Patterns and Drivers of Tree Species Accumulation in Three Biodiversity Hotspots of Northwestern South America
Bibliographic record
Abstract
ABSTRACT Aim This study aims to understand the relative importance of climatic anomalies since the Last Glacial Maximum (LGM) and regional biogeographic (evolutionary) features as drivers of local and regional tree species accumulation across three neotropical biodiversity hotspots in northwestern South America (Northwestern Amazon, Northern Andes and Chocó). We assess the relative influence of phylogenetic structure and dispersal on shaping tree assemblages and the role of climatic and biogeographical features as drivers of species accumulation. Location Northwestern Amazon, Northern Andes and Chocó, northwestern South America. Taxon Angiosperm trees. Methods We analysed data from 49 permanent 1‐ha plots across the three regions. Taxonomic and Phylogenetic structure were quantified using Mean Pairwise Distance (MPD) and Mean Nearest Taxon Distance (MNTD), along with their standardised effect sizes (ses.MPD and ses.MNTD, respectively). We applied generalised linear models (GLMs) to evaluate the effect of climatic anomalies since the LGM and biogeographic region on both ses.MPD and ses.MNTD. Results There were significant differences in species richness, diversity, and ses.MNTD between Northwestern Amazon and both Northern Andes and Chocó, but not between the latter two. In contrast, we did not find significant differences in ses.MPD among regions. Regional diversity was highest in the Northwestern Amazon, followed by Chocó and the Northern Andes. Precipitation seasonality anomaly (PSa) emerged as a significant predictor of ses.MPD, whereas mean annual temperature anomaly (MATa) and biogeographic region were identified as the primary driver of ses.MNTD. Main Conclusions Our findings show the Northwestern Amazon as the region with the highest tree diversity, largely due to higher species accumulation of some dominant clades that have had long time to evolve in a geographic area large enough to promote species coexistence and persistence through time. In contrast, the high diversity in Chocó and Northern Andes was mainly shaped by historical dispersal from tropical and extra‐tropical regions. Ses.MPD was significantly correlated with PSa and temperature seasonality anomaly (TSa), while ses.MNTD showed significant correlations with MATa, TSa and PSa. Geological and evolutionary processes associated with biogeographic regions played a key role determining species accumulation at both local and regional scales.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".