A global assessment of primate responses to landscape structure
Bibliographic record
Abstract
Land-use change modifies the spatial structure of terrestrial landscapes, potentially shaping the distribution, abundance and diversity of remaining species assemblages. Non-human primates can be particularly vulnerable to landscape disturbances, but our understanding of this topic is far from complete. Here we reviewed all available studies on primates' responses to landscape structure. We found 34 studies of 71 primate species (24 genera and 10 families) that used a landscape approach. Most studies (82%) were from Neotropical forests, with howler monkeys being the most frequently studied taxon (56% of studies). All studies but one used a site-landscape or a patch-landscape study design, and frequently (34% of studies) measured landscape variables within a given radius from the edge of focal patches. Altogether, the 34 studies reported 188 responses to 17 landscape-scale metrics. However, the majority of the studies (62%) quantified landscape predictors within a single spatial scale, potentially missing significant primate-landscape responses. To assess such responses accurately, landscape metrics need to be measured at the optimal scale, i.e. the spatial extent at which the primate-landscape relationship is strongest (so-called 'scale of effect'). Only 21% of studies calculated the scale of effect through multiscale approaches. Interestingly, the vast majority of studies that do not assess the scale of effect mainly reported null effects of landscape structure on primates, while most of the studies based on optimal scales found significant responses. These significant responses were primarily to landscape composition variables rather than landscape configuration variables. In particular, primates generally show positive responses to increasing forest cover, landscape quality indices and matrix permeability. By contrast, primates show weak responses to landscape configuration. In addition, half of the studies showing significant responses to landscape configuration metrics did not control for the effect of forest cover. As configuration metrics are often correlated with forest cover, this means that documented configuration effects may simply be driven by landscape-scale forest loss. Our findings suggest that forest loss (not fragmentation) is a major threat to primates, and thus, preventing deforestation (e.g. through creation of reserves) and increasing forest cover through restoration is critically needed to mitigate the impact of land-use change on our closest relatives. Increasing matrix functionality can also be critical, for instance by promoting anthropogenic land covers that are similar to primates' habitat.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".