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Abstract Genomic data and machine learning approaches have gained interest due to their potential to identify adaptive genetic variation across populations and to assess species vulnerability to climate change. By identifying gene–environment associations for putatively adaptive loci, these approaches project changes to adaptive genetic composition as a function of future climate change (genetic offsets), which are interpreted as measuring the future maladaptation of populations due to climate change. In principle, higher genetic offsets relate to increased population vulnerability and therefore can be used to set priorities for conservation and management. However, it is not clear how sensitive these metrics are to the intensity of population and individual sampling. Here, we use five genomic datasets with varying numbers of SNPs (N_SNPs = 7006–1,398,773), sampled populations (N_pop = 23–47) and individuals (N_ind = 185–595) to evaluate the estimation sensitivity of genetic offsets to varying degrees of sampling intensity. We found that genetic offsets are sensitive to the number of populations being sampled, especially with less than 10 populations and when genetic structure is high. We also found that the number of individuals sampled per population had small effects on the estimation of genetic offsets, with more robust results when five or more individuals are sampled. Finally, uncertainty associated with the use of different future climate scenarios slightly increased estimation uncertainty in the genetic offsets. Our results suggest that sampling efforts should focus on increasing the number of populations, rather than the number of individuals per populations, and that multiple future climate scenarios should be evaluated to ascertain estimation sensitivity. 

Under the recently adopted Kunming‐Montreal Global Biodiversity Framework, 196 Parties committed to reporting the status of genetic diversity for all species. To facilitate reporting, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: maintaining genetically distinct populations and ensuring populations are large enough to maintain genetic diversity. The major advantage of these indicators is that they can be estimated with or without DNA‐based data. However, demonstrating their feasibility requires addressing the methodological challenges of using data gathered from diverse sources, across diverse taxonomic groups, and for countries of varying socio‐economic status and biodiversity levels. Here, we assess the genetic indicators for 919 taxa, representing 5271 populations across nine countries, including megadiverse countries and developing economies. Eighty‐three percent of the taxa assessed had data available to calculate at least one indicator. Our results show that although the majority of species maintain most populations, 58% of species have populations too small to maintain genetic diversity. Moreover, genetic indicator values suggest that IUCN Red List status and other initiatives fail to assess genetic status, highlighting the critical importance of genetic indicators. 

Abstract The gut microbiota is critical for host function. Among mammals, host phylogenetic relatedness and diet are strong drivers of gut microbiota structure, but one factor may be more influential than the other. Here, we used 16S rRNA gene sequencing to determine the relative contributions of host phylogeny and host diet in structuring the gut microbiotas of 11 herbivore species from 5 families living sympatrically in southwest Kenya. Herbivore species were classified as grazers, browsers, or mixed-feeders and dietary data (% C4 grasses in diet) were compiled from previously published sources. We found that herbivore gut microbiotas were highly species-specific, and that host taxonomy accounted for more variation in the gut microbiota (30%) than did host dietary guild (10%) or sample month (8%). Overall, similarity in the gut microbiota increased with host phylogenetic relatedness (r = 0.74) across the 11 species of herbivores, but among 7 closely related Bovid species, dietary %C4 grass values more strongly predicted gut microbiota structure (r = 0.64). Additionally, within bovids, host dietary guild explained more of the variation in the gut microbiota (17%) than did host species (12%). Lastly, while we found that the gut microbiotas of herbivores residing in southwest Kenya converge with those of distinct populations of conspecifics from central Kenya, fine-scale differences in the abundances of bacterial amplicon sequence variants (ASVs) between individuals from the two regions were also observed. Overall, our findings suggest that host phylogeny and taxonomy strongly structure the gut microbiota across broad host taxonomic scales, but these gut microbiotas can be further modified by host ecology (i.e., diet, geography), especially among closely related host species.