Accurately detecting steps in genetic diversity across landscapes is important for locating barriers to gene flow, identifying selectively important loci, and defining management units. However, there are many metrics that researchers could use to detect steps and little information on which might be the most robust. Our study aimed to determine the best measure/s for genetic step detection along linear gradients using biallelic single nucleotide polymorphism (SNP) data. We tested the ability to differentiate between linear and step-like gradients in genetic diversity, using a range of diversity measures derived from the q-profile, including allelic richness, Shannon Information, GST, and Jost-D, as well as Bray-Curtis dissimilarity. To determine the properties of each measure, we repeated simulations of different intensities of step and allele proportion ranges, with varying genome sample size, number of loci, and number of localities. We found that alpha diversity (within-locality) based measures were ineffective at detecting steps. Further, allelic richness-based beta (between-locality) measures (e.g., Jaccard and Sørensen dissimilarity) were not reliable for detecting steps, but instead detected departures from fixation. The beta diversity measures best able to detect steps were: Shannon Information based measures, GST based measures, a Jost-D related measure, and Bray-Curtis dissimilarity. No one measure was best overall, with a trade-off between those measures with high step detection sensitivity (GST and Bray-Curtis) and those that minimised false positives (a variant of Shannon Information). Therefore, when detecting steps, we recommend understanding the differences between measures and using a combination of approaches.

Sandra Vardeh

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The Australian range of little penguins, Eudyptula minor, extends around southern Australia, with range-edge sites near the large cities of Perth (west) and Sydney (east). Both range-edges are closer to the equator than the range-core, being likely to experience similar heating with climate change. As a result, movement to one range-edge is not an option for little penguins, unlike in many other species. Therefore, adaptation at the range edge might be very important for little penguins. Capacity for future adaptation depends upon the variability each site holds, and the amount of exchange between sites. In peripheral sites, incoming dispersal might either forestall demographic collapse and replenish genetic variation (good), or overcome local adaptation and increase disease transmission (bad). We aimed to establish the genetic variability in each site, and the exchange (dispersal) of individuals between sites. Genetic markers included biparentally-inherited microsatellites, and maternally-inherited mitochondrial DNA sequence. For microsatellites, no site appeared to have critically low variation, including the peripheral sites, however there was a significant but slight trend of increased variation from east to west. In contrast, mitochondrial DNA showed a pattern of significantly reduced variation at the two range-edges, possibly indicating differential dispersal patterns in males and females. There appear to be two main genetically distinct groups, in the west and the east, but analysis of lifetime dispersal patterns across the Australian range also suggests complex dispersal, sometimes with high dispersal or similarity between locations that are not adjacent. Our work suggests that despite some differentiation, little penguin sites are interdependent due to complex dispersal patterns, and all have valuable genetic variation. In particular, the peripheral sites are not depauperate of variation, and are moderately connected to the remainder of the distribution, so possibly may be able to adapt in response to climate warming.

Gabe O'Reilly

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Celine Frere

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Linear infrastructure stands as one of the main culprits of anthropogenically caused biodiversity decline. As it fragments landscapes, it ultimately results in a myriad of direct and indirect ecological consequences for wildlife. As transportation networks will continue to grow under increasing human population growth, biodiversity will continue to decline making the need to understand and mitigate their impact on species an urgent need for conservation worldwide. The implementation of mitigation measures to alleviate the barrier effect produced by linear transport infrastructure on local fauna is not new, and research has shown that their effectiveness has been shown to be influenced by their design, their placement and the biology of the impacted species. Our understanding of their effectiveness in preventing the longer-term impacts of linear transport infrastructure on habitat connectivity via gene flow, however, remains poorly understood. Here, we used a pre- and post-habitat fragmentation genetic dataset collected as part of an extensive Koala Management Program to ask questions about the immediate and predicted longer-term genetic consequences of linear transport infrastructure on the impacted species. Importantly, using forward migration simulations, we show that to preserve connectivity would need to result in around 20% of the population mixing to avoid long-term genetic drift. These results have important consequences for the management of species at the forefront of linear infrastructure. In particular, the study shows the importance of considering gene flow in our assessment of the effectiveness of fauna crossings.

Gabe O'Reilly

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Gabe O'Reilly

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New sequencing technologies have opened the door to many new research opportunities, but these advances in data collection are not always compatible with some important methods for data analysis. Fis has been a staple calculation in the field of population genetics. Fis can be used to measure either a departure from random mating, or measure underlying selective pressures for or against heterozygote genotypes. However, when using Next Generation Sequencing (NGS) technology on multi-locus gene families it is often impossible to discern which allelic variants are present at each locus. Some important multi-locus gene families are: the major histocompatibility complex (MHC) in animals; homeobox genes in fungi; or the self-incompatibility genes in plants. This in turn makes it impossible to calculate either locus-specific expected heterozygosity, or observed heterozygosity, both of which are required to calculate Fis. Without the ability to calculate Fis from NGS of multi-locus gene families, we need a new multi-locus measure that will allow us to detect the underlining mating, and selective patterns present in such multi-locus genes. This paper provides such a novel multi-locus measure, called 1His. We demonstrate the accuracy of the 1His equation using simulated data, and two datasets taken from natural populations of dolphins and penguins. The introduction of this new measure is particularly important because of the great interest in mating patterns and selection of multi-locus gene families, such as MHC.