As species evolve, they become adapted to their local environments. Detecting the genetic signature of selection and connecting that to the phenotype of the organism, however, is challenging. Here we report using an integrative approach that combines DNA sequencing with structural biology analyses to assess the effect of selection on residues in the mitochondrial DNA of the two species of African elephants. We detected evidence of positive selection acting on residues in complexes I and V, and we used homology protein structure modeling to assess the effect of the biochemical properties of the selected residues on the enzyme structure. Given the role these enzymes play in oxidative phosphorylation, we propose that the selected residues may contribute to the metabolic adaptation of forest and savanna elephants to their unique habitats.

The gut microbiome, or the community of microorganisms inhabiting the digestive tract, is often unique to its symbiont and, in many animal taxa, is highly influenced by host phylogeny and diet. In this study, we characterized the gut microbiome of the African savanna elephant (Loxodonta africana) and the African forest elephant (Loxodonta cyclotis), sister taxa separated by 2.6–5.6 million years of independent evolution. We examined the effect of host phylogeny on microbiome composition. Additionally, we examined the influence of habitat types (forest versus savanna) and diet types (crop‐raiding versus noncrop‐raiding) on the microbiome within L. africana. We found 58 bacterial orders, representing 16 phyla, across all African elephant samples. The most common phyla were Firmicutes, Proteobacteria, and Bacteroidetes. The microbiome of L. africana was dominated by Firmicutes, similar to other hindgut fermenters, while the microbiome of L. cyclotis was dominated by Proteobacteria, similar to more frugivorous species. Alpha diversity did not differ across species, habitat type, or diet, but beta diversity indicated that microbial communities differed significantly among species, diet types, and habitat types. Based on predicted KEGG metabolic pathways, we also found significant differences between species, but not habitat or diet, in amino acid metabolism, energy metabolism, and metabolism of terpenoids and polyketides. Understanding the digestive capabilities of these elephant species could aid in their captive management and ultimately their conservation.

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Livestock depredation is a major conservation challenge globally, causing significant economic losses to pastoralists and threatening large carnivore species outside protected areas. Our study investigated the temporal and spatial distribution of livestock depredation incidences, carnivore species associated with livestock depredation, and assessed mitigation measures in Maasai Mara Conservancies in Southern Kenya. Using daily monitoring of livestock depredation cases, we made comparisons between livestock attacks occurring in predator-proof bomas and those with traditional kraals. A total of 305 livestock depredation incidents were recorded between January and December 2021, translating to a total tally of 1411 livestock maimed or killed. Most livestock depredation incidents occurred during the day (59%) as opposed to night (41%), but this difference was not significant. Livestock depredation incidents in the nighttime occurred mostly inside traditional kraals (34%) and occurred the least in predator-proof kraals (2%). Lions were responsible for more livestock attacks in the grazing fields compared with leopards, hyenas, and wild dogs. Hyenas were more daring and attacked livestock inside traditional bomas relative to lions and leopards. Our study concludes that predator-proof bomas are more effective in minimizing livestock depredation and can be embraced as a sound intervention for human–carnivore co-existence in communities’ wildlife conservation areas.

At Saint Michael’s College, the challenge of quality mentoring inherent in CUREs is addressed using near-peer mentoring. Near-peer mentoring is when an individual completes a course and then serves as a mentor in subsequent iterations (McKenna and Williams 2017). In an undergraduate institution, this can be as simple as a third- or fourth-year student working with a second-year student. Through a CURE developed for a Molecular Genetics lab course, a widely adoptable model has been created.