R. M. Ewers

Variable responses of individual species to tropical forest degradation

The study examines how individual species respond to tropical forest degradation, finding significant variation in responses across populations and surveys.

MAARU: Multichannel Acoustic Autonomous Recording Unit for spatial ecosystem monitoring.

The paper introduces MAARU, a low-cost, open-source multichannel acoustic recorder for spatial ecosystem monitoring, demonstrating its accuracy in localizing sounds and improving species identification.

Tropical invertebrate community assembly processes are robust to a gradient of land use intensity

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.

Is there an accurate and generalisable way to use soundscapes to monitor biodiversity?

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

Logging alters tropical forest structure, while conversion to agriculture reduces biodiversity and functioning

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.

Soundscapes predict species occurrence in tropical forests

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

Oil palm expansion increases the vectorial capacity of dengue vectors in Malaysian Borneo

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.

SAFE Acoustics: an open-source, real-time eco-acoustic monitoring network in the tropical rainforests of Borneo

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.

Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

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

Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device

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.