Genetic connectivity across marginal habitats: the elephants of the Namib Desert

Abstract

Locally isolated populations in marginal habitats may be genetically distinctive and of heightened conservation concern. Elephants inhabiting the Namib Desert have been reported to show distinctive behavioral and phenotypic adaptations in that severely arid environment. The genetic distinctiveness of Namibian desert elephants relative to other African savanna elephant (Loxodonta africana) populations has not been established. To investigate the genetic structure of elephants in Namibia, we determined the mitochondrial (mt) DNA control region sequences and genotyped 17 microsatellite loci in desert elephants (n = 8) from the Hoanib River catchment and the Hoarusib River catchment. We compared these to the genotypes of elephants (n = 77) from other localities in Namibia. The mtDNA haplotype sequences and frequencies among desert elephants were similar to those of elephants in Etosha National Park, the Huab River catchment, the Ugab River catchment, and central Kunene, although the geographically distant Caprivi Strip had different mtDNA haplotypes. Likewise, analysis of the microsatellite genotypes of desert‐dwelling elephants revealed that they were not genetically distinctive from Etosha elephants, and there was no evidence for isolation by distance across the Etosha region. These results, and a review of the historical record, suggest that a high learning capacity and long‐distance migrations allowed Namibian elephants to regularly shift their ranges to survive in the face of high variability in climate and in hunting pressure.

Big brains stabilize populations and facilitate colonization of variable habitats in birds

“…One possible reason for this pattern is that highly encephalized birds have lower risk of extirpation during the early stages of colonization (i.e., when abundances are low 43 ), because of their enhanced ability to withstand environmental change. Similar links between cognition and range expansion have been made in studies documenting the success of highly encephalized species in colonizing novel habitats 16,17,41 and are the basis of our current understanding of the process of human expansion out of Africa 8,44 . Overall, our results suggest that even though environmental variability can be a viable agent of selection in the evolution of cognition (as also concluded by 14,45 ), this particular mechanism is unlikely to have driven many of the most striking cases of encephalization among birds.…”

“…However, in order to evaluate the extent to which this mechanism provides a general explanation for the evolution of cognition in birds, it is critical to explore the direction of causality in the correlation between an enhanced potential for cognition and the occupancy of variable environments. A clear understanding of the sequence of evolutionary events is particularly necessary in this context because the adaptive benefits invoked by the cognitive buffer hypothesis may just as well promote the evolution of cognition in variable habitats, or facilitate instead the secondary colonization of variable habitats by already highly encephalized species 41 . We evaluated the support for these two non-mutually exclusive evolutionary scenarios by using reversible-jump MCMC to estimate models of correlated trait evolution 42 fitted to an exhaustive global sample of non-migratory birds for which brain size is known (N = 1,288 species; Supplemental Data 2).…”

Potential Impacts of Temperature Projections on Selected Large Herbivores in Savanna Ecosystem of Kenya

“…Other studies have demonstrated that elephant can survive extreme climate as indicated in the studies in Namibia [38] and increasing elephants in Botswana [39], where the condition are much severe than our study area. In Kajiado the other drivers like settlements, agriculture, land subdivision and developments may impact the elephants negatively.…”

Incipient signs of genetic differentiation among African elephant populations in fragmenting miombo ecosystems in south-western Tanzania

“…To test for structure on an evolutionary timescale, we analyzed genotypes in STRUCTURE 2.3.4 (Pritchard et al, 2000), denoting a model-based clustering algorithm to multi-locus genotype data (Ishida et al, 2016). We programmed length of the burn-in period to 10,000 and the number of Markov Chain Monte Carlo (MCMC) reps after the burn-in to at least 100,000 steps.…”

“…A species' ability to cope with the changing selective forces resulting from anthropogenic disturbance may be partially determined by the amount of genetic variability in populations as well as the way that variation is structured within and between populations (Archie et al, 2011;Ishida et al, 2016). Evidence for recent emergence of genetic structure within the three studied elephant populations suggests that habitat loss and fragmentation in the areas between Ruaha and Katavi are starting to alter population connectivity.…”