Environmental drivers of tree species richness in the southernmost portion of the Paranaense forests
Bibliographic record
Abstract
Abstract Background The Rio de la Plata grassland region is dominated by temperate grasslands, with the scarce natural forests, influenced floristically by adjacent biogeographical provinces. Uruguay represents the southern limit for many tree species of the Paranaense Province, several of which inhabit the hillside forests. With many species shifting poleward due to climate change, we do not yet know how current environmental factors, particularly climatic ones, are linked to the tree diversity of this flora nowadays. The aim of this study is to understand the geographic pattern of tree richness in the hillside forests of Uruguay, evaluating the water–energy and the environmental heterogeneity hypotheses. The distribution of the hillside forest trees was obtained by compiling and updating the herbaria database and distribution maps of woody plants of Uruguay. The presence/absence of each species, and then the species richness, were georeferenced over a grid that covers Uruguay with 302 cells (660 km2). Over the same grid were compiled environmental variables associated with climate and environmental heterogeneity. The relationship between richness and environmental variables was studied by applying general linear models (GLM). As a strong autocorrelation was detected, a residuals auto-covariate term was incorporated into the GLM, to take into account the species richness spatial structure. Results The tree flora of the hillside forest was composed mainly by Paranaense species that show a latitudinal gradient, with two high richness cores, in the east and northeast of Uruguay. The final model including the environmental variables and the spatial term explained 84% of the variability of tree richness. Species richness showed a positive relationship with precipitation, forest cover, potential evapotranspiration and productivity, while a negative effect of temperature variation was found. The spatial component was the primary predictor, accounting for a 30% of spatial pattern of tree richness. Conclusions This study accounts for a large proportion of the environmental and spatial variations of the tree richness pattern of the Paranense flora in its southernmost portion. It brings support to both water–energy and environmental heterogeneity hypotheses, emphasizing the role of climate and its variation and the habitat availability on the hillside forest diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".