Wildlife-human interactions are increasing in prevalence as urban sprawl continues to encroach into rural areas. Once considered to be unsuitable habitat for most wildlife species, urban/suburban areas now host an array of wildlife populations, many of which were previously restricted to rural or pristine habitats. The presence of some wildlife species in close proximity to dense human populations can create conflict, forcing resource managers to address issues relating to urban wildlife. However, evidence suggests that wildlife residing in urban areas may not exhibit the same life history traits as their rural counterparts because of adaptation to human-induced stresses. This creates difficulty for biologists or managers that must address problems associated with urban wildlife. Population control or mitigation efforts aimed at urban wildlife require detailed knowledge of the habits of wildlife populations in urban areas. This paper describes the history of wildlife in urban areas, provides examples of wildlife populations that have modified their behavior as an adaptation to urban stresses, and discusses the challenges that resource managers face when dealing with urban wildlife.

BackgroundThe movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management.MethodsWe obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season.ResultsWe found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales.ConclusionsThe analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-017-0105-1) contains supplementary material, which is available to authorized users.

: As a first step in understanding structure and dynamics of white‐tailed deer (Odocoileus virginianus) populations, managers require knowledge of population size. Spotlight counts are widely used to index deer abundance; however, detection probabilities using spotlights have not been formally estimated. Using a closed mark—recapture design, we explored the efficiency of spotlights for detecting deer by operating thermal imagers and spotlights simultaneously. Spotlights detected only 50.6% of the deer detected by thermal imagers. Relative to the thermal imager, spotlights failed to detect 44.2% of deer groups (≥1 deer). Detection probabilities for spotlight observers varied between and within observers, ranging from 0.30 (SE = 0.053) to 0.66 (SE = 0.058). Managers commonly assume that although road counts based on convenience sampling designs are imperfect, observers can gather population‐trend information from repeated counts along the same survey route. Our results indicate detection rate varied between and within observers and surveyed transects. If detection probabilities are substantially affected by many variables, and if transect selection is not based on appropriate sampling designs, it may be impractical to correct road spotlight counts for detection probabilities to garner unbiased estimates of population size.

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Good-genes hypotheses predict that development of secondary sexual characters can be an honest advertisement of heritable male quality. We explored this hypothesis using a cervid model (adult, male white-tailed deer, Odocoileus virginianus) to determine whether antler development could provide an honest signal of a male's genetic quality and condition to adversaries. We compared antler, morphometric, hormonal, and parasitic data collected from hunter-harvested deer to characteristics of the Mhc-DRB (Odvi), the most widely studied gene of the major histocompatibility complex (MHC) in Artiodactyla. We detected associations between genetic characteristics at Odvi-DRB and antler development and body mass, suggesting that antler development and body mass may be associated with pathogen resistance in deer and thus may be an honest signal of genetic quality. We also detected associations between Odvi-DRB characteristics and serum testosterone during the breeding season, suggesting that certain MHC characteristics may help deer cope with stresses related to breeding activity. In addition, we observed a negative relationship between degree of antler development and overall abundance of abomasal helminths. Our observations provide support for the hypothesis that antler development in white-tailed deer is an honest signal of quality.