Back

When and where to find the tiny creatures: environmental predictors of the abundance of a miniaturized frog species from Northeast Brazil

de Moura, L. C.; Roberto, I. J.; Guimaraes, M.; Bezerra, F. R.; Araujo, K. C.; dos Santos, E. M.

2025-07-25 ecology
10.1101/2025.07.22.666076 bioRxiv
Show abstract

Amphibians are the most vulnerable terrestrial vertebrates to environmental changes, primarily due to their permeable skin and dependence on water for reproduction. In miniaturized frogs, sensitivity to changes in ecological factors related to humidity may be particularly critical due to their high surface-to-volume ratio, which makes them more susceptible to desiccation. To test this hypothesis, we evaluated the influence of environmental variables on the abundance of calling males of Adelophryne nordestina, a small miniaturized frog with direct development inhabiting the Atlantic Forest leaf litter in northeastern Brazil. We surveyed calling males of A. nordestina over nine months across four transects, each containing 25 survey points, totaling 100 study sites. We employed a Negative Binomial N-Mixture Model to estimate the abundance of calling males and assessed the effect of environmental parameters. Our results indicate that estimated mean detection probability was 0.03, and mean abundance among sites was 85.90. Precipitation, temperature, leaf litter depth, and canopy cover significantly influenced the number of calling males in the study area. We suggest that, as a terrestrial, miniaturized, and direct-developing species, changes in environmental parameters affecting microhabitat humidity impact its reproductive activity. Our findings contribute to the understanding of miniaturized frog ecology and the potential impacts of environmental disturbances on this group.

Published in Austral Ecology · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.