Temporal variation in resource selection of African elephants follows long‐term variability in resource availability

Abstract

The relationship between resource availability and wildlife movement patterns is pivotal to understanding species behavior and ecology. Movement response to landscape variables occurs at multiple temporal scales, from sub‐diurnal to multiannual. Additionally, individuals may respond to both current and past conditions of resource availability. In this paper, we examine the temporal scale and variation of current and past resource variables that affect movement patterns of African elephants (Loxodonta africana) using sub‐hourly movement data from GPS‐GSM collared elephants in Etosha National Park, Namibia. We created detailed satellite‐based spatiotemporal maps of vegetation biomass, as well as distance from surface water, road and fence. We used step selection functions to measure the relative importance of these landscape variables in determining elephants’ local movement patterns. We also examined how elephants respond to information, in locations they have previously visited, on productivity integrated over different temporal scales: from current to historical conditions. Our results demonstrate that elephants choose patches with higher than average annual productivity and grass biomass, but lower tree biomass. Elephants also prefer to walk close to water, roads, and fences. These preferences vary with time of day and with season, thereby providing insights into diurnal and seasonal behavioral patterns and the ecological importance of the landscape variables examined. We also discovered that elephants respond more strongly to long‐term patterns of productivity than to immediate forage conditions, in familiar locations. Our results illustrate how animals with high cognitive capacity and spatial memory integrate long‐term information on landscape conditions. We illuminate the importance of long‐term high temporal resolution satellite imagery to understanding the relationship between movement patterns and landscape structure.

Accounting for space and uncertainty in real‐time location system‐derived contact networks

“…Data sets generated by RTLSs are incredibly versatile and can be used in conjunction with other geographic data (e.g., remotely sensed data) to answer a wide variety of ecological research questions pertaining to individual‐ and population‐level animal behaviors (Kays, Crofoot, Jetz, & Wikelski, 2015). Previous studies have used RTLS data to draw inferences about (a) animals' movement speed and tortuosity (Bastille‐Rousseau et al, 2019; Liu, Xu, & Jiang, 2015; Schiffner, Fuhrmann, Reimann, & Wiltschko, 2018), (b) energy expenditures (Williams et al, 2014), (c) habitat use (Keeley, Beier, & Gagnon, 2016; Thomson et al, 2017; Tsalyuk, Kilian, Reineking, & Marcus, 2019), (d) survival and mortality rates (Klaassen et al, 2013), (e) responses to environmental stimuli (Bastille‐Rousseau et al, 2019; Tsalyuk et al, 2019), and (f) interactions with other individuals, specific locations, or environmental substrates (Chen, Sanderson, White, Amrine, & Lanzas, 2013; Chen & Lanzas, 2016; Dawson, Farthing, Sanderson, & Lanzas, 2019; Spiegel, Leu, Sih, & Bull, 2016; Theurer et al, 2012).…”

“…As RTLSs only produce point data, even when positional accuracy is ≈100% (i.e., approximately all RTLS‐reported coordinates correspond to individuals' true geographic locations) RTLS‐derived contact networks may represent an incomplete picture of potential contacts in a given biological system. For example, point location data collected by ear tag‐ or collar‐based tracking devices, which are often deployed in livestock‐ and wildlife‐monitoring studies, respectively (Chen et al, 2013; Dawson et al, 2019; Theurer et al, 2012; Tsalyuk et al, 2019; Strandburg‐Peshkin et al, 2015; Swain et al, 2008), are not sufficient for describing the space occupied by individuals' bodies. Therefore, contacts involving areas relatively far from the head cannot be captured without introducing substantial amounts of noise and uncertainty (Dawson et al, 2019; Figure 1).…”

Remote Sensing of Environmental Drivers Influencing the Movement Ecology of Sympatric Wild and Domestic Ungulates in Semi-Arid Savannas, a Review

“…Applying landcover classification to a savanna landscape can be challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation [144]. Despite these limitations, Arraut et al (2018) produced a map of the vegetation structure of HNP in seven classes using 2013-2014 Landsat satellite images through a supervised classification process with an overall accuracy (OA) of 83.2% [102].…”

colorist: An r package for colouring wildlife distributions in space–time

“…For example, colorist can also be used to explore the distribution of multiple species or individuals within a single time period. Here we use GPS tracking data collected from African Elephants Loxodonta africana in Etosha National Park, Namibia during 2011 to explore how two individuals used the landscape over the course of a year (Tsalyuk, Kilian, Reineking, & Getz, 2019).…”