Repeated use of the same areas may benefit animals as they exploit familiar sites, leading to consistent home ranges over time that can span generations. Changing risk landscapes may reduce benefits associated with home range fidelity, however, and philopatric animals may alter movement in response to new pressures. Despite the importance of range changes to ecological and evolutionary processes, little tracking data have been collected over the long-term nor has range change been recorded in response to human pressures across generations. Here, we investigate the relationships between ecological, demographic and human variables and elephant ranging behaviour across generations using 16 years of tracking data from nine distinct female social groups in a population of elephants in northern Kenya that was heavily affected by ivory poaching during the latter half of the study. Nearly all groups—including those that did not experience loss of mature adults— exhibited a shift north over time, apparently in response to increased poaching in the southern extent of the study area. However, loss of mature adults appeared to be the primary indicator of range shifts and expansions, as generational turnover was a significant predictor of range size increases and range centroid shifts. Range expansions and northward shifts were associated with higher primary productivity and lower poached carcass densities, while westward shifts exhibited a trend to areas with higher values of primary productivity and higher poached carcass densities relative to former ranges. Together these results suggest a trade-off between resource access, mobility and safety. We discuss the relevance of these results to elephant conservation efforts and directions meriting further exploration in this disrupted society of a keystone species.
This study is part of a long-term individual-based elephant monitoring project centred in Samburu and Buffalo Springs National Reserves in northern Kenya within the Laikipia–Samburu ecosystem (0.3–2.0° N, 36.2–38.3° E) [36] (figure 1). The animals using the unfenced reserves are a part of the second largest elephant population in Kenya [42]. Laikipia–Samburu is made up of a patchwork of land use types including community conservancies, human settlements, agriculture and protected areas [38], and has been monitored intensively for poaching since 2002 as a part of the Convention on International Trade in Endangered Species’ (CITES) Monitoring Illegal Killing of Elephants (MIKE) programme [43]. Movement tracks plotted from earlier (a) and later (b) generations from seven groups collared between 2001 and 2017 (data from groups where only one generation was tracked are not shown). Lines represent 20 (a) and 18 (b) tracked years on a Google Maps base map. Outlines demarcate national reserves and parks, with the two central outlines demarcating the Samburu and Buffalo Springs (left) and Shaba (right) National Reserves complex. Underlying red contours represent poached carcass density during the period when the later generation was tracked (2013–2017). Carcass data were not available for the ecosystem encompassing Meru National Park in the southeast corner. The elephants that use the reserves are monitored by field teams that survey the parks most days along set routes [44]. Records of individuals are maintained through a photo-identification system, and the ages and family histories of most animals are known [37,45]. Analyses of association data recorded during daily surveys were used to define social groups within the hierarchical society [44]. The most cohesive and closely bonded social level in elephant society is the core group, often but not always a family unit of close maternal relatives and their offspring [23,39,44–46]. Members of inter-generational pairs of tracked elephants in this study belonged to the same core social group, such that each pair represented a distinct core group over different time periods. GPS collars recording hourly positions were fitted on immobilized elephants according to protocol of the government of Kenya, administered by a Kenya Wildlife Service veterinary team jointly with the Save the Elephants field team. After collection, movement datasets were cleaned for errors by removing coordinates that could only be reached with speeds exceeding 10 km h−1 (deemed biologically unrealistic), duplicate and incomplete records. For analyses, data were organized into annual datasets with start dates that maximized the number of sampled days in each dataset. Annual datasets had high fix success, averaging 91.5% (ranging from 76.6 to 98.0%) of expected hourly coverage (table 1). The female with the lowest fix success (Amayeta) died 2 months prior to a complete year of tracking. Inter-generational pairs of collared elephants in this study represented disrupted families and included five mother–daughter pairs and two grandmother–granddaughter pairs (table 1). For comparison, we included movement records from two families that did not experience generational turnover that were continuously tracked over the same period, such that nine family lineages were represented in this study. Individuals tracked over the course of the study with birth years in parentheses. The Royals dataset represents three alternately collared relatives that were in the same core social unit throughout the study. See main text for more detail on group characterizations. Distinct core groups correspond to consistent colours across figures. Continuous time stochastic process models that account for inherent autocorrelation were fitted to annual tracking datasets for each dataset in the study (n = 17; Cleopatra and Anastasia are closely associated sisters that were alternately collared and were, therefore, considered a single dataset) to estimate annual autocorrelated kernel density estimation (AKDE) home ranges [47,48]. We estimated both 95% (general) and 50% (core) AKDE home ranges [33]. AKDE home range estimation is robust to inconsistencies in sampling schedules [47] and, therefore, suited to this tracking dataset that spanned several years with variable fix successes. Because tracking datasets varied across individual elephants, we conducted home range analyses on each individual year. Analyses were done using the package ctmm in R v. 3.4.2 [47,49]. To understand whether and how annual home ranges within families change over time, we constructed two sets of normally distributed hierarchical models predicting the response variables latitudinal centroid of home range, longitudinal centroid of home range and home range size. The first set of models predicted these response variables as a function of time to ascertain if range changes were occurring over time, with individual elephant as a random effect and the day on which tracking began as the single predictor variable. The second set of models used the difference in centroids or the difference in home range size between pairs of annual home ranges within a core group over different years (later minus earlier). This set included covariates corresponding to differences in ecological, human, demographic and control variables characterizing the different annual ranges of the focal groups. Specifically, we calculated the difference in mean normalized difference vegetation index values between the former and later annual ranges using data from the MODIS satellite at 250 m spatial resolution and 16-day temporal resolution (https://lpdaac.usgs.gov; product MOD13Q1) averaged over both range areas during the later period (NDVI); the difference in poaching carcass density between the two ranges during the later period calculated as the number of illegally killed carcasses [43] divided by the home range size (poaching); the difference in the combined age of core group adults between the two periods calculated as the sum of the ages of adults in a core group where elephants were considered adults at breeding age (age adults); the difference in the number of coordinates collected between the two datasets (fixes); and whether each comparison of annual home ranges was inter-generational, where the covariate inter-generational was assigned as 0 when the comparison of annual ranges was within one generation (i.e. same individual or among females in the Royals group) and 1 when the comparison was across different generations before and after the older tracked individual died (i.e. mother–daughter or grandmother–granddaughter pairs). As with response variables, all covariates that compared conditions between ranges during the later time period were defined as the value for the later range minus the value for the former range, such that positive differences indicated comparatively higher values in the later range. This structure allowed insight into how later conditions changed between the two ranges, not how conditions changed over time. We excluded Orchid’s most recent range from this comparative analysis because she ventured into a different ecosystem for which we did not have poaching data (figure 1). We included core group identity as a random effect, and standardized continuous predictor variables prior to running models for ease of interpretation. No covariates were correlated above r = |0.5|.
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