Authors: Lawrence ER, Pedersen EJ, Fraser DJ
The broad scale distribution of population-specific genetic diversity (GDP ) across taxa remains understudied relative to species diversity gradients, despite its relevance for systematic conservation planning. We used nuclear DNA data collected from 3678 vertebrate populations across the Americas to assess the role of environmental and spatial variables in structuring the distribution of GDP , a key component of adaptive potential in the face of environmental change. We specifically assessed non-linear trends for a metric of GDP, expected heterozygosity (HE ), and found more evidence for spatial hotspots and cold spots in HE rather than a strict pattern with latitude. We also detected inconsistent relationships between HE and environmental variables, where only 11 of 30 environmental comparisons among taxa groups were statistically significant at the .05 level, and the shape of significant trends differed substantially across vertebrate groups. Only one of six taxonomic groups, freshwater fishes, consistently showed significant relationships between HE and most (four of five) environmental variables. The remaining groups had statistically significant relationships for either two (amphibians, reptiles), one (birds, mammals), or no variables (anadromous fishes). Our study highlights gaps in the theoretical foundation upon which macrogenetic predictions have been made thus far in the literature, as well as the nuances for assessing broad patterns in GDP among vertebrate groups. Overall, our results suggest a disconnect between patterns of species and genetic diversity, and underscores that large-scale factors affecting genetic diversity may not be the same factors as those shaping taxonomic diversity. Thus, careful spatial and taxonomic-specific considerations are needed for applying macrogenetics to conservation planning.
Keywords: biodiversity gradient; environmental variables; genetic diversity; macrogenetics; population genetics; vertebrates;
PubMed: https://pubmed.ncbi.nlm.nih.gov/37365672/
DOI: 10.1111/mec.17059