Associating Seasonal Range Characteristics With Survival of Female White-Tailed Deer

ABSTRACT Delineating populations is critical for understanding population dynamics and managing habitats. Our objective was to delineate subpopulations of migratory female white-tailed deer (Odocoileus virginianus) in the central Black Hills, South Dakota and Wyoming, USA, on summer and winter ranges. We used fuzzy classification to assign radiocollared deer to subpopulations based on spatial location, characterized subpopulations by trapping sites, and explored relationships among survival of subpopulations and habitat variables. In winter, Kaplan-Meier estimates for subpopulations indicated 2 groups: high (S = 0.991 ± 0.005 [x̄ ± SE]) and low (S = 0.968 ± 0.007) weekly survivorship. Survivorship increased with basal area per hectare of trees, average diameter at breast height of trees, percent cover of slash, and total point-center quarter distance of trees. Cover of grass and forbs were less for the high survivorship than the lower survivorship group. In summer, deer were spaced apart with mixed associations among subpopulations. Habitat manipulations that promote or maintain large trees (i.e., basal area = 14.8 m²/ha and average dbh of trees = 8.3 cm) would seem to improve adult survival of deer in winter.     

Effects of Season and Scale on Response of Elk and Mule Deer to Habitat Manipulation

Abstract: Manipulation of forest habitat via mechanical thinning or prescribed fire has become increasingly common across western North America. Nevertheless, empirical research on effects of those activities on wildlife is limited, although prescribed fire in particular often is assumed to benefit large herbivores. We evaluated effects of season and spatial scale on response of Rocky Mountain elk (Cervus elaphus) and mule deer (Odocoileus hemionus) to experimental habitat manipulation at the Starkey Experimental Forest and Range in northeastern Oregon, USA. From 2001 to 2003, 26 densely stocked stands of true fir (Abies spp.) and Douglas-fir (Pseudotsuga menziesii) were thinned and burned whereas 27 similar stands were left untreated to serve as experimental controls. We used location data for elk and mule deer collected during spring (1 Apr-14 Jun) and summer (15 Jun-31 Aug) of 1999–2006 to compare use of treated and untreated stands and to model effects of environmental covariates on use of treated stands. In spring, elk selected burned stands and avoided control stands within the study area (second-order selection; large scale). Within home ranges (third-order selection; small scale), however, elk did not exhibit selection. In addition, selection of treatment stands by elk in spring was not strongly related to environmental covariates. Conversely, in summer elk selected control stands and either avoided or used burned stands proportional to their availability at the large scale; patterns of space use within home ranges were similar to those observed in spring. Use of treatment stands by elk in summer was related to topography, proximity to roads, stand size and shape, and presence of cattle, and a model of stand use explained 50% of variation in selection ratios. Patterns of stand use by mule deer did not change following habitat manipulation, and mule deer avoided or used all stand types proportional to their availability across seasons and scales. In systems similar to Starkey, manipulating forest habitat with prescribed fire might be of greater benefit to elk than mule deer where these species are sympatric, and thus maintaining a mixture of burned and unburned (late successional) habitat might provide better long-term foraging opportunities for both species than would burning a large proportion of a landscape.     

Effects of white-tailed deer on the population dynamics of acorns,seedlings and small saplings of Quercus buckleyi

To measure the effects of white-tailed deer (Odocoileus virginianus) herbivory on seeds, seedlings, and young saplings of Quercus buckleyi on the eastern Edwards Plateau of central Texas, USA, experimental fenced deer exclosures were constructed. Acorns or small Q. buckleyi transplants were placed in each exclosure and in each unfenced control plot. Deer did not significantly affect acorn survival and germination, but did significantly reduce transplant survival and growth rates. The results support the hypothesis that deer are responsible for the failure of recruitment into adult size classes in Q. buckleyi populations in this region. Without adult recruitment of Q. buckleyi, the species composition and possibly even the physiognomy of woodlands on the eastern Edwards Plateau will change markedly. The results of this experiment also indicate that, although juniper (Juniperus ashei) and Q. buckleyi presumably compete for water, light, and nutrients, in the presence of deer junipers can have a positive effect on seedlings and saplings of Q. buckleyi, a case of facilitation. In the presence of deer the transplants increased in height significantly more slowly away from juniper saplings than they did beneath juniper saplings, probably due to the physical protection from browsing that junipers provided to the transplants.     

内蒙古巴彦淖尔盟蒙古野驴的数量、分布和保护对策

20 0 0年 7月中旬至 8月上旬 ,大批蒙古野驴从蒙古共和国进入我国内蒙古自治区巴彦淖尔盟乌拉特中旗和乌拉特后旗境内。 2 0 0 0年 9月对该地区蒙古野驴的数量、分布和保护情况等进行了考察 ,发现蒙古野驴主要活动在巴彦淖尔盟乌拉特中旗和乌拉特后旗中蒙边境沿线的梭形狭长地带。蒙古野驴活动区东端最南点在乌拉特中旗敦德乌素嘎查 (42°1 7′N ,1 0 8°3 0′E) ,距边境 2 3km ,西端最南点在乌拉特后旗乌力吉图嘎查以西 (42°0 8′N ,1 0 6°3 6′E) ,距边境 1 2km。活动区中部的南限在北纬 42°0 1′,距正北方边境 44km ,活动区面积约 5 3 80km2 。根据样带法调查计算估计该分布区蒙古野驴总数约在 1 1 40 0头左右。目前蒙古野驴种群在该地区的分布尚未完全稳定。密集的人口 ,有限的食物资源和偷猎是影响蒙古野驴种群在该分布区稳定生存的主要因素     

Spotlight surveys for white-tailed deer: Monitoring panacea or exercise in futility?

Many monitoring programs for white-tailed deer (Odocoileus virginianus) on both private and public lands across the United States have long relied on the use of road-based spotlight surveys for monitoring population size and trends. Research has suggested spotlight surveys are ineffective and that road-based surveys for deer are biased because of highly variable detection rates. To evaluate variability in detection rates relative to the assumption that repeated surveys along roads will provide reliable trend data for use in calculating deer density estimates, we collected 5 years of thermal-imager and spotlight survey data using a multiple-observer, closed-capture approach. Using a Huggin's closed capture model, data bootstrapping, and variance components analyses, our results suggest that density estimates for white-tailed deer generated from data collected during road-based spotlight surveys are likely not reflective of the standing deer population. Detection probabilities during individual spotlight surveys ranged from 0.00 to 0.80 (median = 0.45) across all surveys, and differed by observer, survey, management unit, and survey transect replicate. Mean spotlight detection probability (0.41) and process standard deviation (0.12) estimates indicated considerable variability across surveys, observers, transects, and years, which precludes the generation of a correction factor or use of spotlight data to evaluate long-term trends at any scale. Although recommended by many state, federal, and non-governmental agencies, our results suggest that the benefit of spotlight survey data for monitoring deer populations is limited and likely represents a waste of resources with no appreciable management information gained. © 2012 The Wildlife Society.     

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

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.     

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

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.