O Nicho Acústico: Integrando a Física, Ecologia E Teoria Da Comunicação

Araújo, Carlos Barros de; Furtado, Shaka Nóbrega Marinho; Vieira, Gustavo H. C.; Simões, Cássio Rachid Meireles de Almeida

doi:10.4257/oeco.2020.2404.01

Cited by 4 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ANH predicts the acoustic output of communities to be partitioned in time and frequency given the potential impact of signal interference and recognition errors among co‐occurring species (Hödl, 1977 ; Duellman & Pyles, 1983 ; Brumm, 2013 ; de Araújo et al ., 2020 ). Consequently, the higher the number of vocally active species in a given community, the higher the expected diversity of acoustic signals in the acoustic space.…”

Section: Discussionmentioning

confidence: 99%

Acoustic indices as proxies for biodiversity: a meta‐analysis

et al. 2022

View full text Add to dashboard Cite

As biodiversity decreases worldwide, the development of effective techniques to track changes in ecological communities becomes an urgent challenge. Together with other emerging methods in ecology, acoustic indices are increasingly being used as novel tools for rapid biodiversity assessment. These indices are based on mathematical formulae that summarise the acoustic features of audio samples, with the aim of extracting meaningful ecological information from soundscapes. However, the application of this automated method has revealed conflicting results across the literature, with conceptual and empirical controversies regarding its primary assumption: a correlation between acoustic and biological diversity. After more than a decade of research, we still lack a statistically informed synthesis of the power of acoustic indices that elucidates whether they effectively function as proxies for biological diversity. Here, we reviewed studies testing the relationship between diversity metrics (species abundance, species richness, species diversity, abundance of sounds, and diversity of sounds) and the 11 most commonly used acoustic indices. From 34 studies, we extracted 364 effect sizes that quantified the magnitude of the direct link between acoustic and biological estimates and conducted a meta-analysis. Overall, acoustic indices had a moderate positive relationship with the diversity metrics (r = 0.33, CI [0.23, 0.43]), and showed an inconsistent performance, with highly variable effect sizes both within and among studies. Over time, studies have been increasingly disregarding the validation of the acoustic estimates and those examining this link have been progressively reporting smaller effect sizes. Some of the studied indices [acoustic entropy index (H), normalised difference soundscape index (NDSI), and acoustic complexity index (ACI)] performed better in retrieving biological information, with abundance of sounds (number of sounds from identified or unidentified species) being the best estimated diversity facet of local communities. We found no effect of the type of monitored environment (terrestrial versus aquatic) and the procedure for extracting biological information (acoustic versus non-acoustic) on the performance of acoustic indices, suggesting certain potential to generalise their application across research contexts. We also identified common statistical issues and knowledge gaps that remain to be addressed in future research, such as a high rate of pseudoreplication and multiple unexplored combinations of metrics, taxa, and regions. Our findings confirm the limitations of acoustic indices to efficiently quantify alpha biodiversity and highlight that caution is necessary when using them as surrogates of diversity metrics, especially if employed as single predictors. Although these tools are able partially to capture changes in diversity metrics, endorsing to some extent the rationale behind acoustic indices and suggesting them as promising bases for future developments, they are far from being direct pr...

show abstract

Section: Discussionmentioning

confidence: 99%

Acoustic indices as proxies for biodiversity: a meta‐analysis

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Processing large datasets is time‐consuming (de Araújo et al., 2021), and automatic classification algorithms often yield inaccurate results when signals are faint or mixed with background noise (Kahl et al., 2021). Training personnel to inspect sound recordings and identify vocally active species based on the unique signal structure might be an alternative (Alquezar & Machado, 2015; de Araújo et al., 2020; Wimmer et al., 2013). Although trained personnel can detect signals with great precision within sound‐rich Neotropical soundscapes (de Araújo et al., 2021; Vielliard, 2004), the amount of data that trained staff can parse is a limiting factor (Gibb et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Acoustic monitoring of anurans and birds in tropical biomes

De Araújo,

Lima,

Albuquerque

et al. 2024

Biotropica

Self Cite

View full text Add to dashboard Cite

Passive acoustic monitoring (PAM) is increasingly popular in ecological research, but recording and analyzing large amounts of data is still a critical bottleneck for the long‐term monitoring of multiple species. We evaluated how temporal and spatial sampling effort affects species diversity estimates using a set of 14,045 1‐min recordings from various neotropical birds and anuran communities. Our goals were to evaluate (i) the daily vocal activity cycle of birds and anurans, (ii) the effect of temporal structure (e.g., number of minutes listened each hour; continuous versus intermittent recordings) on determining the species composition, and (iii) the species–area relationship, and how the number of recorders affects species richness estimates. Based on sampling coverage and completeness, we (iv) evaluate manual inspection schedules for birds and anurans across four biomes of Brazil. We found marked diel variation in vocal activity between taxonomic groups, indicating that birds and anurans are more efficiently detected during early periods of the day and night, respectively. For proper diversity estimates, biomes with higher biodiversity required longer inspecting periods and a larger number of replicates, irrespective of taxa. Although fewer recordings per hour are less informative than full‐hour sampling, species diversity is better estimated when inspected minutes are interspersed over longer periods than inspecting minutes recorded over shorter timespans. Based on our findings, we recommend how to set PAM programs over highly diverse ecosystems.Abstract in Portuguese is available with online material.

show abstract

“…In terrestrial environments, AI are being used in several contexts, such as: rapid assessment of biodiversity (e.g., , describing habitat type and spatial heterogeneity (e.g., Bormpoudakis et al, 2013), characterizing soundscapes (e.g., Towsey et al, 2014), quantifying anthrophony (e.g., Buxton et al, 2017), assessing environmental impact (Ribeiro et al, 2017), monitoring protected areas (e.g., Campos et al, 2021), characterizing landscapes (e.g., Oliveira et al, 2021), assessing the composition of vocally active species and behavior (Farina et al, 2011), and assessing the quality and integrity of habitats (Gómez et al, 2018). It is generally expected that better preserved environments will maintain higher levels of biophony and that those signals will be spread across different frequency bands (Krause, 1987;Farina et al, 2011;Araújo et al, 2020). Knowing that the AI summarizes characteristics of audio data, the divergent results of previous studies could also be related to differences in recording methods and data treatment (Bradfer-Lawrence et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Predicting bird diversity through acoustic indices within the Atlantic Forest biodiversity hotspot

Gaspar,

D. A. Scarpelli,

Oliveira

et al. 2023

Front. Remote Sens.

View full text Add to dashboard Cite

The increasing conversion of natural areas for anthropic land use has been a major cause of habitat loss, destabilizing ecosystems and leading to a biodiversity crisis. Passive acoustic sensors open the possibility of remotely sensing fauna on large spatial and temporal scales, improving our understanding of the current state of biodiversity and the effects of human influences. Acoustic indices have been widely used and tested in recent years, with an aim towards understanding the relationship between indices and the acoustic activity of several taxa in different types of environments. However, studies have shown divergent relationships between acoustic indices and the vocal activity of most soniferous taxa. A combination of indices has, in turn, been reported as a promising tool for representing biodiversity in different contexts. We used uni- and bivariate models to test different combinations of 8 common indices in relation to bird assemblage metrics. We recorded twenty-two study sites in Brazil’s Atlantic Forest and three different types of environments in each site (forest, pasture, and swamp). Our results showed that 1) the best acoustic indices for explaining bird richness, abundance, and diversity were Bioacoustic and Acoustic Complexity; 2) the type of environment (forest, pasture, and swamp) influenced the performance of acoustic indices in explaining bird biodiversity, with the highest score model (biggest R2 value) being a combination between Acoustic Diversity and Bioacoustic indices. Our results do support the use of acoustic indices in monitoring the acoustic activity of birds, but combining indices is encouraged since it provided the best results. However, given the divergence we found across environments, we recommend that sets of indices are tested to determine which of them best describe the biodiversity pattern models for a specific habitat. Based on our results, we propose that biodiversity patterns can be predicted through acoustic patterns. However, the level of confidence will depend on the acoustic index used and on focal taxa of interest (i.e., birds, amphibians, insects, and mammals).

show abstract

O Nicho Acústico: Integrando a Física, Ecologia E Teoria Da Comunicação

Cited by 4 publications

References 23 publications

Acoustic indices as proxies for biodiversity: a meta‐analysis

Acoustic indices as proxies for biodiversity: a meta‐analysis

Acoustic monitoring of anurans and birds in tropical biomes

Predicting bird diversity through acoustic indices within the Atlantic Forest biodiversity hotspot

Contact Info

Product

Resources

About