Inferences about phenological shifts in an Arctic community vary with time-windows

Abstract

Long-term monitoring data have enabled detection of phenological change, yet it remains poorly understood how its temporal dimensions-- duration and choice of start and end years-- influence the inferences drawn. To examine which phenological signals emerge at different temporal scales, we analyzed the longest continuous dataset on high-arctic plant and arthropod phenology, collected from 1996 to 2024 in the Zackenberg valley, northeast Greenland. These data have been used to suggest both rapid advancement of spring in the High Arctic (2007) and little directional change but decadal regime shifts (2023). To reconcile these differing conclusions, we quantified how trend estimates varied across moving time-windows and determined the minimum time-series length required to achieve a high probability of agreement with long-term trends. We find that while trend directionality shifts with temporal windows, confidence in trend estimates increases with time-series length. Using the full time-series, we show dampened signals of warming trends, with annual increases in spring and summer air temperatures by 0.04 [-0.05, 0.13] and 0.05 [-0.01, 0.11] {degrees}C per year, respectively, alongside a 0.82% [-1.85, 0.19] decline in spring snow cover. We also see modest advancement in the seasonal activity of most arthropod taxa (by [~]0.1 days/year), whereas flowering phenology shows no consistent directional change. Shorter time-series revealed cyclical patterns in abiotic drivers yet variable biotic responses, indicating that a single pattern of "climate change" will translate into varied responses within communities. Finally, almost two decades of data were needed to reliably capture long-term trends. Ecologically, these suggest that 1) phenological shifts in the High Arctic are more moderate than early assessments implied and 2) reflect a dynamic balance between species life histories and ongoing climate variability. These may alter interaction potentials within communities, with consequences for ecosystem functioning.