Quantifying the impacts of inbreeding depression on adult reproductive success, survival, and foal birth in the feral horses (Equus ferus caballus) of Sable Island, Nova Scotia
Bibliographic record
Abstract
Inbreeding, the mating between related individuals, often adversely affects individual fitness in a phenomenon known as inbreeding depression (ID). Small and genetically-isolated populations are especially vulnerable to ID, making understanding its causes and consequences important from a wildlife conservation perspective. Quantifying ID in wildlife is challenging, as there are difficulties with collecting long-term life history data needed to estimate fitness and high-quality genetic data needed to precisely estimate inbreeding, especially in free-living populations. In my thesis, I studied ID in the isolated feral horse population of Sable Island, Nova Scotia, by combining individual-based genomic, ecological and life history data from 239 individuals. Genomic data was generated using Single Nucleotide Polymorphism (SNP) arrays, and inbreeding levels were quantified through genomic estimators based on SNPs found in runs of homozygosity, FROH, correlation between uniting gametes, FUNI, and pairwise relatedness. I tested for presence of ID on sex-specific annual adult reproductive success and survival as well as foal births, and the influence of intrinsic and extrinsic variables in modulating ID using generalized linear mixed models and model averaging. Low overall levels of inbreeding was found in the population, and inbreeding was not a significant predictor of any of the fitness traits studied when considered on its own. However, results suggested that ID for female annual reproductive success, female annual survival, and male annual survival may be modulated by environmental conditions such as band size, location, and winter severity. The performance of the estimators FROH and FUNI were similar throughout my analyses. Using a standardized measure of ID, I also show that ID in Sable Island horses is relatively low in comparison to other wildlife populations. This research expands our knowledge of inbreeding depression in natural systems and provides Parks Canada with important information to assist with the management and conservation of Sable Island’s iconic population of feral horses.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".