R. M. Ewers

The paper quantifies how stochastic and deterministic processes influence invertebrate community assembly across a gradient of logging intensity in Malaysian Borneo, finding robustness to land use changes.

This study investigates the generalisability of soundscape analysis for biodiversity monitoring and finds that acoustic features and machine learning models are inconsistent across datasets.

The paper analyzes the impacts of logging and conversion to oil palm on tropical forest structure, biodiversity, and functioning in Borneo, finding that logging affects structure more than biodiversity, while conversion has greater impacts on biodiversity and functioning.

This paper shows that soundscapes can predict species occurrence in tropical forests without needing extensive labeled data, achieving high accuracy for many species.

This study shows that oil palm plantations in Malaysian Borneo increase dengue vectorial capacity by 150% compared to forests due to higher adult mosquito abundance despite similar temperature-dependent trait estimates.

The paper presents an open-source, real-time eco-acoustic monitoring network in Borneo's tropical rainforests, providing code, design, and infrastructure for data collection and analysis.

This paper develops a machine learning method using eco-acoustic data to monitor ecosystem changes and detect anomalies efficiently.

The paper introduces an open-source, low-cost ecosystem monitoring system that autonomously captures and transmits continuous data, outperforming existing technologies at a fraction of the cost.