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Record W2090638568 · doi:10.1093/icb/icq093

Experimental Evolution. Concepts, Methods, and Applications of Selection Experiments. Theodore Garland Jr and Michael R. Rose, editors.

2010· article· en· W2090638568 on OpenAlexaff
Elizabeth G. Boulding

Bibliographic record

VenueIntegrative and Comparative Biology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRose (mathematics)Selection (genetic algorithm)BiologyComputer scienceArtificial intelligenceHorticulture

Abstract

fetched live from OpenAlex

I have been interested in experimental evolution for a long time. In July 1995, I organized a symposium for the meeting of the Society for the Study of Evolution in Montreal, Quebec entitled “Rapid Evolutionary Change in Wild Populations” to which I invited six field biologists (Scott Carroll, Rosemary Grant, Judy Myers, Dolph Schluter, Carmen Parmesan, and Sara Via) and no laboratory scientists. Imagine my disappointment then, when this book arrived and there was only one short chapter on field experiments. In that chapter, Irschick and Reznick argue that unlike laboratory selection experiments, field experiments inform us about mechanisms of population establishment, the prevalence of rapid evolutionary change, and the role of natural and anthropogenic catastrophic events. This seemed relatively little—considering the difficulty in funding long-term field experiments—so I continued to read the remaining 730 pages. Much of this book is about laboratory selection on microorganisms, insects, and mice. What surprised me was my favorite chapters were not those that I would have predicted from their titles. In Chapter 1, the editors argue that laboratory natural selection (LNS) experiments are fundamentally different from the more familiar artificial selection experiments. In LNS experiments, the researchers manipulate the environment, the surviving members of the population are freely allowed to breed, and the researchers periodically monitor the phenotype and genotype of the population over a number of generations. The benefit to studying experimental evolution in the laboratory is that the environmental conditions can be precisely controlled over time, thereby allowing replicate experimental populations to be simultaneously compared with both contemporary and historical control populations. However, the usefulness of LNS experiments as a method of understanding evolution in wild populations is challenged by Huey and Rosenzweig in Chapter 22. It is difficult to know if we should reject the hypothesis that temperature is the cause of geographical clines in wing size based on evidence from a LNS experiment in which Drosophila populations failed to develop a cline. In Chapter 4, Dykhuizen and Dean utilize modern Escherischia coli genomics techniques to show that the fitness of a genotype on particular substrates and in particular thermal environments can be predicted from “the bottom up” based on detailed knowledge of enzyme kinetics. Their research should impress field biologists who have tried to measure lifetime fitness. Towards the end of their chapter, they cheer on Drosophila workers who are taking a similar LNS approach. One of my favorite chapters (Chapter 10) was by Zera and Harshman on the life history physiology of insects. In that chapter, they find differences in lipid allocation between lines of sand crickets selected for long wings (LW) and for short wings (SW). The LW crickets divert more of their lipids to triglycerides, which are needed to sustain flight, whereas the SW divert more to phospholipids, which are important for egg production. This is exciting as it gives a physiological explanation for the negative genetic correlation between fecundity and the production of wings (Table 3 in Chapter 3 by Roff and Fairbairn). Chapter 11 “Behavior and Neurobiology” by Rhodes and Kawecki reviews the challenges in selecting for different behaviors. Lines of mice artificially selected for increased voluntary running behavior had an increased number of nuclei in the part of the brain secreting a stress hormone. This suggests that the mechanism by which increased voluntary running was attained was by activation of the stress response. This theme of indirect effects of selection is continued in Chapter 12 “Selection, Performance, and Physiology” by Swallow and colleagues. Mice selected for high rates of voluntary running showed a higher propensity to attack and kill crickets than did mice from control lines. Another favorite was Chapter 14: “Understanding Evolution thorough the Phages” by Ford and Jessup who investigate coevolutionary dynamics between viruses and their bacterial hosts. They review LNS studies that show that low levels of migration increase local adaptation of phages to environments with different levels of resources. This addition of migration between different environments allows realism with higher migration being correlated with the evolution of more virulent phages. I believe this volume is suitable for new graduate students enrolled in an evolutionary biology seminar course. The authors have made an effort to keep most of their chapters accessible to nonspecialists and most have included a detailed description of their experimental evolutionary research. Rather than allowing the students to fight over who gets to present the Speciation or Aging chapter I suggest a stochastic approach. Being assigned a topic at random might result in some interesting PhD theses on crickets or phages.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.031
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0090.006
Science and technology studies0.0010.013
Scholarly communication0.0030.005
Open science0.0060.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0080.004

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.

Opus teacher head0.029
GPT teacher head0.355
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2010
Admission routes1
Has abstractno

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