Abstract

Approaches to characterise and monitor biodiversity based on the sound signals of ecosystems have become popular in landscape ecology and biodiversity conservation. However, to date, validation studies of how well acoustic indices reflect observed biodiversity patterns have often relied on low levels of either spatial or temporal replication, while focussing on habitats with similar underlying anthropological and geophysical sound characteristics. For acoustic indices to be broadly applicable to biodiversity monitoring, their capacity to measure the ecological facets of soundscapes must be robust to these potential sources of bias. Using two contrasting recording approaches, we examined the efficacy of four commonly used acoustic indices to reflect patterns of observed bird species richness across a tropical forest degradation gradient in Northeast Borneo. The gradient comprised intact and logged forests, riparian forests, remnants, and oil palm plantations, thus providing a highly variable anthrophonic and geophonic soundscape. We compared the degree to which acoustic indices derived from automated versus point count recording methods detected variation in inter-habitat species richness, as well as their capacity to capture changes in species diversity as a consequence of forest degradation quantified by high-resolution LiDAR derived forest canopy heights. We found Acoustic Diversity Index was associated with forest canopy height as measured by both automated recorders and recordings from point counts, whereas the association between canopy height and Acoustic Complexity Index was only detected using point count recordings. For both types of recordings, Acoustic Complexity Index exhibited the strongest relationship with observed bird richness in old growth and logged forest, whereas Acoustic Diversity was not linked, suggesting avian richness does not drive its association with canopy height. No acoustic indices were associated with observed bird richness in oil palm riparian areas. Our findings underscore the potential utility of soundscape approaches to characterise biodiversity patterns in degraded tropical landscapes, and may be used as a proxy for human inventories of bird communities. However, we also show that for acoustic indices to be effective on landscape-wide studies of environmental gradients, adequate spatial replication is required, and care must be taken to control for non-target elements of soundscapes in different habitats.

Highlights

  • Global ecosystems are changing rapidly due to anthropological pressures, resulting in wildlife population declines and extinctions (Ceballos et al, 2017)

  • The model estimated species richness at every point count location, with a median of 78 species per location predicted in old growth, 68 in logged forest, 66 in riparian reserves and 52 in oil palm

  • For Acoustic Complexity, Acoustic Diversity and Normalised Difference in Soundscape Index (NDSI) we found that results from autonomous recorders demonstrated a greater overall variance than those from point count recordings (Fig. 2)

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Summary

Introduction

Global ecosystems are changing rapidly due to anthropological pressures, resulting in wildlife population declines and extinctions (Ceballos et al, 2017). Biodiversity loss weakens the stability of ecosystems upon which human populations depend (Hautier et al, 2015). Recent advances in monitoring include the application of new re­ mote sensing technologies (Pettoretti et al, 2014), such as Light. Detection and Ranging (LiDAR) (Guo et al, 2017) and Synthetic Aperture Radar (Villard et al, 2016). These technologies allow biodi­ versity, human pressures and management interventions to be assessed over spatiotemporal scales that would be logistically unfeasible via ground-based methods alone, providing repeatable and standardised information on a suite of biodiversity indicators (Pettoretti et al, 2014). When combined with powerful new statistical approaches, robust esti­ mates of species occupancy and habitat associations can be derived (Royle et al, 2007)

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