Meta‐analysis of Antarctic phylogeography reveals strong sampling bias and critical knowledge gaps

• Published in: Ecography
• Main Authors: Liu, Xiaoyue P., Duffy, Grant A., Pearman, William S., Pertierra, Luis R., Fraser, Ceridwen I.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Antarctic; Antarctic Peninsula; Southern Ocean; The Antarctic; Victoria Land; Antarc*; Antarctica
• Online Access: http://dx.doi.org/10.1111/ecog.06312; https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.06312; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ecog.06312

Much of Antarctica's highly endemic terrestrial biodiversity is found in small ice‐free patches. Substantial genetic differentiation has been detected among populations across spatial scales. Sampling is, however, often restricted to commonly‐accessed sites and we therefore lack a comprehensive understanding of broad‐scale biogeographic patterns, which could impede forecasts of the nature and impacts of future change. Here, we present a synthesis of published genetic studies across terrestrial Antarctica and the broader Antarctic region, aiming to identify current biogeographic patterns, environmental drivers of diversity and future research priorities. A database of all published genetic research from terrestrial fauna and flora (excl. microbes) across the Antarctic region was constructed. This database was then filtered to focus on the most well‐represented taxa and markers (mitochondrial COI for fauna, and nuclear ITS for flora). The final dataset comprised 7222 records, spanning 153 studies of 335 different species. There was strong taxonomic bias towards flowering plants (52% of all floral data sets) and springtails (54% of all faunal data sets), and geographic bias towards the Antarctic Peninsula and Victoria Land. Recent connectivity between the Antarctic continent and neighbouring landmasses, such as South America and the Southern Ocean Islands (SOIs), was inferred for some groups, but patterns observed for most taxa were strongly influenced by sampling biases. Above‐ground wind speed and habitat heterogeneity were positively correlated with genetic diversity indices overall though environment was a generally poor predictor of genetic diversity. The low resolution and variable coverage of data may also have reduced the power of our comparative inferences. In the future, higher‐resolution data, such as genomic SNPs and environmental modelling, alongside targeting sampling of remote sites and under sampled taxa, will address current knowledge gaps and greatly advance our understanding of evolutionary ...