Cage Matching: Head to Head Competition Experiments of an Invasive Plant Species from Different Regions as a Means to Test for Differentiation
Bibliographic record
Abstract
Many hypotheses are prevalent in the literature predicting why some plant species can become invasive. However, in some respects, we lack a standard approach to compare the breadth of various studies and differentiate between alternative explanations. Furthermore, most of these hypotheses rely on 'changes in density' of an introduced species to infer invasiveness. Here, we propose a simple method to screen invasive plant species for potential differences in density effects between novel regions. Studies of plant competition using density series are a fundamental tool applied to virtually every aspect of plant population ecology to better understand evolution. Hence, we use a simple density series with substitution contrasting the performance of Centaurea solstitialis in monoculture (from one region) to mixtures (seeds from two regions). All else being equal, if there is no difference between the introduced species in the two novel regions compared, Argentina and California, then there should be no competitive differences between intra and inter-regional competition series. Using a replicated regression design, seeds of each species were sown in the greenhouse at 5 densities in monoculture and mixed and grown till onset of flowering. Centaurea seeds from California had higher germination while seedlings had significantly greater survival than Argentina. There was no evidence for density dependence in any measure for the California region but negative density dependence was detected in the germination of seeds from Argentina. The relative differences in competition also differed between regions with no evidence of differential competitive effects of seeds from Argentina in mixture versus monoculture while seeds from California expressed a relative cost in germination and relative growth rate in mixtures with Argentina. In the former instance, lack of difference does not mean 'no ecological differences' but does suggest that local adaptation in competitive abilities has not occurred. Importantly, this method successfully detected differences in the response of an invasive species to changes in density between novel regions which suggests that it is a useful preliminary means to explore invasiveness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".