Consistent and scalable monitoring of birds and habitats along a coffee production intensity gradient

Le résumé fourni par la source

Abstract 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, birds are commonly used as indicators. However, a consistent and reliable methodological approach for monitoring tropical avian communities and habitat quality in a way that is scalable is largely lacking. 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, together with autonomous recording units and a vocalization classifier 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 LiDAR can capture relevant variation in vegetation across the habitat gradient in coffee systems, specifically matching the generally observed pattern that the intensification of coffee production is associated with a decrease in vegetation density and complexity. We also found that bioacoustics can capture known functional signatures of avian communities across this habitat degradation gradient. Thus, we show that these technologies can be used in 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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