PDF(Pubmed)
Abstract：
Using passive acoustic monitoring (PAM) and convolutional neural networks (CNN), we monitored the movements of the two endangered Amazon River dolphin species, the boto (Inia geoffrensis) and the tucuxi (Sotalia fluviatilis) from main rivers to floodplain habitats (várzea) in the Mamirauá Reserve (Amazonas, Brazil). We detected dolphin presence in four main areas based on the classification of their echolocation clicks. Using the same method, we automatically detected boat passages to estimate a possible interaction between boat and dolphin presence. Performance of the CNN classifier was high with an average precision of 0.95 and 0.92 for echolocation clicks and boats, respectively. Peaks of acoustic activity were detected synchronously at the river entrance and channel, corresponding to dolphins seasonally entering the várzea. Additionally, the river dolphins were regularly detected inside the flooded forest, suggesting a wide dispersion of their populations inside this large area, traditionally understudied and particularly important for boto females and calves. Boats overlapped with dolphin presence 9% of the time. PAM and recent advances in classification methods bring a new insight of the river dolphins\' use of várzea habitats, which will contribute to conservation strategies of these species.
摘要：
使用无源声学监测(PAM)和卷积神经网络(CNN)，我们监测了两种濒临灭绝的亚马逊河海豚的活动，从主要河流到Mamirauá保护区（亚马逊，巴西)。根据回声定位点击的分类，我们在四个主要区域检测到了海豚的存在。使用相同的方法，我们自动检测船只通道，以估计船只和海豚存在之间可能的相互作用。CNN分类器的性能很高，回声定位点击和船只的平均精度为0.95和0.92，分别。在河流入口和河道处同步检测到声学活动的峰值，对应于季节性进入várzea的海豚。此外,经常在被洪水淹没的森林里发现河豚，表明他们的人口在这个大区域内广泛分散，传统上研究不足，对boto雌性和小牛特别重要。9％的时间与海豚重叠。PAM和分类方法的最新进展带来了对河豚使用várzea栖息地的新见解，这将有助于这些物种的保护策略。
