Floristic Composition, Species Richness and Diversity of Campo Rupestre Vegetation from the Itacolomi State Park, Minas Gerais, Brazil
Bibliographic record
Abstract
Nevertheless campos rupestres are considered species rich and diverse vegetation formations, phytosociological surveys from the Itacolomi State Park, Minas Gerais, Brazil, are lacking in scientific literature. To close this gap, we compared floristic composition, species richness and diversity from two sites, Lagoa Seca and Calais, both situated within the park. Calais is moderately impacted by extensive pasture, fire, and settling activities. Both surveys contained 15 plots of 10 x 10 m, cardinality of each species was estimated. Beside species richness, the indexes of Shannon-Wiener, Fisher’s ?, the community richness estimator Jackknife 1 and the numbers of endemic, endangered and invasive species were compared. With 107 species, the moderately impacted Calais showed higher species richness than Lagoa Seca (76 species). The indices of Shannon-Wiener and Fisher’s ?, the community richness estimator, as well as point diversity and spatial turnover derived from the species-area relationship (SAR) indicated higher diversity for Calais. From Lagoa Seca, 30% of all species are endemic to the Atlantic Rainforest or to Cerrado, and four species are endangered, not any species found in Lagoa Seca is described as an invasive one. On the other hand, 23 species found in Calais are invasive species, only one from all 107 species is endangered. Only 19% of all species found in Calais are endemic. Nevertheless species richness and diversity differ between both study sites, they are exceptionally high compared to similar vegetation formations from further regions. This justifies the declaration of the park as a local hotspot of biodiversity. Furthermore, our results show that species richness or diversity measures are inappropriate criteria to evaluate the intactness of campo rupestre vegetation. More weight should be put on criteria like numbers of invasive, endemic or endangered species.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".