Known Elephant Numbers and Distribution in Africa

Map and Population table detailing known elephant population in Africa as of 1978

Known Elephant Distribution in Africa: An Updated Map

Map depicting the elephant distribution in Africa as of Spring 1979

Bee Threat Elicits Alarm Call in African Elephants

Unlike the smaller and more vulnerable mammals, African elephants have relatively few predators that threaten their survival. The sound of disturbed African honeybees Apis meliffera scutellata causes African elephants Loxodonta africana to retreat and produce warning vocalizations that lead other elephants to join the flight. In our first experiment, audio playbacks of bee sounds induced elephants to retreat and elicited more head-shaking and dusting, reactive behaviors that may prevent bee stings, compared to white noise control playbacks. Most importantly, elephants produced distinctive “rumble” vocalizations in response to bee sounds. These rumbles exhibited an upward shift in the second formant location, which implies active vocal tract modulation, compared to rumbles made in response to white noise playbacks. In a second experiment, audio playbacks of these rumbles produced in response to bees elicited increased headshaking, and further and faster retreat behavior in other elephants, compared to control rumble playbacks with lower second formant frequencies. These responses to the bee rumble stimuli occurred in the absence of any bees or bee sounds. This suggests that these elephant rumbles may function as referential signals, in which a formant frequency shift alerts nearby elephants about an external threat, in this case, the threat of bees

Elephants rest more when the poaching risk is high and do not recover the lost time within a diel cycle

Poaching for ivory has caused disturbances in the behaviour of elephants. The nature and magnitude of such disturbance are not yet fully understood. Here, we studied the daily activity cycles of 10 elephants tracked for a minimum of two years each in the Samburu ecosystem in Kenya. The elephants were tracked over a spatial or temporal gradient of poaching risk in their distribution area. Laikipia Samburu landscape is shared with humans and is a mosaic of land ownership and management practices, all of which lead to varied levels of illegal killing. Using movement data of elephants tracked on various dates between 2002 and 2016, we studied the daily activity cycle when they were in their different core areas. Using Generalized Additive Models, we found that elephants moved less around midday in high poaching areas. Despite the adaptive shift in activity times, elephants were moving for fewer hours per day in areas with higher risk, reducing total movement daily in risky areas or times. The elephants lost one hour daily from their usual movement time when they went into high-risk areas, which they did not compensate for despite increased activity at dawn and dusk. The level of illegal killing was the best explanatory variable for altering the activity cycle. We infer that such risk avoidance behaviour culminates in the potential reduction of foraging efforts in a risky area. A deficit in their activity time may have consequences for their social life, reproduction, or overall foraging success, aspects of elephant ecology that are not fully understood yet. We discuss the results in light of the increasing need for more fine-scale temporal analyses of the influence of risk on the diel activity of elephants for sites that achieve both movement data and verified records of causes of elephant deaths

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

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

Inferring ecological and behavioral drivers of African elephant movement using a linear filtering approach

Understanding the environmental factors influencing animal movements is fundamental to theoretical and applied research in the field of movement ecology. Studies relating fine-scale movement paths to spatiotemporally structured landscape data, such as vegetation productivity or human activity, are particularly lacking despite the obvious importance of such information to understanding drivers of animal movement. In part, this may be because few approaches provide the sophistication to characterize the complexity of movement behavior and relate it to diverse, varying environmental stimuli. We overcame this hurdle by applying, for the first time to an ecological question, a finite impulse–response signal-filtering approach to identify human and natural environmental drivers of movements of 13 free-ranging African elephants (Loxodonta africana) from distinct social groups collected over seven years. A minimum mean-square error (MMSE) estimation criterion allowed comparison of the predictive power of landscape and ecological model inputs. We showed that a filter combining vegetation dynamics, human and physical landscape features, and previous movement outperformed simpler filter structures, indicating the importance of both dynamic and static landscape features, as well as habit, on movement decisions taken by elephants. Elephant responses to vegetation productivity indices were not uniform in time or space, indicating that elephant foraging strategies are more complex than simply gravitation toward areas of high productivity. Predictions were most frequently inaccurate outside protected area boundaries near human settlements, suggesting that human activity disrupts typical elephant movement behavior. Successful management strategies at the human–elephant interface, therefore, are likely to be context specific and dynamic. Signal processing provides a promising approach for elucidating environmental factors that drive animal movements over large time and spatial scales.This research was supported by NSF GRFP (A. N. Boettiger) and NIH grant GM083863-01 and USDI FWS Grant 98210-8-G745 to W. M. Getz.http://www.esajournals.org/loi/ecol

Human footprint and protected areas shape elephant range across Africa

Over the last two millennia, and at an accelerating pace, the African elephant (Loxodonta spp. Lin.) has been threatened by human activities across its range. We investigate the correlates of elephant home range sizes across diverse biomes. Annual and 16-day elliptical time density home ranges were calculated by using GPS tracking data collected from 229 African savannah and forest elephants (L. africana and L. cyclotis, respectively) between 1998 and 2013 at 19 sites representing bushveld, savannah, Sahel, and forest biomes. Our analysis considered the relationship between home range area and sex, species, vegetation productivity, tree cover, surface temperature, rainfall, water, slope, aggregate human influence, and protected area use. Irrespective of these environmental conditions, long-term annual ranges were overwhelmingly affected by human influence and protected area use. Only over shorter, 16-day periods did environmental factors, particularly water availability and vegetation productivity, become important in explaining space use. Our work highlights the degree to which the human footprint and existing protected areas now constrain the distribution of the world’s largest terrestrial mammal. A habitat suitability model, created by evaluating every square kilometer of Africa, predicts that 18,169,219 km2 would be suitable as elephant habitat—62% of the continent. The current elephant distribution covers just 17% of this potential range of which 57.4% falls outside protected areas. To stem the continued extirpation and to secure the elephants’ future, effective and expanded protected areas and improved capacity for coexistence across unprotected range are essential