Crater lake habitat predicts morphological diversity in adaptive radiations of cichild fishes

Recknagel, H. , Elmer, K. R. and Meyer, A. (2014) Crater lake habitat predicts morphological diversity in adaptive radiations of cichild fishes. Evolution, 68(7), pp. 2145-2155. (doi: 10.1111/evo.12412)

Abstract

Adaptive radiations provide an excellent opportunity for studying the correlates and causes for the origin of biodiversity. In these radiations, species diversity may be influenced by either the ecological and physical environment, intrinsic lineage effects, or both. Disentangling the relative contributions of these factors in generating biodiversity remains a major challenge in understanding why a lineage does or does not radiate. Here, we examined morphological variation in body shape for replicate flocks of Nicaraguan Midas cichlid fishes and tested its association with biological and physical characteristics of their crater lakes. We found that variability of body elongation, an adaptive trait in freshwater fishes, is mainly predicted by average lake depth (N = 6, P < 0.001, R2 = 0.96). Other factors considered, including lake age, surface area, littoral zone area, number of co-occurring fish species, and genetic diversity of the Midas flock, did not significantly predict morphological variability. We also showed that lakes with a larger littoral zone have on average higher bodied Midas cichlids, indicating that Midas cichlid flocks are locally adapted to their crater lake habitats. In conclusion, we found that a lake's habitat predicts the magnitude and the diversity of body elongation in repeated cichlid adaptive radiations.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Elmer, Professor Kathryn and Recknagel, Mr Hans
• Authors: Recknagel, H., Elmer, K. R., and Meyer, A.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
• Journal Name: Evolution
• Publisher: Wiley
• ISSN: 0014-3820
• ISSN (Online): 1558-5646