Effect of the Landscape on Functional and Spatial Connectivity in Magnolia Cubensis (Magnoliaceae) in Two Mountain Massifs of Cuba
Bibliographic record
Abstract
Abstract Landscape features impact gene flow and the spatial patterns of genetic variation between populations of a species. Because many Magnoliaceae species occur in fragmented and highly disturbed landscapes, the family is an excellent model for landscape genetic studies. This research focuses on the subspecies and localities of Magnolia cubensis and aims to: (1) compare the genetic diversity, (2) search for genetic patterns, (3) describe the functional connectivity and (4) access the structural connectivity of the landscape. This study employs 21 microsatellite markers on two subspecies, complemented with landscape characteristics of the Guamuhaya and Sierra Maestra massifs in Cuba. Magnolia cubensis subsp. acunae does not have a well-defined spatial genetic pattern: there is no evidence of isolation by distance or spatial autocorrelation and the little genetic differentiation between the two localities does not reflect the characteristics of the landscape that separates them or the cost values to cross it. Magnolia cubensis subsp. cubensis presents evidence of isolation by distance and the autocorrelation analyses indicate that the approximate scale of the genetic neighborhood is between 35 and 40 km. There is a marked genetic structure with probability values that indicate the existence of three genetic groups. Large genetic differentiation was only found between Gran Piedra and the other localities, which reflects low gene flow. Our results support the recognition of these subspecies at the species level. We define one evolutionary significant unit in Magnolia cubensis subsp. acunae and two evolutionary significant units in Magnolia cubensis subsp. cubensis. These results must be combined with ecological, social and distribution data, in order to obtain a more accurate and realistic perspective of the conservation management strategies for these taxa.
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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 | 0.001 | 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) | 0.001 | 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".