The impact of noise from open-cast mining on Atlantic forest biophony
Introduction
Open-cast mining is known to produce high sound pressure levels through exploratory and production drilling, blasting, cutting, handling of materials, ventilation, crushing, conveying, ore processing and transportation (Donoghue, 2004). This massive noise pollution has the potential to negatively impact wildlife. Mining has been shown to impact breeding birds by reducing their density (Smith et al., 2005), their species diversity, and their population sizes (Saha and Padhy, 2011). Ant-species richness has also been found to decrease owing to mining activity (Queiroz, 2013). Despite the evidence that noise pollution negatively affects wildlife reproduction and longevity (Warren et al., 2006, Slabbekoorn and Ripmeester, 2008, Barber et al., 2009, Francis et al., 2011, Kight and Swaddle, 2011), sound pollution from mining activity is still poorly regulated around the world (Hessel and Sluis-Cremer, 1987, Frank et al., 2003).
Many animal species depend on acoustic signals for intraspecific communication (Catchpole and Slater, 2008). Several studies have demonstrated that high noise levels may reduce habitat quality for many species (Bayne et al., 2008) by masking sound signals and decreasing the efficiency of animal communication (Langemann et al., 1998, Lohr et al., 2003, Brumm, 2004, Bee and Swanson, 2007). Noise can also decrease reproductive success (Halfwerk et al., 2011), as well as altering mating systems (Swaddle and Page, 2007, Habib et al., 2007) and parental care in bird species (Schroeder et al., 2012). Nonetheless, some animal species are capable of adjusting their acoustic signals to communicate in noisy environments, for example, by increasing their amplitude (Brumm et al., 2004, Brumm et al., 2009), shifting frequencies (Slabbekoorn and Peet, 2003, Parks et al., 2007, Nemeth and Brumm, 2009), altering their calling rates (Sun and Narins, 2005), changing call duration (Brumm et al., 2004) or by shifting their time of calling (Fuller et al., 2007, Sousa-Lima and Clark, 2008). Other species exhibit behavioral changes including avoiding noisy areas during foraging (Miksis-Olds et al., 2007, Schaub et al., 2008) and other daily activities (Sousa-Lima and Clark, 2009, Duarte et al., 2011). Area avoidance and acoustic compensatory mechanisms to reduce or offset the effects of noise may alter the acoustic complexity of a community in a given location, resulting in a decrease in species' abundance (Bayne et al., 2008) and/or diversity (Proppe et al., 2013) at noise-polluted sites.
Technophony, which is the sound produced by human-made machinery, has become omnipresent in natural soundscapes (Barber et al., 2011) and, despite evidence demonstrating negative impacts on animals, there is still a lack of official regulation of the noise produced by industrial and exploratory activities in terrestrial natural areas. The Atlantic forest in Brazil is one of the richest and most endangered biomes of the world (Myers et al., 2000) where a high level of mining activity occurs. Despite this high level of mining activity, there are no laws regulating the sound-pollution levels permitted in this biome. In many countries of the world, noise monitoring from industrial activities is required only in respect to its impacts on human health. Consequently, the effects of noise on wildlife that are already known should drive efforts to develop environmental legislation to protect wildlife (Brown et al., 2013).
Passive acoustic monitoring (PAM) methods provide opportunities to evaluate the consequences of different land-use decisions (Blumstein et al., 2011, Joo et al., 2011, Mennitt and Fristrup, 2012, Brown et al., 2012, Brown et al., 2013), especially in environments such as mines, that are difficult to access or monitor using conventional methods (Mellinger and Barlow, 2003, Scott Brandes, 2008). PAM devices can record data during several days continuously and, consequently, a large amount of information can be collected from the acoustic environment. As a result, special software and indices to process audio files rapidly and efficiently are required (Kasten et al., 2012; Aide et al., 2013, Sueur et al., 2014, Villanueva-Rivera and Pijanowski, 2015). In this context, Pieretti et al. (2011) introduced the Acoustic Complexity Index (ACI), which facilitates an indirect and rapid measuring of the complexity of the soundscape. The ACI has been proven to be a useful tool in tracking the dynamics of the sounds produced by animal communities (Farina et al., 2013); this is achieved by describing the spectral complexity of the biophony of soundscapes through the intrinsic variability of biotic sounds. This index has already been applied in noisy environments (Pieretti et al., 2011, Pieretti and Farina, 2013) because it possesses the particular quality of helping to filter out most technophonies, such as trains, cars or airplane transit noise; additionally, Towsey et al. (2014) indicate ACI as one of the best indicators of bird biodiversity among 14 different acoustic indices.
There are no studies investigating how anthropogenic noise affects soundscapes and biophony in mining areas. Considering that, the aim of this study was to investigate noise effects on Atlantic forest soundscape dynamics by comparing the biophony and technophony at a site close to an active open-cast mine and at a habitat-matched site that was distant from the mine or other anthropogenic activities.
Section snippets
Study area
Data were collected at the Environmental station of Peti in the municipalities of São Gonçalo do Rio Abaixo and Santa Bárbara, Minas Gerais state, Brazil (centered at 19°53′57″S and 43°22′07″W). The climate of southeastern Brazil can be divided into two macro-climatic seasons: a hot wet season, from October to March, and a cooler dry season from April to September (Minuzzi et al., 2007).
The reserve is an Atlantic forest fragment of approximately 605 ha located in the upper Rio Doce Basin
Mining noise characterization
Sites close to and far from the mine differed significantly in terms of background noise. The site close to the mine exhibited levels 1–22 dB(Z) higher in comparison with the site far from the mine. The mean Leq, Leqmax and Leqmin of each type of soundscape are presented in Table 1. Noise levels in 1/3 octave bands are shown in Table 1 of the Supplementary Materials. The noise measured using the power spectral density confirmed the results of the noise-level measurements. These demonstrated that
Discussion
Large-scale human activities can have a considerable impact on the daily ecological functions within a community (Francis et al., 2011); in particular, on acoustic communication processes (Rabin et al., 2003, Slabbekoorn and Ripmeester, 2008). Noise is one of the most common threats to environments around the world owing to its, well-established, negative impact on fauna (Brown et al., 2013, Pieretti and Farina, 2013). Although mining is an important economic activity in many parts of the
Conclusion
Many studies have demonstrated the negative impact of noise pollution on animal acoustic communication, as well as revealing the negative impact on species diversity, richness and abundance. Nevertheless, studies into the impact of technophony on the biophony in terrestrial soundscapes in tropical environments are still lacking. Here, it has been shown that sound pollution from open-cast mining activities has a significant impact on the biophonical soundscape of a neighboring tropical forest.
Role of the funding source
The authors would like to thank CNPq for their continuing support. RJY and MR were financially supported by CNPq and FAPEMIG (PPM). MHLD was supported by a FAPEMIG postgraduate scholarship during this research. This study was funded competitively by FAPEMIG from a financial donation made by VALE, but VALE did not in any way restrict our research or contribute to its design, execution or publication.
Acknowledgments
The authors would like to thank all of the staff at the environmental station of Peti who assisted with our study, especially Leotacílio da Fonseca. We are also grateful to Marina Scarpelli, Mariane Kaizer and Renan Duarte for their help during data acquisition and to the engineer, Krisdany Cavalcante, for the help with the noise-level measurements. We thank also the anonymous referees for their useful comments and suggestions on this manuscript.
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