Passive acoustic monitoring, when coupled with automated signal recognition software, allows researchers to perform simultaneous monitoring at large spatial and temporal scales. This technique has been widely used to monitor cetaceans, bats, birds, and anurans but rarely applied to monitor primates. Here, we evaluated the effectiveness of passive acoustic monitoring and automated signal recognition software for detecting the presence and monitoring the roaring behavior of the Black and Gold Howler Monkey (Alouatta caraya) over a complete annual cycle at one site in the Brazilian Pantanal. The diel pattern of roaring activity was unimodal, with high vocal activity around dawn. The howler monkey showed a clear seasonal pattern of roaring activity, with most of the roars detected during the wet season (74.9%, peak activity during November and December). The maximum vocal activity occurred during the period of maximum flowering and fruit production in the study area, suggesting a potential role of roaring in defending major feeding sites, which is in agreement with the findings of previous studies on the species. However, we cannot rule out the possibility that roaring may serve different purposes. Vocal activity was negatively associated with relative air humidity, which might be related to lower vocal activity on wetter and rainy days, while vocal activity was not related to minimum air temperature. Automated signal recognition software allowed us to detect the species in 89% of the recordings in which it was vocally active, but with a reduced time cost, since the time investment for data analyses was 2% of recording time. The good performance of the recognizer might be related to the long and loud roars of the howler monkey. Further research should be performed to evaluate the effectiveness of automated signal recognition for detecting the calls of different species of primates and under different environmental conditions.

中文翻译：

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黑金吼猴的日常和年度发声行为的被动声学监测

被动声学监测与自动信号识别软件相结合，使研究人员能够在大空间和时间尺度上进行同步监测。该技术已广泛用于监测鲸类、蝙蝠、鸟类和无尾目动物，但很少用于监测灵长类动物。在这里，我们评估了被动声学监测和自动信号识别软件在检测黑金吼猴 ( Alouatta caraya) 在巴西潘塔纳尔湿地的一个地点进行一个完整的年度周期。咆哮活动的昼夜模式是单峰的，在黎明前后有高发声活动。吼猴表现出明显的季节性咆哮活动模式，大多数咆哮是在雨季检测到的（74.9%，11 月和 12 月的活动高峰期）。最大的发声活动发生在研究区的最大开花和果实产量期间，表明咆哮在保护主要觅食地点方面具有潜在作用，这与先前对该物种的研究结果一致。但是，我们不能排除咆哮可能用于不同目的的可能性。声音活动与相对空气湿度呈负相关，这可能与雨天和雨天的声音活动较低有关，而声音活动与最低气温无关。自动信号识别软件使我们能够在 89% 的声音活跃的录音中检测到物种，但时间成本减少了，因为数据分析的时间投资是录音时间的 2%。识别器的良好性能可能与吼猴的长而响亮的咆哮有关。应该进行进一步的研究，以评估自动信号识别在检测不同种类灵长类动物和不同环境条件下的呼叫的有效性。因为数据分析的时间投资是记录时间的 2%。识别器的良好性能可能与吼猴的长而响亮的咆哮有关。应该进行进一步的研究，以评估自动信号识别在检测不同种类灵长类动物和不同环境条件下的呼叫的有效性。因为数据分析的时间投资是记录时间的 2%。识别器的良好性能可能与吼猴的长而响亮的咆哮有关。应该进行进一步的研究，以评估自动信号识别在检测不同种类灵长类动物和不同环境条件下的呼叫的有效性。