The EICA is dead? Long live the EICA!
Bibliographic record
Abstract
An important hypothesis for how plants respond to introduction to new ranges is the evolution of increased competitive ability (EICA). EICA predicts that biogeographical release from natural enemies initiates a tradeoff in which exotic species in non-native ranges become larger and more competitive, but invest less in consumer defenses, relative to populations in native ranges. This tradeoff is exceptionally complex because detecting concomitant biogeographical shifts in competitive ability and consumer defense depend upon which traits are targeted, how competition is measured, the defense chemicals quantified, whether defense chemicals do more than defend, whether “herbivory” is artificial or natural, and where consumers fall on the generalist-specialist spectrum. Previous meta-analyses have successfully identified patterns but have yet to fully disentangle this complexity. We used meta-analysis to reevaluate traditional metrics used to test EICA theory and then expanded on these metrics by partitioning competitive effect and competitive tolerance measures and testing Leaf Specific Mass in detail as a response trait. Unlike previous syntheses, our meta-analyses detected evidence consistent with the classic tradeoff inherent to EICA. Plants from non-native ranges imposed greater competitive effects than plants from native ranges and were less quantitatively defended than plants from native ranges. Our results for defense were not based on complex leaf chemistry, but instead were estimated from tannins, toughness traits, and primarily Leaf Specific Mass. Species specificity occurred but did not influence the general patterns. As for all evidence for EICA-like tradeoffs, we do not know if the biogeographical differences we found were caused by tradeoffs per se, but they are consistent with predictions derived from the overarching hypothesis. Underestimating physical leaf structure may have contributed to two decades of tepid perspectives on the tradeoffs fundamental to EICA.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".