Deep learning to extract the meteorological by-catch of wildlife cameras

Microclimate—proximal climatic variation at scales of metres and minutes—can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.ISSN:1354-1013ISSN:1365-248

Winters are changing: snow effects on Arctic and alpine tundra ecosystems

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes and biogeochemical cycling. We also compare studies of natural snow gradients with snow manipulation studies, altering snow depth and duration, to assess time scale difference of these approaches. The number of studies on snow in tundra ecosystems has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. In specific, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by manipulative studies (average 7.9 days advance, 5.5 days delay) were substantially lower than those observed over spatial gradients (mean range of 56 days) or due to interannual variation (mean range of 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates

Winters are changing: snow effects on Arctic and alpine tundra ecosystems

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes and biogeochemical cycling. We also compare studies of natural snow gradients with snow manipulation studies, altering snow depth and duration, to assess time scale difference of these approaches. The number of studies on snow in tundra ecosystems has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. In specific, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by manipulative studies (average 7.9 days advance, 5.5 days delay) were substantially lower than those observed over spatial gradients (mean range of 56 days) or due to interannual variation (mean range of 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates