Reconstructing the degree of mammal defaunation throughout the Caatinga - the largest dry tropical forest region of South America
Bibliographic record
Abstract
Mammal diversity around the world has been increasingly threatened by a myriad of anthropogenic drivers, but particularly overhunting and natural habitat loss. These threats alter the structure of local mammal assemblages and, consequently, their associated ecological interactions. Here, we assess the degree to which the mammal fauna has been defaunated across the 862,818-km2 Caatinga tropical dry forest region of northeastern Brazil. Specifically, we examine potential changes in the structure of medium- to large-bodied mammal assemblages, large-scale spatial patterns of local extinctions, the loss of ecosystem functionality, and the role of human disturbance and protected areas in mammal defaunation. We compiled empirical data for 51 species representing a total of 73 local mammal assemblages that could be defined as exhaustively sampled throughout the region and compared species distribution estimates between contemporary and historical times. Our results show that 90% of the Caatinga lost between 20% and 80% of its mammal species, and the structure of coexisting local assemblages was further downsized by ~77%. Among all 51 species, 37 lost over 50% of their geographic range across the region. Caatinga defaunation is currently associated with a severe loss of ecosystem functionality. Overhunting, agropastoral habitat conversion and ruminant livestock were the main drivers of the extent and severity of local defaunation rates, which were conversely buffered by protected areas. This study informs conservation efforts in arid tropical forest regions dominated by the rural poor, including protection of remnant dry forests and restoration of mammal- and habitat- mediated ecosystem services.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".