On Small Mammal Sympatry in the Southeastern Amazon and Ecological Relationships with Brazil Nut Dispersal and Harvesting
Bibliographic record
Abstract
The Amazon rainforest harbors the planet's highest biodiversity among terrestrial ecosystems; however, the biology and ecology of most of its species are unknown. Niche partitioning is considered a key factor allowing species co-existence, especially for morphological similar species such as spiny rats of the genus Proechimys. I examined the extent to which habitat differentiation, species body mass, and diet could explain the community composition of small mammals at a site in the southeastern Amazon. Moreover, I radio-tagged sympatric species of Proechimys spp. and Mesomys stimulax (an arboreal spiny rat) to obtain detailed autoecology information, including habitat use and use of space. I found support for niche partitioning among species and associated small mammal species with distinct successional phases of gap dynamics. I also observed among Proechimys spp. a typical polygynous organization: females appeared to be territorial against females of any species of their genus; but male territories overlapped with those of several females. Mesomys stimulax showed evidence of monogamy and possible sociality, although sample sizes were small. To identify the importance of small mammals as seed disperser of Brazil nut seeds, I conduct experiments using a combination of fluorescent powder, seed exclosures, and track plates in forests with and without Brazil nut groves, and in forests with and without Brazil nut harvesting. Among small mammals, only Proechimys spp. removed, dispersed, and preyed upon Brazil nut seeds. Proechimys spp. sometimes scatterhoarded these seeds, and hence have the potential to play a significant role in recruiting new Brazil nut trees. I also trapped small mammals and measured forest structures on the same sites used for the seed dispersal experiment, to determine the ecological effects of Brazil nut harvest on small mammal communities; however, my results showed little evidence of changes associated with the seed exploitation. My results highlight the importance of habitat heterogeneity in structuring small mammal communities, and indicate that forest management practices that alter habitats, such as partial logging, also can be expected to alter small mammal composition and diversity. Proechimys spp. have the potential to play an important role in the ecological restoration of intensive exploited Brazil nut groves.
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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 | 0.000 | 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".