Breaking down the components of the competition‐colonization trade‐off: New insights into its role in diverse systems
Bibliographic record
Abstract
Performance trade-offs between competition and colonization can be an important mechanism facilitating regional coexistence of competitors. However, empirical evidence for this trade-off is mixed, raising questions about the extent to which it shapes diverse ecological communities. Here, we outline a framework that can be used to improve empirical tests of the competition-colonization trade-off. We argue that tests of the competition-colonization trade-off have been diverted into unproductive paths when dispersal mode and/or competition type have been inadequately defined. To generate comparative predictions of associations between dispersal and competitive performance, we develop a conceptual trait-based framework that clarifies how dispersal mode and type of competitor shape this trade-off at the stage of dispersal and establishment in a variety of systems. Our framework suggests that competition-colonization trade-offs may be less common for passively dispersing organisms when competitive dominants are those best able to withstand resource depletion (competitive response), and for active dispersers when traits for dispersal performance are positively associated with resource pre-emption (competitive effect). The framework presented here is designed to provide common ground for researchers working in different systems in order to prompt more effective assessment of this performance trade-off and its role in shaping community structure. By delineating key system properties that mediate the trade-off between competitive and colonization performance and their relationship to individual-level traits, researchers in disparate systems can structure their predictions about this trade-off more effectively and compare across systems more clearly.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".