Fragmentation and Translocation Distort the Genetic Landscape of Ungulates: Red Deer in the Netherlands
Bibliographic record
Abstract
At present, many ungulate populations have a complex history of isolation and translocation. Consequently, ungulate populations may have experienced substantial reductions of the level of overall gene flow, yet simultaneously have augmented levels of long-distance gene flow. To investigate the effect of this dual anthropogenic impact on the genetic landscape of ungulates, we genotyped 35K SNPs in 47 red deer (Cervus elaphus) of The Netherlands, including putative autochthonous relic populations as well as allochthonous populations established in private estates and rewilding areas. We applied FST and ordination analyses to determine the meta-population genetic structure and thereby the occurrence of hybridization. At population level, we investigated levels of inbreeding through individual-based diversity measures, including Runs of Homozygosity. We found that both spatial genetic structure and within-population genetic variation differed markedly from patterns assumed from present-day abundance and distribution. Notwithstanding the small spatial scale, red deer populations formed distinct genetic clusters, and some had higher genetic similarity to distant than to nearby populations. Moreover, the putative autochthonous relic deer populations had much reduced levels of polymorphism and multi-locus heterozygosity, despite relatively large current population size. Accordingly, genomes of these deer contained a high proportion of long (>5Mb) Runs of Homozygosity. Whereas the observed high levels of inbreeding warrant defragmentation measures, the presence of adjacent autochthonous and allochthonous genetic stocks imply that facilitation of geneflow would cause genetic homogenization. Such distortions of the genetic landscape of ungulates creates management dilemmas that cannot be properly anticipated without baseline genetic monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".