https://cpw.cvlcollections.org/files/original/e621ebf5e7fdc07b356a348a6a523368.pdf 9e9726e4f9ba572b5078654d395d633c PDF Text Text The research in this publication was partially or fully funded by Colorado Parks and Wildlife. Dan Prenzlow, Director, Colorado Parks and Wildlife • Parks and Wildlife Commission: Marvin McDaniel, Chair • Carrie Besnette Hauser, Vice-Chair Marie Haskett, Secretary • Taishya Adams • Betsy Blecha • Charles Garcia • Dallas May • Duke Phillips, IV • Luke B. Schafer • James Jay Tutchton • Eden Vardy �Diversity and Distributions A Journal of Conservation Biogeography Diversity and Distributions, (Diversity Distrib.) (2016) 22, 547–557 BIODIVERSITY RESEARCH Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid Joseph M. Northrup1*, Charles R. Anderson Jr2 and George Wittemyer1,3 1 Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA, 2 Mammals Research Section, Colorado Parks and Wildlife, Fort Collins, CO, USA, 3 Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA ABSTRACT Aim The space an animal uses over a given time period must provide the resources required for meeting energetic needs, reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology, conservation and management. Location The Piceance Basin, Colorado, USA. Methods We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages second-order properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage), individual and anthropogenic factors on interannual variation in range use of individual deer using a hierarchical Bayesian regression framework. Results Mule deer demonstrated remarkable spatial philopatry, with a median of 50% overlap (range: 8–78%) in year-to-year utilization distributions. Environmental conditions were the primary driver of both philopatry and range size, with anthropogenic disturbance playing a secondary role. Main conclusions Philopatry in mule deer is suspected to reflect the impor- *Correspondence: Joseph M. Northrup, Department of Fish, Wildlife and Conservation Biology, Colorado State University, Campus Delivery 1474, Fort Collins, CO, USA, 80523. E-mail: joe.northrup@gmail.com tance of spatial familiarity (memory) to this species and, therefore, factors driving spatial displacement are of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategies. Keywords animal movement, energy development, home range, Odocoileus hemionus, utilization distribution The space an animal uses must contain all of the requisite resources for survival and reproduction during a given period. Thus, information on the drivers of space-use provides valuable insight into animal ecology. How animals use space is fundamental to their social structure (Vonhof et al., 2004) and habitat selection (Johnson, 1980), as well as broader ecological and evolutionary processes, including ecosystem stability (Makarieva et al., 2005) and the adaptive potential of populations (Stiebens et al., 2013). Furthermore, anthropogenic perturbations can alter space-use (Sawyer et al., 2006; Webb et al., 2011; Northrup et al., 2015), with impacts to fitness, and population dynamics (Dzialak et al., 2011). ª 2016 John Wiley & Sons Ltd DOI: 10.1111/ddi.12417 http://wileyonlinelibrary.com/journal/ddi INTRODUCTION 547 �J. M. Northrup et al. Information on these perturbations is essential to conservation and management and facilitates prediction of responses to global environmental change. The advent of global positioning system (GPS) radio collars and the proliferation of their use in ecological studies (Cagnacci et al., 2010) have provided unprecedented ability to examine space-use. However, generated data have a high degree of spatial and temporal dependence that violates assumptions of traditional methods, particularly home range estimators. Recent advances in movement modelling provide new avenues for exploring range dynamics using high-resolution GPS data (e.g. Wall et al., 2014; Fleming et al., 2015). Such methods that incorporate the movement process into estimates of spatial distribution allow for statistically robust application of high-resolution movement data. These approaches can provide detailed representations of space-use, allowing improved inference on the spatial factors influencing behaviour. Two aspects of space-use dynamics that are of primary importance to animal ecology are fidelity to annual and seasonal ranges (philopatry), and range size. There is ample theory supporting the evolutionary benefits of philopatry at broad and fine scales. Memory or learning that enhances knowledge of forage resources is important in optimal foraging (Eliassen et al., 2009; Olsson & Brown, 2010; Berger-Tal & Avgar, 2012), where the efficiency of searches increases relative to experience with successful foraging sites (Benhamou, 1994). Motor learning allows for animals to better avoid predators and discourage intruders (Stamps, 1995). In addition, philopatry is expected to vary with the predictability and heterogeneity of habitat, the cost of changing ranges, age and life expectancy (Switzer, 1993). The empirical literature demonstrates the broad propensity for philopatric behaviour across a range of species (Phillips et al., 1998; Lesage et al., 2000; Dalerum et al., 2007; Webb et al., 2011; Morrison & Bolger, 2012), indicating the widespread importance of this strategy. Although some species show high philopatry regardless of variation in environmental or anthropogenic factors (e.g. Lesage et al., 2000; Tracz et al., 2010), variation in philopatry has been related to breeding status (Morrison & Bolger, 2012), population density (Lesage et al., 2000) and natural and anthropogenic disturbance (Linnell & Andersen, 1995; Faille et al., 2010; Webb et al., 2011). Intraspecific variation in range size often is examined under the framework of optimal foraging theory. This theory predicts that animals will maximize energy intake while minimizing movement (Charnov, 1976; Pyke et al., 1977), and thus individuals in areas of greater forage should use smaller areas. Empirical studies have provided validation for these theoretical underpinnings in roe deer (Capreolus capreolus; Tufto et al., 1996; Kjellander et al., 2004; Said & Servanty, 2005), red deer (Cervus elaphus; Rivrud et al., 2010) and moose (Alces alces; van Beest et al., 2011). These predictions are complicated during different life history stages, and by inter- and intraspecific interactions (Fretwell & Lucas, 1969; Brown et al., 1999; Frid & Dill, 2002). Accordingly, range 548 size has been found to vary with factors not directly related to forage, including reproductive status (Said et al., 2005; van Beest et al., 2011), age (Said et al., 2009), climate and weather (Sweanor & Sandegren, 1989; Rivrud et al., 2010; van Beest et al., 2011), population density (Tufto et al., 1996), landscape heterogeneity (Kie et al., 2002), and anthropogenic development (Walter et al., 2009; Faille et al., 2010; Webb et al., 2011). Anthropogenic disturbance alters habitat selection (Northrup et al., 2012), and displaces animals (Linnell & Andersen, 1995; Stephenson et al., 1996; Sawyer et al., 2006; Webb et al., 2011). Such displacement can impact philopatry and range size, but studies of this process have been infrequent and show equivocal results (e.g. Edge et al., 1985; Tracz et al., 2010). Assessing the impacts of anthropogenic disturbance informs our understanding of how animals perceive these stressors and can provide insight into the resilience of populations that might be otherwise elusive; for example, high site philopatry in the face of declining habitat quality might increase the vulnerability of populations (Faille et al., 2010). In North America, anthropogenic disturbance related to hydrocarbon extraction has increased rapidly in recent years, leading to displacement of wildlife (Sawyer et al., 2006; Dzialak et al., 2011; Harju et al., 2011; Webb et al., 2011). Mule deer (Odocoileus hemionus Raf.) populations have experienced dramatic declines across their range over a similar time period (Unsworth et al., 1999). Displacement resulting from development has recently been identified as a potentially aggravating factor (e.g. Sawyer et al., 2006). Mule deer are recognized to be highly philopatric at broad scales (i.e. at the study area level across years and seasons Robinette, 1966; Garrott et al., 1987). Thus, their space-use dynamics are relevant to management and offer insight into their susceptibility to disturbance from landscape alterations. Our objectives were to assess the drivers of philopatry and range size in female mule deer on their summer and winter ranges. Building from the above literature, we made the following predictions: (1) deer would use smaller areas with greater availability of forage and would show lower philopatry with greater annual differences in forage, (2) deer would use smaller areas and show greater philopatry when they had a greater access to thermal and predatory cover, (3) older animals would use smaller areas and display greater philopatry, (4) animals in better condition would use smaller areas and show greater philopatry, and (5) anthropogenic disturbance would displace deer, causing them to use larger ranges and show lower philopatry. METHODS Study area The study was conducted in the Piceance Basin in north-west Colorado (Fig. 1). The area is comprised of a pinyon pine (Pinus edulis Engelm.) and Utah Juniper (Juniperus osteosperma Torr.) shrubland complex with high topographic Diversity and Distributions, 22, 547–557, ª 2016 John Wiley & Sons Ltd �Space-use dynamics in a North American cervid thickness of subcutaneous rump fat and the longissimus dorsi muscle were measured using a portable ultrasound (Stephenson et al., 1998, 2002; Cook et al., 2001), and age was estimated (Robinette et al., 1957; Hamlin et al., 2000). The BCS and fat measurements were used to calculate the percentage ingesta-free body fat (hereafter fat) of each deer following Cook et al. (2010). Each individual was fit with a GPS radio collar (G2110D; Advanced Telemetry Systems, Isanti, MN, USA) set to attempt a relocation on one of two schedules: (1) hourly between September 1 and June 30 and once every two hours between July 1 and August 31 (deer captured in December 2010 and March 2011), and (2) every 30 min between September 1 and June 15 and hourly between June 16 and August 31 (deer captured after March 2011). Different duty cycles were used because of a change in the battery life of collars. Following recapture, death or collar release, GPS data were downloaded. We censored data potentially impacted by capture and removed data from deer that died (Northrup et al., 2014; Appendix S1), and examined the remaining data for outliers (Appendix S1). The remaining data were categorized as being on winter or summer range, while data during migration were excluded. Migrations were determined visually in ARCMAP 10.1 (Environmental Systems Research Institute, Redlands, CA, USA). Estimation of space-use Figure 1 Location of study area, nearest town (Meeker, Colorado) and outlines of summer and winter mule deer distribution. Underlying grey scale represents elevation. diversity. For a detailed description of the vegetation of the area, see Bartmann & Steinert (1981) and Bartmann et al. (1992). The dominant anthropogenic activity was natural gas development with drilling activity declining throughout the study (density of drill rigs = 0.02 km�1 in 2011, 0.007 km�1 in 2012 and 0.002 km�1 in 2013). The study was focused on the Ryan Gulch winter range area and the corresponding Roan Plateau summer range (Fig. 1). Deer in this area are migratory. Deer data Between December 2010 and December 2013, we captured and subsequently recaptured adult (> 1 year old) female mule deer using helicopter net gunning. Fifty individual deer were tracked for multiple years (1–3 years; see Appendix Table S1.1 in ‘Supporting Information’ for details). All procedures were approved by the Colorado State University (protocol ID: 10-2350A) and Colorado Parks and Wildlife (protocol ID: 15-2008) Animal Care and Use Committees. For more details on capture procedure, see Northrup et al. (2014). Upon capture, deer were weighed, a body condition score (BCS) was estimated (Cook et al., 2001, 2007, 2010), the Diversity and Distributions, 22, 547–557, ª 2016 John Wiley & Sons Ltd A continuous-time correlated random walk model (Johnson et al., 2008a,b) was fit to data from each individual deer, year and season (summer or winter) combination using the ‘CRAWL’ package (Johnson et al., 2008b) in the R statistical software (R Core Team, 2013). This model represents an Ornstein–Uhlenbeck process and can be used to estimate the probability of an animal being at any location at any point during a sampling period given its recorded positions. Extended over a specified time period and area, these probabilities can be combined to produce a utilization distribution (UD). This model incorporates autocorrelation in movement into the predictions of space-use thereby allowing for incorporation of behavioural dynamics that are clustered in time. Further, variance in the estimates of locations is directly incorporated into the UD, addressing concerns over uncertainty in the UD itself (as discussed by Fieberg et al., 2005). Although theoretically the above UDs are continuous in space and time, in practice both the sampling area and interval are discrete. Thus, deer locations were predicted for every minute between the first and last location in each dataset, and the probability of use was summed over a 5 m grid and weighted to ensure the resulting UDs summed to 1. A sensitivity analysis was conducted to determine the optimal cell size and sampling interval (see Appendix S2 in ‘Supporting Information’). Following UD estimation, three metrics related to range size and philopatry were calculated. For the first two metrics, we calculated the area of the smallest polygons containing 549 �J. M. Northrup et al. 50% and 99% of the density of the UDs (hereafter the 50% and 99% highest density ranges). The third metric was the overlap of the UDs coming from any 2 years for which we had deer data. The overlap metric was calculated as: I P overlap ¼ i¼1 I P UD1i \ UD2i ; (1) UD1i [ UD2i i¼1 where I represents the number of cells over which the UDs were calculated (see Appendix Figure S2.2 for illustrative example). The result is a value ranging from 0 (no overlap) to 1 (complete overlap with identical probabilities in each cell). All analyses were conducted using the R statistical software. Factors influencing overlap and range size To address our predictions, we fit a series of regression models with the overlap and size metrics as the response variables. In addition to the covariates for age and condition, we created a set of spatial covariates related to forage availability (snow depth during winter and normalized difference vegetation index [NDVI]), thermal and predatory cover (treed land cover and terrain ruggedness), and a suite of anthropogenic features (see Appendix S3 and Appendix Table S3.1 in ‘Supporting Information’). For the overlap analysis, the outlines of the ranges for the 2 years of interest were merged and the annual differences in covariate values were calculated (for covariates that varied across years). In the analysis of range size, by chance the number of facilities and well pads will increase as the area used increases, causing an artefactual correlation between the number of pads and range size. Although formulating these covariates as density can theoretically ameliorate these issues, all deer had between 1 and 3 pads or facilities in their range, causing an artefactual negative relationship between range size and density. Thus, these covariates were excluded, although the density of pipelines and roads was maintained. The body fat of deer was estimated from the regression equation presented in Cook et al. (2010) and thus has uncertainty associated with each estimate. To incorporate this uncertainty into our models, we estimated the true fat within the model, putting a normal prior on the true fat with mean equal to the observed fat and standard deviation as presented in Cook et al. (2010; see Appendix S4 in ‘Supporting Information’). For the overlap analysis, uncertainty was incorporated into the fat measures from each year separately and then the difference between years was calculated at each iteration in the algorithm presented below. Models were fit to the overlap and size metrics using beta and gamma regression, respectively, in a Bayesian framework in R and JAGS using the ‘RJAGS’ package (Plummer, 2012). Because there were multiple years of data from individual deer, models were fit with intercepts varying by individual 550 (Appendix S4). All continuous covariates other than fat were � x� standardized x�� and pairwise correlations among all covarir ates were calculated. For the overlap models, there was high correlation among the change in producing and drilling well pads across years (r > 0.8), because wells transition from drilling to production and thus a decline in drilling must be followed by an increase in production. As these covariates were measuring the same process, we chose only to test the effect of drilling, as this is presumably the most disturbing period of development. We next tested for multicollinearity using condition numbers following the guidance of Lazaridis (2007). Following the above tests, we fit a series of models incorporating all combinations of covariates that were correlated at less than |0.7|. We intended to test global models, but had numerous correlated covariates, as well as multiple representations of snow, NDVI and road density. Thus, we fit a series of models with combinations of covariates that were not highly correlated (Appendix Tables S4.2, S4.4, S4.6, S4.8, S4.10, S4.12). The Watanabe–Akaike information criteria (Watanabe, 2010), asymptotically equivalent to leave-one-out cross-validation and appropriate for hierarchical Bayesian models (Gelman et al., 2013; Hooten & Hobbs, 2014), were used to compare models. Each algorithm was run for 125,000 iterations, discarding the first 25,000 as burn-in, to construct posterior distributions for each parameter. Two chains were obtained for each model, using starting values that were expected to be overdispersed relative to the posterior distribution, and convergence was assessed using the Gelman–Rubin diagnostic (Gelman & Rubin, 1992) and by examining trace plots of each parameter. Lastly, we performed posterior predictive checks on the best models (Gelman & Hill, 2007; Appendix Table S4.1). RESULTS Utilization distribution overlap We estimated ranges for 49 deer across the three-year study period equating to 106 winter range estimates and 99 summer range estimates (Appendix Table S1.1). All tracked deer returned to the same general area on both summer and winter range in all years. Overlap values of UDs for both seasons were nearly identical for ranges separated by 1 year and 2 years (1 year winter �x = 0.29 and 2 years �x = 0.32, Wilcox rank sum test W = 830, P = 0.31; 1 year summer �x = 0.49, and 2 years �x = 0.48, W = 608, P = 0.88). There was greater overlap in mule deer UDs during summer (median = 0.50 overlap, range: 0.10–0.78) than winter (median = 0.31, range: 0.08–0.49; W = 6375, P < 0.0001). Posterior predictive checks indicated relatively low discrepancy between the predicted and real data (Appendix Table S4.1). The overlap of the UDs during summer declined weakly with an increase in the number of well pads with active drilling, while increasing with the proportion of the range comprised of treed land cover (Table 1). During winter, overlap was negatively related to the density of natural gas facilities Diversity and Distributions, 22, 547–557, ª 2016 John Wiley & Sons Ltd �Space-use dynamics in a North American cervid Table 1 Covariates, median coefficient estimates (coeff.) and proportion of posteriors (prop.) falling above and below 0 for beta regression models fit to the biannual overlap (degree of philopatry) in the utilization distributions during summer and winter for female mule deer in the Piceance Basin of north-west Colorado, USA. Descriptions of all covariates can be found in Appendix S3 and Appendix Table S3.1. (Fig. 2), the difference in the average NDVI between years, the difference in total depth of snowfall between years, the difference in pipeline density between years and the proportion of the range comprised of treed land cover (Table 1, Fig. 2). Range size Covariates* Summer Overall intercept Tree Difference in density of drilling well pads Difference in average NDVI Density of major roads Terrain ruggedness Fat Age Winter Overall intercept Density of natural gas facilities Difference in total snow depth Tree Difference in average NDVI Difference in density of pipelines Age Difference in density of drilling well pads Difference in fat Density of all roads Median coeff. Prop. < 0 Prop. > 0 �0.04 0.13 �0.05 0.59 0.09 0.73 0.41 0.91 0.27 �0.03 �0.03 �0.03 �0.01 0.00 0.68 0.62 0.60 0.63 0.49 0.32 0.38 0.40 0.37 0.51 �0.85 �0.17 �0.12 �0.07 �0.06 �0.06 �0.03 �0.02 1.00 1.00 0.99 0.85 0.96 0.90 0.65 0.68 0.00 0.00 0.01 0.15 0.04 0.10 0.35 0.32 �0.01 �0.01 0.66 0.56 0.34 0.44 *See Appendix S3 and Appendix Table S3.1 for descriptions of covariates. Deer used a greater overall area during the winter (99% size �x = 6.3, range: 1.61–19 km2) than summer (99% size �x = 2.27, range: 0.6–7 km2; Wilcox rank sum test W = 1203, P < 0.0001) and the area they used most intensively during the winter (50% size �x = 0.77, range: 0.18–1.53 km2) also was greater than during summer (50% size �x = 0.25, range: 0.08–0.65 km2; Wilcox rank sum test W = 11868, P < 0.0001). Posterior predictive checks indicated that there was low discrepancy between predicted and real data during the winter, but during the summer, the squared deviance residuals of the real data were greater than those of the simulated data for a large proportion of the MCMC iterations (Appendix Table S4.1). These results likely indicate that the model was not adequately capturing the variance in the data during the summer. The 99% summer range size was positively related to the body fat of deer in the following December and the density of pipelines and negatively related to terrain ruggedness, age, the proportion of the range comprised of treed land cover and the peak NDVI (Table 2; Fig. 3). Similarly, the size of the 50% summer range was positively related to the fat of the deer in the following December and negatively related to deer age, terrain ruggedness, the density of pipelines and the average NDVI of the range (Fig. 3; Table 2). Figure 2 Predictions of range overlap (degree of philopatry) with variation in (a) the density of gas and other facilities during winter and (b) the absolute difference in total depth of snowfall between years during winter for mule deer in the Piceance Basin, north-west Colorado, USA. Gradient represents the density of the posterior predicted values. Diversity and Distributions, 22, 547–557, ª 2016 John Wiley & Sons Ltd 551 �J. M. Northrup et al. Table 2 Covariates, median coefficient estimates (coeff.) and proportion of posteriors (prop.) falling above and below 0 for gamma regression models fit to the size of the 99 and 50 percent highest density ranges during summer and winter for female mule deer in the Piceance Basin of north-west Colorado, USA. Descriptions of all covariates can be found in Appendix S3 and Appendix Table S3.1. Covariates* Summer 99% Overall intercept Terrain ruggedness Age Maximum NDVI Tree Density of pipelines Fat Density of all roads Summer 50% Overall intercept Terrain ruggedness Average NDVI Density of pipelines Age Fat Tree Density of major roads Winter 99% Overall intercept Density of pipelines Terrain ruggedness Tree Total snow fall Density of all roads Age Average NDVI Fat Winter 50% Overall intercept Terrain ruggedness Total snow fall Density of major roads Tree Age Maximum NDVI Fat Median coeff. Prop. < 0 Prop. > 0 14.12 �0.10 �0.08 �0.06 �0.06 0.05 0.04 0.02 0 0.94 0.92 0.88 0.82 0.17 0.00 0.37 1 0.06 0.08 0.12 0.18 0.83 1.00 0.63 12.05 �0.08 �0.07 �0.07 �0.06 0.03 0.03 0.03 0.00 0.95 0.98 0.93 0.93 0.00 0.24 0.24 1.00 0.05 0.02 0.07 0.07 1.00 0.77 0.76 15.48 �0.22 �0.18 0.17 0.06 0.05 �0.04 �0.02 0.01 0.00 1.00 0.98 0.01 0.21 0.24 0.77 0.67 0.26 1.00 0.00 0.02 0.99 0.79 0.76 0.23 0.34 0.74 9.60 �0.17 �0.05 �0.04 0.04 �0.03 �0.03 0.02 0.00 1.00 0.82 0.84 0.20 0.78 0.74 0.02 1.00 0.00 0.18 0.16 0.80 0.22 0.26 0.98 *See Appendix S3 and Appendix Table S3.1 for descriptions of covariates. The 99% winter range size was negatively related to terrain ruggedness and the density of pipelines, while positively related to the proportion of the range comprised of treed land cover and weakly positively related to snow depth (Table 2). The size of the 50% winter range was negatively related to terrain ruggedness, and weakly negatively related to snow depth and the density of major roads, while positively related to the proportion of the range comprised of treed land cover (Table 2). Similar to summer, fatter deer also had larger 50% highest density winter ranges. 552 DISCUSSION Range philopatry An understanding of space-use and philopatry is fundamental to animal ecology. Animals are philopatric for numerous reasons, including foraging benefits of memory and learned resource locations (Benhamou, 1994; Eliassen et al., 2009), and enhanced predator avoidance (Stamps, 1995). Mule deer in our study were highly philopatric during both seasons, highlighting the importance of spatial familiarity to this species. Deer displayed greater philopatry during summer than winter, which fits with theory on this topic and deer biology. During summer, energy acquisition is the primary driver of behaviour as deer birth and rear fawns, an energetically costly activity, and accrue fat for winter (Tollefson et al., 2010). During this time, deer exhibited high philopatry within a small, intensively used space. The high philopatry during summer might also be related to past success in raising fawns in an area, although we note that fawn survival is low (Pojar & Bowden, 2004). During winter, temporal and spatial variation in snow makes the landscape more dynamic and deer are primarily concerned with energy conservation (Torbit et al., 1985). Philopatry in winter was lower than in summer as predicted by theory (Switzer, 1993). At this time, fawns also are more mobile, which might further influence the lower degree of philopatry during winter. Deer also displayed fine-scale variation in philopatry in accordance with a number of our predictions. As predicted, during winter, philopatry decreased with larger differences in forage availability, measured by snow and NDVI. We posit that these predictions did not hold during summer because forage availability is consistently high (i.e. productivity is substantial even in relatively poor years). Rather, deer displayed greater philopatry where ranges had greater cover during summer. Given that predation risk for fawns is high during this time (Pojar & Bowden, 2004), consistent access to familiar cover might be more important than during winter, reflecting seasonal differences in the degree of philopatry. The predicted relationship between philopatry did not hold during winter, potentially because deer tend to forage in open areas on winter range (Northrup et al., 2015) and thus are structuring philopatry around forage resources. Contrary to predictions, age and condition did not influence philopatry, although both influenced range size (see Discussion below). We interpret this as indicating that environmental factors have an overriding influence on philopatry. Lastly, and critical for the management and conservation of this species, anthropogenic development influenced philopatry, although differently than predicted. There was lower philopatry during the winter on ranges with more industrial facilities, and when pipeline density increased. During summer, there was a weak negative relationship between philopatry and increases in drilling activity. These results suggest development locally displaced deer. The strong response to facilities could be an issue of management Diversity and Distributions, 22, 547–557, ª 2016 John Wiley & Sons Ltd �Space-use dynamics in a North American cervid et al., 2011). In the light of the apparent importance of philopatric space-use strategies to deer, our results provide an example of how anthropogenic development and land use changes could alter a fundamental behaviour evolved to enhance deer foraging success and predator avoidance, although the level of disturbance we observed did not appear to invoke demographic consequences because this population increased during our investigations (Anderson, 2014). Range size Figure 3 Predictions of range size (km2) against normalized difference vegetation index (NDVI) for the respective season and range size for mule deer in the Piceance Basin, north-west Colorado, USA. Gradient represents the density of the posterior predicted values. concern. Facilities are some of the busiest and loudest features associated with development and also comparatively permanent on the landscape relative to well pad activity, which declines over time, and, accordingly, appear to be more disruptive to deer range dynamics. While it is recognized that deer avoid well pads (Sawyer et al., 2006; Northrup et al., 2015), we found relatively weak impacts of these features on philopatry. This result may be a function of the low rate of development activity during the study or the ability of deer to ameliorate impacts through fine-scale adjustments. Interestingly, there is comparatively little information in the literature on the response of ungulates to natural gas facilities and our results provide evidence that these may be more disruptive to deer behaviour than well pads. Identification of the factors driving philopatry in our study area aligns generally with our understanding of deer behaviour and biology. Deer rely heavily on the use of wellknown areas. Such behaviour is expected to be selected for when heterogeneity in sites is low and habitat is predictable (Switzer, 1993), supporting our findings that departures from high philopatry occur because of environmental or humaninduced landscape dynamics. Importantly, changes in philopatric strategies might occur only after a time-lag (Switzer, 1993), meaning impacts of anthropogenic changes on the landscape may be stronger than found here. Although development activity is not currently high enough to cause abandonment of ranges (e.g. as seen by Sawyer et al., 2006), they influence deer habitat selection (Northrup et al., 2015) and our current findings showed that some features elicited reduced use of the previous year’s range. In other systems, displacement from hydrocarbon development potentially has led to reduced survival and reproduction in elk (Dzialak Diversity and Distributions, 22, 547–557, ª 2016 John Wiley & Sons Ltd Optimal foraging theory provides a useful framework for understanding range size dynamics. In areas of higher productivity, animals are expected to use smaller areas (Charnov, 1976; Pyke et al., 1977). Deer in our study largely adhered to this prediction, with productivity being an important determinant of range size. Deer used substantially smaller areas during summer, when range quality is higher, than during winter. During summer, deer also used smaller areas when productivity was greater, although range size and productivity relationships changed during the low productivity winter. These results resemble findings for other ungulate species (Tufto et al., 1996; Rivrud et al., 2010; van Beest et al., 2011), supporting the generality of the range size–productivity relationship. The lack of a strong relationship during winter likely reflects the consistently low-quality forage available on winter range and the focus of deer on energy conservation over forage intake (Wallmo et al., 1977). During all seasons and for both the 99% and 50% ranges, deer in areas with greater terrain ruggedness had smaller ranges as predicted. While we used terrain ruggedness as a measure of predatory cover, more rugged terrain likely requires greater energetic expenditure to traverse and thus could lead to deer using smaller ranges. During the winter, deer increased the size of their 99% ranges when snow was deeper, as predicted, but decreased the size of their 50% range. We posit that a reduction in the size of the core area in response to deeper snow is related to employment of an energy conservation strategy characterized by less movement. Deer also showed contrasting responses to treed habitat (increasing the 99% range with more treed habitat during winter, and decreasing the 99% range during summer and 50% range during winter). During the winter, deer in this area use treed habitat primarily for cover during the day (Northrup et al., 2015), and thus more treed habitat in their range could drive the need for more area to access foraging resources. During summer, forage resources are readily available and deer are rearing fawns and thus might be prioritizing thermal and predatory cover in treed areas. The characteristics of age and condition also influenced the area deer used. Older deer used smaller areas, which might indicate that older animals are better at optimally using space or are better able to monopolize preferred range. In contrast, fatter deer had larger ranges supporting other work showing that fatter deer used more energy during the winter (Monteith et al., 2013). Matching summer results 553 �J. M. Northrup et al. indicate that similar dynamics are at play. These results indicate that during winter, there is some benefit to having a larger range, potentially linked to access to welfare factors (i.e. limiting resources, thermal cover) or reducing predation risk. We caution that summer results are potentially influenced by fawn rearing success, which strongly impacts deer condition. As such, greater fat stores may relate to fawn loss (truncating lactation costs), complicating interpretation of the relationship between fat and range size. Unfortunately, we lack information on reproduction to resolve this relationship. Lastly, increased density of pipelines and roads was associated with smaller range sizes. This contrasted with our predictions, with the exception of the increase in the 99% range during summer (the weakest response among all seasons and range sizes). Construction of pipelines and roads necessitates the removal of overstorey plants (in our case pinyon pine and juniper), which can be followed by reseeding and/or understorey vegetation response. As such, we suspect more pipelines and roads could be related to greater forage availability with the reduced range size reflecting the benefits of such treatments. However, we documented lower philopatry in response to changes in pipeline density, so there does appear to be some displacement related to the initial construction of pipelines, after which they might offer forage resources. As with climate variation, anthropogenic development clearly influenced space-use in our study system. In particular, the high degree of philopatry during summer, the period for rearing fawns and accruing fat, suggests that activities that displace deer from their preferred summer range may be of particular concern. During winter, our results suggest that the lower quality forage and dynamic landscape increase deer space-use requirements. Increased development could further exacerbate the nutritional limitations of winter range if it reduces resources. The finding that deer responded strongly to forage availability indicates that during poor years, deer require more area and might be particularly susceptible to anthropogenic impacts. While well pads are known to strongly influence the habitat selection process of deer in our study system (Northrup et al., 2015), our general finding that range size and philopatry were not as strongly influenced by well pad activity indicates that environmental factors most strongly influence range use behaviour in this area. With respect to these findings, it appears that there are opportunities for certain development features (particularly pipelines) to be managed and implemented in a manner that might have benefits for deer, while the negative impacts of other features (particularly industrial facilities) should be the focus of mitigation strategies. ACKNOWLEDGEMENTS Space-use estimation With increasing sophistication of GPS collar technology, our ability to collect highly detailed movement data is growing. The simultaneous advancement in analytical methods provides unprecedented ability to understand behaviour. We used novel methodology developed in the animal movement literature to leverage the complexity of these data. This method enabled highly detailed examination of space-use dynamics at fine scales. Many classic approaches, such as kernel density estimation (Worton, 1989), do not incorporate the animal’s movement behaviour in their estimation approach, resulting in UDs reflecting the assumptions of a point process. Our approach ensured that space-use estimation captured the movement process. When assessing the relationship between fine-scaled behaviours and landscape dynamics, employing technically appropriate techniques is necessary. CONCLUSIONS The success of philopatric strategies is based on predictability (Switzer, 1993). Environmental and anthropogenic changes can thus negatively impact philopatric species. Climate change will likely drive variability in precipitation and vegetative productivity, reducing the predictability of ecological systems and potentially decreasing the benefits of philopatric strategies. As such, we may expect philopatric species to be particularly susceptible to climate change unless they exhibit plasticity not seen in this study. However, management options for addressing this issue are limited. 554 Mule deer capture and monitoring was funded and/or supported by Colorado Parks and Wildlife (CPW), White River Field Office of Bureau of Land Management, ExxonMobil Production/XTO Energy, Federal Aid in Wildlife Restoration (W-185-R), Safari Club International, the Colorado State Severance Tax, EnCana Corp., Williams/WPX Energy, Shell Exploration and Production, Marathon Oil Corp., The Mule Deer Foundation and Colorado Mule Deer Assn. We thank L. Wolfe, C. Bishop, D. Finley, and D. Freddy (CPW) and numerous field technicians for project assistance, L. 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Diversity and Distributions, 22, 547–557, ª 2016 John Wiley & Sons Ltd SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Appendix S1 Mule deer capture data and data cleaning. Appendix S2 Analysis of sensitivity of utilization distributions to sampling interval and cell spacing, and schematic of philopatry calculation. Appendix S3 Covariates used in regression models and description of their derivation. Appendix S4 Model structures and formulation, results tables, and posterior predictive checks. BIOSKETCHES Joseph M. Northrup is a postdoctoral researcher with Colorado Parks and Wildlife and Colorado State University. His research is focused on understanding how human-caused environmental change impacts wildlife populations. Charles R. Anderson Jr is the leader of the Mammals Research Section at Colorado Parks and Wildlife. His research is focused on population dynamics of large mammals, wildlife–human interactions and applying wildlife ecology to inform future management decisions. George Wittemyer is an Associate Professor in the Department of Fish, Wildlife and Conservation Biology at Colorado State University. His research is focused on applying spatial ecological approaches to study the impacts of human activities on wildlife populations. Author contributions: All authors conceived the ideas and wrote the paper, C.R.A. oversaw data collection, and J.M.N. analysed the data. Editor: Bethany Bradley 557 � https://cpw.cvlcollections.org/files/original/4b03249b9edccf80c5a04ea56d112941.zip 37d37fdfaea95d72648bdedf71364065 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal Articles Description An account of the resource CPW peer-reviewed journal publications Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Environmental dynamics and anthropogenic development alter philopatry and space‐use in a North American cervid Description An account of the resource <p class="article-section__sub-title section1"><strong>Aim</strong></p> <p>The space an animal uses over a given time period must provide the resources required for meeting energetic needs, reproducing and avoiding predation. Anthropogenic landscape change in concert with environmental dynamics can strongly structure space-use. Investigating these dynamics can provide critical insight into animal ecology, conservation and management.</p> <p class="article-section__sub-title section1"><strong>Location</strong></p> <p>The Piceance Basin, Colorado, USA.</p> <p class="article-section__sub-title section1"><strong>Methods</strong></p> <p>We applied a novel utilization distribution estimation technique based on a continuous-time correlated random walk model to characterize range dynamics of mule deer during winter and summer seasons across multiple years. This approach leverages second-order properties of movement to provide a probabilistic estimate of space-use. We assessed the influence of environmental (cover and forage), individual and anthropogenic factors on interannual variation in range use of individual deer using a hierarchical Bayesian regression framework.</p> <p class="article-section__sub-title section1">Results</p> <p>Mule deer demonstrated remarkable spatial philopatry, with a median of 50% overlap (range: 8–78%) in year-to-year utilization distributions. Environmental conditions were the primary driver of both philopatry and range size, with anthropogenic disturbance playing a secondary role.</p> <p class="article-section__sub-title section1"><strong>Main conclusions</strong></p> <p>Philopatry in mule deer is suspected to reflect the importance of spatial familiarity (memory) to this species and, therefore, factors driving spatial displacement are of conservation concern. The interaction between range behaviour and dynamics in development disturbance and environmental conditions highlights mechanisms by which anthropogenic environmental change may displace deer from familiar areas and alter their foraging and survival strategies.</p> Bibliographic Citation A bibliographic reference for the resource. Recommended practice is to include sufficient bibliographic detail to identify the resource as unambiguously as possible. Northrup, J. M., C. R. Anderson Jr, and G. Wittemyer. 2016. Environmental dynamics and anthropogenic development alter philopatry and space‐use in a North American cervid. Diversity and Distributions 22:547–557. <a href="https://doi.org/10.1111/ddi.12417" target="_blank" rel="noreferrer noopener">https://doi.org/10.1111/ddi.12417</a> Creator An entity primarily responsible for making the resource Northrup, Joseph M. Anderson Jr, Charles R. Wittemyer, George Subject The topic of the resource Animal movement Energy development Home range <em>Odocoileus hemionus</em> Utilization distribution Extent The size or duration of the resource. 11 pages Date Created Date of creation of the resource. 2016-01-05 Rights Information about rights held in and over the resource <a href="http://rightsstatements.org/vocab/InC-NC/1.0/" target="_blank" rel="noreferrer noopener">In Copyright - Non-Commercial Use Permitted</a> Format The file format, physical medium, or dimensions of the resource application/pdf Language A language of the resource English Is Part Of A related resource in which the described resource is physically or logically included. Diversity and Distributions Type The nature or genre of the resource Article