沿咖啡生产强度梯度对鸟类和栖息地进行一致且可扩展的监测

bioRxiv Pub Date : 2024-07-16 DOI:10.1101/2024.07.12.603271
Marius Somveille, Joe Grainger-Hull, Nicole Ferguson, Sarab S. Sethi, Fernando González-García, Valentine Chassagnon, Cansu Oktem, Mathias Disney, G. Bautista, John Vandermeer, Ivette Perfecto
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引用次数: 0

摘要

与农业集约化相关的土地利用变化是热带地区生物多样性丧失的主要驱动因素。为了评估热带农产品生产系统中栖息地与生物多样性之间的关系(这对于认证和检查生物多样性友好型农业实践的成功与否至关重要),鸟类通常被用作指标。然而,要在整个年度周期内以可扩展的方式持续可靠地监测鸟类群落如何受到土地利用变化的影响,使用传统的调查方法具有挑战性。在这项研究中,我们探讨了是否可以利用被动声学监测所收集的音频数据的自动分析以及遥感数据的分析,来有效监测与咖啡生产集约化相关的栖息地退化梯度上的鸟类生物多样性。咖啡是热带森林地区生产的一种重要作物,其生产正在不断扩大和加强,咖啡生产系统形成了一个生态复杂性梯度,从森林般遮荫的多元栽培到茂密的阳光曝晒单一栽培。我们利用激光雷达技术勘测栖息地,并结合自主记录装置和发声分类算法,评估了位于墨西哥南部的一个咖啡景观中的鸟类群落组成,该景观由一个阴生咖啡农场、一个阳生咖啡农场和一个森林遗迹组成。我们发现,将激光雷达与连续采集的生物声学数据的自动分析相结合,可以捕捉到作为咖啡景观中栖息地质量函数的鸟类群落的预期功能特征。因此,我们证明这种方法是监测生物多样性如何对热带地区土地利用集约化做出反应的可靠方法。这种方法的一个主要优势是,它有可能以成本效益高的方式大规模部署,帮助设计和认证生物多样性友好型生产景观。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Consistent and scalable monitoring of birds and habitats along a coffee production intensity gradient
Land use change associated with agricultural intensification is a leading driver of biodiversity loss in the tropics. To evaluate the habitat-biodiversity relationship in production systems of tropical agricultural commodities, which is critical for certifying and examining the success of biodiversity-friendly agricultural practices, birds are commonly used as indicators. However, consistently and reliably monitoring how bird communities are affected by land use change throughout the annual cycle in a way that can be scalable is challenging using traditional survey methods. In this study, we examined whether the automated analysis of audio data collected by passive acoustic monitoring, together with the analysis of remote sensing data, can be used to efficiently monitor avian biodiversity along the gradient of habitat degradation associated with the intensification of coffee production. Coffee is an important crop produced in tropical forested regions, whose production is expanding and intensifying, and coffee production systems form a gradient of ecological complexity ranging from forest-like shaded polyculture to dense sun-exposed monoculture. We used LiDAR technology to survey the habitat, in combination with autonomous recording units and a vocalisation classification algorithm to assess bird community composition in a coffee landscape comprising a shade-grown coffee farm, a sun coffee farm, and a forest remnant, located in southern Mexico. We found that combining LiDAR with the automated analysis of continuously collected bioacoustics data can capture the expected functional signatures of avian communities as a function of habitat quality in the coffee landscape. Thus, we show that this approach can be a robust way to monitor how biodiversity responds to land use intensification in the tropics. A major advantage of this approach is that it has the potential to be deployed cost-effectively at large scales to help design and certify biodiversity-friendly productive landscapes.
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