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Crop diversity underpins the productivity, resilience and adaptive capacity of agriculture. Loss of this diversity, termed crop genetic erosion, is therefore concerning. While alarms regarding evident declines in crop diversity have been raised for over a century, the magnitude, trajectory, drivers and significance of these losses remain insufficiently understood. We outline the various definitions, measurements, scales and sources of information on crop genetic erosion. We then provide a synthesis of evidence regarding changes in the diversity of traditional crop landraces on farms, modern crop cultivars in agriculture, crop wild relatives in their natural habitats and crop genetic resources held in conservation repositories. This evidence indicates that marked losses, but also maintenance and increases in diversity, have occurred in all these contexts, the extent depending on species, taxonomic and geographic scale, and region, as well as analytical approach. We discuss steps needed to further advance knowledge around the agricultural and societal significance, as well as conservation implications, of crop genetic erosion. Finally, we propose actions to mitigate, stem and reverse further losses of crop diversity.

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.

We compare the two main classes of measures of population structure in genetics: (i) fixation measures such as FST, GST, and θ and (ii) allelic differentiation measures such as Jost's D and entropy differentiation. These two groups of measures quantify complementary aspects of population structure, which have no necessary relationship with each other. We focus especially on empirical aspects of population structure relevant to conservation analyses. At the empirical level, the first set of measures quantify nearness to fixation, while the second set of measures quantify relative degree of allelic differentiation. The two sets of measures do not compete with each other. Fixation measures are often misinterpreted as measures of allelic differentiation in conservation applications; we give examples and theoretical explanations showing why this interpretation can mislead. This misinterpretation has led to the mistaken belief that the absolute number of migrants determines allelic differentiation between demes when mutation rate is low; we show that in the finite island model, the absolute number of migrants determines nearness to fixation, not allelic differentiation. We show that a different quantity, the factor that controls Jost's D, is a good predictor of the evolution of the actual genetic divergence between demes at equilibrium in this model. We also show that when conservation decisions require judgments about differences in genetic composition between demes, allelic differentiation measures should be used instead of fixation measures. Allelic differentiation of fast‐mutating markers can be used to rank pairs or sets of demes according to their differentiation, but the allelic differentiation at coding loci of interest should be directly measured in order to judge its actual magnitude at these loci.

Identifying and monitoring locally adaptive genetic variation can have direct utility for conserving species at risk, especially when management may include actions such as translocations for restoration, genetic rescue, or assisted gene flow. However, genomic studies of local adaptation require careful planning to be successful, and in some cases may not be a worthwhile use of resources. Here, we offer an adaptive management framework to help conservation biologists and managers decide when genomics is likely to be effective in detecting local adaptation, and how to plan assessment and monitoring of adaptive variation to address conservation objectives. Studies of adaptive variation using genomic tools will inform conservation actions in many cases, including applications such as assisted gene flow and identifying conservation units. In others, assessing genetic diversity, inbreeding, and demographics using selectively neutral genetic markers may be most useful. And in some cases, local adaptation may be assessed more efficiently using alternative approaches such as common garden experiments. Here, we identify key considerations of genomics studies of locally adaptive variation, provide a road map for successful collaborations with genomics experts including key issues for study design and data analysis, and offer guidelines for interpreting and using results from genomic assessments to inform monitoring programs and conservation actions.

The rapidly emerging field of macrogenetics focuses on analysing publicly accessible genetic datasets from thousands of species to explore large-scale patterns and predictors of intraspecific genetic variation. Facilitated by advances in evolutionary biology, technology, data infrastructure, statistics and open science, macrogenetics addresses core evolutionary hypotheses (such as disentangling environmental and life-history effects on genetic variation) with a global focus. Yet, there are important, often overlooked, limitations to this approach and best practices need to be considered and adopted if macrogenetics is to continue its exciting trajectory and reach its full potential in fields such as biodiversity monitoring and conservation. Here, we review the history of this rapidly growing field, highlight knowledge gaps and future directions, and provide guidelines for further research.Leigh and colleagues describe the potential of the emerging field of macrogenetics to improve conservation and biodiversity management. Challenges preventing the field from reaching its full promise are highlighted and possible solutions and a framework for future macrogenetic studies are proposed.

Plant collections held by botanic gardens and arboreta are key components of ex situ conservation. Maintaining genetic diversity in such collections allows them to be used as resources for supplementing wild populations. However, most recommended minimum sample sizes for sufficient ex situ genetic diversity are based on microsatellite markers, and it remains unknown whether these sample sizes remain valid in light of more recently developed next‐generation sequencing (NGS) approaches. To address this knowledge gap, we examine how ex situ conservation status and sampling recommendations differ when derived from microsatellites and single nucleotide polymorphisms (SNPs) in garden and wild samples of two threatened oak species. For Quercus acerifolia, SNPs show lower ex situ representation of wild allelic diversity and slightly lower minimum sample size estimates than microsatellites, while results for each marker are largely similar for Q. boyntonii. The application of missing data filters tends to lead to higher ex situ representation, while the impact of different SNP calling approaches is dependent on the species being analyzed. Measures of population differentiation within species are broadly similar between markers, but larger numbers of SNP loci allow for greater resolution of population structure and clearer assignment of ex situ individuals to wild source populations. Our results offer guidance for future ex situ conservation assessments utilizing SNP data, such as the application of missing data filters and the usage of a reference genome, and illustrate that both microsatellites and SNPs remain viable options for botanic gardens and arboreta seeking to ensure the genetic diversity of their collections.

Conservation success depends on translating theory into practical guidance and tools that are relevant and useful for non‐scientists. While the complexity of population genetics has challenged the usage of straightforward metrics for conservation, several practical guidelines have been advanced, such as those regarding effective population size (Ne). Allendorf et al. highlight limitations of Ne as a metric for practical use. Specifically, they demonstrate that while Ne is sufficient for predicting heterozygosity, it is not predictive of the number of alleles, another key variable in conservation genetics. This has important implications for Ne‐based metrics, such as the Ne 500 indicator recently adopted in the Convention on Biological Diversity's Kunming–Montreal Global Biodiversity Framework. As developers and advocates of the Ne 500 indicator, we agree with this assessment, and acknowledge that Ne does not comprehensively predict changes in allelic variation. In this article we briefly summarize several major points in Allendorf et al. and provide practical suggestions to better account for allelic variation during indicator assessments. These suggestions include reporting major declines in Nc as part of genetic assessments, clearly articulating the intention and caveats of the Ne 500 indicator, integrating simulations into genetic assessments, and assessing the number of genetically distinct populations. We conclude that the Ne 500 indicator remains a valuable metric uniquely capable of capturing critical aspects of a species' genetic status while remaining accessible and interpretable to policymakers and other non‐geneticists. By acknowledging the limitations of focusing solely on Ne and providing options for more thorough and nuanced understandings of genetic diversity, we hope to guide future usage of the Ne 500 indicator and help bridge the gap between conservation genetics theory and practice.

Premise In this study, we use simulations to determine how pollen flow and sampling constraints can influence the genetic conservation found in seed collections. Methods We simulated genotypes of parental individuals and crossed the parentals based on three different ranges of pollen flow (panmictic, limited, and highly limited) to create new seed sets for sampling. We tested a variety of sampling scenarios modeled on those occurring in nature and calculated the proportion of alleles conserved in each scenario. Results We found that pollen flow greatly influences collection outcomes, with panmictic pollen flow resulting in seed sets containing 21.6% more alleles than limited pollen flow and 48.6% more alleles than highly limited pollen flow, although this impact diminishes when large numbers of maternal plants are sampled. Simulations of realistic seed sampling (sampling more seed from some plants and fewer from others) showed a relatively minor impact (<2.5%) on genetic diversity conserved compared to ideal sampling (uniform sampling across all maternal plants). Discussion We conclude that future work must consider limited pollen flow, but collectors can be flexible with their sampling in the field as long as many unique maternal plants are sampled. Simulations remain a fruitful method to advance ex situ sampling guidelines.

Hybridization is a potential tool for incorporating stress tolerance in plants, particularly to pests and diseases, in support of restoration and conservation efforts. Butternut ( Juglans cinerea ) is a species for which hybridization has only recently begun being explored. This North American hardwood tree is threatened due to Ophiognomonia clavigignenti-juglandacearum ( Ocj ), the causal fungus of butternut canker disease (BCD), first observed in 1967. Observational evidence in some wild J. cinerea populations indicates that naturalized hybrids of J. cinerea with Japanese walnut ( Juglans ailantifolia ) may be more tolerant to BCD than non-admixed J. cinerea , but this has not been formally tested in a controlled trial. We aimed to examine potential BCD tolerance within and between J. cinerea and J. cinerea × J. ailantifolia hybrids and to determine if there is a difference in canker growth between BCD fungal isolates. Five-year-old J. cinerea and hybrid trees were inoculated with two Ocj fungal isolates collected from natural infections found in two different sites in Indiana, United States, and a blank control (agar only). Measurements of both artificially induced and naturally occurring cankers were taken at 8-, 12-, 20-, 24-, and 32-month post-inoculation. Differences in canker presence/absence and size were observed by fungal isolate, which could help explain some of the differences in BCD severity seen between J. cinerea populations. Smaller and fewer cankers and greater genetic gains were seen in hybrid families, demonstrating that hybrids warrant further evaluation as a possible breeding tool for developing BCD-resistant J. cinerea trees.

Recent scientific evidence shows that genetic diversity must be maintained, managed, and monitored to protect biodiversity and nature's contributions to people. Three genetic diversity indicators, two of which do not require DNA‐based assessment, have been proposed for reporting to the Convention on Biological Diversity and other conservation and policy initiatives. These indicators allow an approximation of the status and trends of genetic diversity to inform policy, using existing demographic and geographic information. Application of these indicators has been initiated and here we describe ongoing efforts in calculating these indicators with examples. We specifically describe a project underway to apply these indicators in nine countries, provide example calculations, address concerns of policy makers and implementation challenges, and describe a roadmap for further development and deployment, incorporating feedback from the broader community. We also present guidance documents and data collection tools for calculating indicators. We demonstrate that Parties can successfully and cost‐effectively report these genetic diversity indicators with existing biodiversity observation data, and, in doing so, better conserve the Earth's biodiversity.