Undesirable ambient noise during wildlife surveys decreases detection probability

The soundscape is an integral component of natural environments, but it can pose acoustic challenges for wildlife monitoring when undesirable noise interferes with detection. Here, we present a simple approach to reduce the influence of background noise on point count surveys. Using N-mixture models in a Bayesian framework, we estimated detection probability, relative abundance, and population trends for eight bird species and one mammal. Models incorporated acoustic covariates and were fit to 12,048 five-minute point counts from 746 locations from 2019 to 2024. Observers rated ambient noise on a 1–10 scale in all years and used a smartphone app to measure soundscape volume in 2019. Each species was modelled separately for detections within ≤50 m and >50 m from the observer. We hypothesised that both sound metrics would reduce the detection probability, especially for distant detections. Ambient noise was informative in more cases than soundscape volume. Contrary to expectations, soundscape volume more strongly affected detections within 50 m, whereas ambient noise had a greater effect beyond 50 m. Our findings suggest that incorporating soundscape assessments can help correct undercounting bias in relative abundance estimates, improve the comparability of point count surveys, and be easily implemented with minimal logistical burden.