Differential responses of marsh predators to rainfall-induced habitat loss and subsequent variations in prey availability
Bibliographic record
Abstract
Rainfall has increased in many regions during recent decades, but most information is from dryland ecosystems, which precludes generalizations about its ecological consequences. We explored the effects of increased flooding on the Geoffroy’s cat, Leopardus geoffroyi (d’Orbigny and Gervais, 1844), and pampas fox, Pseudalopex gymnocercus (G. Fischer, 1814), exposed to an abnormally rainy period in marshes at Mar Chiquita, Argentina. In particular, we assessed the effects of flooding on (i) habitat use by L. geoffroyi and P. gymnocercus, (ii) abundance of their main prey, and (iii) functional responses of predators to variations in prey abundance. Overall, simple regression analysis identified negative effects of flooding on abundance of prey (rodents, waterbirds, and arthropods), but structural-equation modeling and logistic generalized linear models identified differential effects of rainfall on habitat use by and functional responses of predators, respectively. Habitat use by L. geoffroyi was more negatively affected by interannual variability in flooding-induced habitat loss, particularly through its effect on waterbirds. At the same time, habitat use by P. gymnocercus was less affected, likely because this species was less dependent on prey from flooded areas and used higher elevation habitats. Given that most native grasslands in elevated areas have been converted to agriculture, the more specialized L. geoffroyi faces a greater threat if current trends of climate change persist in this region.
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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 | 0.001 |
| 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".