Biology subjects

Erb, W. M.

Publications and source records attributed to Erb, W. M..

3 recordsLinked to original sources

bioRxiv2024

A Systematic Review of the Impacts of El Nino-Driven Drought, Fire, and Smoke on Non-Human Primates in Southeast Asia

During the El Nino phase of the El Nino-Southern Oscillation (ENSO), much of Southeast Asia experiences intense droughts and surging temperatures, exacerbating wildfires and creating a blanket of hazardous haze across much of the region. These patterns are predicted to worsen with climate change, further exacerbating the vulnerability of the regions primate species, 94% of which are threatened with extinction. Here, we report findings from a systematic search of the literature and synthesise the current state of knowledge regarding the impacts of El Nino-driven heat, drought, fire, and smoke on non-human primates in Southeast Asia. Our review shows that habitat loss and degradation driven by El Nino-induced drought and fire causes changes to diet, activity patterns, social interactions, and physical condition, and leads to population crowding, reduced group sizes, increased infant mortality, displacement of individuals, and local extirpation. Further, prolonged exposure to smoke alters behaviour and worsens the health of primates. Notably, two studies presented evidence for the recovery of primate populations in habitats damaged by fire if forest is allowed to regenerate. We highlight significant gaps in our understanding of the impacts of El Nino on non-human primates, particularly the need for research that encompasses the interconnected factors of rainfall, temperature, fire, and smoke, as well as long-term effects on reproduction and mortality. Due to the unpredictable nature of El Nino, we acknowledge the difficulties of planning research in this area and emphasise the potential of long-term research sites to add to our understanding of this key conservation issue.

ecology↗

bioRxiv2024

Automated detection of Bornean white-bearded gibbon (Hylobates albibarbis) vocalisations using an open-source framework for deep learning

Passive acoustic monitoring is a promising tool for monitoring at-risk populations of vocal species, yet extracting relevant information from large acoustic datasets can be time-consuming, creating a bottleneck at the point of analysis. To address this, we adapted an open-source framework for deep learning in bioacoustics to automatically detect Bornean white-bearded gibbon (Hylobates albibarbis) "great call" vocalisations in a long-term acoustic dataset from a rainforest location in Borneo. We describe the steps involved in developing this solution, including collecting audio recordings, developing training and testing datasets, training neural network models, and evaluating model performance. Our best model performed at a satisfactory level (F score = 0.87), identifying 98% of the highest-quality calls from 90 hours of manually-annotated audio recordings and greatly reduced analysis times when compared to a human observer. We found no significant difference in the temporal distribution of great call detections between the manual annotations and the models output. Future work should seek to apply our model to long-term acoustic datasets to understand spatiotemporal variations in H. albibarbis calling activity. Overall, we present a roadmap for applying deep learning to identify the vocalisations of species of interest which can be adapted for monitoring other endangered vocalising species.

ecology↗

bioRxiv2023

Vocal complexity in the long calls of Bornean orangutans

Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable "long call" vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: 1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, 2) distinguish among call types using supervised classification and unsupervised clustering, and 3) compare the performance of different feature sets. Using 46 acoustic features, we applied machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We additionally used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrogram representations. Supervised approaches showed low inter-observer reliability and poor classification accuracy, indicating that pulse types were not discrete. We propose an updated pulse classification approach that is highly reproducible across observers and exhibits strong classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and quantify gradedness in animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-a-vis graded repertoires.

animal behavior and cognition↗