In the Arctic tundra, snow and ice conditions critically determine whether wildlife populations thrive or decline. Factors such as snow depth, hardness, the timing of snowfall and spring melt, and the occurrence of solid ground ice can significantly impact the survival and reproduction of various animal species. As climate change is rapidly altering the Arctic, it is essential to understand snow-wildlife interactions. However, a major challenge lies in the lack of snow-ice datasets that are tailored to the spatial and temporal scales relevant to wildlife studies. The complexity and ever-changing nature of snowpack characteristics make it inherently difficult to quantify these conditions accurately. One phenomenon particularly challenging to capture are rain-on-snow (ROS) events: winter rain leading to the formation of ground ice. These increasingly common events can have severe consequences for herbivores by blocking their access to food, potentially leading to reduced reproduction and high mortality. Unfortunately, different scientific disciplines use different datasets to define and quantify ROS events, leading to inconsistencies in the outcomes and making it challenging to understand their impacts on wildlife. To overcome these challenges and data limitations, an interdisciplinary collaboration between ecologists, snow scientists, and remote sensing experts is necessary. Bringing together international experts from these different fields for a workshop in Svalbard in February 2025, the WISE project has initialised a new collaboration at this interface, with a focus on the high Arctic. The goals of this collaboration are to stimulate the exchange of knowledge and experiences, explore synergies in studying snow-wildlife interactions, and contribute to the co-development of snow-ice data products that match ecologists' needs. By combining expertise from various disciplines, this collaborative effort has the potential to provide a more comprehensive understanding of the intricate relationships between snow conditions and wildlife populations in a rapidly changing Arctic. The organised workshop provided the platform to meet and get to know each other, discuss recent developments and outstanding challenges, and scope out opportunities for collaborative efforts.
In snow-dominated environments, snow and ice conditions such as snow depth and hardness, the timing of snow onset and spring melt, and the occurrence of basal ground icing, critically determine wildlife population dynamics. To predict the impacts of climate change on Arctic tundra ecosystems, understanding snow-wildlife relationships is therefore critical. However, to date, this understanding is severely hampered by a chronic lack of snow-ice data products at wildlife-relevant spatial and temporal scales. Snowpack characteristics are complex and constantly evolving, and therefore inherently challenging to quantify. In particular, it has proven difficult to capture and characterise rain-on-snow (ROS) events, which are increasing in frequency and magnitude across the Arctic. ROS events can be impactful in blocking herbivores’ access to forage, which may lead to reduced reproduction and high mortality. However, to date, ROS events are notoriously difficult to quantify and their impacts in time and space are challenging to characterise, as different disciplines use different datasets to define and quantify ROS, leading to inconsistencies in the outcome. Overcoming these challenges and data limitations requires interdisciplinary collaboration between ecologists, snow scientists, and remote sensing experts. To this end, we here propose a new interdisciplinary collaboration between pan-Arctic wildlife ecologists, snow scientists, and experts in remote sensing, with a regional focus on the high Arctic. The collaboration, initiated through a joint workshop, aims to stimulate knowledge, experience, and data exchange, explore synergies in studying snow-wildlife interactions in the high Arctic, and to, ultimately, contribute to co-developing snow-ice data products that match ecologists’ needs.
