Adaptation and the spatiotemporal mosaic of selection
Bibliographic record
Abstract
Adaptive divergence due to variation in selective pressures is believed to have generated much of the biodiversity observed today.Despite this, it is commonly assumed that selective pressures are consistent through space and time.In particular, studies often examine selection in a single population at a single time point and take that to represent the selection to which a whole species is subject.However, recent work has demonstrated that considerable spatial and temporal variation in selection exists.The focus of my dissertation is thus on understanding the mechanisms and characteristics of spatiotemporal variation in selection and how this can affect adaptive phenotypic divergence.In my first chapter, I formally quantified spatial variation in selection estimates across many published studies, and found that spatial variation in selection was frequently present.Variation in the strength of selection was more common than variation in the direction of selection.For my subsequent chapters, I used the Trinidadian guppy system, an iconic model of the interaction between natural and sexual selection.In this system, females prefer males with higher colour, but this higher colour makes males more susceptible to predation.Variation among populations in predation regime (presence or absence of piscivores) is thus thought to be a strong selective driver of adaptive divergence among natural populations of guppies.In my second chapter, building on the general insights of the first chapter regarding the magnitude and characteristics of spatial variation in selection, I then inferred the amount of spatiotemporal variation in selection on male guppy colour by quantifying colour in six populations of guppies over six years.I found that selection varied primarily in space (i.e.across different predation regimes) and that this spatial variation was relatively consistent through time.In my third chapter, I used this knowledge of the importance of spatial variation in selective pressures to see if I could repeatedly "drive" evolution in predictable course, I might never have discovered just how fascinating evolution is to study.Thank you, Andrew, for your advice, mentorship, and training while allowing me the freedom to pursue my own research interests.My supervisory committee has also been a great support and I have enjoyed (seriously!
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".