Biology subjects

Bjorkman, A. D.

Publications and source records attributed to Bjorkman, A. D..

4 recordsLinked to original sources

bioRxiv2025

Climate-driven specialisation in plant-pollinator networks peaks outside the tropics

Pollination is a key ecological process sustaining biodiversity and food security, yet global patterns of plant-pollinator specialisation have remained unresolved. Using the largest global dataset of quantitative networks (>3,400 networks, >110,000 interactions), we show that the latitudinal specialisation gradient (LSG) exists, but it is non-linear, hemispherically asymmetric, and strongly taxon-dependent. Network-level and pollinator specialisation were lowest in the tropics and peaked at northern mid-latitudes, whereas plants tended to become more specialised toward higher latitudes. Climate consistently outperformed latitude, species richness, and environmental productivity as a predictor of these patterns. Specialisation declined with increasing temperature, rose with moderate rainfall before declining at the wettest sites, and increased with temperature seasonality, but plants and pollinators responded differently to these drivers. Functional groups diverged strongly: ectothermic insects were most specialised in cooler, seasonal climates, while birds showed weaker links to latitude but reduced specialisation in wetter regions. These findings demonstrate that climate, rather than latitude or species richness, structures global variation in specialisation. Because warmer and less seasonal climates promote generalisation, climate change is likely to disrupt the most specialised pollination systems, unevenly across taxa and regions, with important consequences for biodiversity and ecosystem stability.

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↗

bioRxiv2023

Widespread reductions in body size are paired with stable assemblage biomass.

Biotic responses to global change include directional shifts in organismal traits. Body size, an integrative trait that determines demographic rates and ecosystem functions, is often thought to be shrinking in the Anthropocene. Here, we assess the prevalence of body size change in six taxon groups across 5,032 assemblage time-series spanning 1960-2020. Using the Price equation to partition this change into within-species body size versus compositional changes, we detect prevailing decreases in body size through time. Change in assemblage composition contributes more to body size changes than within-species trends, but both components show substantial variation in magnitude and direction. The biomass of assemblages remains remarkably stable as decreases in body size trade-off with increases in abundance. One-Sentence SummaryVariable within-species and compositional trends combine into shrinking body size, abundance increases and stable biomass.

ecology↗

bioRxiv2018

Forest loss as a catalyst of population and biodiversity change

Global assessments have highlighted land-use change as a key driver of biodiversity change. However, we lack real-world global-scale estimates of how habitat transformations such as forest loss and gain are reshaping biodiversity over time. Here, we quantify the influence of 150 years of forest cover change on populations and ecological assemblages worldwide and across taxa by analyzing change in 6,667 time series. We found that forest loss simultaneously intensified ongoing increases and decreases in abundance, species richness and temporal species replacement (turnover) by up to 48%. Temporal lags in these responses extended up to 50 years and increased with species generation time. Our findings demonstrate that land-use change precipitates divergent population and biodiversity change, highlighting the complex biotic consequences of deforestation and afforestation.\n\nOne Sentence SummaryDeclines in forest cover amplify both gains and losses in population abundance and biodiversity over time.

ecology↗