Non‐native species have higher consumption rates than their native counterparts
Bibliographic record
Abstract
Non-native species can be major drivers of ecosystem alteration, especially through changes in trophic interactions. Successful non-native species have been predicted to have greater resource use efficiency relative to trophically analogous native species (the Resource Consumption Hypothesis), but rigorous evidence remains equivocal. Here, we tested this proposition quantitatively in a global meta-analysis of comparative functional response studies. We calculated the log response ratio of paired non-native and native species functional responses, using attack rate and maximum consumption rate parameters as response variables. Explanatory variables were consumer taxonomic group and functional feeding group, habitat, native assemblage latitude, and non-native species taxonomic distinctiveness. Maximum consumption rates for non-native species were 70% higher, on average, than those of their native counterparts; attack rates also tended to be higher, but not significantly so. The magnitude of maximum consumption rate effect sizes varied with consumer taxonomic group and functional feeding group, being highest in favour of non-natives for molluscs and herbivores. Consumption rate differences between non-native and native species tended to be greater for freshwater taxa, perhaps reflecting sensitivity of insular freshwater food webs to novel consumers; this pattern needs to be explored further as additional data are obtained from terrestrial and marine ecosystems. In general, our results support the Resource Consumption Hypothesis, which can partly explain how successful non-native species can reduce native resource populations and restructure food webs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".