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Anthropogenic noise alters tropical soundscapes: insights from acoustic network analysis
Samuel Silva1, Natália Saturnino1, Robert Young2
1Pontifical Catholic University of Minas Gerais, Belo Horizonte, Brazil.
Abstract:
Anthropogenic noise is an increasingly pervasive driver of environmental change, with mining emerging as a major global source of acoustic disturbance, particularly in tropical ecosystems that harbour most of the world's biodiversity. Because animal acoustic communication underpins key behaviours such as mate attraction, territory defence and predator avoidance, understanding how noise impacts soundscapes is critical for conservation. However, robust metrics capable of capturing these impacts remain limited. Here, we propose co-occurrence patterns as a novel proxy to quantify the effects of noise pollution on soundscapes. We conceptualise soundscapes as dynamic networks formed by biophony, geophony and anthropophony. We monitored soundscapes in the Atlantic Forest (Minas Gerais, Brazil), comparing sites located near an opencast mine and 2.5 km away. Four passive acoustic recorders sampled continuously for seven days bimonthly over one year. We focused on mining truck passages as the dominant noise source and analysed animals' acoustic signals co-occurrence before, during and after these events to construct acoustic networks. Network Portrait Divergence was performed to assess structural differences between networks. Network metrics (nodes, edges and centrality) and divergences revealed clear shifts in soundscape structure. Complexity was highest during the wet season without noise, while lowest complexity occurred during noisy periods in the dry season. Although central species retained their relative positions, overall network dynamics changed during noise events. These results suggest that anthropogenic noise alters soundscape structure rather than simply displacing key species, compromising overall acoustic dynamics. Our findings highlight acoustic networks as a promising and scalable tool to detect and interpret noise impacts on biodiversity.
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