Acoustic indices as proxies for biodiversity: a meta-analysis

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 proxies for biodiversity. To guide more efficient use and future research, we review their principal theoretical and practical shortcomings, as well as prospects and challenges of acoustic indices in biodiversity assessment. Altogether, we provide the first comprehensive and statistically based overview on the relation between acoustic indices and biodiversity and pave the way for a more standardised and informed application for biodiversity monitoringThis study was supported by a research project funded by the Comunidad de Madrid and the European Social Fund (PEJ2018-AI/AMB-9957, to D. L.). We thank Camille Desjonquères for her valuable comments on the study design, Alison Cooper for her exhaustive and insightful revision of the manuscript, and anonymous reviewers for their significant contribution. I. A. and L. S. M. S. acknowledge research grants provided by the Comunidad de Madrid (PEJ-2018-AI/ AMB-9957, to D. L.) and the Ministerio de Economía, Industria y Competitividad of Spain (PEJ-2018-004603-A, to D. L.), respectively, together with the support of the European Social Fund. H. L. was supported by the FPI program of the Ministerio de Ciencia e Innovacion of Spain (grant CGL2017-86926-P). D. L. also acknowledges a postdoctoral grant provided by the Comunidad de Madrid (2020-T1/AMB-20636, Atraccion de Talento Investigador, Spain) and a research project funded by the Ministerio de Economía, Industria y Competitividad (CGL2017-88764-R, MINECO/AEI/FEDER, Spain

Spatial heterogeneity and habitat configuration overcome habitat composition influences on alpha and beta mammal diversity

The effects of habitat fragmentation on different taxa and ecosystems are subject to intense debate, and disentangling them is of utmost importance to support conservation and management strategies. We evaluated the importance of landscape composition and configuration, and spatial heterogeneity to explain α‐ and β‐diversity of mammals across a gradient of percent woody cover and land use diversity. We expected species richness to be positively related to all predictive variables, with the strongest relationship with landscape composition and configuration, and spatial heterogeneity respectively. We also expected landscape to influence β‐diversity in the same order of importance expected for species richness, with a stronger influence on nestedness due to deterministic loss of species more sensitive to habitat disturbance. We analyzed landscape structure using: (a) landscape metrics based on thematic maps and (b) image texture of a vegetation index. We compared a set of univariate explanatory models of species richness using AIC, and evaluated how dissimilarities in landscape composition and configuration and spatial heterogeneity affect β‐diversity components using a Multiple Regression on distance Matrix. Contrary with our expectations, landscape configuration was the main driver of species richness, followed by spatial heterogeneity and last by landscape composition. Nestedness was explained, in order of importance, by spatial heterogeneity, landscape configuration, and landscape composition. Although conservation policies tend to focus mainly on habitat amount, we advocate that landscape management must include strategies to preserve and improve habitat quality and complexity in natural patches and the surrounding matrix, enabling landscapes to harbor high species diversity

AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring

Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires the identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27 hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources can be found on our GitHub repository https://github.com/soundclim/anuraset

Database

Dataset used in the manuscrip

Data from: Satellite image texture for the assessment of tropical anuran communities

The relationship between environmental heterogeneity and biodiversity represents a cornerstone of ecological research. While environmental descriptors over large extents usually have medium to low spatial resolution, in-situ measures provide accurate information for limited areas, and a gap remains in providing remote descriptors that represent local environmental structure. Texture from satellite images can represent fine-scale heterogeneity over wide spatial coverage, but to date, it has mostly been used to predict general aspects of species diversity, such as richness. Here, we assess the utility of image textures from high resolution satellite images (RapidEye 3A) and in-situ variables to predict differences in the composition of anuran communities in a tropical savanna (Cerrado) of Brazil. While in-situ measures accounted for compositional differences of the whole community, two measures of image textures were associated only with the variation of species within the Hylidae family (adj. R² = 0.16 and 0.14). Comparatively, image textures predicted ~2/3 of the variation explained by in-situ­ measures (adj. R² = 0.23). When both approaches were combined, a greater compositional variation was achieved (adj. R² = 0.28), with 1/5 of it shared by both in-situ and textures, and 1/5 attributed solely to texture. Our findings suggest that image texture can complement the assessment of environmental heterogeneity acting on the assembly of local anuran communities. This approach can be valuable for explicitly including spatial heterogeneity in biological assessments over broad spatial extents, especially for biological groups strongly filtered by environmental conditions

AnuraSet: A dataset for benchmarking neotropical anuran calls identification in passive acoustic monitoring

raw_data.zip Raw audio data collected in the field. It is composed of sub-folders that represent each monitoring site. Each sub-folder is composed of audio .wav files that follow the name of {site}_{date}_{time}.wav. weak_labels.csv Annotation at a 1-minute level where each raw audio data it is assigned a value representing the anuran calling activity: 0 is absence; 1 is Low; 2 is Moderate; and 3 is High. The CSV file is composed of two columns representing the site and the file name and species columns with the anuran calling activity. strong_labels.zip Annotation at a high level with temporal limits (beginning and end) of audio segments containing species-specific calls with an inter-call interval of less than 1 second. As in raw_data, each sub-folder represents a monitoring site and the files are .txt containing (i) call beginning; (ii) call end; and (iii) the species name and audio quality. anuraset.zip Preprocessed dataset with 93378 3-second audio samples input for benchmarking. The dataset folder contains 2 files and one folder containing separate folders per site. The samples are WAV audio files with fixed 3-second lengths, obtained with 22.05 kHz sampling frequency and 16-bit depth. The two other files are a README file describing the structure and construction of the dataset and a metadata CSV file containing the labels for each sample.Abstract: Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires the identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27 hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources can be found on our GitHub repository https://github.com/soundclim/anuraset