ABSTRACT Passive acoustic monitoring (PAM) is a promising, if underused, technology for primate conservation. Successful PAM requires an understanding of the target species' vocal activity patterns and the factors that influence them, but this information remains scarce for most vocal primates. This is true for sportive lemurs ( Lepilemur spp.), which are understudied but otherwise excellent candidates for PAM, being highly vocal and threatened. We deployed autonomous audio recorders to measure vocal activity in the Critically Endangered Nosy Be sportive lemur ( Lepilemur tymerlachsoni ), sampling a 4‐h window from twilight each night for two lunar cycles. Our objectives were to identify suitable call types for monitoring, evaluate a user‐friendly automated call detection algorithm, assess temporal variation in vocal activity, and examine how environmental variables and moon illumination influence vocal activity. Automated call detection found an estimated 38% of all target calls but generated a high rate of false positives (96%). Among three call types, “ouah” calls were common and had the highest detection rate (51%), making them suitable target calls. Call rates were highest in the fourth hour following twilight, increased with temperature and moon illumination, and decreased during rainfall. We also observed variation in vocal activity between recording dates and sites, highlighting the need for sufficient temporal and spatial replication. We present recommendations for improving survey design, detection probability, and population inferences from PAM. The recommendations are specific to L . tymerlachsoni and may guide similar work on other sportive lemurs, although species‐specific differences in vocal behavior and ecology must also be considered.
Martin et al. (Sun,) studied this question.
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