No abstract

Understanding whether populations can adapt in situ or whether interventions are required is of key importance for biodiversity management under climate change. Landscape genomics is becoming an increasingly important and powerful tool for rapid assessments of climate adaptation, especially in long-lived species such as trees. We investigated climate adaptation in Eucalyptus microcarpa using the DArTseq genomic approach. A combination of F outlier and environmental association analyses were performed using >4200 genomewide single nucleotide polymorphisms (SNPs) from 26 populations spanning climate gradients in southeastern Australia. Eighty-one SNPs were identified as putatively adaptive, based on significance in F outlier tests and significant associations with one or more climate variables related to temperature (70/81), aridity (37/81) or precipitation (35/81). Adaptive SNPs were located on all 11 chromosomes, with no particular region associated with individual climate variables. Climate adaptation appeared to be characterized by subtle shifts in allele frequencies, with no consistent fixed differences identified. Based on these associations, we predict adaptation under projected changes in climate will include a suite of shifts in allele frequencies. Whether this can occur sufficiently rapidly through natural selection within populations, or would benefit from assisted gene migration, requires further evaluation. In some populations, the absence or predicted increases to near fixation of particular adaptive alleles hint at potential limits to adaptive capacity. Together, these results reinforce the importance of standing genetic variation at the geographic level for maintaining species' evolutionary potential.

Eucalypts are the cornerstone of ecological restoration efforts across the highly modified agricultural landscapes of southern Australia. \u27Local provenancing\u27 is the established strategy for sourcing germplasm for ecological restoration plantings, yet this approach gives little consideration to the persistence of these plantings under future climates. This paper provides a synopsis of recent and ongoing research that the authors are undertaking on climate adaptation in eucalypts, combining new genomic approaches with ecophysiological evidence from provenance trials. These studies explore how adaptive diversity is distributed within and among populations, whether populations are buffered against change through capacity for phenotypic plasticity, and how this informs provenancing strategies. Results to date suggest that eucalypts have some capacity to respond to future environmental instability through adaptive phenotypic plasticity or selection of putatively adaptive alleles. Despite this, growing evidence suggests that eucalypts will still be vulnerable to change. Provenancing strategies that exploit adaptations found in non-local provenances could thus confer greater climate-resilience in ecological restoration plantings, although they will also need to account for potential interactions between climate adaptations and other factors (e.g. cryptic evolutionary variation, non-climate-related adaptations, herbivory and elevated CO2)

Revegetation plantings are a key management tool for ecological restoration. Revegetation success is usually measured using ecological traits, however, genetic diversity should also be considered as it can influence fitness, adaptive capacity and long-term viability of revegetation plantings and ecosystem functioning. Here we review the global literature comparing genetic diversity in revegetation plantings to natural stands. Findings from 48 studies suggest variable genetic outcomes of revegetation, with 46% demonstrating higher genetic diversity in revegetation than natural stands and 52% demonstrating lower diversity. Levels of genetic diversity were most strongly associated with the number of source sites used—where information was available, 69% of studies showing higher genetic diversity in revegetation reported using multiple provenances, compared with only 33% for those with lower diversity. However, with a few exceptions, it was unclear whether differences in genetic diversity between revegetation and natural stands were statistically significant. This reflected insufficient reporting of statistical error and metadata within the published studies, which limited conclusions about factors contributing to patterns. Nonetheless, our findings indicate that mixed seed sourcing can contribute to higher genetic diversity in revegetation. Finally, we emphasize the type of metadata needed to determine factors influencing genetic diversity in revegetation and inform restoration efforts.

Our data did not support the effectiveness of the inSPOT intervention among a predominantly heterosexual population in a large urban STI clinic.

In order to contribute to evolutionary resilience and adaptive potential in highly modified landscapes, revegetated areas should ideally reflect levels of genetic diversity within and across natural stands. Landscape genomic analyses enable such diversity patterns to be characterized at genome and chromosomal levels. Landscape-wide patterns of genomic diversity were assessed in Eucalyptus microcarpa, a dominant tree species widely used in revegetation in Southeastern Australia. Trees from small and large patches within large remnants, small isolated remnants and revegetation sites were assessed across the now highly fragmented distribution of this species using the DArTseq genomic approach. Genomic diversity was similar within all three types of remnant patches analysed, although often significantly but only slightly lower in revegetation sites compared with natural remnants. Differences in diversity between stand types varied across chromosomes. Genomic differentiation was higher between small, isolated remnants, and among revegetated sites compared with natural stands. We conclude that small remnants and revegetated sites of our E. microcarpa samples largely but not completely capture patterns in genomic diversity across the landscape. Genomic approaches provide a powerful tool for assessing restoration efforts across the landscape.

Identifying genomic patterns associated with adaptation in wild populations can provide information to support management strategies as well as facilitate fundamental discoveries (Garner et al., 2016;Sgrò et al., 2011). We can improve our understanding of the response of species to changing climates and their evolutionary potential by leveraging knowledge about adaptive genetic variation in

Genotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results. These effects could be amplified in downstream predictions, including management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for adaptation to environment. Using empirical and simulated datasets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the dataset, lessening the power to detect adaptive variants (i.e. simulated true positives) with strong and weak strength of selections. Regardless, strength of selection was a good predictor for GEA detection, but even SNPs under strong selection went undetected. We further show that filtering can significantly impact the predictions of adaptive capacity of species in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending largely on the study system, availability of genomic resources, and desired objectives of the study.