Intertidal species distribution evaluation and prediction with the consideration of physiological and genetic traitsExtreme Events

Thursday 3 July from 12:00 to 12:15

Yunwei Dong1

1Ocean University of China, China

Understanding the genetic basis of local adaptation in thermal performance is essential for predicting how species distributions will shift due to anthropogenic climate change. Many species inhabit multiple biogeographic regions, and the uniquely adapted populations in each area may respond differently to future ocean warming. This study examined phylogeographic patterns, thermal sensitivity, and genetic differentiation in intertidal species.

Whole-genome sequencing, based on newly assembled chromosome-level genomes, revealed the genetic structure of these species. Both genetic factors and heat stress drive adaptive divergence across multiple levels of biological organization, from individuals to entire biogeographic regions. Considering the genetic diversity related to variations in heat tolerance, a physiological species distribution model (pSDM) was used to predict how different genetic subgroups will distribute themselves in response to climate change.

These findings demonstrate that even if a species’ range remains stable, it can still experience a significant loss of adaptive diversity due to climate change. The integrated approach presented here, which includes both physiological and adaptive genetic variation within a biogeographic framework, offers new insights into how marine species may respond to global warming.

Biography

Yunwei’s research focuses on developing an integrated understanding of the impacts of climate change and human activity on the biogeographic patterning of intertidal species. His work employs a wide range of experimental approaches - from molecular biology to biogeographic analysis - to provide a mechanistic understanding of the drivers of these distribution shifts and to enable construction of models for predicting structure and function of ecosystems under different environmental change scenarios.