Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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.
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".