Victorian Alpine Plot Network (Alpine Long Term Monitoring - Community Changes): Multi-taxa Phylogenomic Data, 2012–2013

Global change poses significant and urgent challenges for biodiversity conservation. Species<br>persistence under a rapidly changing environment ultimately depends on abilities to disperse<br>to favourable habitats or adapt in situ by plastic or evolutionary mechanisms. Conservation<br>strategies preserving endemism and adaptive potential are critical.<br><br>This study aims to investigate the phylogeographic history of Victorian Alpine plants<br>using high-density genetic markers. Multi-taxa genomic data was compared to determine<br>common phylogeographic patterns and identify evolutionary processes shaping biodiversity.<br>Spatial patterns of genetic structure were used to delineate evolutionary bioregions and<br>refugia of high conservation value.<br><br>Life-history traits have seldom been explicitly within a landscape genetic framework.<br>Spatial isolation is a key component of genetic structure for sessile organisms. This study<br>demonstrates that life-history traits are primary drivers of inter-population connectivity and<br>genetic structure. Differences across taxa impacted on patterns of genetic structure on fine<br>spatial scales, while common patterns were observed at broad scales regardless of life-history<br>traits.<br><br>These findings complement other Australian Alpine genetic studies indicate that flora<br>and fauna in Victorian Alps share a common genetic structure and phylogeographic history<br>driven by unique processes. The geomorphology of the Victorian Alps has clearly driven the<br>evolutionary trajectories of the native flora and fauna. This approach could inform evidence<br>based conservation policy.<br><br>Previously undelineated cryptic species were revealed by this study—highlighting<br>limitations of traditional taxonomy and the utility of new approaches. This project<br>demonstrates how genomic technologies can characterise evolutionary processes at landscape<br>scales, and detect important patterns in at-risk ecosystems.