Structure in vital rates, internal source-sink dynamics, and their influence on current population expansion for the feral horses (Equus ferrus caballus) of Sable Island, Nova Scotia
Bibliographic record
Abstract
Population-level dynamics are affected by temporal variation in individual vital rates of survival and reproduction, which are in turn influenced by habitat-specific processes. Variation in habitat quality within a population’s range can drive movement of individuals between different areas, and so there may be a relationship between variation in vital rates and spatial heterogeneity in population growth (λ). I investigated this relationship for the feral horses (Equus ferus caballus) of Sable Island, Nova Scotia, Canada, from 2008−2010. The horses (n = 484 in September 2010) form a closed population that is free from human interference and predation. I analyzed annual population growth using age-structured projection matrix models parameterized with survival and fertility data collected from almost every female (98.7% of females). I found some evidence of temporal variation in growth during the two years I studied the population (λ2008−2009 = 1.065, λ2009−2010 = 1.117). Age structure appears to have converged to a stable age distribution, suggesting this growth rate has been sustained in the years leading up to the end of my study. Variation in vital rates of adult fertility and foal survival made the largest contribution to annual variation in population growth. Future growth is predicted to be most influenced by proportional changes in adult survival, which remained relatively unchanged between 2008 and 2010. The population can be stratified into three spatially distinct subunits found across a west−east longitudinal gradient of water resources (access to permanent ponds vs. ephemeral water sources and holes dug in sand). I assessed the existence of source-sink dynamics to determine if individual movements between subunits could explain spatial heterogeneity in population growth. I found that spatial heterogeneity in growth appears to be most influenced by immigration and emigration events between subunits. Evidence suggests that current growth of the overall Sable Island horse population is made possible by individual emigration from more productive into less productive subunits; in particular, a source presented in the west of the island where permanent water ponds are located.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".