Search

Natural marine soundscapes are being threatened by increasing anthropic noise, particularly in shallow coastal waters. To preserve and monitor these soundscapes, understanding them is essential. Here, we propose a new method for semi-supervised categorization of shallow marine soundscapes, with further interpretation of these categories according to concurrent environmental conditions. The proposed methodology uses a nonlinear mapping of short-term spectrograms to a two-dimensional space, followed by a density-based clustering algorithm to identify similar sound environments. A random forest classifier, based on additional environmental data, is used to predict their occurrence. Finally, explainable machine learning tools provide insight into the ecological explanation of the clusters. This methodology was tested in the Belgian part of the North Sea, and resulted in clearly identifiable categories of soundscapes that could be explained by spatial and temporal environmental parameters, such as distance to the shore, bathymetry, tide or season. Classifying soundscapes facilitates their identification, which can be useful for policy making or conservation programs. Soundscape categorization, as proposed in this work, could be used to monitor acoustic trends and patterns in space and time that might provide useful indicators of biodiversity and ecosystem functionality change. [ABSTRACT FROM AUTHOR]

Acoustic metrics (AM) assist our interpretation of acoustic environments by aggregating a complex signal into a unique number. Numerous AM have been developed for terrestrial ecosystems, with applications ranging from rapid biodiversity assessments to characterizing habitat quality. However, there has been comparatively little research aimed at understanding how these metrics perform to characterize the acoustic features of marine habitats and their relation with ecosystem biodiversity. Our objectives were to 1) assess whether AM are able to capture the spectral and temporal differences between two distinct Antarctic marine acoustic environment types (i.e., pelagic vs. on‐shelf), 2) evaluate the performance of a combination of AM compared to the signal full frequency spectrum to characterize marine mammals acoustic assemblages (i.e., species richness–SR–and species identity) and 3) estimate the contribution of SR to the local marine acoustic heterogeneity measured by single AM. We used 23 different AM to develop a supervised machine learning approach to discriminate between acoustic environments. AM performance was similar to the full spectrum, achieving correct classifications for SR levels of 58% and 92% for pelagic and on‐shelf sites respectively and > 88% for species identities. Our analyses show that a combination of AM is a promising approach to characterize marine acoustic communities. It allows an intuitive ecological interpretation of passive acoustic data, which in the light of ongoing environmental changes, supports the holistic approach needed to detect and understand trends in species diversity, acoustic communities and underwater habitat quality. [ABSTRACT FROM AUTHOR]

Species composition is constrained by two upper-level processes in ecological contexts where the dispersion of organisms is not severely limited, namely selection and ecological drift. This intuitive framework has motivated a constant flow of empirical models for linking the species matrix to the local environmental descriptors, in which the environment rarely explains more than 30–40% of the variation in species composition. In most cases, researchers only approximate the environmental axes that drive fitness differences between species, as the list of measured descriptors reflect both logistical constraints and hypothesis-driven questions. Moreover, contextual factors, such as the species pool size (SPS) and the spatial extent of the sampled area, could moderate species–environment associations through sampling effects and dispersal limitations. This study’s objective was to quantify the influence of contextual factors (i.e., related to the circumstances in which the study was conducted) on the species–environment association strength on the basis of a synthesis of 156 models of forest bird communities. Our results reveal that factors related to the SPS and the number of independent environmental axes studied affect our capacity to detect selection, whereas spatial factors such as the study’s spatial extent and latitude are less important determinants. The study context explains almost a third of the observed variation in the strength of the species– environment association. We conclude that strong species–environment associations can be found for properly designed studies of forest bird communities, which raises the question of whether ecologists have underestimated the importance of selection in community assembly processes. [ABSTRACT FROM AUTHOR]

Anthropogenic noise has been shown to alter the transmission environment and distort acoustic signals, prompting vocalizing species to use compensatory mechanisms. Through a meta-analysis we investigated the relative importance of biological and contextual factors predisposing species to shift their singing/calling frequencies in response to anthropogenic noise. We gathered data from 36 studies, synthesizing information on more than 160 experiments and 60 bird and anuran species. To estimate the breadth of frequency shift, we calculated a standardized effect size using Hedges' g. We fitted a multilevel linear mixed-effect model on g as the dependent variable weighted by its inverse variance, with typical frequency, body mass, experimental condition, and noise source type as independent terms. Our results reveal broader shifts in smaller bird species when compared with bigger species, an effect that was emphasized in the low-frequency component of the song spectrum. Birds increased their dominant frequencies when confronted to anthropogenic noise, whereas anurans were less prone to such shifts. Human-altered acoustic environments can be considered a novel selective force impelling change to the communication patterns of many vocalizing species. [ABSTRACT FROM AUTHOR]

In this article, the author focuses on the impact of anthropogenic noise masking on fish behaviors and their acoustic signals by commenting on Roca et al. meta-analysis. Topics discussed include investigation of masking effect magnitude on receivers, an importance of comparability and reliability of methodologies; and the use of plasticity for finding genetic evolution.