Spatial distributions and resource selection by mule deer in an arid environment: Responses to provision of water

Self Cite

Climate models predict that shifts in temperature and precipitation patterns are likely to occur across the globe. Changing climate will likely have strong effects on arid environments as a result of increased temperatures, increasing frequency and intensity of droughts, and less consistent pulses of rainfall. Therefore, understanding the link between patterns of precipitation, temperature, and population performance of species occupying these environments will continue to increase in importance as climatic shifts occur within these natural ecosystems. We sought to evaluate how individual, maternal, population, and environmental, particularly temperature and precipitation, level factors influence population performance of a large herbivore in an arid environment. We used mule deer (Odocoileus hemionus) as a representative species and quantified juvenile survival to test hypotheses about effects of environmental factors on population performance. Precipitation events occurring in mid‐ to late‐pregnancy (January–April) leading to spring green‐up, as indexed by normalized difference in vegetation index, had the strongest positive effect on juvenile survival and recruitment. In addition, larger neonates had an increased probability of survival. Our findings indicate that timing and amount of precipitation prior to parturition have strong influences on maternal nutritional condition, which was passed on to young. These results have important implications for understanding how animal populations may benefit from timing of precipitation during spring and prior to parturition, especially in arid environments.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Timing of precipitation in an arid environment: Effects on population performance of a large herbivore

Heffelfinger

Stewart

Bush

et al. 2018

Self Cite

Selection of den sites and chronology of denning by black bears in the eastern Sierra Nevada and western Great Basin

Long,

Stewart,

Shoemaker

et al. 2024

Self Cite

Use of dens during winter is an important strategy for American black bears (Ursus americanus) for both energy conservation and reproduction; and occupancy of suitable den sites has implications for reproductive fitness. Denning strategies may change as a result of changing climatic conditions and habitat loss. Black bears occupy arid environments in the eastern Sierra Nevada and the western ranges of the Great Basin Ecosystem. Our objectives were to identify: (1) which physical characteristics of habitat influenced selection of den sites at multiple spatial scales and (2) which environmental factors influenced timing of entrance and exit of dens by females and males. We evaluated selection of den sites by black bears at three spatial scales (300, 1000, and 4000 m) from 2011 to 2022. Terrain ruggedness was important for selection of den sites at all spatial scales. Within a 300‐m buffer from the den, bears selected den sites with rugged terrain, lower horizontal visibility, and greater canopy cover, resulting in more concealment and protection than that of the surrounding environment. Within 1000‐ and 4000‐m buffers around each den, bears selected den sites with rugged terrain, northern aspects, and steep slopes. At the 4000‐m scale, we observed interactions between sex with slope and distance to roads; females selected den sites on steeper slopes and closer to roads than did males. Females remained in the dens longer than males by entering earlier in the autumn and exiting later in the spring. Male bears exited their dens earlier with increasing consecutive days above freezing temperatures, but that relationship was weak for females. Knowing what characteristics are important for selection of den sites, and influence timing of denning, will be important for understanding how shifting climatic patterns will affect bears, particularly in arid environments that may be prone to wider fluctuations in climatic drivers of denning in the future.

A machine‐learning approach for extending classical wildlife resource selection analyses

Shoemaker

Heffelfinger

Jackson

et al. 2018

Self Cite

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.