Iseli, E. | bioRxiv Science

bioRxiv2024

Early signs of plant community responses to climate warming along mountain roads in Switzerland

O_LIGlobal warming is pushing species to shift their ranges towards higher latitudes and elevations, causing a reassembly of plant communities potentially accompanied by community thermophilization (i.e., an increasing number or cover of thermophilic species, sometimes at the expense of mesic or cold-adapted species). Given the large variation typically observed in the magnitude and direction of range shifts, quantifying community thermophilization might provide a sensitive method to detect range shifts within short time periods and across limited spatial extents. Assessing changes in plant community composition as a whole might integrate early signs of range shifts across co-occurring species while accounting for changes in both occurrence and abundance. C_LIO_LIHere, we combine an assessment of (i) species-level range shifts, (ii) changes in species richness and (iii) changes in community-inferred temperatures along three mountain roads in Switzerland to ask whether plant communities have responded to warming climate over a 10 year period, and whether community thermophilization is an appropriate metric for early detection of these changes. C_LIO_LIWe found a community thermophilization signal of +0.13{degrees}C over the 10-year study period based on presence-absence data only. Despite significant upward shifts of species upper range limits in the lower part of the studied elevational gradient and a decrease in species richness at high elevations, significant thermophilization was not detectable if community- inferred temperatures were weighted by species covers. Low cover values of species that were gained or lost over the study period and their similar species-specific temperatures to resident species explained the discrepancy between the thermophilization detected in either cover-weighted or unweighted models. C_LIO_LISynthesis. Our work shows that plant species are shifting to higher elevations along roadsides in the western Swiss Alps and that this translates into a detectable warming signal of plant communities within 10 years. However, the species-level range shifts and the community-level warming effect are mostly based on low cover values of gained/lost species, preventing the detection of community thermophilization signals when incorporating cover changes. We therefore recommend using unweighted approaches for early detection of community-level responses to changing climate, ideally set into context by also assessing species-level range shifts. C_LI

ecology↗

bioRxiv2023

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

Microclimate - proximal climatic variation at scales of meters 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, with deep learning tools to 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, yet ecologically relevant, meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africas Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. Our best model achieves 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. Unlike previous studies we test model performance in remote and novel locations, distinguishing overcast and sunny conditions in Svalbard, Norway with 79.3% accuracy (93.9% consensus accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabeled 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.

ecology↗