Quantifying community keystoneness in metacommunities under disturbance
Bibliographic record
Abstract
Understanding how metacommunities respond to natural and anthropogenic disturbances is a key objective in ecology. In this study, we introduce an analytical framework to identify communities whose extirpation triggers stronger (hereafter keystone communities) or weaker (hereafter idle communities) cascading effects on extinction and colonization events that ultimately drive temporal changes in compositional patterns of the remaining communities. These cascading dynamics illustrate how the loss of communities disrupts connectivity, altering dispersal patterns that ultimately shape species occupancy and dominance at the landscape scale. Through mechanistic simulation models that reproduce in silico the design of removal experiments, we demonstrated that our framework accurately estimates ‘keystoneness', ranking local communities by their role in maintaining metacommunity compositional patterns over time. We also use our framework and simulation models to derive a mechanistic understanding of community keystoneness, demonstrating how landscape characteristics and species pool attributes jointly shape the role of communities within the metacommunity. A key feature of our framework is its ability to generate community keystoneness estimates that are weakly correlated to local diversity, thus providing a new metric for assessing the relevance and conservation value of local communities under a metacommunity context. This feature is particularly important in cases where high local diversity reflects an influx of individuals into demographic sinks, a common consequence of human activities near natural areas. To showcase the unique insights of this framework, we examined and contrasted the effects of artificial light at night (ALAN) on the diversity and keystoneness of a moth metacommunity sampled over two decades. We conclude with a discussion of the framework's underlying assumptions, emphasizing its relevance for addressing both conceptual and applied ecological questions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".