Biology subjects

Linhart, P.

Publications and source records attributed to Linhart, P..

4 recordsLinked to original sources

bioRxiv2026

BioDCASE: Using data challenges to make community advances in computational bioacoustics

Computational bioacoustics has seen significant advances in recent decades. However, the rate of insights from automated analysis of bioacoustic audio lags behind our rate of collecting the data - due to key capacity constraints in data annotation and bioacoustic algorithm development. Gaps in analysis methodology persist: not because they are intractable, but because of resource limitations in the bioacoustics community. To bridge these gaps, we advocate the open science method of data challenges, structured as public contests. We conducted a bioacoustics data challenge named BioDCASE, within the format of an existing event (DCASE). In this work we report on the procedures needed to select and then conduct useful bioacoustics data challenges. We consider aspects of task design such as dataset curation, annotation, and evaluation metrics. We report the three tasks included in BioDCASE 2025 and the resulting progress made. Based on this we make recommendations for open community initiatives in computational bioacoustics.

animal behavior and cognition↗

bioRxiv2025

Assessing the Effective Range for Individual Acoustic Identification: Comparison of Manual and Automatic Methods

Individual Acoustic Monitoring (IAM), especially when combined with passive acoustic monitoring (PAM), offers a non-invasive alternative to traditional mark-recapture methods to gain insights into species demography. Few studies have examined how identification decreases over distance along with degradation of identity cues. In this study, we conducted a song transmission experiment to quantify the range at which individual Yellowhammers (Emberiza citrinella) can be reliably identified. Songs from ten males (20 songs per individual, covering their full repertoire) were broadcast and re-recorded along a 200 m transect using AudioMoth recorders. Comparing manual classification by human observers with BirdNET classifier adapted for individual identification, we assess how well individuals could be distinguished at increasing distances. Humans were confident in assigning identity to a larger proportion of songs. Where identity has been assigned, both human and BirdNET were highly reliable at short distances (up to 50 m) discriminating even between very similar song types shared among males. At moderate distances (100 - 150 m), simple augmentation boosted BirdNETs performance remarkably, almost matching human classification accuracy. Our results indicate that individual recognition remains reliable up to 100 meters where both accuracy and agreement between assignment methods were high. Our results confirm that automated systems offer promising tools for large-scale, non-invasive individual monitoring, but challenges in accuracy and robustness persist at greater distances. We highlight the difference between signal detectability and individual identification range (much shorter) and its importance for optimizing PAM array designs.

zoology↗

bioRxiv2025

Social environment affects vocal individuality in a non-learning species

Individual recognition is fundamental to the social behaviour of many animals. In high-density populations, animals encounter and compete with conspecific rivals more frequently, which should enhance the individuality of their signals to facilitate recognition among neighbours. We investigated vocal individuality in male territorial calls of two populations of little owls (Athene noctua) with different densities. Further, to explore the potential influence of local population distribution on individuality, we also examined isolated males without neighbours and clumped males with neighbours. Our findings indicate higher individuality at higher densities across both scenarios, measured using two individuality metrics: Beechers information statistic and Discrimination score. Additionally, clumped males exhibited significantly lower acoustic niche overlaps compared to isolated males, suggesting that the immediate social environment might be more influential than larger-scale population density patterns. This study is the first to demonstrate that vocal individuality in a territorial species is influenced by conspecific density, similar to findings in group-living and colonial species.

animal behavior and cognition↗

bioRxiv2019

Measuring individual identity information in animal signals: Overview and performance of available identity metrics

O_LIIdentity signals have been studied for over 50 years but there is no consensus as to how to quantify individuality. While there are a variety of different metrics to quantify individual identity, or individuality, these methods remain un-validated and the relationships between them unclear. C_LIO_LIWe contrasted three univariate and four multivariate metrics (and their different computational variants) and evaluated their performance on simulated and empirical datasets. C_LIO_LIOf the metrics examined, Beechers information statistic (HS) was the best one and could easily and reliably be converted into the commonly used discrimination score (and vice versa) after accounting for the number of individuals and calls per individual in a given dataset. Although Beechers information statistic is not entirely independent of sampling parameters, this problem can be removed by reducing the number of parameters or by increasing the number of individuals. C_LIO_LIBecause it is easily calculated, has superior performance, can be used to describe single variables or signal as a whole, and because it tells us the maximum number of individuals that can be discriminated given a set of measurements, we recommend that individuality should be quantified using Beechers information statistic. C_LI

ecology↗