Data from: Trails-as-transects: phenology monitoring across heterogeneous microclimates in Acadia National Park, Maine
McDonough MacKenzie, Caitlin; Miller-Rushing, Abraham J.; Primack, Richard B. (2019), Data from: Trails-as-transects: phenology monitoring across heterogeneous microclimates in Acadia National Park, Maine, Dryad, Dataset, https://doi.org/10.5061/dryad.1n93p40
Climate‐driven shifts in phenology, which are being observed worldwide, affect ecosystem services, trophic interactions, and community composition, presenting challenges to managers in protected areas. Resource management benefits from local, species‐specific phenology information. However, phenology monitoring programs in heterogeneous landscapes typically require serendipitous historical records or many years of contemporary data before trends in phenological responses to changes in climate can be analyzed. Here, we used a trails‐as‐transects approach to rapidly accumulate monitoring data across environmental gradients on three mountains in Acadia National Park, Maine, USA, and compared our results to phenological changes observed in Concord, Massachusetts, USA. In four years of intensive monitoring of transects on three mountains, we found large variability in spring temperatures across the mountains, but consistent patterns of advancing flower and leaf phenology in warmer microclimates. Reduced sampling intensity would have yielded similar results, but a shorter duration would not have revealed these patterns. The plants in Acadia responded to warming spring temperatures by shifting leaf and flower phenology in the same direction (earlier), but at a reduced rate (as measured in d/°C), in comparison with plants in southern New England (e.g., Concord, Massachusetts, USA). Our approach takes advantage of topographical complexity and associated microclimate gradients to substitute for long time series, allowing for rapid assessment of phenological response to climate. Other climate gradients (e.g., urban‐to‐rural, latitudinal, or coastal‐to‐inland) could work similarly. This intensive monitoring over a short time period quickly builds a robust dataset and can inform management decisions regarding future monitoring strategies, including sampling designs for citizen science‐based phenology monitoring programs.
National Science Foundation, Award: DEB-1501266