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

Spatial genetic patterns can be influenced by a broad range of factors across a landscape. The hypothesis that heterogeneous vegetation and habitat fragmentation rather than water flow influence genetic patterns in two riparian plant species with different niches was tested. Genotyping by sequencing was used to assess the genetic diversity and structure of each species at 12 sites across a river catchment. Generalized dissimilarity modelling examined the relative influence that vegetation type and habitat fragmentation had on patterns of genetic differentiation across the landscape. Restricted gene flow in the widespread species, Callistachys lanceolata, resulted in lower genetic differentiation than that exhibited by Astartea leptophylla, a restricted riparian species with high gene flow. Geographic distance and vegetation type explained the patterns of genetic differentiation in the widespread species, whereas habitat fragmentation and, to a lesser extent, vegetation type explained patterns in the restricted species. Water flow was not found to have significant impacts on patterns of genetic diversity in riparian plant species with restricted and widespread distribution. Impacts of vegetation type on genetic differentiation were most likely due to change in canopy density and associated pollinator communities in the vegetation types across the catchment. Reduced connectivity caused by habitat fragmentation was evident in the restricted riparian species, while reduced connectivity in the widespread species was related to the change of vegetation type between sites. Natural causes of reduced connectivity as well as anthropogenic causes need to be considered in future work to predict persistence and resilience under a changing climate.

Microsatellite markers have demonstrated their value for performing paternity exclusion and hence exploring mating patterns in plants and animals. Methodology is well established for diploid species, and several software packages exist for elucidating paternity in diploids; however, these issues are not so readily addressed in polyploids due to the increased complexity of the exclusion problem and a lack of available software. We introduce polypatex, an r package for paternity exclusion analysis using microsatellite data in autopolyploid, monoecious or dioecious/bisexual species with a ploidy of 4n, 6n or 8n. Given marker data for a set of offspring, their mothers and a set of candidate fathers, polypatex uses allele matching to exclude candidates whose marker alleles are incompatible with the alleles in each offspring-mother pair. polypatex can analyse marker data sets in which allele copy numbers are known (genotype data) or unknown (allelic phenotype data) - for data sets in which allele copy numbers are unknown, comparisons are made taking into account all possible genotypes that could arise from the compared allele sets. polypatex is a software tool that provides population geneticists with the ability to investigate the mating patterns of autopolyploids using paternity exclusion analysis on data from codominant markers having multiple alleles per locus.

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.

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