco Animal Care and Control to summarize a decade of coyote sightings and human-coyote interactions in San Francisco and to characterize spatiotemporal patterns of attitudes and interaction types in relation to housing density, socioeconomics, pollution and human vulnerability metrics, and green space availability. We found that human-coyote conflict reports have been significantly increasing over the past 5 years and that there were more conflicts during the coyote pup-rearing season (April-June), the dry season (June-September), and the COVID-19 pandemic. Conflict reports were also more likely to involve dogs and occur inside of parks, despite more overall sightings occurring outside of parks. Generalized linear mixed models revealed that conflicts were more likely to occur in places with higher vegetation greenness and median income. Meanwhile reported coyote boldness, hazing, and human attitudes toward coyotes were also correlated with pollution burden and human population vulnerability indices. Synthesis and applications: Our results provide compelling evidence suggesting that human-coyote conflicts are intimately associated with social-ecological heterogeneities and time, emphasizing that the road to coexistence will require socially-informed strategies. Additional long-term research articulating how the social-ecological drivers of conflict (e.g., human food subsidies, interactions with domestic species, climate-induced droughts, socioeconomic disparities, etc.) change over time will be essential in building adaptive management efforts that effectively mitigate future conflicts from occurring.

This study measured the population dynamics of coyotes in the grasslands and creosote shrublands of McKenzie Flats, Sevilleta National Wildlife Refuge. The study was begun in January, 1992, and continued quarterly each year. Â Coyotes were sampled via scat counts along the roads of McKenzie Flats during winter, spring, summer, and fall of each year. The entire road transect was 21.5 miles in length. Scat counts over a week period (number of scats/mile/day) in each season along the roads were used to calculate the densities of coyotes (number of coyotes per square kilometer). Results from 1992 to 2002 indicated that autumn was the peak density period of the year, with generally steady declines through the year until the following autumn. Coyote populations appeared to fluctuate seasonally, but remained relatively stable at 0.27 +/- 0.03 (SE) coyotes per km2 during summer periods (this likely represents the "breeding pair" density, during which coyote pairs have set up territories and are raising young, but the pups have not as yet joined the parents in foraging activities).

Human development has major implications for wildlife populations. Urban-exploiter species can benefit from human subsidized resources, whereas urban-avoider species can vanish from wildlife communities in highly developed areas. Therefore, understanding how the density of different species varies in response to landcover changes associated with human development can provide important insight into how wildlife communities are likely to change and provide a starting point for predicting the consequences of those changes. Here, we estimated the population density of five common mesocarnivore species (coyote (Canis latrans), bobcat (Lynx rufus), red fox (Vulpes vulpes), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana)) along an urban to rural gradient in the greater Fayetteville Area, Northwest Arkansas, USA between November 2021, and March 2022. At each study site, we applied the Random Encounter Model (REM) to data from motion-triggered cameras to calculate the density of our five focal species. Coyote density ranged from 0–3.47 with a mean of 0.4 individuals/km2. Raccoon density ranged from 0–93.26 with a mean of 4.2 individuals/ km². Bobcat density ranged from 0–8.87 with a mean of 0.33 individuals/km². Opossum density ranged from 0–27.35 with a mean of 0.76 individuals/km². Red fox density ranged from 0–0.73, with a mean of 0.02 individuals/km². We used generalized linear models to evaluate the density of each species against environmental and anthropogenic variables. Coyotes and raccoons occurred in the greatest densities in areas with high anthropogenic noise levels, suggesting that both species are synanthropic and able to co-exist in areas of high human activity. Alternatively, Virginia opossum and red fox densities were greatest in open, developed areas (lawns, golf courses, cemeteries, and parks) and were absent (red fox) or rare (opossum) in natural areas. We found no evidence that bobcat density varied along the urban to rural gradient studied, but this lack of evidence may have been driven by the small spatial scale of many of our sites in relation to space needs of this wide-ranging species. The density estimates we report based on game camera data of unmarked animals were consistent with reports from the literature for these same species derived from traditional methods, providing additional support to the REM as a viable, non-invasive method to calculate density of unmarked species.

Coyotes (Canis latrans) in urban landscapes provide important food web functions and ecological services but can also trigger human-wildlife conflict when their diet includes anthropogenic resources or domestic pets. As adaptable omnivores, coyotes adjust their diet to their environment, routinely switching among food items to accommodate spatial and seasonal differences in availability. To evaluate coyotes’ potential impacts within the food web of urban Long Beach, California where human-wildlife conflict involving coyotes may occur, we analyzed 115 scat samples collected once every two weeks from two open space fragments inside the urban matrix. We hypothesized that differences in scat composition would correlate with seasonal and site differences, with greater use of anthropogenic resources during the dry season supplementing lower prey availability, and greater consumption of wild mammal prey during the wet season when rabbits and small mammals reproduce. We found coyote diet was pr...

Carnivores are among the most conspicuous, charismatic and economically important mammals in shortgrass steppe, yet relatively is little is known about their populations or of the ecological factors that determine their distribution and abundance, in part because densities tend to be low. Mammalian carnivores represent the top predators in grassland food webs, consuming rodents, rabbits, young ungulates and other small vertebrates. In addition, shortgrass steppe is the primary habitat of the swift fox (Vulpes velox), a species of special conservation concern throughout most of its range. Fox populations are thought to be limited by predation from coyotes (Canis latrans), the most common carnivore in these grasslands and a species of interest, both for its ecological roles and well as a target species for human exploitation, ie hunting and predator control.

In 1994, we implemented a low-intensity sampling scheme to monitor long-term changes in relative abundance of mammalian carnivores and help us examine interactions between these predators and their small mammal prey, including rodents and rabbits. We estimated relative abundance of carnivores using scat surveys along a fixed route. Four times each year (January, April, July, October), we drove a 32-km route consisting of pasture two-track and gravel roads on the CPER. We first drove the route to remove all scats (‘PRE-census’); we then returned ~14 d later and counted the number of scats deposited on the route (‘CENSUS’). We recorded the species that deposited the scat and estimated the scat age based on external appearance (4 categories). Beginning in 1997, we recorded the vegetation (habitat) type and topographic position of all scat locations to describe habitat use. Latrines are indicated by locations containing multiple scats.

We used the ‘CENSUS’ data to calculate a scat index, defined as the number of scats deposited per km of road per night. The scat index can be used to estimate population density using equations for coyotes (Knowlton 1982) and swift foxes (Schauster et al. 2002) that described the rate of scat deposition from surveys where density was known. To estimate density and compare trends among seasons and years, we omitted scats collected along the 8.3 km of the route that occurred on gravel county roads. These roads are graded sporadically, sometimes between pre-census and census surveys, which tended to remove scats. (NOTE: these observations are NOT omitted in the dataset).

Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, contemporary approaches for modelling species interactions predominantly centre around occupancy states. Here, we use simulations to illuminate the inadequacies of modelling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to bias in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an occupancy-abundance model designed explicitly for modelling abundance-mediated species interactions involving two or more species. When modelling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modelling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=...). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83392

Carnivores are among the most conspicuous, charismatic and economically important mammals in shortgrass steppe, yet relatively is little is known about their populations or of the ecological factors that determine their distribution and abundance, in part because densities tend to be low. Mammalian carnivores represent the top predators in grassland food webs, consuming rodents, rabbits, young ungulates and other small vertebrates. In addition, shortgrass steppe is the primary habitat of the swift fox (Vulpes velox), a species of special conservation concern throughout most of its range. Fox populations are thought to be limited by predation from coyotes (Canis latrans), the most common carnivore in these grasslands and a species of interest, both for its ecological roles and well as a target species for human exploitation, ie hunting and predator control. In 1994, we implemented a low-intensity sampling scheme to monitor long-term changes in relative abundance of mammalian carnivores and help us examine interactions between these predators and their small mammal prey, including rodents and rabbits. We estimated relative abundance of carnivores using scat surveys along a fixed route. Four times each year (January, April, July, October), we drove a 32-km route consisting of pasture two-track and gravel roads on the CPER. We first drove the route to remove all scats (‘PRE-census’); we then returned ~14 d later and counted the number of scats deposited on the route (‘CENSUS’). We recorded the species that deposited the scat and estimated the scat age based on external appearance (4 categories). Beginning in 1997, we recorded the vegetation (habitat) type and topographic position of all scat locations to describe habitat use. Latrines are indicated by locations containing multiple scats. We used the ‘CENSUS’ data to calculate a scat index, defined as the number of scats deposited per km of road per night. The scat index can be used to estimate population density using equations for coyotes (Knowlton 1982) and swift foxes (Schauster et al. 2002) that described the rate of scat deposition from surveys where density was known. To estimate density and compare trends among seasons and years, we omitted scats collected along the 8.3 km of the route that occurred on gravel county roads. These roads are graded sporadically, sometimes between pre-census and census surveys, which tended to remove scats. (NOTE: these observations are NOT omitted in the dataset).

Data used in manuscript, Dynamic connectivity assessment for a terrestrial predator in a metropolitan region, containing coyote steps within the Greater Toronto Area, Canada with vegetation density (NDVI), impervious surface (building density), human population density, and distance to linear features extracted for each start and end step. Linear features labeled following traffic: low (LT), medium (MT), high (HT), and function hiking trails (NT) and public service (PS; (ie. railways and transmission lines).

Code Repository: https://github.com/tgelmi-candusso/Dynamic-connectivity-assessment-for-a-terrestrial-predator-in-a-metropolitan-region

Coyotes (n = 27; Figure 1) were monitored between 2012 and 2021 for 245 ± 136 days (mean ± standard deviation). Coyotes were live-trapped with padded foothold traps, approved by the Ontario Ministry of Natural Resources Wildlife Animal Care Committee (protocols 75-12, 75-13, 75-14 ) or captured with nets by the Toronto Wildlife Centre, and fitted with self-releasing GPS-collars (Lotek Wildcell SG, Newmarket, Canada), recording location data, resampled following the median sampling frequency in order to maintain a constant sampling frequency for each individual (1-3 hours; Appendix S1: Table S1, http://doi.org/10.1002/fee.2633 ). The data were well balanced in terms of demographic traits (12 females/15 males, 19 adults/eight juveniles, 22 residents/15 transients). Residents and transients were distinguished based on movement characteristics.

From consecutive GPS-collar locations, we calculated the turning angle and step length with the steps_by_burst() function from the R package amt (Signer et al. 2019). After fitting the distributions to observed step lengths and turning angles, we generated nine random available steps for each observed step using the random_steps() function from the R package amt (Signer et al. 2019). We standardized the fixed variables included in the model and extracted their values at the endpoint of each step.

The fixed variables included four urban landscape covariates: vegetation density (normalized difference vegetation index or NDVI), human population density, impervious surface, and linear features. To measure the spatiotemporal dynamic responses of coyotes, we included the interaction of the fixed variables with three temporal scales (diel cycles, biological seasons, and climate seasons) and three demographic traits (coyote age, sex, and social status).

More information on the data and how it was used available at http://doi.org/10.1002/fee.2633

Pima County diffuse movement areas: D1 – D21 (Wildlife movement within a wildland block) D1. Sierra Pinta/O’Neil Hills/Agua Dulce Mountains Wildland Blocks: Within Cabeza Prieta National Wildlife Refuge Species Identified: Bighorn sheep; Javelina; Mule deer; Sonoran pronghorn Current Threats/Barriers: None listed Notes: Within both Yuma County and Pima County D2. San Cristobal Valley/Antelope Hills Wildland Blocks: Within Cabeza Prieta National Wildlife Refuge Species Identified: Bighorn sheep; Javelina; Mule deer; Sonoran pronghorn Current Threats/Barriers: None listed Notes: Within both Yuma County and Pima County D3. Growler Valley Wildland Blocks: Within Barry M. Goldwater Air Force Range, Cabeza Prieta National Wildlife Refuge and Organ Pipe Cactus National Monument Species Identified: Bighorn sheep; Javelina; Mule deer; Sonoran pronghorn Current Threats/Barriers: Border activies; Military activities, OHV use (drug traffic and law enforcement) Notes: None listed D4. Childs Valley Wildland Blocks: Mostly within Cabeza Prieta National Wildlife Refuge; Childs/Little Ajo Mountains Species Identified: Bighorn sheep; Javelina; Mule deer; Sonoran pronghorn Current Threats/Barriers: None listed Notes: None listed D5. Bates Well Wildland Blocks: Within Cabeza Prieta National Wildlife Refuge and Organ Pipe Cactus National Monument Species Identified: Bighorn sheep; Javelina; Mule deer; Sonoran pronghorn Current Threats/Barriers: Border activities; OHV use Notes: None listed D6. Batamote/Pozo Redondo/Souceda/Sikort Chuapo Mountains Wildland Block Wildland Blocks: Within Batamote/Pozo Redondo/Souceda/Sikort Chuapo Mountains Wildland Block Species Identified: Bighorn sheep; Desert tortoise; Javelina; Mule deer, White-tailed deer Current Threats/Barriers: Agriculture (grazing); Border activities; OHV use (recreational, drug traffic, law enforcement); Pipeline (El Paso Gas expansion); Solar energy development Notes: Important areas include ridge line overlap and Burro Gap; A major road to aerial gunnery training goes through this area. Large portion of wildland block is within Tohono O’odham Nation. D7. Ajo Range/Barajita Valley Wildland Block Wildland Blocks: Within Ajo Range/Barajita Valley Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. Large portion of wildland block is within Tohono O’odham Nation. Portion of wildland block is within Mexico. D8. Gu Vo Hills/Mesquite Mountains/La Quituni Valley Wildland Block Wildland Blocks: Within Gu Vo Hills/Mesquite Mountains/La Quituni Valley Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. Wildland block is completely within Tohono O’odham Nation and Mexico. D9. Sierra Blanca/Brownell Mountains/Window Valley Wildland Block Wildland Blocks: Within Sierra Blanca/Brownell Mountains/Window Valley Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. Wildland block is completely within Tohono O’odham Nation. D10. Quijotoa Mountains/Quijotoa Valley Wildland Block Wildland Blocks: Within Quijotoa Mountains/Quijotoa Valley Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. Wildland block is completely within Tohono O’odham Nation. D11. Sells/Gu Oidak Valley Wildland Block Wildland Blocks: Within Sells/Gu Oidak Valley Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. Wildland block is completely within Tohono O’odham Nation. D12. Comobabi Mountains Wildland Block Wildland Blocks: Within Comobabi Mountains Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. Wildland block is completely within Tohono O’odham Nation. D13. Alvarez/Artesa Mountains/Baboquivari/Vamori Valley Wildland Block Wildland Blocks: Within Alvarez/Artesa Mountains/Baboquivari/Vamori Valley Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. Wildland block is completely within Tohono O’odham Nation and Mexico. D14. Baboquivari/Coyote/Quinlan Mountains Wildland Block Wildland Blocks: Within Baboquivari/Coyote/Quinlan Mountains Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. Large portion of wildland block is within Tohono O’odham Nation. Wildland block contains Coyote Mountains Wilderness, Baboquivari Peak Wilderness, Buenos Aires National Wildlife Refuge and extends into Mexico. D15. Silver Bell/Waterman Mountains/Samaniego Hills Wildland Block Wildland Blocks: Within Silver Bell/Waterman Mountains/Samaniego Hills Wildland Block Species Identified: Bighorn sheep; Desert Tortoise Current Threats/Barriers: Agriculture (grazing); Exotic species (buffelgrass); High density residential development; Industrial/commercial development; Mining (limestone); OHV use Notes: Mostly within Ironwood Forest National Monument. Partially within Tohono O’odham Nation. Patented mining claims have willing seller. D16. Roskruge Mountains Wildland Block Wildland Blocks: Within Roskruge Mountains Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was drawn on a map but without a datasheet. D17. Buenos Aires National Wildlife Refuge Wildland Block Wildland Blocks: Within Buenos Aires National Wildlife Refuge Wildland Block Species Identified: None listed Current Threats/Barriers: None listed Notes: Wildland block was indicated via email and based entirely on land ownership. D18. Sierrita Mountains Wildland Block Wildland Blocks: Within Sierrita Mountains Wildland Block Species Identified: Javelina; Mountain lion; White-tailed deer Current Threats/Barriers: Agriculture (grazing); Industrial/commercial development; Low density residential development; Mining; OHV use Notes: Wildland block partially within Buenos Aires National Wildlife Refuge. The rest of the wildland block has low land stewardship status. D19. Tumacacori/San Luis Mountains Wildland Block Wildland Blocks: Within Tumacacori/San Luis Mountains Wildland Block Species Identified: Jaguar Current Threats/Barriers: High density residential development; High traffic gravel road; Low density residential development; Paved roads; Powerlines Notes: Wildland block mostly within Coronado National Forest but includes habitat outside boundaries. D20. Mission Mine Wildland Block Wildland Blocks: Within Mission Mine Wildland Block Species Identified: None listed Current Threats/Barriers: High traffic gravel road (Batamote Road); Mining Notes: Wildland block is mostly within Tohono O’Odham Nation (San Xavier). Copper mining exists within the wildland block (Mission Mine). This mine may create habitat for certain species. D21. Santa Rita Experimental Range/Coronado National Forest Wildland Block Wildland Blocks: Within Santa Rita Experimental Range/Coronado National Forest Wildland Block Species Identified: None listed Current Threats/Barriers: Mining (proposed Rosemont Mine) Notes: Wildland block was digitized based on land ownership. Pima County, Arizona State Land Department, University of Arizona and US Forest Service support lands in this wildland block. Pima County landscape movement areas: L1 – L40 (Wildlife movement between wildland blocks) L1. Crater Range/Childs/Little Ajo Mountains to Batamote/Sauceda Mountains Area Connected: Batamote/Pozo Redondo/Souceda Mountains Wildland Block – Crater Range/Childs/Little Ajo Mountains within Barry M. Goldwater Air Force Range and