Species-Specific Responses of Carnivores to Human-Induced Landscape Changes in Central Argentina
Bibliographic record
Abstract
The role that mammalian carnivores play in ecosystems can be deeply altered by human-driven habitat disturbance. While most carnivore species are negatively affected, the impact of habitat changes is expected to depend on their ecological flexibility. We aimed to identify key factors affecting the habitat use by four sympatric carnivore species in landscapes of central Argentina. Camera trapping surveys were carried out at 49 sites from 2011 to 2013. Each site was characterized by 12 habitat attributes, including human disturbance and fragmentation. Four landscape gradients were created from Principal Component Analysis and their influence on species-specific habitat use was studied using Generalized Linear Models. We recorded 74 events of Conepatus chinga, 546 of Pseudalopex gymnocercus, 193 of Leopardus geoffroyi and 45 of Puma concolor. We found that the gradient describing sites away from urban settlements and with low levels of disturbance had the strongest influence. L. geoffroyi was the only species responding significantly to the four gradients and showing a positive response to modified habitats, which could be favored by the low level of persecution by humans. P. concolor made stronger use of most preserved sites with low proportion of cropland, even though the species also used sites with an intermediate level of fragmentation. A more flexible use of space was found for C. chinga and P. gymnocercus. Our results demonstrate that the impact of human activities spans across this guild of carnivores and that species-specific responses appear to be mediated by ecological and behavioral attributes.
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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 | 0.000 | 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) | 0.001 | 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".