Conservation often involves trade-offs between competing goals and objectives. While trade-offs between biodiversity conservation and human needs have been emphasised (e.g. Hirsch et al. 2011; McShane et al. 2011), it is also important to take into account trade-offs between conserving different aspects of biodiversity. In Addo Elephant National Park (AENP), South Africa, managers seek to balance the local conservation of African elephants (Loxodonta africana) and the ecological processes they provide with protecting rare plants and providing for tourism.

The park was founded in 1931 to protect the remnant population of elephants in the Eastern Cape Province following depletion by hunting (Hall-Martin 1980). Fences were erected in 1954 to prevent conflict with farmers, and the population rapidly grew to its current level of over 500 elephants (Hall-Martin 1980; SANParks 2011). Although elephants comprise around 80% of the herbivore biomass in AENP (Landman et al. 2012), the park is also home to 19 other large mammal species (SANParks 2008). This diversity of species attracts national and international tourists, with over 90% of tourists rating wildlife viewing as a ‘very important’ motivation for visiting (Boshoff et al. 2007). Demand by tourists for wildlife viewing opportunities sometimes leads to stocking high densities of tourist-favoured species, such as elephants. Such actions, however, may fail to meet tourism objectives and may conflict with the South African National Parks (SANParks) mandate to preserve diverse, healthy ecosystems (Maciejewski & Kerley 2014). This mismatch is especially crucial in AENP, which contains a wide variety of rare and important plant species in addition to animals (Lombard et al. 2001). The park contains portions of five of the nine South African biomes, comprising 43 different vegetation units (SANParks 2008). Concerns about disturbance of vegetation by elephants have been raised by several studies that documented loss of plant biodiversity and biomass (Kerley & Landman 2006; Landman, Kerley & Schoeman 2008; Lombard et al. 2001). A better understanding of the factors influencing elephant movement is needed to inform conservation decisions by park managers regarding wildlife, rare plants and tourism (Ferreira et al. 2011). Such information can be used to identify where high elephant use coincides with rare plant hotspots, suggesting locations for enclosure with fences to protect rare plants from elephants and other herbivores. It can also help guide tourist viewing to maintain a balance between ecological health and visitor satisfaction. Currently elephants are fenced into three discrete sections of AENP, occupying about 60 000 ha of the total 160 000 ha area of the park (Ferreira et al. 2011). Understanding how elephants use resources in the currently accessible areas of AENP will clarify possible effects of future expansion of access to other areas of the park.

We investigate elephant resource selection through satellite imagery and movement data to evaluate how elephant space use responds to vegetation productivity (greenness), artificial water points, slope and terrain ruggedness across space and time in AENP. While numerous studies consider the influence of vegetation, water and other factors on elephant movement and distribution (e.g. Chamaillé-Jammes et al. 2013; Loarie, Van Aarde & Pimm 2009b; Marshal et al. 2011; Roever, Van Aarde & Leggett 2012), they are generally conducted in savanna systems, featuring distinct wet and dry seasons, with differing factors influencing elephant habitat selection in each season (Loarie et al. 2009b; Roever et al. 2012). AENP, on the contrary, exhibits no strong seasonal rainfall pattern (Ferreira et al. 2011; Gough & Kerley 2006) and features evergreen succulent thicket in the main elephant areas (Landman et al. 2008), rather than the savanna vegetation of trees and grasses typical of elephant populations elsewhere. It is unclear how elephant resource use in aseasonal thicket will differ from resource use in savanna systems.

As a set of non-exclusive hypotheses, we predict that elephant resource selection in the aseasonal thicket of AENP will correspond to that observed in savanna environments. While selection patterns may well differ in an aseasonal thicket ecosystem, these hypotheses provide a basis for comparison. Elephants must consume great quantities of forage to sustain their large body size and high absolute metabolic requirements (Hopcraft, Olff & Sinclair 2010). Positive selection for green vegetation has been indicated in several studies of elephant habitat use (Loarie et al. 2009b; Pittiglio et al. 2012; Wall et al. 2013; but see Boettiger et al. 2011). Elephants are water-dependent herbivores and typically require access to drinking water every day or two (Chamaillé-Jammes et al. 2013; Owen-Smith et al. 2006). Water availability alters elephant movement and distribution patterns (Chamaillé-Jammes, Valeix & Fritz 2007; Loarie, Van Aarde & Pimm 2009a; Shannon et al. 2009; Smit, Grant & Whyte 2007a), with elephants typically selecting for areas near water. Steep slopes require high energy expenditures from elephants because of their large body size, and thus tend to be avoided (Roever et al. 2012; Wall, Douglas-Hamilton & Vollrath 2006). Rugged terrain is preferentially used by elephants because of the presence of greater nutrient concentrations and forage density compared with less rugged areas (Nellemann, Moe & Rutina 2002). In summary, if elephants in aseasonal AENP follow the patterns exhibited by elephants in seasonal systems, we expect them to show (1) a positive association with vegetation greenness, (2) a negative association with distance to water, (3) a negative association with steeper slopes and (4) a positive association with more rugged terrain.

We use step selection functions (Forester, Im & Rathouz 2009; Fortin et al. 2005) to evaluate the hypotheses identified above, measuring elephant resource selection at a daily scale in AENP. Environmental covariates are interacted with functions of time to investigate annual variability in selection patterns, while three data sets of daily movement steps recorded in the morning (06:00), midday (12:00) and afternoon (16:00) are analysed to reflect circadian patterns of resource selection. We use the results of these analyses to suggest implications for management that balance the needs of elephants, the demands of tourists and the potential of future elephant expansion to other areas of AENP.

The seven collared elephants in this study (six in the Main Camp – Colchester section of AENP plus one in the Nyathi section) each contributed between 504 and 1046 locations to the SSF analyses (Table 1-A2). Depending on the time of the day, elephants exhibited daily steps that averaged between 2151.5 m and 2267.5 m (Table 1-A2). Results were similar when the elephant occupying the Nyathi section of AENP was excluded (Table 2-A2).

All three daily movement data sets had clearly supported top models (Table 2). The full model including all covariates and time-varying selection coefficients (Model 30 in Table 1) was the best for all three data sets, with Akaike weights between 0.98 and 1.00. Selection patterns varied across the daily data sets (Table 3), indicating both daily and annual variation in resource selection by female elephants in AENP. Predictive performance of the daily models varied across data sets for both interpolation and extrapolation (Table 3). Interpolative predictive performance, as indicated by k-fold cross-validation, was high for both the 12:00 and 16:00 daily selection models, while the 06:00 model performed poorly. Extrapolative predictive performance, predicting use for the Nyathi elephant from Main Camp – Colchester resource selection models, showed strong predictive ability at 12:00, but weak performance at 06:00 and 16:00.

We tested four hypotheses of elephant selection based on patterns observed in savanna systems, namely that elephants would show (1) a positive association with vegetation greenness, (2) a negative association with distance to water, (3) a negative association with steeper slopes and (4) a positive association with more rugged terrain. Elephant selection of NDVI varied across the year, with the strongest selection in the winter (July–September) and the least selection in the summer (November–February; Figure 2a). Daily selection of greener vegetation (high NDVI values) in the morning (06:00) was consistently strong across the year, with the exception of February, aligning with the first hypothesis. At midday (12:00), however, selection patterns were more nuanced, with elephants avoiding greener vegetation from mid-November to March and selecting for greener vegetation from late May to September. Elephants showed little selection or avoidance of NDVI in the afternoon (16:00) throughout the year, contrasting with the first hypothesis. Consistent with the second hypothesis, female elephants in AENP tended to select areas closer to water (negative selection coefficient for distance to water), though this varied both diurnally and annually (Figure 2b). Elephants showed a strong selection for areas near water points in the summer (November–mid-February) at midday and in the afternoon, and the weakest relationship in the morning. Annual patterns in selection coefficients for slope at midday and in the afternoon (Figure 2c) generally supported the third hypothesis, with a predominantly negative relationship that was strongest from October to February and weakest from May to August. Conversely, daily selection patterns in the morning showed little relationship with slope in the summer, but a positive selection for steeper slopes in mid-winter (mid-May–mid-July). Variability in selection coefficient values across the year for terrain ruggedness was similar in the morning, midday and afternoon (Figure 2d). While some annual fluctuations in selection were apparent, 95% confidence intervals for all 3 hours always overlapped zero, indicating little support for selection or avoidance of rugged terrain and contrasting with the fourth hypothesis.

The relative probability of use predicted across the Main Camp – Colchester area using the SSF models varied throughout the day and across the year (Figure 3; Appendix 4). Predicted use in the morning typically emphasised higher probabilities of use in different parts of the park than at midday and noon. On average across the study period, elephant use was predicted to be higher in the southern portions of the elephant-accessible area in the morning and in the northern parts at midday (Figure 4). Use in the afternoon was more evenly distributed across the elephant-accessible area (Figure 4). When extrapolated to the Greater AENP, the midday model predicted the highest probabilities of use in the currently available areas of Main Camp and Colchester as well as the botanical reserves and southern portion of Nyathi (Figure 5). Predicted use was also relatively high in the northernmost portion of the park around Darlington Dam. The central areas of AENP tended to exhibit lower relative probabilities of use according to the midday model.

Our findings are consistent with our first three hypotheses in that elephants in Subtropical Thicket responded positively to feeding opportunities (vegetation greenness) and water and negatively to slope. This provides some confidence in the ability to extrapolate findings between biomes in terms of resource selection by elephants. The fourth hypothesis of a positive association of elephants with more rugged terrain was not supported. The latter hypothesis was predicated upon patterns of increased nutrient distribution being associated with rugged terrain in savanna landscapes (Nellemann et al. 2002). The lack of support for this hypothesis was expected on the basis of the generally high nutrients in Subtropical Thicket soils (Kerley & Landman 2006). Hence, given the oligotrophic nature of many savanna soils (East 1984), the lack of success in extrapolating this pattern between biomes likely reflects a biome-scale difference rather than a change in elephant resource selection.

There is increasing recognition of the importance of understanding dynamic patterns both in animal movement (Bowler & Benton 2005; Mueller & Fagan 2008) and resource selection (McLoughlin et al. 2010). Animals occupying dynamic environments face substantial variability at multiple spatial and temporal scales to which they must respond if they are to obtain the necessary resources to survive and reproduce. The interaction of these changes in environmental conditions and individual behavioural responses results in larger patterns of population-level movement and distribution (Mueller & Fagan 2008). Understanding the drivers of these space use patterns enables managers to incorporate spatiotemporal patterns of species distribution and impacts into their conservation decisions seeking to balance multiple objectives. Our results indicate that female elephants in AENP exhibit dynamic patterns of resource selection at both daily and annual scales.

Analysis of elephant selection for green vegetation, distance to water and slope indicates variable selection patterns over time both annually and daily. Annual variability in resource selection (Evans & Harris 2012; Jachowski, Slotow & Millspaugh 2012; Shannon et al. 2006), movement patterns (Leggett 2010; Loarie et al. 2009a; Wittemyer et al. 2008) and home range size (Osborn 2003; Shannon et al. 2006) has been previously reported for elephants, though typically in the context of seasonal systems. Our study shows that in the aseasonal context of AENP, elephants also show variable selection patterns across the year. For NDVI, distance to water and slope, the most notable divergences from the hypotheses of selection based on patterns seen in savanna systems occur during the winter (approximately May–October; Figure 2). While rainfall patterns are aseasonal in AENP, temperature follows much more consistent patterns, with the winter generally being cooler than the summer (Figure 1-A1). Ambient temperature influences habitat selection in elephants (Kinahan, Pimm & Van Aarde 2007). The cooler temperatures in the winter may relax the need for elephants to remain close to water, allowing them to select for areas of greener vegetation as seen in the positive midday selection coefficients for NDVI during this period.

Daily variation in elephant movement and resource selection is apparent in many systems (e.g. Cook, Henley & Parrini 2015; Galanti et al. 2006; Graham et al. 2009). These patterns, however, are typically associated with human activity and perceptions of risk by elephants (but see Loarie et al. 2009a). While previous studies often break daily patterns into day–night comparisons, we record finer levels of variability, with selection patterns in the morning often differing from those observed at midday and in the afternoon. This is most pronounced in the selection of green vegetation, but is also somewhat apparent for distance to water and slope. Boettiger et al. (2011) suggest that elephant foraging strategies in seasonal savannas of northern Kenya are more complex than simply selecting areas with the highest productivity, as has been indicated elsewhere (e.g. Loarie et al. 2009b; Wall et al. 2013). Our study finds similar complexity of selection in the aseasonal system of AENP, where elephants consistently select for greener vegetation (higher NDVI values) in the morning, but exhibit negative or neutral selection patterns later in the day. A trade-off has been noted for many herbivores between balancing water requirements and nutritional needs (e.g. Chamaillé-Jammes et al. 2013; Redfern et al. 2003). For elephants, the need to regularly access drinking water (Chamaillé-Jammes et al. 2013) constrains habitat use options (Redfern et al. 2003), limiting the ability of elephants to consistently select for green vegetation. While the small size of AENP and relative frequency of water points in the elephant-accessible areas mean that water is not limiting in the traditional sense of a lack of sufficient water, the localisation of available water interacting with the regular need to access water by elephants may still constrain movement decisions. Indeed, water can still serve as a constraining factor even in systems with very high water availability (Redfern et al. 2003). In AENP, we propose that the concentration of available water at borehole-fed artificial water points leads elephants to function like central-place foragers, as has been seen for other species (Rozen-Rechels et al. 2015). Elephants may venture away from water points in the morning, seeking to maximise their forage intake and meet their nutritional requirements, before returning to water points in the middle of the day. This could explain the generally positive selection for greener vegetation and weaker relationship with distance to water observed in the morning data. As a generalist forager (Kerley & Landman 2006), elephants may use a variety of habitats to meet their nutritional requirements. However, the finite water availability in AENP leads to similar patterns of use when drinking, as common targets constrain resource selection strategies (Boettiger et al. 2011). This diversity of options for elephant use in the morning may explain why the morning SSF model had substantially poorer interpolative prediction ability compared with the midday and afternoon models.

In addition to elephants balancing forage–water trade-offs through daily variability in habitat use, they may be able to further minimise trade-offs through habitat modification. High elephant utilisation around artificial water points leads to changes in vegetation structure and composition, with succulent thicket vegetation being largely replaced by grass (Landman et al. 2012). O’Connor, Goodman and Clegg (2007) suggested that elephants should favour green grasses and forbs as a food source because of their high potential intake rate. Dietary studies indicate this is the case in AENP, as Cynodon dactylon, the main grass growing around highly utilised water points (Landman et al. 2012), comprises a significant proportion of elephant diet (Landman et al. 2008; Paley & Kerley 1998). Elephants thus may be creating favourable conditions that enhance their forage opportunities, similar to patterns seen in other systems (Du Toit & Olff 2014). These modified areas may allow elephants to meet their water requirement while minimising their cost in reduced forage opportunities.

All location data in this study are for female elephants. Patterns may differ for male elephants, as studies from various parts of southern Africa have found behavioural, diet and movement differences between sexes (Evans & Harris 2012; Shannon, Mackey & Slotow 2013; Smit, Grant & Whyte 2007b). Similarly, Whitehouse and Schoeman (2003) reported differences between female and male elephant movement patterns in AENP. In addition, Duffy et al. (2011) suggested that sexual activity may alter elephant movement patterns, even overriding the importance of factors such as water. It is unclear what influence male elephants in AENP may have had on female movement and habitat selection or how the patterns described here for female herds reflect habitat selection decisions made by males. However, GPS collars were recently placed on several male elephants in AENP (A. Gaylard, unpublished data), enabling future comparison of habitat selection patterns for male elephants with those observed here.

Behavioural trade-offs are common in nature. We find this to be true for female elephants in the aseasonal system of AENP, which employ dynamic patterns of resource selection at daily and annual scales to meet their competing requirements for forage, water and other resources. In the summer, selection patterns for vegetation productivity (NDVI), distance to water and slope generally conform to those exhibited by elephants in savanna systems, but these relationships become weaker or reversed in the winter. At daily scales, resource selection by elephants varies in the morning from patterns seen at midday and in the afternoon, likely reflecting temporal reactions of elephants to the trade-off between acquiring sufficient forage and water. These results reinforce the importance of considering dynamic resources in studies of animal selection (McLoughlin et al. 2010). For elephants, future research should expand consideration of daily variability in movement and resource selection from situations relating to human–elephant conflict to consider other aspects of resource use.

Protected area managers also seek to balance trade-offs between competing objectives. In AENP, managers must weigh trade-offs between the desires of tourists and the conservation needs of elephants, rare plants and other elements of biodiversity. Our findings provide an opportunity to promote ‘smarter tourism’, improving viewing success by guiding tourists towards areas where they are likely to see elephants and improving tourist access infrastructure in such areas, rather than by increasing elephant numbers. In general, areas near waterholes are likely to be highly utilised by elephants at midday and in the afternoon, offering rewarding tourist viewing opportunities, but are less likely to be used by elephants in the morning. The results also offer insights into possible effects of future expansion of elephant access in AENP, predicting where elephant use may concentrate if elephants are given access to new areas. Such information can suggest priorities for detailed assessment of vegetation communities to determine where key areas of rare plants occur prior to elephant access. Such information may allow establishment of new botanical reserves, helping protect plants from elephant impacts and improving the likelihood of sustainable outcomes in the park.