Predicting weed invasion in Canada under climate change: Evaluating evolutionary potential
Bibliographic record
Abstract
Clements, D. R. and DiTommaso, A. 2012. Predicting weed invasion in Canada under climate change: Evaluating evolutionary potential. Can. J. Plant Sci. 92: 1013–1020. Many weed species have already advanced northward from the United States into Canada, and their number threatens to increase with warming trends under climate change. For many weed species, this range expansion can be attributed to evolutionary adaptation by northern populations occurring in areas experiencing warmer climates and longer growing seasons in recent decades. In this paper, we examine the potential for invasive plant species to be selected for one or more of 10 character traits: (1) high growth rate, (2) wide climatic or environmental tolerance, (3) short generation time, (4) prolific or consistent reproduction, (5) small seed size, (6) effective dispersal, (7) uniparental reproduction capacity, (8) no specialized germination requirements, (9) high competitive ability, and (10) effective defenses against natural enemies. If any one of these traits is selected for in an invasive species, it would provide the affected species with the potential for a more expansive invasion range than anticipated by models that assume a static genotype. Four weed species of interest exhibiting potential northward range expansion within North America were evaluated: an obligatory outcrossing annual dicot, Himalayan balsam (Impatiens glandilifera Royle), a mostly selfing annual dicot, velvetleaf (Abutilon theophrasti Medic.), a perennial dicot that reproduces via rhizomes but forms fertile hybrids, Japanese knotweed [Fallopia japonica (Houtt.) Ronse Decr.], and a primarily selfing perennial grass, johnsongrass [Sorghum halapense (L.) Pers.]. Evidence for potential evolutionary responses to climate change was observed among particular traits for each of the four species, despite population genetic differences. The population genetics of invasive plants are difficult to model, as is climate change itself, but consideration of weed evolution to whatever degree possible should lead to improved predictive power of such models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".