Climate change and weed adaptation: can evolution of invasive plants lead to greater range expansion than forecasted?
Bibliographic record
Abstract
ClementsDR & DitommasoA (2011). Climate change and weed adaptation: can evolution of invasive plants lead to greater range expansion than forecasted?Weed Research51, 227–240. Summary Invasive plants are frequently viewed as harbingers of climate change owing to their potential to cause economic and ecological damage in the process of expanding their ranges. Models are being developed to help predict the range expansion of these plants, based on known tolerance ranges. Success of weeds has often been attributed to an ‘all‐purpose genotype’, implying a high level of phenotypic plasticity. However, recent work has shown that many species are capable of relatively rapid genetic change as well, enhancing their ability to invade new areas in response to anthropogenic ecosystem modification. We thus predict that range expansion by many invasive species will exceed that predicted by modelling approaches that do not consider potential evolutionary change. We highlight a number of cases where weeds have expanded their latitudinal ranges or are predicted to do so in response to climatic selection pressures. We also list ten traits as likely targets for natural selection under climate change. The lag phase commonly observed for invasive species may frequently be a result of the time needed for the invader to evolve to fit the new habitat. During this present period of climate change, many invasive plant populations are likely to be in the process of developing adaptations that could lead to exponential population growth in the near future. Thus, assessment of the risk of invasive species owing to changing climate must incorporate evolutionary potential.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".