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Record W4391212723 · doi:10.1093/jmammal/gyad129

Sexual Segregation in Ungulates: Ecology, Behavior, and Conservation

2024· article· en· W4391212723 on OpenAlexaffabout
Mark S. Boyce

Bibliographic record

VenueJournal of Mammalogy · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEcologySexual behaviorBehavioral ecologyConservation biologyGeographyBiologyPsychology

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0410.003

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2024
Admission routes2
Has abstractyes

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