Can competitive effects and responses of alien and native species predict invasion outcomes?
Bibliographic record
Abstract
The relative competitive ability of native and alien species, which consists of competitive effect (CE) and response (CR), has often been invoked as a key determinant of invasion success. Previous studies have reported that an alien species with a high CE and /or a low CR would successfully invade a native species. However, no studies have yet empirically examined the hypothesis or tested the consistency of invasion outcomes predicted by the CE-CR framework and modern species coexistence theory (MCT). To fill this research gap, we conducted a pairwise competition experiment between five alien and five native species, quantified CE and CR based on their biomass in the absence and presence of one competitor, and predicted invasion outcomes based on both CE-CR and MCT frameworks. We have demonstrated theoretically that the CE and CR frequently measured in previous work are only approximations of interspecific competitive coefficients, and thus could not completely predict the invasion outcomes. As we expected, the invasion outcomes predicted by the CE-CR framework were partially consistent with the predictions by the MCT framework. Specifically, aliens with low CR and high CE tended to exclude natives, while aliens with high CR and low CE tended to be excluded by natives according to MCT. In contrast, pairs of stable coexistence and priority effects did not conform to the theoretical expectation. Despite the theoretical defects of the CE-CR framework, it can provide some useful value in predicting the invasion outcomes, especially when intrinsic growth rate and intraspecific competition coefficients are not available. Our study is the first to compare invasion outcomes separately derived from qualitative (the CE-CR framework) and quantitative (the MCT framework) methods. We recommend that future research should adopt quantitative approaches such as MCT as far as possible, to more comprehensively understand and predict the biotic outcomes of interacting species.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".