Acoustic detection rate can outperform traditional survey approaches in estimating relative densities of breeding waders

IF 1.8 3区 生物学 Q1 ORNITHOLOGY
Ibis Pub Date : 2024-11-05 DOI:10.1111/ibi.13375
David Jarrett, Stephen G. Willis
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Abstract

Passive acoustic devices are increasingly being used to monitor biodiversity. However, few studies have compared the accuracy of acoustic surveys and traditional surveys against ground-truthed data. Here, we assess whether acoustic recorders used in conjunction with an artificial intelligence (AI) classifier can predict the relative breeding density of four wader species better than traditional fieldworker transect surveys. In a 27-km2 upland study site, acoustic data were collected at 83 sampling points and analysed using the BirdNet bird-sound classifier to estimate vocal detection rate at each location; we also carried out concurrent transect bird surveys. To ground-truth these approaches, intensive field surveys were undertaken to identify each breeding territory of our focal species. With both the acoustic dataset and the transect dataset, we used similar analytical approaches (random forest regression trees) to predict relative territory density across the study site, and then compared these predictions with the territory density obtained from the intensive field surveys. The classifier performed well at identifying the presence of target species' vocalizations within 3-s periods for Lapwing (accuracy = 0.911), Curlew (0.826) and Oystercatcher (0.841), but less well for Golden Plover (0.699). For Curlew and Oystercatcher, the predictions obtained from the acoustic approach were a better fit to actual territory density than the transect approach. In contrast, for Lapwing and Golden Plover, the transect predictions outperformed the acoustic predictions, with the acoustic model particularly poor for Golden Plover. We attributed these differences to the performance of the classifier, species' ecology and vocal behaviour. Data gathering for the acoustic approach was more time-efficient than the transect surveys, requiring less than a quarter of the fieldworker days. We conclude that there is high potential for acoustic approaches to augment traditional methods, although species' ecological characteristics should be considered: species that vocalize more frequently, at higher amplitudes and hold larger territories will be better-suited to sampling-based acoustic methods.

Abstract Image

在估计繁殖涉禽的相对密度方面,声探测率优于传统的调查方法
被动声装置越来越多地被用于监测生物多样性。然而,很少有研究将声学测量和传统测量的准确性与地面真实数据进行比较。在这里,我们评估了与人工智能(AI)分类器结合使用的声学记录仪是否能比传统的田野工作者样带调查更好地预测四种涉水物种的相对繁殖密度。在一个27平方公里的高地研究地点,收集了83个采样点的声学数据,并使用BirdNet鸟类声音分类器进行分析,以估计每个地点的声音检测率;我们还同时进行了鸟类样带调查。为了验证这些方法,我们进行了密集的实地调查,以确定我们的焦点物种的每个繁殖区域。对于声学数据集和样带数据集,我们使用相似的分析方法(随机森林回归树)来预测整个研究地点的相对领土密度,然后将这些预测结果与密集实地调查获得的领土密度进行比较。该分类器在3-s时间内识别目标物种叫声的准确率分别为:田凫(0.911)、鸻(0.826)和蛎鹬(0.841),但对金鸻(0.699)的准确率较低。对于杓鹬和蛎鹬来说,声学方法的预测结果比样带方法更符合实际的领土密度。相比之下,对于田凫和金鸻,样带预测优于声学预测,金鸻的声学模型特别差。我们将这些差异归因于分类器的性能、物种的生态和发声行为。声学方法的数据收集比样带调查更省时,所需时间不到现场工作人员的四分之一。我们得出的结论是,声学方法有很大的潜力来增强传统方法,尽管物种的生态特征应该被考虑在内:发声频率更高、振幅更高、占据更大领土的物种将更适合基于采样的声学方法。
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来源期刊
Ibis
Ibis 生物-鸟类学
CiteScore
4.60
自引率
9.50%
发文量
118
审稿时长
6-12 weeks
期刊介绍: IBIS publishes original papers, reviews, short communications and forum articles reflecting the forefront of international research activity in ornithological science, with special emphasis on the behaviour, ecology, evolution and conservation of birds. IBIS aims to publish as rapidly as is consistent with the requirements of peer-review and normal publishing constraints.
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