Behavioral and Demographic Responses of Mule Deer to Energy Development on Winter Range

Anthropogenic habitat modification is a major driver of global biodiversity loss. In North America, one of the primary sources of habitat modification over the last 2 decades has been exploration for and production of oil and natural gas (hydrocarbon development), which has led to demographic and behavioral impacts to numerous wildlife species. Developing effective measures to mitigate these impacts has become a critical task for wildlife managers and conservation practitioners. However, this task has been hindered by the difficulties involved in identifying and isolating factors driving population responses. Current research on responses of wildlife to development predominantly quantifies behavior, but it is not always clear how these responses scale to demography and population dynamics. Concomitant assessments of behavior and population‐level processes are needed to gain the mechanistic understanding required to develop effective mitigation approaches. We simultaneously assessed the demographic and behavioral responses of a mule deer population to natural gas development on winter range in the Piceance Basin of Colorado, USA, from 2008 to 2015. Notably, this was the period when development declined from high levels of active drilling to only production phase activity (i.e., no drilling). We focused our data collection on 2 contiguous mule deer winter range study areas that experienced starkly different levels of hydrocarbon development within the Piceance Basin. We assessed mule deer behavioral responses to a range of development features with varying levels of associated human activity by examining habitat selection patterns of nearly 400 individual adult female mule deer. Concurrently, we assessed the demographic and physiological effects of natural gas development by comparing annual adult female and overwinter fawn (6‐month‐old animals) survival, December fawn mass, adult female late and early winter body fat, age, pregnancy rates, fetal counts, and lactation rates in December between the 2 study areas. Strong differences in habitat selection between the 2 study areas were apparent. Deer in the less‐developed study area avoided development during the day and night, and selected habitat presumed to be used for foraging. Deer in the heavily developed study area selected habitat presumed to be used for thermal and security cover to a greater degree. Deer faced with higher densities of development avoided areas with more well pads during the day and responded neutrally or selected for these areas at night. Deer in both study areas showed a strong reduction in use of areas around well pads that were being drilled, which is the phase of energy development associated with the greatest amount of human presence, vehicle traffic, noise, and artificial light. Despite divergent habitat selection patterns, we found no effects of development on individual condition or reproduction and found no differences in any of the physiological or vital rate parameters measured at the population level. However, deer density and annual increases in density were higher in the low‐development area. Thus, the recorded behavioral alterations did not appear to be associated with demographic or physiological costs measured at the individual level, possibly because populations are below winter range carrying capacity. Differences in population density between the 2 areas may be a result of a population decline prior to our study (when development was initiated) or area‐specific differences in habitat quality, juvenile dispersal, or neonatal or juvenile survival; however, we lack the required data to contrast evidence for these mechanisms. Given our results, it appears that deer can adjust to relatively high densities of well pads in the production phase (the period with markedly lower human activity on the landscape), provided there is sufficient vegetative and topographic cover afforded to them and populations are below carrying capacity. The strong reaction to wells in the drilling phase of development suggests mitigation efforts should focus on this activity and stage of development. Many of the wells in this area were directionally drilled from multiple‐well pads, leading to a reduced footprint of disturbance, but were still related to strong behavioral responses. Our results also indicate the likely value of mitigation efforts focusing on reducing human activity (i.e., vehicle traffic, light, and noise). In combination, these findings indicate that attention should be paid to the spatial configuration of the final development footprint to ensure adequate cover. In our study system, minimizing the road network through landscape‐level development planning would be valuable (i.e., exploring a maximum road density criteria). Lastly, our study highlights the importance of concomitant assessments of behavior and demography to provide a comprehensive understanding of how wildlife respond to habitat modification

The role of environmental, structural and anthropogenic variables on underpass use by African savanna elephants (Loxodonta africana) in the Tsavo Conservation Area

Wildlife crossing structures are effective interventions for mitigating fragmentation of habitats by linear infrastructure. The 2017 construction of a new railway cutting through the Tsavo Con- servation Area (TCA), home to the largest elephant population in Kenya, affected wildlife movement and habitat connectivity. Although numerous studies have investigated the use of wildlife crossing structures by a wide range of species, few have focused on their use by mega- herbivores. In this study, we examined use of 41 wildlife crossing structures by African savanna elephants (Loxodonta africana) along a 133 km section of new railway in Tsavo, Kenya. We used a generalized linear mixed modeling approach to assess the relationship between elephant crossing rate over 28 months between July 2017 to April 2021 and explanatory factors including crossing structure attributes, livestock presence and proximity to highways, water points and human settlement. We found that structural attributes of crossing structures were most strongly associ- ated with the elephant crossing rate, particularly height and its interaction with type of crossing structure (bridges, wildlife underpasses and culverts). Higher crossing structures were associated with higher crossing rate, with the largest influence of height at culverts and wildlife underpasses. Although bridges comprised only 19.5 % of the 41 available crossing structures, they accounted for a disproportionately high number of elephants crossing events (56 %). The results demon- strated the importance of bridges over designated crossing structures for elephants, with pre- dicted seasonal counts of elephant crossings being 0.31 for average sized culverts, 2.88 for wildlife underpasses and 5.86 for bridges. The environmental and anthropogenic variables were not strongly associated with elephant crossing rate. Our findings have direct application for future siting and design of crossing structures across elephant rang

Demographic Variables for Wild Asian Elephants Using Longitudinal Observations

Detailed demographic data on wild Asian elephants have been difficult to collect due to habitat characteristics of much of the species’ remaining range. Such data, however, are critical for understanding and modeling population processes in this endangered species. We present data from six years of an ongoing study of Asian elephants (Elephas maximus) in Uda Walawe National Park, Sri Lanka. This relatively undisturbed population numbering over one thousand elephants is individually monitored, providing cohort-based information on mortality and reproduction. Reproduction was seasonal, such that most births occurred during the long inter-monsoon dry season and peaked in May. During the study, the average age at first reproduction was 13.4 years and the 50th percentile inter-birth interval was approximately 6 years. Birth sex ratios did not deviate significantly from parity. Fecundity was relatively stable throughout the observed reproductive life of an individual (ages 11–60), averaging between 0.13–0.17 female offspring per individual per year. Mortalities and injuries based on carcasses and disappearances showed that males were significantly more likely than females to be killed or injured through anthropogenic activity. Overall, however, most observed injuries did not appear to be fatal. This population exhibits higher fecundity and density relative to published estimates on other Asian elephant populations, possibly enhanced by present range constriction. Understanding the factors responsible for these demographic dynamics can shed insight on the future needs of this elephant population, with probable parallels to other populations in similar settings

A model for leveraging animal movement to understand spatio-temporal disease dynamics

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model ‘Movement-driven modelling of spatio-temporal infection risk’ (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology

The influence of social structure, habitat, and host traits on the transmission of Escherichia coli in wild elephants

Social structure is proposed to influence the transmission of both directly and environmentally transmitted infectious agents. However in natural populations, many other factors also influence transmission, including variation in individual susceptibility and aspects of the environment that promote or inhibit exposure to infection. We used a population genetic approach to investigate the effects of social structure, environment, and host traits on the transmission of Escherichia coli infecting two populations of wild elephants: one in Amboseli National Park and another in Samburu National Reserve, Kenya. If E. coli transmission is strongly influenced by elephant social structure, E. coli infecting elephants from the same social group should be genetically more similar than E. coli sampled from members of different social groups. However, we found no support for this prediction. Instead, E. coli was panmictic across social groups, and transmission patterns were largely dominated by habitat and host traits. For instance, habitat overlap between elephant social groups predicted E. coli genetic similarity, but only in the relatively drier habitat of Samburu, and not in Amboseli, where the habitat contains large, permanent swamps. In terms of host traits, adult males were infected with more diverse haplotypes, and males were slightly more likely to harbor strains with higher pathogenic potential, as compared to adult females. In addition, elephants from similar birth cohorts were infected with genetically more similar E. coli than elephants more disparate in age. This age-structured transmission may be driven by temporal shifts in genetic structure of E. coli in the environment and the effects of age on bacterial colonization. Together, our results support the idea that, in elephants, social structure often will not exhibit strong effects on the transmission of generalist, fecal-oral transmitted bacteria. We discuss our results in the context of social, environmental, and host-related factors that influence transmission patterns

Predicting Dispersal and Conflict Risk for Wolf Recolonization in Colorado

1. The colonization of suitable yet unoccupied habitat due to natural dispersal or human introduction can benefit recovery of threatened species. Predicting habitat suitability and conflict potential of colonization areas can facilitate conservation planning. 2. Planning for reintroduction of gray wolves (Canis lupus) to the United States state of Colorado is underway. Assessing which occupancy sites minimize the likelihood of human-wolf conflict during dispersal events and seasonal movements is critical to the success of this initiative. 3. We used a spatial absorbing Markov chain (SAMC) framework, which extends random walk theory and probabilistically accounts for both movement behavior and mortality risk, to compare the viability of potential occupancy sites (public lands \u3e 500 km2 to minimally meet wolf pack range area). The SAMC framework produced spatially explicit predictions of wolf dispersal, philopatry and conflict risk ahead of recolonization prior to reintroduction efforts. Our SAMC model included: (1) movement resistance based on terrain, roads and housing density; (2) mortality risk and potential conflict (absorption) based on livestock presence, social tolerance, land ownership and state boundaries; and (3) site fidelity based on habitat quality. Using this model, we compared 21 public land units by deriving predictions of: (A) relative survival time outside each site, (B) intensity of use and retention time within each site, and (C) the probability of use on adjacent public lands. We also predicted and mapped potential conflict hot spots associated with each site. 4. Among the units assessed, a complex of United States Forest Service Wilderness areas near Aspen, chiefly the Hunter-Fryingpan and Collegiate Peaks Wilderness areas, had the best overall rankings when comparing predictions of each metric. The area balances high-quality, well-connected habitat with relatively low livestock density and high social tolerance. 5. Synthesis and applications. Our findings highlight the utility of the SAMC framework for assessing colonization areas and the capacity to identify locations for effective proactive management, especially of conflict prone species. The flexibility of the SAMC framework enables predicting likely areas of philopatry and human-wildlife conflict using spatially explicit metrics which can improve the success of conservation translocations and management of species with changing geographic extents

Role of social structure in establishment of an invasive large mammal after translocation

Background Data on the movement behavior of translocated wild pigs is needed to develop appropriate response strategies for containing and eliminating new source populations following translocation events. We conducted experimental trials to compare the home range establishment and space-use metrics, including the number of days and distance traveled before becoming range residents, for wild pigs translocated with their social group and individually. Results We found wild pigs translocated with their social group made less extensive movements away from the release location and established a stable home range ~5 days faster than those translocated individually. We also examined how habitat quality impacted the home range sizes of translocated wild pigs and found wild pigs maintained larger ranges in areas with higher proportion of low-quality habitat. Conclusion Collectively, our findings suggest translocations of invasive wild pigs have a greater probability of establishing a viable population near the release site when habitat quality is high and when released with members of their social unit compared to individuals moved independent of their social group or to low-quality habitat. However, all wild pigs translocated in our study made extensive movements from their release location, highlighting the potential for single translocation events of either individuals or groups to have far-reaching consequences within a much broader landscape beyond the location where they are released. These results highlight the challenges associated with containing populations in areas where illegal introduction of wild pigs occurs, and the need for rapid response once releases are identified

Deriving spatially explicit direct and indirect interaction networks from animal movement data

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers