National Institute for Science and Technology in Wetlands (INAU), Federal University of Mato Grosso (UFMT), Computational Bioacoustics Research Unit (CO.BRA), Cuiabá, Mato Grosso, Brazil.
Postgraduate Program in Ecology and Biodiversity Conservation, Institute of Biosciences, Federal University of Mato Grosso, Cuiabá, Mato Grosso, Brazil.
Am J Primatol. 2021 Mar;83(3):e23241. doi: 10.1002/ajp.23241. Epub 2021 Feb 4.
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.
被动声学监测与自动化信号识别软件相结合，可以使研究人员同时在大空间和时间尺度上进行监测。该技术已广泛用于监测鲸目动物、蝙蝠、鸟类和两栖动物，但很少应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化信号识别软件在巴西潘塔纳尔的一个地点监测黑金吼猴（Alouatta caraya）全年存在和监测其咆哮行为的有效性。咆哮活动的日活动模式呈单峰型，黎明前后活动较高。吼猴表现出明显的季节性咆哮活动模式，大部分咆哮声是在雨季（74.9%，活动高峰期在 11 月和 12 月）检测到的。最大的发声活动发生在研究区域内开花和果实产量最大的时期，这表明咆哮在保护主要觅食地方面可能发挥了作用，这与之前对该物种的研究结果一致。然而，我们不能排除咆哮可能具有不同目的的可能性。发声活动与相对空气湿度呈负相关，这可能与较湿和雨天的发声活动较低有关，而发声活动与最低空气温度无关。自动化信号识别软件使我们能够在其发声的 89%的录音中检测到该物种，但时间成本降低，因为数据分析的时间投入仅为录音时间的 2%。识别器的良好性能可能与吼猴长而响亮的咆哮声有关。应进一步研究评估自动化信号识别检测不同种类灵长类动物叫声和不同环境条件下的有效性。
