Genetic structure of a Virginia opossum (<i>Didelphis virginiana</i>) population inhabiting a fragmented agricultural ecosystem
Bibliographic record
Abstract
Fragmentation has drastically altered the quality of habitats throughout numerous ecosystems, often leading to dramatic changes in the composition of wildlife communities. The ecology and associated movement behavior of a species may also be modified as a result of forest fragmentation, resulting in changes in genetic composition of the affected species. In this research, we evaluated the genetic structure of the Virginia opossum ( Didelphis virginiana Kerr, 1792) at the landscape and local scales in a fragmented, agricultural ecosystem in northern Indiana using 13 microsatellite loci. We examined 290 samples from opossums inhabiting 28 discrete habitat patches, and evaluated partitioning of genetic variation of opossums among and within habitat patches. We observed low but significant levels of genetic structure (FST = 0.005) overall, and pairwise comparisons of FSTvalues among habitat patches also were relatively low. Relatedness within patches was highly variable (–0.077 ≤ rxy ≤ 0.060), with a few patches exhibiting significantly higher levels of relatedness than random expectations, and we detected no evidence of sex-biased natal dispersal. These results contrast with previous field studies that documented male-biased dispersal in the Virginia opossum, indicating dispersal in this species is plastic and dependent upon local environmental conditions.
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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 | 0.001 | 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".