Soundscapes predict species occurrence in tropical forests
Soundscapes predict species occurrence in tropical forests
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DOI：
10.1111/oik.08525
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发表时间：
2021-12-08
期刊：
影响因子：
3.4
通讯作者：
Picinali, Lorenzo
中科院分区：
文献类型：
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作者：
Sethi, Sarab S.;Ewers, Robert M.;Picinali, Lorenzo
准确的发生数据对于保护重要物种或濒危物种是必要的，但获取这些数据通常是缓慢、费力和昂贵的。自动声学监测为人工调查提供了可扩展的替代方案，但识别物种发声需要大量手动注释的训练数据集，并且并不总是可能的（例如，对于较少研究或沉默的物种）。需要一种新的方法，使用更小和更粗糙标记的音频数据集快速预测物种的发生。在马来西亚沙巴州热带森林退化梯度的20分钟录音中，我们调查了当地的声景是否可以用来推断32种鸟类和7种爬行动物的存在。利用来自卷积神经网络（CNN）的声学特征，我们通过在临时粗糙标记点计数数据集上训练我们的模型来表征物种指示性音景。在两个类群的39个物种中，声景观成功预测了34个物种的发生，曲线下面积（AUC）指标在0.53 ~ 0.87之间。具有较强时间发生模式的物种获得了最高的精度。声景观比地上碳密度更能预测物种的发生，地上碳密度是一种通常用于量化森林退化梯度中栖息地质量的指标。研究结果表明，声景观可以有效地预测各种物种的发生，为数据驱动的大规模生境适宜性评估提供了新的方向。
Accurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g. for lesser studied or silent species). A new approach is needed that rapidly predicts species occurrence using smaller and more coarsely labelled audio datasets. We investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species in 20 min recordings across a tropical forest degradation gradient in Sabah, Malaysia. Using acoustic features derived from a convolutional neural network (CNN), we characterised species indicative soundscapes by training our models on a temporally coarse labelled point-count dataset. Soundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics from 0.53 up to 0.87. The highest accuracies were achieved for species with strong temporal occurrence patterns. Soundscapes were a better predictor of species occurrence than above-ground carbon density - a metric often used to quantify habitat quality across forest degradation gradients. Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species and provide a new direction for data driven large-scale assessments of habitat suitability.