Testing the predictability of morphological evolution in contrasting thermal environments

• Published in: Evolution
• Main Authors: Pilakouta, Natalie, Humble, Joseph L., Hill, Iain D.C., Arthur, Jessica, Costa, Ana P.B., Smith, Bethany A., Kristjánsson, Bjarni K., Skúlason, Skúli, Killen, Shaun S., Lindström, Jan, Metcalfe, Neil B., Parsons, Kevin J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press 2023
• Subjects: Iceland
• Online Access: https://eprints.gla.ac.uk/289440/; https://eprints.gla.ac.uk/289440/1/289440.pdf

Gaining the ability to predict population responses to climate change is a pressing concern. Using a “natural experiment,” we show that testing for divergent evolution in wild populations from contrasting thermal environments provides a powerful approach, and likely an enhanced predictive power for responses to climate change. Specifically, we used a unique study system in Iceland, where freshwater populations of threespine sticklebacks (Gasterosteus aculeatus) are found in waters warmed by geothermal activity, adjacent to populations in ambient-temperature water. We focused on morphological traits across six pairs from warm and cold habitats. We found that fish from warm habitats tended to have a deeper mid-body, a subterminally orientated jaw, steeper craniofacial profile, and deeper caudal region relative to fish from cold habitats. Our common garden experiment showed that most of these differences were heritable. Population age did not appear to influence the magnitude or type of thermal divergence, but similar types of divergence between thermal habitats were more prevalent across allopatric than sympatric population pairs. These findings suggest that morphological divergence in response to thermal habitat, despite being relatively complex and multivariate, are predictable to a degree. Our data also suggest that the potential for migration of individuals between different thermal habitats may enhance nonparallel evolution and reduce our ability to predict responses to climate change.