Anthropogenic Matrices Favor Homogenization of Tree Reproductive Functions in a Highly Fragmented Landscape
Bibliographic record
Abstract
Species homogenization or floristic differentiation are two possible consequences of the fragmentation process in plant communities. Despite the few studies, it seems clear that fragments with low forest cover inserted in anthropogenic matrices are more likely to experience floristic homogenization. However, the homogenization process has two other components, genetic and functional, which have not been investigated. The purpose of this study was to verify whether there was homogenization of tree reproductive functions in a fragmented landscape and, if found, to determine how the process was influenced by landscape composition. The study was conducted in eight fragments in southwest Brazil. The study was conducted in eight fragments in southwestern Brazil. In each fragment, all individual trees were sampled that had a diameter at breast height ≥3 cm, in ten plots (0.2 ha) and, classified within 26 reproductive functional types (RFTs). The process of functional homogenization was evaluated using additive partitioning of diversity. Additionally, the effect of landscape composition on functional diversity and on the number of individuals within each RFT was evaluated using a generalized linear mixed model. appeared to be in a process of functional homogenization (dominance of RFTs, alpha diversity lower than expected by chance and and low beta diversity). More than 50% of the RFTs and the functional diversity were affected by the landscape parameters. In general, the percentage of forest cover has a positive effect on RFTs while the percentage of coffee matrix has a negative one. The process of functional homogenization has serious consequences for biodiversity conservation because some functions may disappear that, in the long term, would threaten the fragments. This study contributes to a better understanding of how landscape changes affect the functional diversity, abundance of individuals in RFTs and the process of functional homogenization, as well as how to manage fragmented landscapes.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".