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Enregistrement 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 sur OpenAlexaff
Elizabeth G. Boulding

Notice bibliographique

RevueIntegrative and Comparative Biology · 2010
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueCephalopods and Marine Biology
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésRose (mathematics)Selection (genetic algorithm)BiologyComputer scienceArtificial intelligenceHorticulture

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,037
score de la tête « metaresearch » (Gemma)0,031
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,198

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0370,031
Méta-épidémiologie (sens strict)0,0050,006
Méta-épidémiologie (sens large)0,0050,002
Bibliométrie0,0090,006
Études des sciences et des technologies0,0010,013
Communication savante0,0030,005
Science ouverte0,0060,003
Intégrité de la recherche0,0030,007
Charge utile insuffisante (le modèle a refusé de juger)0,0080,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,355
Écart entre enseignants0,327 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2010
Routes d'admission1
Résumé présentnon

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