Biology subjects

Boughey, K.

Publications and source records attributed to Boughey, K..

2 recordsLinked to original sources

bioRxiv2023

Applying genomic approaches to identify historic population declines in European forest bats

O_LIAnthropogenically-driven environmental changes over the past two centuries have led to severe biodiversity loss, most prominently in the form of loss of populations and individuals. Better tools are needed to assess the magnitude of these wildlife population declines. Anecdotal evidence suggests European bat populations have suffered substantial declines in the past few centuries. However, there is little empirical evidence of these declines that can be used to put more recent population changes into historic context and set appropriate targets for species recovery. C_LIO_LIThis study is a collaboration between academics and conservation practitioners to develop molecular approaches capable of providing quantitative evidence of historic population changes and their drivers that can inform the assessment of conservation status and conservation management. We generated a genomic dataset for the Western barbastelle, Barbastella barbastellus, a globally Near Threatened and regionally Vulnerable bat species, including colonies from across the species British and Iberian ranges. We used a combination of landscape genetics and approximate Bayesian computation model-based inference of demographic history to identify both evidence of population size changes and possible drivers of these changes. C_LIO_LIWe found that levels of genetic diversity increased and inbreeding decreased with increasing broadleaf woodland cover around the colony location. Genetic connectivity was impeded by artificial lights and facilitated by the combination of rivers and broadleaf woodland cover. C_LIO_LIThe demographic history analysis showed that both the northern and southern British barbastelle populations have declined by 99% over the past 330-548 years. These declines may have been triggered by loss of large oak trees and native woodlands due to shipbuilding during the early colonial period. C_LIO_LISynthesis and applications. Genomic approaches can be applied to provide a better understanding of the conservation status of threatened species, within historic and contemporary context, and inform their conservation management. This study shows how we can bridge the implementation gap and promote the application of genomics in conservation management through co-designing studies with conservation practitioners and co-developing applied management targets and recommendations. C_LI

genomics↗

bioRxiv2022

Towards a General Approach for Bat Echolocation Detection and Classification

O_LIAcoustic monitoring is a scalable approach for assessing bat populations, yet automating the detection and classification of bat echolocation calls remains challenging, particularly in data-scarce regions. Although deep learning (DL) is increasingly applied to this task, most existing approaches repurpose computer-vision architectures and generate a single prediction per spectrogram clip, offering limited robustness to variable background noise and potentially constraining generality across regions and species assemblages. C_LIO_LIHere, we develop BatDetect2, an open-source DL pipeline for the joint detection and classification of bat echolocation calls. BatDetect2 builds on a 2D convolutional architecture and incorporates two targeted modifications: (i) a temporal self-attention layer designed to capture long-range structure across call sequences, and (ii) convolutional layers augmented with frequency coordinates to explicitly encode frequency information directly. We evaluate model generality using five diverse datasets from four different regions: UK, Mexico, Australia, and Brazil, and conduct ablation analyses using a UK dataset spanning 17 bat species. We further assess whether a trained model can detect echolocation calls from species absent from the training data. C_LIO_LIBatDetect2 consistently outperforms a traditional call-parameter extraction baseline across all datasets and evaluation metrics. Ablation analyses show that the inclusion of temporal self-attention yields a substantial species classification performance gain, increasing mean Average Precision (mAP) from 0.83 to 0.88, while frequency-coordinate augmentation provides no measurable benefit. When applied to novel species assemblages without retraining, model detection performance varies across datasets, with Average Precision ranging from 0.60 to 0.98. C_LIO_LIOverall, BatDetect2 demonstrates strong and transferable performance across acoustically and taxonomically diverse regions. By jointly detecting and classifying all bat calls present in each input clip, the pipeline provides a practical and extensible tool for passive acoustic monitoring. The full training pipeline and a pretrained UK model are released through the open-source Python package batdetect2, enabling practitioners to develop and deploy models using their own data. C_LI

ecology↗