Gaps in terrestrial soundscape research: It's time to focus on tropical wildlife

Underwater Chatter for the Win: A First Assessment of Underwater Soundscapes in Two Bays along the Eastern Cape Coast of South Africa

In 2014, the South African government launched ‘Operation Phakisa’ under which port developments play a significant role in supporting ocean economic growth. These developments will likely increase vessel traffic to and from South African ports, making it imperative to monitor for changes in underwater sound budgets with potential negative effects on marine life. However, no soundscape studies have been conducted around South Africa, resulting in an absence of baseline measurements. This study provides a first description of the underwater soundscape in St. Francis Bay and Algoa Bay, Eastern Cape. Soundscape measurements identified major soundscape contributors, temporal patterns in broadband sound levels, and underlying environmental drivers. Applicability of modelled vessel noise and wind noise maps to predict large-scale spatial variation in sound budgets was assessed. Our study shows that sounds from biological sources and wind dominated at all recording sites, with fish choruses driving temporal patterns as a function of time of year and position of the sun. Sound from vessels was present at all sites but most notable in long-term spectral levels measured in Algoa Bay. Sound propagation models predicted a further increase in the contribution of vessel noise towards shipping lanes and east Algoa Bay. Our study provides a building block to monitor for shifts in sound budgets and temporal patterns in these two bays under a developing ocean economy. Furthermore, our study raises concerns that vessel noise is likely a significant contributor in shallow waters elsewhere along the South African coast where vessel density is known to be higher (i.e., Durban and Cape Town).

Temporal Soundscape Patterns in a Panamanian Tree Diversity Experiment: Polycultures Show an Increase in High Frequency Cover

In this ecoacoustic study we used the setting of a tropical tree diversity planted forest to analyze temporal patterns in the composition of soundscapes and to test the effects of tree species richness on associated biodiversity measured as acoustic diversity. The analysis of soundscapes offers easy, rapid and sustainable methods when assessing biodiversity. During the last years the quantification of regional or global acoustic variability in sounds and the analysis of different soundscapes has been evolving into an important tool for biodiversity conservation, especially since case studies confirmed a relationship between land-use management, forest structure and acoustic diversity. Here we analyzed soundscapes from two seasons (dry and rainy season) and aurally inspected a subset of audio recordings to describe temporal patterns in soundscape composition. Several acoustic indices were calculated and we performed a correlation analysis and a non-metric multidimensional scaling analysis to identify acoustic indices that: (i) were complementary to each other and such represented different aspects of the local soundscapes and (ii) related most strongly to differences in acoustic composition among tree species richness, season and day phase. Thus, we chose “High Frequency Cover,” “Bioacoustic Index,” and “Events Per Second” to test the hypothesis that acoustic diversity increases with increasing tree species richness. Monocultures differed significantly from polycultures during night recordings, with respect to High Frequency Cover. This index covers sounds above 8 kHz and thus represents part of the orthopteran community. We conclude that increasing tree species richness in a young tropical forest plantation had positive effects on the vocalizing communities. The strongest effects were found for acoustic activity of the orthopteran community. In contrast to birds, orthopterans have smaller home ranges, and are therefore important indicator species for small scale environmental conditions.

Hay meadow vibroscape and interactions within insect vibrational community

Our experiences shape our knowledge and understanding of the world around us. The natural vibrational environment (vibroscape) is hidden to human senses but is nevertheless perceived and exploited by the majority of animals. Here, we show that the vibroscape recorded on plants in a temperate hay meadow is a dynamic low-frequency world, rich in species-specific vibrational signals. The overall vibroscape composition changed throughout the season and also depended on the plant species, as well as on the spatial position of individual plants within the meadow. Within the studied community, vibrationally signaling species sharing this communication channel avoided interference primarily by partitioning vibrational space on a fine temporal scale. The vibroscape is a reliable source of information in the environment and expands our understanding of ecological and evolutionary processes.

Visual Active Learning for Labeling: A Case for Soundscape Ecology Data

Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models increases, it is crucial that more effective approaches to labeling are developed. In this paper, we report the development of a methodology and a framework to support labeling, with an application case as background. The methodology performs visual active learning and label propagation with 2D embeddings as layouts to achieve faster and interactive labeling of samples. The framework is realized through SoundscapeX, a tool to support labeling in soundscape ecology data. We have applied the framework to a set of audio recordings collected for a Long Term Ecological Research Project in the Cantareira-Mantiqueira Corridor (LTER CCM), localized in the transition between northeastern São Paulo state and southern Minas Gerais state in Brazil. We employed a pre-label data set of groups of animals to test the efficacy of the approach. The results showed the best accuracy at 94.58% in the prediction of labeling for birds and insects; and 91.09% for the prediction of the sound event as frogs and insects.