Sexual Segregation in Ungulates: Ecology, Behavior, and Conservation
Notice bibliographique
Résumé
For many species of mammals, sexes are separated through much of the year except during mating season when they get together to breed. Indeed, seasonal habitat segregation happens in each of the ungulate taxa reviewed in this book: Moose (Alces alces), elk (Cervus canadensis), Mule Deer (Odocoileus hemionus), White-tailed Deer (O. virginianus), and Bighorn Sheep (Ovis canadensis). A few other species are mentioned but much of the book focuses on these case studies, which is to be expected given that Bowyer has had a life-long career studying these species. Indeed, I found these case studies to be highlights of the book. Bowyer presents a literature review of the many mechanisms that have been proposed to explain sexual segregation. He dismisses most of these concluding that sexual segregation is a consequence of 2 drivers—food and risk of predation. In fact, he attempts to cement his conclusion by defining sexual segregation to be “differential use of space or other resources by the sexes outside the mating season.” However, other mammalogists will cling to multiple causes for sexual segregation, including social segregation caused by differential activity rhythms between the sexes. Furthermore, a more comprehensive definition is found in the Sexual Segregation and Aggregation Statistic (Bonenfant et al. 2007), where social segregation can happen in the absence of habitat segregation. I found a few errors in this book. Bowyer claims that used and available habitats cannot overlap substantially when estimating habitat selection (p. 52). By definition, however, all used resource units come from the set of available resource units (Johnson et al. 2006); thus, used and available resource units must overlap. Distributions of used and available resource units must be different to detect selection but these 2 distributions must overlap. I also reject his view that carrying capacity does not vary seasonally (p. 107) because seasonally forced dynamics are fundamental to many complex system behaviors in population biology. I would have preferred an online appendix with the equations that motivated his claims. Yet, overall, this is a scholarly work properly documented with modern literature and containing a very useful index. Graduate students with an interest in behavioral ecology will find that Bowyer’s book stimulates ideas and presents opportunities for future synthesis. His book follows one edited by Ruckstuhl and Neuhaus (2006) titled “Sexual Selection in Vertebrates: Ecology of the Two Sexes.” Together these volumes provide a window into the rich natural history of sexual segregation along with a plethora of plausible explanations. If Bowyer is right that it all boils down to food and predators, we already have a powerful baseline for a framework in the theory of optimal foraging. In my opinion what is needed is a true synthesis in the form of mathematical theory that can integrate proposed mechanisms to predict when we will see sexual segregation.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,041 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».