Ecological coherence in abundance dynamics across terrestrial and marine assemblages
Bibliographic record
Abstract
Abstract Understanding how communities respond to environmental change requires assessing not just overall variability but also the structure of co-variation among taxa. We frame this idea under the Ecological Coherence (EC) framework, which generalizes previous notions such as community synchrony or coherence. EC captures the structure of co-responses among taxa within assemblages and can be expressed through two complementary objects: (1) the co-response matrix C , which contains all pairwise correlations between taxa and can be used to identify clusters of taxa with coherent responses as well as the contributions of individual species to community-wide coherence; and (2) the EC distribution , which summarizes the overall profile of co-responses by capturing their shape, spread, and central tendency across the community. By combining these two views, EC moves beyond single summary metrics and provides a richer picture of how coherence is organized within communities. Analyzing the EC distribution across 341 terrestrial and 105 marine assemblages worldwide, we found a general prevalence of weak correlations and a few strong, directional correlations. We also found that it varies with community composition, sampling effort, and biogeographic region. Moreover, the C matrix consistently identified a small subset of taxa with strong correlations to many others, suggesting a promising path to detecting those that may play central roles in amplifying or buffering community responses to environmental change. Our findings on EC pave the way for deeper investigations into what drives the diversity of ecological responses to environmental changes and how it shapes community dynamics, while also underscoring the need for strategically distributing ecological monitoring across trophic guilds and functional roles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".