Resistance evolution, from genetic mechanism to ecological context
Bibliographic record
Abstract
Resistance evolution, from genetic mechanism to ecological contextRegina S. Baucom1, Veronica Iriart2, Julia Kreiner3, and Sarah Yakimowski41Ecology and Evolutionary Biology Department, University of Michigan, Ann Arbor, Michigan, USA2Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA3Biodiversity Research Centre & Department of Botany, The University of British Columbia, Vancouver, BC V6T 1Z44Department of Biology, Queen’s University, Kingston, ON K7L 3N6CorrespondenceRegina S. Baucom, Ecology and Evolutionary Biology Department, University of Michigan, Ann Arbor, Michigan, 48109.Email: rsbaucom@umich.edu*Authors contributed equallyPesticide use by humans has induced strong selective pressures, reshaping evolutionary trajectories, ecological networks, and even influencing ecosystem dynamics. The evolution of pesticide resistance across weeds, insects, and fungi often leads to negative impacts on both human health and the economy while concomitantly providing excellent systems for studying the process of evolution. In fact, the study of pesticide resistance has been a feature of evolutionary biology since the Evolutionary Synthesis, with Dobzhansky noting in his book The Genetics and Origins of Species (1937) that cyanide resistance in the California red scale constituted the “best proof of the effectiveness of natural selection yet obtained”. Following the pioneering work of James Crow and others in the 1950’s—which greatly expanded our knowledge of the genetics underlying adaptation—the study of pesticide resistance has shed light on a variety of topics, such as the repeatability of phenotypic evolution across the landscape, ‘hotspots’ of evolution across the genome, and information on the number and type of genetic solutions that populations may employ to strong selection pressures.Landscape level approaches have come to the forefront over the last 20 years of resistance evolution research, often taking advantage of the fact that replicated populations of the same species are exposed to the same pesticide. Further, the resistance evolution field is turning more attention to the ecological context within which resistance evolution occurs, likely stemming, at least in part, from an historical focus on fitness costs (Cousens & Fournier-Level 2018; Baucom 2019). This special feature, ‘Resistance evolution, from genetic mechanism to ecological context’ in Molecular Ecology captures the current state of resistance evolution with contributions broadly addressing the question ‘What has the rapid evolution of pesticide resistance taught us about genome dynamics and adaptation as well as the ecological context within which resistance evolution occurs?’ Below, we contextualize the manuscripts in this special issue that provide insight into the state of the art investigations of resistance evolution across various species of insects, weeds and fungi.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".