Recalculating route: dispersal constraints will drive the redistribution of Amazon primates in the Anthropocene
Bibliographic record
Abstract
Climate change will redistribute the global biodiversity in the Anthropocene. As climates change, species might move from one place to another, due to local extinctions and colonization of new environments. However, the existence of permeable migratory routes precedes faunal migrations in fragmented landscapes. Here, we investigate how dispersal will affect the outcome of climate change on the distribution of Amazon's primate species. We modeled the distribution of 80 Amazon primate species, using ecological niche models, and projected their potential distribution on scenarios of climate change. Then, we imposed landscape restrictions to primate dispersal, derived from a natural biogeographical barrier to primates (the main tributaries of the Amazon river) and an anthropogenic constraint to the migration of many canopy‐dependent animals (deforested areas). We also highlighted potential conflict zones, i.e. regions of high migration potential but predicted to be deforested. Species response to climate change varied across dispersal limitation scenarios. If species could occupy all newly suitable climate, almost 70% of species could expand ranges. Including dispersal barriers (natural and anthropogenic), however, led to range expansion in only less than 20% of the studied species. When species were not allowed to migrate, all of them lost an average of 90% of the suitable area, suggesting that climate may become unsuitable within their present distributions. All Amazon primate species may need to move as climate changes to avoid deleterious effects of exposure to non‐analog climates. The effect of climate change on the distribution of Amazon primates will ultimately depend on whether landscape permeability will allow climate‐driven faunal migrations. The network of protected areas in the Amazon could work as ‘stepping stones’ but most are outside important migratory routes. Therefore, protecting important dispersal corridors is foremost to allow effective migrations of the Amazon fauna in face of climate change and deforestation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".