Perceptions of Similarity Can Mislead Provenancing Strategies—An Example from Five Co-Distributed Acacia Species

Abstract

Ecological restoration requires balancing levels of genetic diversity to achieve present-day establishment as well as long-term sustainability. Assumptions based on distributional, taxonomic or functional generalizations are often made when deciding how to source plant material for restoration. We investigate this assumption and ask whether species-specific data is required to optimize provenancing strategies. We use population genetic and environmental data from five congeneric and largely co-distributed species of Acacia to specifically ask how different species-specific genetic provenancing strategies are based on empirical data and how well a simple, standardized collection strategy would work when applied to the same species. We find substantial variability in terms of patterns of genetic diversity and differentiation across the landscape among these five co-distributed Acacia species. This variation translates into substantial differences in genetic provenancing recommendations among species (ranging from 100% to less than 1% of observed genetic variation across species) that could not have been accurately predicted a priori based on simple observation or overall distributional patterns. Furthermore, when a common provenancing strategy was applied to each species, the recommended collection areas and the evolutionary representativeness of such artificially standardized areas were substantially different (smaller) from those identified based on environmental and genetic data. We recommend the implementation of the increasingly accessible array of evolutionary-based methodologies and information to optimize restoration efforts.

All Populations Matter: Conservation Genomics of Australia’s Iconic Purple Wattle, Acacia purpureopetala

“…The matrix of polymorphic loci recovered for A. purpureopetala suggests that the available genetic variation residing in this species are very similar to other more widespread Acacias [16]. However, we found very low levels of diversity (Ho, He and ar) at the field sites, suggesting repeated levels of inbreeding through biparental inbreeding and self-fertilisation [59].…”

“…The R package SNPrelate [40] was used to estimate population pairwise Fst values based on the estimator of [41]. These diversity estimates were also calculated for three comparable sized data sets (sample size and sequencing depth for SNPs) of three species of Acacia (A. linifolia, A. longifolia and A suaveolens) with similar available data (DArTseq SNPs, samples size and sequencing depth) previously described in [16], allowing for a better understanding of levels of inferred genetic diversity in the study species.…”

“…d.f degree of freedom, SS = Sum of squares, Phi-statistics (Φ) according to [55]. Comparable genomic data for three additional species of Acacia (available to us through the Restore and Renew project [16,32]) allowed us to assess the levels of inferred diversity against other species with similar phylogenetic background. While the three additional species occur across a much bigger area (estimates of Area of Occupancy and Extent of Occurrence provided in Table S1), similar numbers of SNPs were found in three of the four species with the same sequencing depth as provided by Dart Pty Ltd.…”

“…The Ho, He, and ar as well as pairwise Fst estimates amongst field sites (Table S2; 0.2 to 0.9) were most like A. suaveolens, and this species has been found to have a mixed mating system with high levels of selfing within some populations [56]. More details of the three additional species of Acacia species are available [16].…”

“…Figure S3: This plot illustrates how the proportion of total diversity captured through increasing sampling from the available sample set in the A. purpureopetala DArTseq data set does not increase beyond a sample size of 60 individuals. We used OptGenMix (Bragg et al 2020) to estimate the proportion of diversity based on maximising the genetic distance between samples. Figure S4 Plot showing predicted compositional dissimilarity against observed compositional dissimilarity.…”