Although bioacoustics is increasingly used to study species and environments for their monitoring and conservation, detecting calls produced by species of interest is prohibitively time consuming when done manually. Here we compared four methods for detecting and identifying roar-barks of maned wolves (Chrysocyon brachyurus) within long sound recordings: (1) a manual method, (2) an automated detector method using Raven Pro 1.4, (3) an automated detector method using XBAT and (4) a mixed method using XBAT's detector followed by manual verification. Recordings were done using a song meter installed at the Serra da Canastra National Park (Minas Gerais, Brazil). For each method we evaluated the following variables in a 24-h recording: (1) total time required analysing files, (2) number of false positives identified and (3) number of true positives identified compared to total number of target sounds. Automated methods required less time to analyse the recordings (77 -93 min) when compared to manual method (189 min), but consistently presented more false positives and were less efficient in identifying true positives (manual ¼ 91.89%, Raven ¼ 32.43% and XBAT ¼ 84.86%). Adding a manual verification after XBAT detection dramatically increased efficiency in identifying target sounds (XBAT þ manual ¼ 100% true positives). Manual verification of XBAT detections seems to be the best way out of the proposed methods to collect target sound data for studies where large amounts of audio data need to be analysed in a reasonable time (111 min, 58.73% of the time required to find calls manually).

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Passive Acoustic Monitoring (PAM) provides the way to feature the status, presence and trends of species’ distribution in different landscapes, under different environmental conditions. The howler monkeys are known for investing a long time and energy in the vocal type known as roar, an important vocal type for intra- and inter-group dynamics. The aim of the present study was to evaluate the detection, distribution and vocal behavior of wild howler monkeys based on the PAM methodology due to the need of having a fast and effective method to evaluate this species’ population presence and status given the successive yellow fever outbreaks lethally affecting it. The study was carried out at Fontes do Ipiranga State Park, Southeastern São Paulo City, Brazil. The evaluation of howler monkeys roar vocal behavior’s temporal pattern was carried out by taking into consideration the number of roaring events counted based on time. Vocalization occurrences observed between stations were compared to express the roaring events on an hourly basis. In total, 1,531 hours of recordings were analyzed and it allowed detecting roars in all PAM stations. The number of roaring events ranged from 71 to 142 per station. The present study has shown the useful application of PAM to accurately detect the presence of howler monkey groups based on vocal behavior. Furthermore, this method helps assessing groups’ distribution and daily occurrence, besides giving tips about how they are distributed in the area based on their handling ability.