Extending the concept of keystone species to communities and ecosystems
Bibliographic record
Abstract
Keystone species are defined as having disproportionate importance in their community. This concept has proved useful and is now often used in conservation ecology. Here, we introduce the concept of keystone communities (and ecosystems) within metacommunities (and metaecosystems). We define keystone and burden communities as communities with impacts disproportionately large (positive or negative respectively) relative to their weight in the metacommunity. We show how a simple metric, based on the effects of single-community removals, can characterise communities along a 'keystoneness' axis. We illustrate the usefulness of this approach with examples from two different theoretical models. We further distinguish environmental heterogeneity from species trait heterogeneity as determinants of keystoneness. We suggest that the concept of keystone communities/ecosystems will be highly beneficial, not only as a fundamental step towards understanding species interactions in a spatial context, but also as a tool for the management of disturbed landscapes.
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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 | 0.001 |
| 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".