Wet season environments drive local adaptation in the drought-sensitive timber tree Dicorynia guianensis in French Guiana
Bibliographic record
Abstract
Climate change poses threats to biodiversity, particularly in tropical rainforests. How tropical rainforest tree species will respond to climate change is uncertain because their extent of local adaptation, its drivers and genetic basis remain poorly known. Characterizing these and the risks of maladaptation in future climates can inform on possible responses and help design strategies for the conservation. This study focuses on Dicorynia guianensis (Fabaceae), a widespread tree species in French Guiana, known for its sensitivity to drought. We performed genome resequencing on 87 individuals sampled in 11 sites across French Guiana to investigate the genetic structure, diversity, the drivers and the genetic basis of local adaptation. Genetic structure analysis identified three distinct groups: western, inland, and eastern, with similar levels of genetic diversity and distributed in areas with different environmental conditions. Six methods applied to detect genomic signatures of selection revealed region-specific selective sweeps and overlap between SNPs identified through outlier analysis or genome-environment association analyses. The most relevant environmental drivers of selection were potential evapotranspiration of the wettest quarter and precipitation of the coldest quarter, indicating that environmental variables related to high rainfall during the wet season are stronger drivers of local adaptation of D. guianensis than drought. Sites located in inland French Guiana had higher risks of climatic maladaptation than coastal sites. Our results contribute to the understanding of local adaptation and risk of maladaptation in tropical trees. They emphasize the need for area-specific approaches in managing tropical tree under the pressures of climate change.
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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) | 0.001 | 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".