Inferences on Frog Responses to Forest Cover Depend on Sampling Method and Reproductive Mode

ABSTRACT Sampling choices can bias assessments of biodiversity responses to habitat loss, compromising conservation strategies in vulnerable ecosystems such as the Brazilian Atlantic Forest. We evaluated the efficiency of three frog sampling methods (active search, plot sampling, and pitfall traps) across Atlantic Forest remnants to estimate species richness and abundance of forest‐specialist species during rapid assessment surveys. We also examined the effectiveness of these methods in assessing the influence of forest cover on different functional groups of frogs: water‐dependent and water‐independent species. Across 146 sampling days, we recorded 1450 individuals from 53 species. Active searches yielded the highest species richness (50 species), surpassing plot sampling (18 species) and pitfall traps (16 species). Active search was more efficient than plot and pitfall methods for estimating richness, while active search and plot were more efficient than pitfall traps for estimating abundance. Among the statistically significant models assessing the effects of forest cover on estimated richness, the highest fits were obtained for models that used data from the three methods combined (90% of the variance) or active search combined with pitfall (88%). For water‐dependent species, the models that provided significant results with highest fits used data from the three methods combined (93%) or active search combined with pitfall traps (92%). Active searches combined with plot sampling improved detection of forest cover effects on frog abundance (60%), mainly for water‐independent species (71%). Richness of water‐dependent species had stronger sensitivity to forest loss, while water‐independent species had abundance declines associated with forest loss. Despite being labor‐intensive, pitfall traps uniquely detected rare taxa, emphasizing their complementary role. For rapid assessments, we advocate for integrating active searches with targeted passive methods, as prioritizing multi‐method approaches combined with functional trait analyses can enhance the detection of ecologically distinct groups and generate more robust data to guide biodiversity conservation efforts.