Fragmentation reduces community‐wide taxonomic and functional diversity of dispersed tree seeds in the Central Amazon
Bibliographic record
Abstract
The Amazon harbors one of the most diverse tree floras on earth, and most species depend on mutualists for pollination and seed dispersal. This makes them susceptible to reproductive decline in fragmented forest because many of these mutualists suffer area-related extinction in fragments. It remains unknown, however, whether this highly biodiverse tree flora will reproduce and ultimately persist in fragmented forest. We conducted a 2-yr study of seed fall in an experimentally fragmented, highly diverse Central Amazonian forest. We determined the effect of fragment size (1, 10, 100 ha and continuous forest control) on the density, species richness, functional diversity and functional composition of seeds separated into two data sets: dispersed tree seeds, and undispersed tree seeds. Our results show a 3× reduction in the density of undispersed, non-pioneer tree seeds in fragments of all sizes, indicating reduced seed production of the non-pioneer tree community. The density of dispersed tree seeds was reduced by 6× in fragments of all sizes, while species richness was reduced by 6× in 1-ha fragments and by 3× in 10- and 100-ha fragments compared to intact forest. This provides evidence of reduced community-wide seed dispersal, which became more pronounced with declining fragment size. The functional diversity (FRic) of dispersed tree seeds was reduced 9.6× in small fragments, and significant shifts in the functional composition for 8 of the 10 reproductive and ecological traits studied were identified, suggesting compromised ecosystem functioning. These functional compositional shifts provide evidence for disrupted mutualistic processes in fragments, which include loss of pollination by bees, especially small eusocial (meliponid) bees, and loss of dispersal by primates and large birds, which reduced the frequency of large-seeded tree species. Fragments also lost rare and mature-forest species, and collectively these changes suggest that future tree communities in fragmented Amazonian landscapes will retain a taxonomically and functionally impoverished species pool with a biased functional composition unless efforts are undertaken to conserve dispersal by large frugivores and pollination by meliponid bees.
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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 | 0.000 | 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".