Fitness & major histocompatibility complex diversity of two bottlenose dolphin populations
Bibliographic record
Abstract
Low genetic diversity diminishes the potential to adapt to change and thus reduces individual fitness as well as population viability. Therefore, it is crucial to identify genetic variation that affects fitness and population viability. Until recently, the search for the genetic basis of fitness variation, and the related field of conservation genetics, have mostly relied on neutral genetic markers, such as mitochondrial DNA and microsatellites. However, ecological and evolutionary processes relevant to fitness can only be inferred by non-neutral genes, such as those of the major histocompatibility complex (MHC). \nIn this thesis I examined the interplay between demographic parameters, fitness and MHC genetic diversity of two West Australian populations of bottlenose dolphins (Tursiops cf aduncus): a large population (N ≈ 3000), in Shark Bay (SB) and a much smaller population (N ≈ 250) off Bunbury (BB). With collaborators, I assessed demographic parameters, characterized MHC variation, and investigated the relationship between fitness and MHC diversity. \nIn chapter 2, our demographic analysis showed that the SB population, which is comparatively unimpacted by human activity, was much larger and displayed greater reproductive output than the urban BB population. Reproductive rates of the SB population showed large temporal fluctuations. In chapter 3, using a combination of Sanger and next-generation Illumina MiSeq sequencing, we identified a large variety of MHC sequence variants. We also classified MHC supertypes, variants which encode particular antigen binding sites with differing physiochemical properties. These MHC supertypes may serve as an improved marker for biologically relevant genetic variation. Transcription of some of the MHC sequence variants was confirmed in both populations. Chapter 4 shows that the larger SB population displayed greater MHC diversity—nucleotide diversity and a novel measure of ‘supertype diversity’—than the BB population. The SB population is, therefore, potentially more robust to environmental changes. In chapter 5 we report a strong association between MHC diversity and individual fitness: For example, offspring produced by mothers who were heterozygous for one MHC locus were much more likely to survive to weaning age, than offspring of homozygous mothers. Thus the MHC variation identified in this thesis provides a reliable basis for further studies on the relationship between genetic diversity, fitness and population biology.
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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 | 0.000 |
| 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) | 0.001 | 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".