Biology subjects

Metcalf, O. C.

Publications and source records attributed to Metcalf, O. C..

5 recordsLinked to original sources

bioRxiv2026

Unsupervised source separation and clustering of soundscape recordings predicts avian richness in hyperdiverse systems

1: Passive Acoustic Monitoring (PAM) has emerged as a leading approach for biodiversity research, but analysing the vast quantities of data produced by this method remains challenging. Acoustic indices are often employed to estimate species richness in soundscape recordings, but their generalisability and overall efficacy in speciose regions has increasingly been challenged. An alternative, multi-species classification models, still suffer from performance limitations, particularly in the tropics. 2: We introduce an unsupervised approach that estimates avian species richness using an integrated source separation model trained on birds (BirdMixIT), a leading bird vocalization embedding model (Perch 2.0), and hierarchical agglomerative clustering. We count the clusters of embeddings generated from separated channels to estimate the species richness in recordings at differing timescales. 3: We test the generalisability and predictive performance of our approach, "source-clustering", using a large combined dataset featuring 103 total hours of manually-annotated soundscape recordings from the Amazon rainforest in Peru and Brazil. To compare the performance of source-clustering to existing approaches, we estimated species richness in the same recordings with both traditional acoustic indices and Perch classifications at various confidence thresholds. 4: Our source-clustering method performed consistently well between our separate datasets. Source-clustering performed better for predicting avian species richness than traditional acoustic indices at all levels, and was complementary with Perch, performing similarly overall to its best settings combinations, though it was more robust for short recordings, and should be more resilient in locations with less training data. Source-clustering generally predicted species richness better than the best-performing Perch settings at the 15-second, 1-minute, and 15-minute temporal resolutions, while most settings of Perch performed better at the 1-hour resolution. 5: Source-clustering can complement or exceed the performance of presently established methods for estimating avian species richness from soundscape recordings, namely traditional acoustic indices and the out-of-box versions of leading automated identification modes. This is particularly true at narrow temporal resolutions, in systems where training data is scarce, and where species density is high enough to generate significant overlapping vocalisations, rendering it especially useful in hyperdiverse tropical ecosystems where other methods for interpreting soundscape recordings face persistent challenges.

bioinformatics↗

bioRxiv2026

Anthropogenic disturbance inverts microclimate stratification in tropical forests

Tropical rainforests generate and maintain their own microclimate regimes and the resultant cooler, more humid and stable environments foster the hyperdiversity typical of these ecosystems. Although the temporal and spatial (horizontal) distributions of microclimates have been relatively well studied, the vertical dimension has received less attention, and little is known about how forest disturbance affects the vertical stratification of microclimates in tropical forests. In this study, we examine how the vertical distribution of temperatures varies between undisturbed and burned Amazonian forests. We installed five vertical transects with temperature dataloggers distributed at 7 different heights to collect data over multiple days during the end of the dry season. We investigated how anthropogenic disturbance (fire) mediates the vertical stratification of microclimate and whether microclimate buffering (i.e, the difference between understorey and canopy temperatures) varies according to the forest structure. We showed that anthropogenic disturbance can cause an inversion in the vertical stratification of microclimates, with burned forests having hotter temperatures (up to 2 {degrees}C) in the understorey than in the canopy during the day - the opposite of what is found in undisturbed forests (typically 3 {degrees}C cooler). During the night, while understorey and canopy temperatures are similar in undisturbed forests, we found that, in burned forests, understorey temperatures were up to 2 {degrees}C cooler than in the canopy. Microclimate buffering by day was best explained by aboveground carbon stocks, with higher temperature buffering in more carbon rich forests. Our study shows that anthropogenic disturbance alters the vertical stratification of temperatures in Amazonian forests, leading to significant temporal changes along the diel cycle. Future research should focus on understanding these changes across a wider range of disturbance regimes, and explore the consequences for biodiversity and ecosystem functions from the canopy to the forest floor.

ecology↗

bioRxiv2025

The reliability of acoustic classification models for determining avian vocalisation patterns

Automated detection and classification of species vocalisations offers the potential to utilise acoustic datasets across unprecedented spatial and temporal scales. However, classification algorithms inevitably generate errors, and error rates vary with context. While methods for quantifying error rates in ecoacoustics are well established, there is limited research on what level of model performance is sufficient to reliably determine avian vocalisation patterns. Using an extensive fully expert-labelled acoustic dataset from Peru (18 hours, 6 sites), we examined changes in the probability of detecting target bird vocalisations in the first hour after dawn to address three key questions: (1) How sensitive are models predicting detection probability over time to reductions in classification accuracy? (2) To what extent does aggregating detections over longer time periods impact classification accuracy? Additionally, we used the labelled dataset to assess how the creation and composition of a test dataset for assessing classifier performance can impact the reliability of accuracy metrics: (3) Are estimates of classification precision robust when test and deployment datasets are not independent and identically distributed? Our results indicate that poor classification performance--especially low precision--can lead to misleading inferences about temporal patterns of vocalisations. Aggregating classifier predictions over longer time periods improved recall but often resulted in misleading patterns of vocal behaviour by reducing precision and temporal resolution. We also demonstrate that precision can be substantially overestimated when species presences are rarer in the deployment dataset than in test data. These findings highlight the importance of cautious application of automated classification in acoustic ecology and the need for accuracy assessment methods tailored to the intended ecological analysis.

ecology↗

bioRxiv2023

Listening to tropical forest soils

PAM has proven to be an effective tool for monitoring biotic soundscapes in the marine, terrestrial, and aquatic realms. Recently it has been suggested that it could also be an effective method for monitoring soil fauna, but has so far been used in only four studies in temperate and polar regions. We present the first study of soil soundscapes in tropical forests, using a novel analytical pipeline allowing for the use of in-situ recording of soundscapes with minimal soil disturbance. We found significant differences in soil soundscapes between burnt and unburnt forests and the first indications of a diel cycle in soil soundscapes. These promising results and methodological advances highlight the potential of PAM for large-scale and long-term monitoring of soil biodiversity. We use the results to discuss research priorities, including relating soil biophony to biodiversity, community structure and ecosystem functioning, and the use of appropriate hardware and analytical techniques.

ecology↗

bioRxiv2020

Optimising tropical forest bird surveys using passive acoustic monitoring and repeated short-duration point counts

Estimation of avian biodiversity is a cornerstone measure of ecosystem condition, with turnover in avian community composition underpinning many studies of land-use change in tropical forests. Surveys conducted using autonomous recorders have been frequently found to be more efficient than traditional point-count surveys. However, there has been limited research into optimal survey duration, despite autonomous recordings allowing for many more repeats of short-duration surveys with relative ease in comparison to traditional survey methods. We use an acoustic dataset collected from a region of very high avian biodiversity - the eastern Brazilian Amazon - to test the effect of using short-duration surveys to increase temporal coverage without increasing total survey duration. We use this dataset to assess whether a survey protocol consisting of 240 15-second surveys at 29 locations, short-duration surveys, has an influence on resulting alpha and gamma diversity, and detection frequency, than standard-duration surveys of four 15-minute surveys per location. We find that repeated short-duration surveys outperform longer duration surveys in every metric considered herein, with short-duration surveys predicted to detect approximately 50% higher alpha diversity, and 10% higher gamma diversity. Short-duration surveys also detect species more often, at more survey locations. Conversely, standard-duration surveys are almost four times more likely to produce false negatives (i.e. to fail to detect species presence). Whilst there is no difference between the proportion of uncommon species detected by the two methods, when considering species detected multiple times at multiple locations, short-duration surveys detected three times more uncommon species than standard-duration surveys. We conclude that short-duration recorded surveys should be considered the primary method for sampling the species richness of bird communities in tropical forests and is likely to be preferable to longer duration or traditional surveys in most environments.

ecology↗