Biology subjects

Campos-Cerqueira, M.

Publications and source records attributed to Campos-Cerqueira, M..

3 recordsLinked to original sources

bioRxiv2026

Overcoming software bottlenecks for scalable passive acoustic monitoring: insights from a global expert assessment

1.Passive acoustic monitoring (PAM) enables non-invasive sampling of wildlife across broad spatial, temporal and taxonomic scales. Its ongoing and widespread use has generated unprecedented volumes of acoustic data, shifting the primary bottleneck from data collection to the storage, processing, integration, and interpretation of PAM outputs. Although many software tools exist to address these challenges, differences in their design, scope, and usability often create fragmented and complex analytical workflows. To identify the key barriers and opportunities shaping the implementation of PAM surveys, we conducted a structured expert solicitation involving 30 international practitioners working across terrestrial and aquatic ecosystems. Experts identified and ranked their most critical pain points in current PAM workflows, spanning data storage, processing, and interpretation. The top challenge identified related to accurate species identification using deep learning and artificial intelligence (AI) models, especially in noisy soundscapes or for underrepresented taxa. Eight additional priority challenges included workflow fragmentation, limited availability of user-friendly analytical and visualisation tools, uneven access to software, manual validation bottlenecks, computational constraints, and difficulties in data handling, standardisation, and sharing. Participants also proposed practical mitigation strategies for these priority challenges, supported by step-by-step guidance to help overcome key barriers. Together, these insights provide a roadmap toward more scalable, open-access, and collaborative software systems, which are increasingly essential to realise the full potential of PAM in global biodiversity monitoring.

ecology↗

bioRxiv2023

A comparison of convolutional neural networks and few-shot learning in classifying long-tailed distributed tropical bird songs

Biodiversity monitoring depends on reliable species identification, but it can often be difficult due to detectability or survey constraints, especially for rare and endangered species. Advances in bioacoustic monitoring and AI-assisted classification are improving our ability to carry out long-term studies, of a large proportion of the fauna, even in challenging environments, such as remote tropical rainforests. AI classifiers need training data, and this can be a challenge when working with tropical animal communities, which are characterized by high species richness but only a few common species and a long tail of rare species. Here we compare species identification results using two approaches: convolutional neural networks (CNN) and Siamese Neural Networks (SNN), a few-shot learning approach. The goal is to develop methodology that accurately identifies both common and rare species. To do this we collected more than 600 hours of audio recordings from Barro Colorado Island (BCI), Panama and we manually annotated calls from 101 bird species to create the training data set. More than 40% of the species had less than 100 annotated calls and some species had less than 10. The results showed that Siamese Networks outperformed the more widely used convolutional neural networks (CNN), especially when the number of annotated calls is low.

ecology↗

bioRxiv2022

A framework for the quantification of soundscape diversity using Hill numbers

O_LISoundscape studies are increasingly common to capture landscape-scale ecological patterns. Yet, several aspects of soundscape diversity quantification remain unexplored. Although some processes influencing acoustic niche usage may operate in the 24h domain, most acoustic indices only capture the diversity of sounds co-occurring in sound files at a specific time of day. Moreover, many indices do not consider the relationship between the spectral and temporal traits of sounds simultaneously. To provide novel insights into landscape-scale patterns of acoustic niche usage at broader temporal scales, we present a workflow to quantify soundscape diversity through the lens of functional ecology. C_LIO_LIOur workflow quantifies the functional diversity of sound in the 24-hour acoustic trait space. We put forward an entity, the Operational Sound Unit (OSU), which groups sounds by their shared functional properties. Using OSUs as our unit of diversity measurement, and building on the framework of Hill numbers, we propose three metrics that capture different aspects of acoustic trait space usage: (i) soundscape richness; (ii) soundscape diversity; (iii) soundscape evenness. We demonstrate the use of these metrics by (a) simulating soundscapes to assess if the indices possess a set of desirable behaviours; and (b) quantifying the soundscape richness and evenness along a gradient in species richness to illustrate how these metrics can be used to shed unique insights into patterns of acoustic niche usage. C_LIO_LIWe demonstrate that: (a) the indices outlined herein have desirable behaviours; and (b) the soundscape richness and evenness are positively correlated with the richness of soniferous species. This suggests that the acoustic niche space is more filled where taxonomic richness is higher. Moreover, species-poor acoustic communities have a higher proportion of rare sounds and use the acoustic space less effectively. As the correlation between the soundscape and taxonomic richness is strong (>0.8) and holds at low sampling intensities, soundscape richness could serve as a proxy for taxonomic richness. C_LIO_LIQuantifying the soundscape diversity through the lens of functional ecology using the analytical framework of Hill numbers generates novel insights into acoustic niche usage at a landscape scale and provides a useful proxy for taxonomic richness measurement. C_LI

ecology↗