Biology subjects

Ewers, R. M.

Publications and source records attributed to Ewers, R. M..

16 recordsLinked to original sources

bioRxiv2025

Stand age drives changes in the biodiversity and microclimate of oil palm plantations

Tree plantation age strongly dictates its environment. As they mature, the canopy closes and habitat complexity tends to increase, which in turn affects biodiversity. However, for oil palm, one of the most important and widespread tree plantations, we lack a detailed understanding of how this process affects microclimate and biodiversity. We therefore compiled environmental and biodiversity data of oil palm plantations that were repeatedly sampled over a 6 year period in Malaysian Borneo. Using these data, we made a chronosequence of the microclimate and biodiversity of oil palm plantations between the ages 5 and 18. We found that oil palm plantations increasingly buffer temperature and humidity over time, becoming cooler and less dry. Animal responses differed between taxa: spider and beetle abundance increased, ant abundance decreased, and mammal and dung-beetle abundances stayed constant. The diversity of beetles in general increased and family-level community composition changed, while dung-beetle and mammal diversity stayed constant. Some taxa are therefore likely limited by the harsh microclimate of young plantations, while increased habitat complexity and fruit and leaf production in older plantations could also drive increased abundances of other taxa. Our results reveal that oil palm plantations undergo considerable changes, even over a decade after establishment. Correspondingly, plantation age should be considered in studies into oil palm plantations impact on climate and biodiversity, and landscape level diversity could be increased by allowing multiple oil palm stand-ages to co-exist.

ecology↗

bioRxiv2025

Sustainable cattle management by communities supports African wildlife

Community-based conservation (CBC) initiatives aim to reconcile biodiversity protection with local livelihoods, yet their effectiveness in protecting wildlife remains uncertain, often hinging on local management1,2. We evaluated a globally significant CBC model in Kenyas Greater Maasai Mara Ecosystem (GME), where conservancies, run jointly by Maasai landowners and the tourism sector, employ rotational cattle grazing to support both wildlife and pastoralism3,4. Using a [~]1200 km2 grid of 180 camera traps across gradients of livestock pressure in Maasai Mara National Reserve and three conservancies in 2018, we collected and analysed over 2 million images with a customised AI-powered pipeline. We found a positive impact of observed cattle pressure on mammal community occupancy and species richness, except for at the highest levels of cattle grazing. However, sheep and goat grazing and proximity to infrastructure had a negative impact. These results provide evidence that wildlife and pastoralism can coexist under community-led stewardship5, but only with active management and targeted control of emerging threats. AI tools such as our image classifier may contribute to more adaptive community-led management of these areas6. As conservation policy shifts beyond formal protected areas, our findings support CBC as a scalable model for conserving biodiversity within working landscapes, offering a pathway to meet global targets while maintaining local livelihoods7.

ecology↗

bioRxiv2025

Accelerating loss of resilience in Bornean rainforests

Climate change and human activity are eroding tropical rainforest resilience, risking tipping points of mass forest dieback towards savannah-like or treeless states. Here, we extend a close analysis of tipping-point early-warning signals in satellite-sensed vegetation data from the Amazon to Borneo, revealing resilience loss via critical slowing down of recovery from perturbation. Specifically, we examine spatiotemporal patterns in rising lag-1 autocorrelation and variance to assess whether, when and where resilience loss accelerated, and how these patterns relate to climate, human activity and animal biodiversity. We find that Bornean rainforests lost resilience over 1991-2016, with a marked acceleration from a breakpoint in 2004. Resilience loss accelerated earlier in subregions with higher human footprint, and was more severe in drier, more human-impacted subregions before the breakpoint. Post-breakpoint, resilience loss was stronger in wetter and less biodiverse areas. We also uncover the apparent reallocation of post-breakpoint resilience loss to areas that had previously appeared more resistant. Climate and bird and mammal richness appear to be most strongly correlated with this pattern. Our results provide enhanced evidence of Bornean rainforest resilience loss and its potential drivers, underscoring the urgent need for ecological conservation and climate change mitigation in Borneo and worldwide.

ecology↗

bioRxiv2024

Interpretable and Robust Machine Learning for Exploring and Classifying Soundscape Data

The adoption of machine learning in Passive Acoustic Monitoring (PAM) has improved prediction accuracy for tasks like species-specific call detection and habitat quality estimation. However, these models often lack interpretability, and PAM generates vast amounts of non-informative data, as soundscapes are typically information sparse. Here, we developed ecologically interpretable methods that accurately predict land use from audio while filtering unwanted data. Audio from habitats in Southern India (evergreen forests, deciduous forests, scrublands, grasslands) was collected and categorised by land use (reference, disturbed, and agriculture). We used Gaussian Mixture Models (GMMs) on top of a Convolutional Neural Network (CNN)-based feature extractor to predict land use. Thresholding based on likelihood values from GMMs improved model accuracy by excluding uninformative data, enabling our method to outperform models such as Random Forests and Support Vector Machines. By analysing areas of acoustic feature space driving predictions, we identified "keystone" soundscape elements for each land use, including both biotic and anthropogenic sources. Our approach provides a novel method for ecologically meaningful interpretation and exploration of large acoustic datasets independent of specific feature extractors. Our study paves the way for soundscape monitoring to deliver robust and trustworthy habitat assessments on scales that would not otherwise be possible.

ecology↗

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↗

bioRxiv2023

Stochastic dispersal assembly dominates tropical invertebrate community assembly regardless of land use intensity.

Understanding how community assembly processes drive biodiversity patterns is a central goal of community ecology. While it is generally accepted that ecological communities are assembled by both stochastic and deterministic processes, quantifying their relative importance remains challenging. Few studies have investigated how the relative importance of stochastic and deterministic community assembly processes vary among taxa and along gradients of habitat degradation. Using data on 1,645 arthropod species across seven taxonomic groups in Malaysian Borneo, we quantified the importance of ecological stochasticity and of a suite of community assembly processes across a gradient of logging intensity. The relationship between logging and community assembly varied depending on the specific combination of taxa and stochasticity metric used, but, in general, the processes that govern invertebrate community assembly were remarkably robust to changes in land use intensity.

ecology↗

bioRxiv2022

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

Acoustic monitoring has the potential to deliver biodiversity insight on vast scales. Whilst autonomous recording networks are being deployed across the world, existing analytical techniques struggle with generalisability. This limits the insight that can be derived from audio recordings in regions without ground-truth calibration data. By calculating 128 learned features and 60 soundscape indices of audio recorded during 8,023 avifaunal point counts from diverse ecosystems, we investigated the generalisability of soundscape approaches to biodiversity monitoring. Within each dataset, we found univariate correlations between several acoustic features and avian species richness, but features behaved unpredictably across datasets. Training a machine learning model on compound indices, we could predict species richness within datasets. However, models were uninformative when applied to datasets not used for training. We found that changes in soundscape features were correlated with changes in avian communities across all datasets. However, there were cases where avian communities changed without an associated shift in soundscapes. Our results suggest that there are no common hallmarks of biodiverse soundscapes across ecosystems. Therefore, soundscape monitoring should only be used when high quality ground-truth data exists for the region of interest, and in conjunction with more targeted and accurate in-person ecological surveys. By better understanding how to use interpret data reliably, we hope to unlock the scale at which acoustic monitoring can be used to deliver true impact for land managers and scientists monitoring biodiversity around the world. SummaryWhilst eco-acoustic monitoring has the potential to deliver biodiversity insight on vast scales, existing analytical approaches behave unpredictably across studies. We collated 8,023 audio recordings with paired manual avifaunal point counts to investigate whether soundscapes could be used to monitor biodiversity across diverse ecosystems. We found that neither univariate indices nor machine learning models were predictive of species richness across datasets, but soundscape change was consistently indicative of community change. Our findings indicate that there are no common features of biodiverse soundscapes, and that soundscape monitoring should be used cautiously and in conjunction with more reliable in-person ecological surveys.

ecology↗

bioRxiv2022

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

The impacts of degradation and deforestation on tropical forests are poorly understood, particularly at landscape scales. We present an extensive ecosystem analysis of the impacts of logging and conversion of tropical forest to oil palm from a large-scale study in Borneo, synthesizing responses from 82 variables categorized into four ecological levels spanning a broad suite of ecosystem properties: 1) structure and environment, 2) species traits, 3) biodiversity, and 4) ecosystem functions. Responses were highly heterogeneous and often complex and non-linear. Variables that were directly impacted by the physical process of timber extraction, such as soil structure, were sensitive to even moderate amounts of logging, whereas measures of biodiversity and ecosystem functioning were generally resilient to logging but more affected by conversion to oil palm plantation. One-Sentence SummaryLogging tropical forest mostly impacts structure while biodiversity and functions are more vulnerable to habitat conversion.

ecology↗

bioRxiv2021

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

Changes in land-use and the associated shifts in environmental conditions can have large effects on the transmission and emergence of disease. Mosquito-borne disease are particularly sensitive to these changes because mosquito growth, reproduction, survival and susceptibility to infection are all thermally sensitive traits, and land use change dramatically alters local microclimate. Predicting disease transmission under environmental change is increasingly critical for targeting mosquito-borne disease control and for identifying hotspots of disease emergence. Mechanistic models offer a powerful tool for improving these predications. However, these approaches are limited by the quality and scale of temperature data and the thermal response curves that underlie predictions. Here, we used fine-scale temperature monitoring and a combination of empirical, laboratory and temperature-dependent estimates to estimate the vectorial capacity of Aedes albopictus mosquitoes across a tropical forest - oil palm plantation conversion gradient in Malaysian Borneo. We found that fine-scale differences in temperature between logged forest and oil palm plantation sites were not sufficient to produce differences in temperature-dependent trait estimates using published thermal performance curves. However, when measured under field conditions a key parameter, adult abundance, differed significantly between land-use types, resulting in estimates of vectorial capacity that were 1.5 times higher in plantations than in forests. The prediction that oil palm plantations would support mosquito populations with higher vectorial capacity was robust to uncertainties in our adult survival estimates. These results provide a mechanistic basis for understanding the effects of forest conversion on mosquito-borne disease risk, and a framework for interpreting emergent relationships between land-use and disease transmission. As rising demand for palm oil products drives continued expansion of plantations, these findings have important implications for conservation, land management and public health policy at the global scale. AUTHOR SUMMARYThe large scale modification of landscapes by humans has contributed to the rise of emerging and re-emerging mosquito-borne diseases. While the association between athropogenic land-use change and these shifts in disease risk are frequently observed, our understanding of exactly how land-use change mechanistically alters disease risk remains unclear. Changes in local environmental conditions (e.g. temperature) may play an important role, due to the effects on mosquito life-history, but are rarely measured at scales relevant these small-bodied ectotherms. Here we measure the impact of tropical forest conversion to oil palm plantation on each of the components that determine the potential for mosquitoes to transmit pathogens (vectorial capacity). By combining fine-scale temperature data from the field, published temperature responses of mosquitoes and field data on adult mosquito populations, we show that land-use change from forest to plantation can be expected to increase vectorial capacity by 150%. Our results highlight the need to advance field research into fundamental mosquito ecology, and to more critically evaluate the increased risk of Aedes-borne disease in dynamic working landscapes against the benefits of economic development.

ecology↗

bioRxiv2020

Soundscapes predict species occurrence in tropical forests

O_LIAccurate occurrence data is necessary for the conservation of keystone or endangered species, but acquiring it is usually slow, laborious, and costly. Automated acoustic monitoring offers a scalable alternative to manual surveys, but identifying species vocalisations requires large manually annotated training datasets, and is not always possible (e.g., for silent species). A new, intermediate approach is needed that rapidly predicts species occurrence without requiring extensive labelled data. C_LIO_LIWe investigated whether local soundscapes could be used to infer the presence of 32 avifaunal and seven herpetofaunal species across a tropical forest degradation gradient in Sabah, Malaysia. We developed a machine-learning based approach to characterise species indicative soundscapes, training our models on a coarsely labelled manual point-count dataset. C_LIO_LISoundscapes successfully predicted the occurrence of 34 out of the 39 species across the two taxonomic groups, with area under the curve (AUC) metrics of up to 0.87 (Bold-striped Tit-babbler Macronus bornensis). The highest accuracies were achieved for common species with strong temporal occurrence patterns. C_LIO_LISoundscapes were a better predictor of species occurrence than above-ground biomass - a metric often used to quantify habitat quality across forest degradation gradients. C_LIO_LISynthesis and applications: Our results demonstrate that soundscapes can be used to efficiently predict the occurrence of a wide variety of species. This provides a new direction for audio data to deliver large-scale, accurate assessments of habitat suitability using cheap and easily obtained field datasets. C_LI

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↗

bioRxiv2018

Forest quality, forest area and the importance of beta-diversity for protecting Borneo’s beetle biodiversity

The lowland forest of Borneo is threatened by rapid logging for timber export and clearing for the expansion of timber and oil palm plantations. This combination of processes leaves behind landscapes dotted with small, often heavily-degraded forest fragments. The biodiversity value of such fragments, which are easily dismissed as worthless, is uncertain. We collected 187 taxa of rove beetles across a land-use gradient in Sabah, Malaysia, spanning pristine tropical lowland forest to heavily-degraded forest. Using these data, we identified shifts in alpha-, beta-, and gamma-diversity in response to forest quality and distance, then applied our findings from continuous expanses of forest to make predictions on hypothetical forest areas. We found that maintaining high forest quality is more important than forest area for conserving rare taxa (those important for conserving biodiversity per se), and that very small areas (10 ha) are likely to harbour the same richness of rove beetles as larger areas (100 ha) of equal forest quality. We estimate a decline in richness of 36% following heavy logging (removal of 95% of the vegetation biomass) from a forest area of 100 ha or less. Maintaining large forest area as well as high forest quality is important for conserving community composition, likely to be more important for conserving ecosystem functioning. We predict a decline of 35% in community diversity in conversion of a 100 ha area of unlogged forest to a 10 ha area of heavily-logged forest. Despite significant declines in alpha-diversity, beta-diversity within small rainforest areas may partially mitigate the loss of gamma-diversity, reinforcing the concept that beta-diversity is a dominant force determining the conservation of species in fragmented landscapes. In contrast to previous findings on larger animals, our results suggest that even small fragments of degraded forest might be important reservoirs of invertebrate biodiversity in tropical agriculture landscapes. These fragments, especially of lightly-logged forest, should be conserved where they occur and form an integral part of management for more sustainable agriculture in tropical landscapes.

ecology↗

bioRxiv2017

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

O_LIAutomated methods of monitoring ecosystems provide a cost-effective way to track changes in natural systems dynamics across temporal and spatial scales. However, methods of recording and storing data captured from the field still require significant manual effort.\nC_LIO_LIHere we introduce an open source, inexpensive, fully autonomous ecosystem monitoring unit for capturing and remotely transmitting continuous data streams from field sites over long time-periods. We provide a modular software framework for deploying various sensors, together with implementations to demonstrate proof of concept for continuous audio monitoring and time-lapse photography.\nC_LIO_LIWe show how our system can outperform comparable technologies for fractions of the cost, provided a local mobile network link is available. The system is robust to unreliable network signals and has been shown to function in extreme environmental conditions, such as in the tropical rainforests of Sabah, Borneo.\nC_LIO_LIWe provide full details on how to assemble the hardware, and the open-source software. Paired with appropriate automated analysis techniques, this system could provide spatially dense, near real-time, continuous insights into ecosystem and biodiversity dynamics at a low cost.\nC_LI

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