Causes and consequences of an unusually male-biased adult sex ratio in an unmanaged feral horse population
Bibliographic record
Abstract
1. The adult sex ratio (ASR) is important within ecology due to its predicted effects on behaviour, demography, and evolution, but research examining the causes and consequences of ASR bias have lagged behind studies of sex ratios at earlier life stages. Although ungulate ASR is relatively well-studied, exceptions to the usual female-biased ASR challenge our understanding of the underlying drivers of biased ASR, and provide an opportunity to better understand its consequences. 2. Some feral ungulate populations, including multiple horse populations, exhibit unusually male-biased ASR. For example, research suggests that the feral horse (Equus ferus caballus) population on Sable Island, Nova Scotia, Canada, may exhibit a male-biased ASR. Such exceptions to the rule provide a valuable opportunity to reveal the contributions of environmental context and trait differences to ASR bias. 3. We aimed to test for bias in Sable Island horse ASR, identify the demographic drivers of bias, and explore its demographic and social consequences. To do this, we used life-history, movement, and group membership information for hundreds of horses followed through a long-term individual-based study between 2007 and 2018. 4. Sable Island horse ASR is male-biased, and this skew has increased over time, reaching 62% male in 2018. Our life table response experiment suggested that ASR skew was driven predominantly by male-biased adult survival. Further analyses pointed to sex-biased survival being driven by reduced female survival post-reproduction. Male-biased ASR was associated with reduced harem sizes, an increase in the number of social groups on the island, and reduced reproduction in young females. 5. Our results support the idea that male-biased ASR in feral ungulate populations may be caused by a combination of high population density and high reproductive output. We suggest that female-biased mortality may be caused by females continuing to reproduce at high density, and thus being more susceptible to resource shortages. Thus, our results highlight the strong context-dependence of ASR. Furthermore, our work indicates the potential for ASR to substantially alter a population's social organisation. Such changes in social structure could have knock-on consequences for demography by altering the formation/stability of social relationships, or competition for matings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".