Niche-dependent forest and savanna fragmentation in Tropical South America during the Last Glacial Maximum
Bibliographic record
Abstract
The refugia hypothesis, often used to explain Amazonia's high biodiversity, initially received ample support but has garnered increasing criticism over time. Palynological, phylogenetic, and vegetation model reconstruction studies have been invoked to support the opposing arguments of extensive fragmentation versus a stable Amazonian Forest during Pleistocene glacial maxima. Here, we test the past existence of forest fragments and savanna connectivity by bias-correcting vegetation distributions from a Dynamic Vegetation Model (DVM) driven by paleoclimate simulations for South America during the Last Glacial Maximum (LGM). We find evidence for fragmented forests akin to refugia with extensive tropical humid forests to the west and forest islands in central/southern Amazonia. Drier ecosystems of Northern Llanos, Caatinga and Cerrado may have merged into continuous savanna/grasslands that dominated the continent. However, our reconstructions suggest taller, dense woodland/tropical savanna vegetation and areas of similar bioclimate connected disparate forest fragments across Amazonia. This ecotonal biome may have acted as a corridor for generalist forest and savanna species, creating connectivity that allows for range expansion during glacial periods. Simultaneously, it could have served as a barrier for specialists, inducing diversification through the formation of 'semi-refugia'.
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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".