Mapping ecoacoustic hot spots and moments of biodiversity to inform conservation and urban planning. (July 2021)

• Record Type: Journal Article
• Authors: Holgate, Briana; Maggini, Ramona; Fuller, Susan
• Is Part Of: Ecological indicators. Volume 126(2021)
• Publication Date: 2021-07
• Subjects: ACI Acoustic Complexity Index -- SAC Spatial Autocorrelation -- BRT Boosted Regression Tree -- RAC Residual Autocorrelation -- tc tree complexity -- lr learning rate -- RE Regional Ecosystems; Ecoacoustics -- Acoustic complexity index -- Spatial interpolation -- Biodiversity -- Conservation planning -- Urbanisation; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2021.107627
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Spatio-temporal patterns in biodiversity were mapped in a peri -urban environment. Ecoacoustic index data were interpolated and modelled using boosted regression trees. Landform, vegetation type and extent were key predictors of biodiversity. Ecoacoustic analyses can guide conservation decision-making and urban planning. Abstract: As the rate of urbanisation continues to increase, widespread habitat clearing within peri-urban landscapes contributes to significant environmental impacts, including loss of biological diversity. Acoustic recording has recently been identified as an effective tool for monitoring biodiversity and ecosystem health. With increasing pressure from urbanisation, it is critical that spatial and temporal variability in biodiversity is mapped across future development sites to enable sound decision-making and to deliver ecological urban design outcomes. This study used ecoacoustic monitoring to map biodiversity patterns in space and time to identify hot spots and hot moments of biodiversity activity across a peri-urban landscape in south-east Queensland, Australia. In this study, a hot spot represents an increase in acoustic activity at a given spatial location, whereas hot moments represent an increase in acoustic activity at a given time point. An acoustic index (Acoustic Complexity Index, ACI) was used as a proxy for biodiversity and visualised through spatial interpolation. The acoustic data were statistically modelled using Boosted Regression Trees (BRT). This approach enabled predictors related to acoustic complexity to be identified, including vegetation and landform. Results of this study have shown that ecoacoustic data can be used to map hot spots and hot moments of biodiversity and support more informed conservation decision-making in future urban planning frameworks, to avoid or mitigate negative impacts on biodiversity.