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ABSTRACT Many international, national, state, and local organizations prioritize the ranking of threatened and endangered species to help direct conservation efforts. For example, the International Union for Conservation of Nature (IUCN) assesses the Green Status of species and publishes the influential Red List of threatened species. Unfortunately, such conservation yardsticks do not explicitly consider genetic or genomic diversity (GD), even though GD is positively associated with contemporary evolutionary fitness, individual viability, and with future evolutionary potential. To test whether populations of genome sequences could help improve conservation assessments, we estimated GD metrics from 82 publicly available mammalian datasets and examined their statistical association with attributes related to conservation. We also considered intrinsic biological factors, including trophic level and body mass, that could impact GD and quantified their relative influences. Our results identify key population GD metrics that are both reflective and predictive of IUCN conservation categories. Specifically, our analyses revealed that Watterson's theta (the population mutation rate) and autozygosity (a product of inbreeding) are associated with the current Red List categorization, likely because demographic declines that lead to “listing” decisions also reduce levels of standing genetic variation. We argue that by virtue of this relationship, conservation organizations like IUCN could leverage emerging genome sequence data to help categorize Red List threat rankings (especially in otherwise data‐deficient species) and/or enhance Green Status assessments to establish a baseline for future population monitoring. Thus, our paper (1) outlines the theoretical and empirical justification for a new GD‐based assessment criterion, (2) provides a bioinformatic pipeline for estimating GD from population genomic data, and (3) suggests an analytical framework that can be used to measure baseline GD while providing quantitative GD context for consideration by conservation authorities.

Epigenetic variation is often characterized by modifications to DNA that do not alter the underlying nucleotide sequence, but can influence behavior, morphology, and physiological phenotypes by affecting gene expression and protein synthesis. In this review, we consider how the emerging field of ecological epigenetics (eco-epi) aims to use epigenetic variation to explain ecologically relevant phenotypic variation and predict evolutionary trajectories that are important in conservation. Here, we focus on how epigenetic data have contributed to our understanding of wild populations, including plants, animals, and fungi. First, we identified published eco-epi literature and found that there was limited taxonomic and ecosystem coverage and that, by necessity of available technology, these studies have most often focused on the summarized epigenome rather than locus- or nucleotide-level epigenome characteristics. We also found that while many studies focused on adaptation and heritability of the epigenome, the field has thematically expanded into topics such as disease ecology and epigenome-based ageing of individuals. In the second part of our synthesis, we discuss key insights that have emerged from the epigenetic field broadly and use these to preview the path toward integration of epigenetics into ecology. Specifically, we suggest moving focus to nucleotide-level differences in the epigenome rather than whole-epigenome data and that we incorporate several facets of epigenome characterization (e.g., methylation, chromatin structure). Finally, we also suggest that incorporation of behavior and stress data will be critical to the process of fully integrating eco-epi data into ecology, conservation, and evolutionary biology.

As we continue to convert green spaces into roadways and buildings, connectivity between populations and biodiversity will continue to decline. In threatened and endangered species, this trend is particularly concerning because the cessation of immigration can cause increased inbreeding and loss of genetic diversity, leading to lower adaptability and higher extirpation probabilities in these populations. Unfortunately, monitoring changes in genetic diversity from management actions such as assisted migration and predicting the extent of introduced genetic variation that is needed to prevent extirpation is difficult and costly in situ. Therefore, we designed an agent-based model to link population-wide genetic variability and the influx of unique alleles via immigration to population stability and extirpation outcomes. These models showed that management of connectivity can be critical in restoring at-risk populations and reducing the effects of inbreeding depression. However, the rescued populations were more similar to the migrant source population (average FST range 0.05-0.10) compared to the historical recipient population (average FST range 0.23-0.37). This means that these management actions not only recovered the populations from the effects of inbreeding depression, but they did so in a way that changed the evolutionary trajectory that was predicted and expected for these populations prior to the population crash. This change was most extreme in populations with the smallest population sizes, which are representative of critically endangered species that could reasonably be considered candidates for restored connectivity or translocation strategies. Understanding how these at-risk populations change in response to varying management interventions has broad implications for the long-term adaptability of these populations and can improve future efforts for protecting locally adapted allele complexes when connectivity is restored.