The importance of snow in species distribution models of arctic vegetation

Abstract

Snow cover is characteristic of high‐latitude and ‐altitude ecosystems where snowpack properties regulate many ecological patterns and processes. Nevertheless, snow information is only rarely used as a predictor in species distribution models (SDMs). Methodological difficulties have been limiting both the quality and quantity of available snow information in SDMs. Here, we test whether incorporating remotely sensed snow information in baseline SDMs (using five climate‐topography‐soil variables) improves the accuracy of species occurrence and community level predictions. We use vegetation data recorded in 1200 study sites spanning a wide range of environmental conditions characteristic of mountain systems at high‐latitudes. The data consist of 273 species from three ecologically different and evolutionarily distant taxonomical groups: vascular plants, mosses, and lichens. The inclusion of the snow persistence variable significantly improved the predictive performance of the distribution and community level predictions. The improvements were constant, irrespective of the evaluation metric used or the taxonomic group in question. Snow was the most influential predictor for 36% of the species and had, on average, the second highest variable importance scores of all the environmental variables considered. Consequently, models incorporating snow data produced markedly more refined distribution maps than simpler models. Snow information should not be neglected in the construction of species distribution models where ecosystems characterized by seasonal snow cover are concerned.

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

“…), our study highlights the underused potential of Landsat imagery in this context (Macander et al. ; Niittynen and Luoto ; Niittynen et al. ).…”

Influence of microclimate and geomorphological factors on alpine vegetation in the Western Swiss Alps

“…Arabis caerulea, Salix herbacea). Based on correlations between vegetation plots and environmental variables, the growing degree days and the growing season length appeared to be between the most explicative variables, confirming how snow persistence influences vegetation communities (Braun, 1913;Niittynen and Luoto, 2018).…”

Comparing temperature data sources for use in species distribution models: From in‐situ logging to remote sensing

“…In northern Norway, especially, the relatively mild climate and humid air from the ocean result in thick winter snow packs that can provide a significant decoupling between air, surface and soil temperature (Pauli et al, ; Thompson et al, ). Such an insulating snow pack can affect plant life in several ways, through its effects on overwintering survival, productivity, reproductive success and nutrient and water availability (Niittynen & Luoto, ), with both positive (e.g., fewer frost events) and negative effects (e.g., limited growing season) observed. For many species in the region, especially low‐growing forbs and graminoids, we have shown that using near‐surface soil temperatures instead of free‐air temperatures, which allows the incorporation of these snow cover effects, is crucial to describe the distribution of small‐stature plants accurately (Niittynen & Luoto, ; Randin, Vuissoz, et al, ).…”