Can seed exchange networks explain the morphological and genetic diversity in perennial crop species? The case of the tropical fruit tree <scp><i>Dacryodes edulis</i></scp> in rural and urban Cameroon
Bibliographic record
Abstract
Societal Impact Statement Crop seed exchange networks, shaped by social dynamics, strongly influence the organization and breadth of plant diversity in human‐managed environments. Integrating an urban and market perspective, this study explores the diversity dynamics of a socio‐economically important Central African fruit tree species, the African plum tree. Tree owners in urban, peri‐urban and rural sites use seeds from different origins as their main propagation material, leading to locational variations in genetic diversity and structure. This analysis contributes toward building a framework to inform the research agenda of cultivated African fruit trees, by highlighting the important role of urban centers in safeguarding crop genetic resources. Summary Biocultural factors constrain the dynamics of crop species diversity. Here, we considered different aspects of the social, spatial and temporal dynamics of morphological and genetic diversity in a multi‐purpose perennial crop, the African plum tree ( Dacryodes edulis ). We assessed (i) how seed exchange networks were organized along urbanization gradients, and how they influenced the distribution of species diversity; (ii) the temporal dynamic of seed exchange network by characterizing species genetic diversity through time. To do so, the study was carried out in Cameroon, where we focused on three urbanization gradients, covering urban, peri‐urban and rural areas, corresponding to three different ethnic groups (Bamileke, Bassa, Beti). We combined interviews with tree owners and nuclear microsatellite‐based genetic analyses. Tree owners from urban and peri‐urban sites primarily used distant seed sources, acquired in the market or from their village of origin, as propagation material, whereas tree owners in rural sites relied primarily on village‐level seeds. In turn, genetic diversity was not evenly distributed, with rural sites exhibiting their own genetic clusters. On the contrary, the genetic diversity of urban sites was enhanced by extensive human‐mediated seed flows. Looking at trees from different age classes, we found that genetic diversity was stable over time. Overall, this first attempt to combine different levels of diversity for African plum trees in commercially connected areas expands the scope for in situ intraspecific conservation by highlighting the contribution of urbanized areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".