Notes from the past show how local variability can stymie urchins and the rise of the reds in the Gulf of Maine
Bibliographic record
Abstract
Abstract The impacts of global change—from shifts in climate to overfishing to land use change—can depend heavily on local abiotic context. Building an understanding of how to downscale global change scenarios to local impacts is often difficult, however, and requires historical data across large gradients of variability. Such data are often not available—particularly in peer reviewed or gray literature. However, these data can sometimes be gleaned from casual records of natural history—field notebooks, data sheet marginalia, course notes, and more. Here, we provide an example of one such approach for the Gulf of Maine, as we seek to understand how environmental context can influence local outcomes of region‐wide shifts in subtidal community structure. We explore a decade of hand‐drawn algal cover maps around Appledore Island made by Dr. Art Borror while teaching at the Shoals Marine Lab. Appledore's steep wave exposure gradient—from exposed to the open ocean to fully protected—provides a living laboratory to test interactions between global change and local conditions. We then recreate Borror's methods two and a half decades later. We show that overfishing‐driven urchin outbreaks in the 1980s were slowed or stopped by wave exposure and benthic topography. Similarly, local variation appears to have curtailed current invasions by filamentous red algae. Last, some formerly dominant kelps have disappeared over the past 40 years—an observation verified by subtidal surveys. Global change is altering life in the seas around us. While underutilized, solid natural history observations stand as a key resource for us to begin to understand how global change will translate to the heterogeneous mosaic of life in a future Gulf of Maine and other ecosystems around the world.
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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 | 0.001 | 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) | 0.001 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".