U.S. Department of Agriculture: Animal and Plant Health Inspection Service
United States Department of Agriculture Wildlife Services: Staff Publications
Document Type
Article
Date of this Version
2023
Citation
Claunch, N.M., C.M. Goodman, B.M. Kluever, N. Barve, R.P. Guralnick, and C.M. Romagosa. 2023. Commonly collected thermal performance data can inform species distributions in a data-limited invader. Scientific Reports 13:15880. doi: 10.1038/s41598-023-43128-4
Abstract
Predicting potential distributions of species in new areas is challenging. Physiological data can improve interpretation of predicted distributions and can be used in directed distribution models. Nonnative species provide useful case studies. Panther chameleons (Furcifer pardalis) are native to Madagascar and have established populations in Florida, USA, but standard correlative distribution modeling predicts no suitable habitat for F. pardalis there. We evaluated commonly collected thermal traits– thermal performance, tolerance, and preference—of F. pardalis and the acclimatization potential of these traits during exposure to naturally-occurring environmental conditions in North Central Florida. Though we observed temperature-dependent thermal performance, chameleons maintained similar thermal limits, performance, and preferences across seasons, despite long-term exposure to cool temperatures. Using the physiological data collected, we developed distribution models that varied in restriction: time-dependent exposure near and below critical thermal minima, predicted activity windows, and predicted performance thresholds. Our application of commonly collected physiological data improved interpretations on potential distributions of F. pardalis, compared with correlative distribution modeling approaches that predicted no suitable area in Florida. These straightforward approaches can be applied to other species with existing physiological data or after brief experiments on a limited number of individuals, as demonstrated here.
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Comments
U.S. government work