Biology subjects

Durbach, I.

Publications and source records attributed to Durbach, I..

3 recordsLinked to original sources

bioRxiv2025

Acoustic individual identification in a species of field cricket using deep learning

Individual animal identification is essential for wildlife conservation and management, helping to estimate animal abundance and related parameters. We assessed the feasibility of identifying individual field crickets (Plebeiogryllus guttiventris) from their calls using deep learning. In a closed population of known individuals, the best models recognized crickets with up to 99.9% accuracy when trained and tested on calls from the same night, and 63.2% when tested on calls from nights not used in training. When matching pairs of calls, without knowledge of all individuals in the population, models identified pairs of calls from the same night with 97% accuracy, falling to 87.1% if calls were from different nights. Accuracy remained high when tested on individuals not observed during training (within night: 94.1%; across nights 78.8%). Pooling training data across multiple nights improved test accuracy for all models. Deep learning outperformed random forests, particularly on harder tasks, although both were able to discriminate individuals. Analysis of temperature effects on cricket calling behavior showed that higher temperatures were associated with shorter chirp durations and higher carrier frequencies. However, adjusting spectrograms for chirp duration alone, or for both duration and frequency, did not improve performance. The results are the first to demonstrate AIID using wild recordings of an insect species, and further highlight the potential of deep learning-based AIID for non-invasive monitoring of animal populations.

bioinformatics↗

bioRxiv2025

From Shadows to Data: A Robust Population Assessment of Snow Leopards in the Highland Crossroads

The snow leopard (Panthera uncia) is a flagship species of the greater Himalayan region - referred to as the Third Pole - and symbolizes integrity of this ecological system. Within the greater Himalayas, Pakistan holds special significance as the north of the country represents a confluence of four major mountain ranges (Hindu Kush, Pamir, Karakoram, and Himalaya). However, robustly surveying and monitoring elusive, low-density species such as snow leopards has historically been difficult in the region. As a result, our understanding of the spatial patterns in density and overall population size of snow leopards has remained conjectural in the highland crossroads of northern Pakistan. This lack of objective information is an obstacle to realizing effective conservation planning for the species in Pakistan, as well as the broader ecosystem within which it plays a key role. This study aimed to empirically derive population estimates for snow leopards in Pakistan, based on robust camera trapping. Extensive camera trapping was conducted covering about 39% of the snow leopard range in Pakistan from 2010 to 2019, spread across the four major mountain ranges in the north of the country. A total of 828 cameras were placed over 26,540 trap days, resulting in 4,712 photos of snow leopards obtained from 65 different locations. Among the 53 unique individuals identified, the majority (53%) were detected only once, with an overall recapture frequency of 2.28 times per individual. Spatial capture-recapture (SCR) was employed for population and density estimation. Model selection strongly favored a model in which density was positively associated with elevation, and camera type influenced baseline encounter rates. The estimated population size for snow leopards in this highland crossroads was 127 (95% CI 88-182) adult animals, with a mean density of 0.13 (95% CI 0.09-0.19) animals per 100 km{superscript 2}. Examination of the density predictions revealed that higher density areas were associated with protected areas and greater prey biomass, highlighting the importance of these two key factors. This research provides the first robust population estimate for snow leopards in this region, establishing a foundation for long-term population monitoring and assessing the effectiveness of conservation measures. We recommend the integration of complementary approaches, such as non-invasive genetic methods, to validate and refine population estimates.

ecology↗

bioRxiv2020

Automated detection of Hainan gibbon calls for passive acoustic monitoring

1O_LIExtracting species calls from passive acoustic recordings is a common preliminary step to ecological analysis. For many species, particularly those occupying noisy, acoustically variable habitats, the call extraction process continues to be largely manual, a time-consuming and increasingly unsustainable process. Deep neural networks have been shown to offer excellent performance across a range of acoustic classification applications, but are relatively underused in ecology. C_LIO_LIWe describe the steps involved in developing an automated classifier for a passive acoustic monitoring project, using the identification of calls of the Hainan gibbon (Nomascus hainanus), one of the worlds rarest mammal species, as a case study. This includes preprocessing - selecting a temporal resolution, windowing and annotation; data augmentation; processing - choosing and fitting appropriate neural network models; and postprocessing - linking model predictions to replace, or more likely facilitate, manual labelling. C_LIO_LIOur best model converted acoustic recordings into spectrogram images on the mel frequency scale, using these to train a convolutional neural network. Model predictions were highly accurate, with per-second false positive and false negative rates of 1.5% and 22.3%. Nearly all false negatives were at the fringes of calls, adjacent to segments where the call was correctly identified, so that very few calls were missed altogether. A postprocessing step identifying intervals of repeated calling reduced an eight-hour recording to, on average, 22 minutes for manual processing, and did not miss any calling bouts over 72 hours of test recordings. Gibbon calling bouts were detected regularly in multi-month recordings from all selected survey points within Bawangling National Nature Reserve, Hainan. C_LIO_LIWe demonstrate that passive acoustic monitoring incorporating an automated classifier represents an effective tool for remote detection of one of the worlds rarest and most threatened species. Our study highlights the viability of using neural networks to automate or greatly assist the manual labelling of data collected by passive acoustic monitoring projects. We emphasise that model development and implementation be informed and guided by ecological objectives, and increase accessibility of these tools with a series of notebooks that allow users to build and deploy their own acoustic classifiers. C_LI

ecology↗