Long‐distance gene flow in <i>Acacia senegal</i>: Hope for disturbed and fragmented populations
Bibliographic record
Abstract
Abstract Even though pollen and seed dispersals are some of the important factors that determine tree species survival across landscapes, gene dispersal data of important tropical dryland tree species such as Acacia senegal that are undergoing various population disturbances remain scarce. Understanding patterns of gene dispersal in these ecosystems is important for conservation, landscape restoration and tree improvement. We investigated pollen and seed mediated gene flow in two A. senegal populations of contrasting state (less disturbed and heavily disturbed) using nine microsatellites and 128 genotyping‐by‐sequencing single nucleotide polymorphism (SNPs) multilocus genotypes of two growth stages (juvenile and adult trees) and their spatial locations. We performed parentage assignments using likelihood approach and undertook spatial genetic structure (SGS) analyses for the two growth stages through correlation among kinship coefficients and geographical distances between pair of individuals. The SNPs showed higher resolving power and assignment rates than microsatellites; however, a combination of the two marker‐types improved the assignment rate and provided robust parentage assessments. We found evidence of long‐distance (up to 210 m) pollination events for both populations; however, the majority of seed dispersal was found closer to the putative maternal parent. On average, parentage analysis showed high amounts of pollen (40%) and seed (20%) immigration in both populations. Significant positive SGS was found only for the adult cohorts in the less disturbed population for distance classes 20 and 40 m, indicating historical short‐distance seed dispersals. Our results suggest long‐distance gene flow within the species and we recommend conservation of remnant and isolated populations or individual trees to promote genetic connectivity.
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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 | 0.001 |
| 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".