Factors shaping gene flow in red deer (<i>Cervus elaphus</i>) in seminatural landscapes of central Europe
Bibliographic record
Abstract
We studied gene flow and connectivity between three subpopulations and nine groups of red deer ( Cervus elaphus L., 1758) occurring in forests in northeastern Poland and western Belarus. The red deer in this region mostly originated from translocated individuals that were introduced primarily in the 19th and 20th centuries. The genetic structure of the population has been identified during the previous study. Using 14 microsatellite loci, we detected 14 first-generation migrants between the three subpopulations and 21 among the nine groups of deer. The number of effective migrants (Nm) was estimated to be 2.5 individuals/generation between the subpopulations and 6.2 individuals/generation between the groups. About 80% of first-generation migrants moved less than 150 km. The gene flow of hinds and stags was similar. A least cost path (LCP) analysis was performed using different habitat types: deciduous and mixed forests, coniferous forests, wetlands, meadows, arable lands, scarce settlements, dense settlements, and waters. No significant barriers to dispersal were detected, but individual dispersal was restricted in space by the significant isolation by distance. The best model, explaining the genetic distance (FST/1 – FST) between the forests, suggested that LCP corridor length limited gene flow and high forest cover within LCP corridors increased gene flow among the forests.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".