The Effects of Cold Tolerance on the Distribution of Two Extreme Altitude Lizard Species in the Qinghai–Tibetan Plateau

TL;DR

This study improves predictions of lizard habitats on the Qinghai–Tibetan Plateau by incorporating cold tolerance into models, leading to more accurate and smaller suitable area estimates.

Contribution

The study introduces cold tolerance data into species distribution models for two high-altitude lizard species, improving prediction accuracy.

Findings

Incorporating cold tolerance data reduced predicted suitable areas by 37.13% compared to traditional models.

The difference between models was concentrated at the edges of high suitability areas.

Cold tolerance data improved model accuracy by reflecting physiological limits of survival.

Abstract

Predicting where species occur is crucial for their research, but traditional models often overestimate suitable areas because they rely only on general climate. This is especially problematic for animals in extreme environments like the Qinghai–Tibetan Plateau, where surviving the harsh winter is critical. Our study aimed to improve distribution maps for two extreme altitude lizard species by adding a key piece of physiological information: their ability to withstand cold stress during winter hibernation. We compared maps based on just climate data with new maps that also incorporated this cold-tolerance trait. Our results showed that the new, physiology-informed models were smaller and more accurate, removing areas where winters are too severe for the lizards to survive. This proves that including an animal’s physiological limits in models is essential for realistic predictions. Creating more accurate habitat maps is valuable because it helps conservationists focus their efforts on the areas that are truly vital for protecting unique species in a changing world.

Species distribution models (SDMs) have been widely used to predict potentially suitable habitats for species. However, traditional SDMs have been criticized for ignoring the physiological processes by which species respond to their environment. Integrating physiological tolerance into the model is essential to improve the prediction accuracy of SDMs. Currently, this approach has not been applied in the study of Phrynocephalus erythrurus and Phrynocephalus theobaldi, which are part of the world’s highest reptiles and endemic to Qinghai–Tibetan Plateau. In this study, based on experiments, we found that the critical thermal minimum (CTmin) of the Phrynocephalus theobaldi was 0.85 °C. Further, we studied the effect of cold tolerance on the prediction of potential areas for these two reptile species. The high suitability area predicted by the SDMs incorporating cold tolerance data were 37.13% smaller than that predicted by the traditional SDMs. The difference between the two SDMs is primarily concentrated at the edges of the high suitability areas. The incorporation of cold tolerance data influenced the model’s predictions by effectively reducing the extent of edges of the high suitability areas. Our findings have theoretical significance for optimizing SDM predictions and provide a robust scientific basis for biodiversity conservation in the extreme-altitude ecosystems of the Qinghai–Tibetan Plateau.

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