Trails‐as‐transects: phenology monitoring across heterogeneous microclimates in Acadia National Park, Maine
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.161 | 0.024 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".