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Record W4398209955 · doi:10.1186/s13717-024-00517-5

Environmental drivers of tree species richness in the southernmost portion of the Paranaense forests

2024· article· en· W4398209955 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEcological Processes · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersUniversidad de la República Uruguay
KeywordsSpecies richnessGeographyTree (set theory)AgroforestryEcologyForestryEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

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 km 2 ). 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.213
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it