Ten Ways That Weed Evolution Defies Human Management Efforts Amidst a Changing Climate
Bibliographic record
Abstract
The ability of weeds to evolve is key to their success, and the relationship between weeds and humans is marked by co-evolution going back to the agricultural revolution, with weeds evolving to counter human management actions. In recent years, climate change has emerged as yet another selection pressure imposed on weeds by humans, and weeds are likewise very capable of adapting to this latest stress of human origin. This review summarizes 10 ways this adaptation occurs: (1) general-purpose genotypes, (2) life history strategies, (3) ability to evolve rapidly, (4) epigenetic capacity, (5) hybridization, (6) herbicide resistance, (7) herbicide tolerance, (8) cropping systems vulnerability, (9) co-evolution of weeds with human management, and (10) the ability of weeds to ride the climate storm humans have generated. As pioneer species ecologically, these 10 ways enable weeds to adapt to the numerous impacts of climate change, including warming temperatures, elevated CO2, frequent droughts and extreme weather events. We conclude that although these 10 ways present formidable challenges for weed management, the novelty arising from weed evolution could be used creatively to prospect for genetic material to be used in crop improvement, and to develop a more holistic means of managing agroecosystems.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".