Biology subjects

Orme, C. D. L.

Publications and source records attributed to Orme, C. D. L..

5 recordsLinked to original sources

bioRxiv2024

Rapid spread of African Swine Fever across Borneo

African Swine Fever (ASF) reached the island of Borneo at the end of 2020. The first mortalities occurred in wild bearded pigs (Sus barbatus) in Sabah, north-east Borneo. The virus then began to spread across the island but, confounded by COVID 19 lockdowns the spread was difficult to monitor on the ground. The Babi Hutan Project was launched in April 2021 to gather data on pig sightings using citizen science. Here we bring together the data from this project and other online sources to show how the virus encompassed almost the entire island within a one-year period. The speed of spread appears to increase with time following an exponential model: we estimate an average speed of spread of 0.89km/day after 100 days since the first observation and at 4.28km/day after 400 days. We include recommendations of next steps for the bearded pigs of Borneo.

ecology↗

bioRxiv2024

Variable responses of individual species to tropical forest degradation

The functional stability of ecosystems depends greatly on interspecific differences in responses to environmental perturbation. However, responses to perturbation are not necessarily invariant among populations of the same species, so intraspecific variation in responses might also contribute. Such inter-population response diversity has recently been shown to occur spatially across species ranges, but we lack estimates of the extent to which individual populations across an entire community might have perturbation responses that vary through time. We assess this using 524 taxa that have been repeatedly surveyed for the effects of tropical forest logging at a focal landscape in Sabah, Malaysia. Just 39 % of taxa - all with non-significant responses to forest degradation - had invariant responses. All other taxa (61 %) showed significantly different responses to the same forest degradation gradient across surveys, with 6 % of taxa responding to forest degradation in opposite directions across multiple surveys. Individual surveys had low power (< 80 %) to determine the correct direction of response to forest degradation for one-fifth of all taxa. Recurrent rounds of logging disturbance increased the prevalence of intra-population response diversity, while uncontrollable environmental variation and/or turnover of intraspecific phenotypes generated variable responses in at least 44 % of taxa. Our results show that the responses of individual species to local environmental perturbations are remarkably flexible, likely providing an unrealised boost to the stability of disturbed habitats such as logged tropical forests.

ecology↗

bioRxiv2024

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

1.1 - Acoustic localisation, which relies on simultaneous multi-microphone recording, adds spatial information to recorded audio and has been used in ecosystem monitoring to count individuals to improve abundance estimates, locate illegal activities such as logging/poaching, and monitor behaviour such as habitat use or species interactions. Studies have shown many advantages of acoustic localisation, but uptake remains limited as the equipment is often expensive, inaccessible, or only suitable for short-term deployments. 2 -Here, we present a low-cost, open-source, 6-channel recorder built entirely from commercially available components which can be integrated into a solar-powered, networked system. The MAARU (Multichannel Acoustic Autonomous Recording Unit) works in long-term autonomous, passive, and ad-hoc deployments. We introduce MAARUs hardware and software and present the results of lab and field tests investigating the devices durability, localisation accuracy, and other applications. 3 -MAARU provides multichannel data with similar costs and power demands to equivalent omnidirectional recorders. MAARU devices have been deployed in the UK and Brazil, where we have shown MAARUs can accurately localise pure tones up to 6kHz and 65dB bird calls as far as 8m away ({+/-}10{degrees} range, 100% and >60% of signals respectively), louder calls may have even further detection radii. We also show how beamforming can be used on MAARU devices to improve species ID confidence scores by 20%+ and recall by 10%+ when using the BirdNET automated bird identification algorithms. 4 -MAARU is an accessible, low-cost option for those looking to explore spatial soundscape ecology accurately and easily. Ultimately, the added directional element of the multichannel recording provided by MAARU allows for a new type of exploration into sonic environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/576628v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1078827org.highwire.dtl.DTLVardef@17bd5b1org.highwire.dtl.DTLVardef@10a6696org.highwire.dtl.DTLVardef@1d518f4_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2020

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

O_LIAutomated monitoring approaches offer an avenue to deep, large-scale insight into how ecosystems respond to human pressures. Since sensor technology and data analyses are often treated independantly, there are no open-source examples of end-to-end, real-time ecological monitoring networks. C_LIO_LIHere, we present the complete implementation of an autonomous acoustic monitoring network deployed in the tropical rainforests of Borneo. Real-time audio is uploaded remotely from the field, indexed by a central database, and delivered via an API to a public-facing website. C_LIO_LIWe provide the open-source code and design of our monitoring devices, the central web2py database and the ReactJS website. Furthermore, we demonstrate an extension of this infrastructure to deliver real-time analyses of the eco-acoustic data. C_LIO_LIBy detailing a fully functional, open-source, and extensively tested design, our work will accelerate the rate at which fully autonomous monitoring networks mature from technological curiosities, and towards genuinely impactful tools in ecology. C_LI

ecology↗

bioRxiv2019

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

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labour-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we developed a generalisable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed ecosystem soundscapes from a wide variety of biomes into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, paving the way for real-time detection of irregular environmental behaviour including illegal activity. Our highly generalisable approach, and the common set of features, will enable scientists to unlock previously hidden insights from eco-acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.

ecology↗