The effect of latitude on the efficacy of acoustic indices to predict biodiversity: A meta-analysis

Pan, Wuyuan; Goodale, Eben; Jiang, Aiwu; Mammides, Christos

doi:10.1016/j.ecolind.2024.111747

Cited by 7 publications

(5 citation statements)

References 57 publications

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“…Our results partially agree with the previous literature, with most of the indices that are thought to best reflect actual biological diversity also showing stronger relationships with herbivory. At the same time, a possible explanation for the somewhat inconsistent relationships might be that the study is located in a highly biodiverse region, whereas acoustic indices perform better at capturing biodiversity at higher latitudes (Pan et al 2024 ). Conducting similar studies to what we have presented here in temperate systems could help clarify which indices perform better.…”

Section: Discussionmentioning

confidence: 99%

Exploring potential relationships between acoustic indices and ecosystem functions: a test on insect herbivory

Martini,

Chen,

Mammides

et al. 2024

Oecologia

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Biodiversity loss is a global concern. Current technological advances allow the development of novel tools that can monitor biodiversity remotely with minimal disturbance. One example is passive acoustic monitoring (PAM), which involves recording the soundscape of an area using autonomous recording units, and processing these data using acoustic indices, for example, to estimate the diversity of various vocal animal groups. We explored the hypothesis that data obtained through PAM could also be used to study ecosystem functions. Specifically, we investigated the potential relationship between seven commonly used acoustic indices and insect leaf herbivory, measured as total leaf damage and as the damage from three major insect feeding guilds. Herbivory was quantified on seedlings in 13 plots in four subtropical forests in south China, and acoustic data, representing insect acoustic complexity, were obtained by recording the evening soundscapes in those same locations. Herbivory levels correlated positively with the acoustic entropy index, commonly reported as one of the best-performing indices, whose high values indicate higher acoustic complexity, likely due to greater insect diversity. Relationships for specific feeding guilds were moderately stronger for chewers, indicating that the acoustic indices capture some insect groups more than others (e.g., chewers include soniferous taxa such as crickets, whereas miners are mostly silent). Our findings suggest that the use of PAM to monitor ecosystem functions deserves to be explored further, as this is a research field with unexplored potential. Well-designed targeted studies could help us better understand how to best use novel technologies to monitor ecosystem functions.

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Section: Discussionmentioning

confidence: 99%

Exploring potential relationships between acoustic indices and ecosystem functions: a test on insect herbivory

Martini,

Chen,

Mammides

et al. 2024

Oecologia

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“…Using indices to track activity levels of specific taxa is an alternative use of acoustic indices because many were originally developed to be used as proxies for species richness [17,20,21] and composition [9]. However, two recent meta-analysis revealed only moderate positive relationships between acoustic indices and diversity metrics, highlighting their limitations [22,23]. These findings do not undermine their usage but further encourage the development of alternative applications and more tests [22,24].…”

Section: Introductionmentioning

confidence: 99%

Time and habitat structure shape insect acoustic activity in the Amazon

Do Nascimento,

Pérez-Granados,

Alencar

et al. 2024

Phil. Trans. R. Soc. B

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Insects are the most diverse animal taxon on Earth and play a key role in ecosystem functioning. However, they are often neglected by ecological surveys owing to the difficulties involved in monitoring this small and hyper-diverse taxon. With technological advances in biomonitoring and analytical methods, these shortcomings may finally be addressed. Here, we performed passive acoustic monitoring at 141 sites (eight habitats) to investigate insect acoustic activity in the Viruá National Park, Brazil. We first describe the frequency range occupied by three soniferous insect groups (cicadas, crickets and katydids) to calculate the acoustic evenness index (AEI). Then, we assess how AEI varies spatially and temporally among habitat types, and finally we investigate the relationship between vegetation structure variables and AEI for each insect category. Overall, crickets occupied lower and narrower frequency bands than cicadas and katydids. AEI values varied among insect categories and across space and time. The highest acoustic activity occurred before sunrise and the lowest acoustic activity was recorded in pastures. Canopy cover was positively associated with cricket acoustic activity but not with katydids. Our findings contribute to a better understanding of the role of time, habitat and vegetation structure in shaping insect activity within diverse Amazonian ecosystems. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.

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“…However, species identi cation presents di culties when monitoring entire communities, especially in biodiverse areas with large numbers of species and limited training data. Acoustic indices have emerged as a popular alternative for assessing biodiversity without needing to identify individual species (Pan et al, 2024;Stowell and Sueur, 2020). The underlying principle is that more biodiverse communities have more heterogeneous acoustic environments due to a larger variety of acoustic signals (Buxton et al, 2018a).…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of acoustic indices in surveying biodiversity has been extensively investigated in a variety of settings (Alcocer et al, 2022;Pan et al, 2024), including their use as proxies for animal species diversity in temperate and tropical regions (Bicudo et al, 2023;Eldridge et al, 2018;Mammides et al, 2017) and as tools for rapid assessments (Rajan et al, 2019;Sueur et al, 2008). Most studies have focused on birds (Alcocer et al, 2022), using the indices to quantify metrics related to species richness, diversity, and abundance, as well as the abundance and diversity of animal vocalizations (Alcocer et al, 2022;Buxton et al, 2018a).…”

Section: Introductionmentioning

confidence: 99%

“…Most studies have focused on birds (Alcocer et al, 2022), using the indices to quantify metrics related to species richness, diversity, and abundance, as well as the abundance and diversity of animal vocalizations (Alcocer et al, 2022;Buxton et al, 2018a). For the most part, studies have tested the indices individually, yielding mixed results (Alcocer et al, 2022;Pan et al, 2024 Mammides et al, 2024). To accomplish this, researchers have turned to machine learning methods such as the Random Forest Regression (Buxton et al, 2018b;Mammides et al, 2024;Sethi et al, 2023), which are considered more robust than common parametric regression techniques in this context because they make fewer assumptions about the data, are more suitable for high-dimensional settings, and can have higher predictive power.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach for calculating prediction uncertainty when using acoustic indices and machine learning algorithms to monitor animal communities

Mammides,

Huang,

Sree

et al. 2024

Preprint

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There is a growing interest in using passive acoustic monitoring methods to survey biodiversity. Many studies have investigated the efficacy of acoustic indices in monitoring animal communities, particularly bird species richness, with mixed results. It has been suggested that combining multiple acoustic indices could improve accuracy. To accomplish this, researchers have employed machine learning methods, such as the Random Forest Regression, which are considered more robust in this context. However, most machine learning methods have a limitation in that they do not provide well-calibrated uncertainty quantification measures for their predictions. Quantifying uncertainty with the use of appropriate prediction intervals is of paramount importance for making informed management decisions. In this study, we propose addressing this issue using a Machine Learning framework, called Conformal Prediction, which has been developed to provide guaranteed coverage prediction intervals. Specifically, we examine the application of a recently proposed combination of Conformal Prediction with Gaussian Process Regression using data collected through bird and acoustic surveys at biodiverse sites in Cyprus and Australia. Our goal is to demonstrate how the Conformal Prediction framework can be used to assess the models’ prediction accuracy and associated uncertainty when monitoring biodiversity using acoustic indices and machine learning methods. Moreover, we discuss how the framework can be integrated into a wider range of ecological applications to help make more informed conservation management decisions.

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The effect of latitude on the efficacy of acoustic indices to predict biodiversity: A meta-analysis

Cited by 7 publications

References 57 publications

Exploring potential relationships between acoustic indices and ecosystem functions: a test on insect herbivory

Exploring potential relationships between acoustic indices and ecosystem functions: a test on insect herbivory

Time and habitat structure shape insect acoustic activity in the Amazon

A novel approach for calculating prediction uncertainty when using acoustic indices and machine learning algorithms to monitor animal communities

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