Life history traits explain the intra-seasonal abundance pattern of rare land snail species Vertigo moulinsiana: bridging the theory-application gap

TL;DR

This study shows how the life history traits of a rare land snail species affect its population patterns and vulnerability to climate change.

Contribution

The study integrates life history and physiological traits into a predictive population model to explain vulnerability to snow cover loss.

Findings

Snow cover provides critical thermal buffering for V. moulinsiana populations.

Loss of snow cover leads to increased mortality and population decline.

Life history traits should be integrated into ecological models for better conservation planning.

Abstract

Vertigo moulinsiana, a rare and vulnerable land snail species, faces increasing threats from climate change, particularly due to the loss of snow cover and its associated thermal buffering effects. In this study, we develop a population dynamics model to explore how life history traits, including overwintering strategies and seasonal reproduction, shape the intra-seasonal abundance patterns of V. moulinsiana. Using empirical data and simulated snow cover disappearance scenarios, we demonstrate the critical role of snow as an insulating layer that maintains stable subnivium (a microhabitat located at the interface between the snowpack and the ground) conditions. Without this layer, populations experience significant declines due to increased exposure to freezing temperatures and heightened mortality during snowless winters. Our findings highlight the vulnerability of V. moulinsiana to extreme winter conditions and emphasize the importance of integrating life history traits into ecological models. These insights provide a practical framework for conservation by identifying critical periods of vulnerability and habitat features (e.g., subnivium-like refugia) that can buffer populations against climate extremes and should be prioritized in management planning. The model is parameterized and validated using empirical data previously collected by the authors, offering a novel synthesis of life history and physiological traits in a predictive population framework.

The online version contains supplementary material available at 10.1038/s41598-025-10471-7.

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