Tracking community change via network coherence
Bibliographic record
Abstract
Abstract Understanding how ecological communities respond to environmental change remains a key challenge for biodiversity monitoring. To characterize such responses, we need tools that capture how coherently species respond across a community, and to predict their consequences, we must account for ecological interactions. We first introduce the Ecological Coherence (EC) framework, which describes how species’ co-responses are structured within a community. Building on this foundation, we extend it to Ecological Network Coherence (ENC), which embeds co-responses within the network of interactions by restricting them to interacting species. Both are expressed through two complementary representations: a response correlation matrix and the distribution of its values. The first can reveal aspects such as coherent or incoherent modules and the roles species play in shaping coherence, whereas the second provides a profile whose shape may serve as an early-warning indicator of instability. These can be applied to both intrinsic responses (environmental performance) and realized responses (abundance dynamics), derived from currently available monitoring data. We illustrate this approach in two empirical systems: a tropical pollination network, where interacting mutualists were more coherent in their temperature responses than the broader community, and a marine food web, where coherence in abundance trends shifted during collapse. Using a Lotka–Volterra model, we further show that ENC distributions with higher variance—reflecting stronger positive and negative co-responses—increase the risk of instability or amplification in dynamics. We also find that species influential in both the correlation matrix and the interaction matrix are key drivers of major dynamic shifts. These results point to the importance of further exploring ENC distributions as potential early-warning indicators of ecological disruption.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".