Biology subjects

Sethi, S. S.

Publications and source records attributed to Sethi, S. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Fly, land, listen: Autonomous intermittent locomotion enables scalable low-noise drone ecoacoustic surveys

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

ecology↗

bioRxiv2025

Robotics-assisted acoustic surveys could deliver reliable, landscape-level biodiversity insights

Terrestrial remote sensing approaches, such as acoustic monitoring, deliver finely resolved and reliable biodiversity data. However, the scalability of surveys is often limited by the effort, time and cost needed to deploy, maintain and retrieve sensors. Autonomous unmanned aerial vehicles (UAVs, or drones) are emerging as a promising tool for fully autonomous data collection, but there is considerable scope for their further use in ecology. In this study, we explored whether a novel approach to UAV-based acoustic monitoring could detect biodiversity patterns across a varied tropical landscape in Costa Rica. We simulated surveys of UAVs employing intermittent locomotion-based sampling strategies on an existing dataset of 26,411 hours of audio recorded from 341 static sites, with automated detections of 19 bird species (n = 1,819) and spider monkey (n = 2,977) vocalisations. We varied the number of samplers deployed in a single survey (sampling intensity) and whether the samplers move between sites randomly, in a pre-determined route to minimise travel time, or by adaptively responding to real-time detections (sampling strategy) and measured the impact on downstream ecological analyses. We found that observed avian species richness and spider monkey occupancy was not impacted by sampling strategy, but that sampling intensity had a strong influence on downstream metrics. Whilst our simulated UAV surveys were effective in capturing broad biodiversity trends, such as spider monkey occupancy and avian habitat associations, they were less suited for exhaustive species inventories, with rare species often missed at low sampling intensities. As autonomous UAV systems and acoustic AI analyses become more reliable and accessible, our study shows that combining these technologies could deliver valuable biodiversity data at scale.

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

Consistent and scalable monitoring of birds and habitats along a coffee production intensity gradient

Land use change associated with agricultural intensification is a leading driver of biodiversity loss in the tropics. To evaluate the habitat-biodiversity relationship in production systems of tropical agricultural commodities, which is critical for certifying and examining the success of biodiversity-friendly agricultural practices, birds are commonly used as indicators. However, consistently and reliably monitoring how bird communities are affected by land use change throughout the annual cycle in a way that can be scalable is challenging using traditional survey methods. In this study, we examined whether the automated analysis of audio data collected by passive acoustic monitoring, together with the analysis of remote sensing data, can be used to efficiently monitor avian biodiversity along the gradient of habitat degradation associated with the intensification of coffee production. Coffee is an important crop produced in tropical forested regions, whose production is expanding and intensifying, and coffee production systems form a gradient of ecological complexity ranging from forest-like shaded polyculture to dense sun-exposed monoculture. We used LiDAR technology to survey the habitat, in combination with autonomous recording units and a vocalisation classification algorithm to assess bird community composition in a coffee landscape comprising a shade-grown coffee farm, a sun coffee farm, and a forest remnant, located in southern Mexico. We found that combining LiDAR with the automated analysis of continuously collected bioacoustics data can capture the expected functional signatures of avian communities as a function of habitat quality in the coffee landscape. Thus, we show that this approach can be a robust way to monitor how biodiversity responds to land use intensification in the tropics. A major advantage of this approach is that it has the potential to be deployed cost-effectively at large scales to help design and certify biodiversity-friendly productive landscapes.

ecology↗

bioRxiv2024

National-scale acoustic monitoring of avian biodiversity and phenology

Billions of birds migrate annually, triggered by endogenous behaviors but also by ecoclimatic drivers which are shifting with climate change. These dynamics play out over huge spatiotemporal scales, making monitoring of phenology challenging with traditional biodiversity survey approaches. In this study, over a complete spring migration season (April through June), we collected 37,429 hours of audio from 28 networked sensors in forests across Norway using a nationwide passive acoustic monitoring (PAM) system. We applied an open-source detection algorithm to automatically classify bird vocalizations; through expert validation we found the algorithm classified 57 species (14 full migrants) with at least 80% precision. Using these automated detections, we developed regional arrival curves for three common migratory passerines: Willow Warbler, Common Chiffchaff, and Spotted Flycatcher. We then demonstrate that PAM detections can be used to train audio species distribution models that map how species vocalization probability changes across Norway during spring migration. Lastly, we discuss how PAM can complement existing manual surveys to support the design and implementation of effective policy and conservation measures.

ecology↗

bioRxiv2024

Unlocking the soundscape of coral reefs with artificial intelligence

Passive acoustic monitoring can offer insights into the state of coral reef ecosystems at low-costs and over extended temporal periods. Comparison of whole soundscape properties can rapidly deliver broad insights from acoustic data, in contrast to the more detailed but time-consuming analysis of individual bioacoustic signals. However, a lack of effective automated analysis for whole soundscape data has impeded progress in this field. Here, we show that machine learning (ML) can be used to unlock greater insights from reef soundscapes. We showcase this on a diverse set of tasks using three biogeographically independent datasets, each containing fish community, coral cover or depth zone classes. We show supervised learning can be used to train models that can identify ecological classes and individual sites from whole soundscapes. However, we report unsupervised clustering achieves this whilst providing a more detailed understanding of ecological and site groupings within soundscape data. We also compare three different approaches for extracting feature embeddings from soundscape recordings for input into ML algorithms: acoustic indices commonly used by soundscape ecologists, a pretrained convolutional neural network (P-CNN) trained on 5.2m hrs of YouTube audio and a CNN trained on individual datasets (T-CNN). Although the T-CNN performs marginally better across the datasets, we reveal that the P-CNN is a powerful tool for identifying marine soundscape ecologists due to its strong performance, low computational cost and significantly improved performance over acoustic indices. Our findings have implications for soundscape ecology in any habitat. Author SummaryArtificial intelligence has the potential to revolutionise bioacoustic monitoring of coral reefs. So far, a limited set of work has used machine learning to train detectors for specific sounds such as individual fish species. However, building detectors is a time-consuming process that involves manually annotating large amounts of audio followed by complicated model training, this must then be repeated all over again for any new dataset. Instead, we explore machine learning techniques for whole soundscape analysis, which compares the acoustic properties of raw recordings from the entire habitat. We identify multiple machine learning methods for whole soundscape analysis and rigorously test these using datasets from Indonesia, Australia and French Polynesia. Our key findings show use of a neural network pretrained on 5.2m hours of unrelated YouTube audio offers a powerful tool to produce compressed representations of reef audio data, conserving the datas key properties whilst being executable on a standard personal laptop. These representations can then be used to explore patterns in reef soundscapes using "unsupervised machine learning", which is effective at grouping similar recordings periods together and dissimilar periods apart. We show these groupings hold relationships with ground truth ecological data, including coral coverage, the fish community and depth.

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

Automatic vocalisation detection delivers reliable, multi-faceted, and global avian biodiversity monitoring

Tracking biodiversity and its dynamics at scale is essential if we are to solve global environmental challenges. Detecting animal vocalisations in passively recorded audio data offers a highly automatable, inexpensive, and taxonomically broad way to monitor biodiversity. However, uptake is slow due to the expertise and labour required to label new data and fine-tune algorithms for each deployment. In this study, we applied an off-the-shelf bird vocalisation detection model, BirdNET, to 152,376 hours of audio comprising of datasets from Norway, Taiwan, Costa Rica, and Brazil. We manually listened to a subset of detections for each species in each dataset and found precisions of over 80% for 89 of the 139 species (100% for 57 species). Whilst some species were reliably detected across multiple datasets, the performance of others was dataset specific. By filtering out unreliable detections, we could extract species and community level insight on diel (Brazil) and seasonal (Taiwan) temporal scales, as well as landscape (Costa Rica) and national (Norway) spatial scales. Our findings demonstrate that, with a relatively fast validation step, a single vocalisation detection model can deliver multi-faceted community and species level insight across highly diverse datasets; unlocking the scale at which acoustic monitoring can deliver immediate applied impact.

ecology↗

bioRxiv2023

Snowmobile noise alters bird vocalization patterns during winter and pre-breeding season

Noise pollution poses a significant threat to ecosystems worldwide, disrupting animal communication and causing cascading effects on biodiversity. In this study, we focus on the impact of snowmobile noise on avian vocalizations during the non-breeding winter season, a less-studied area in soundscape ecology. We developed a pipeline relying on deep learning methods to detect snowmobile noise and applied it to a large acoustic monitoring dataset collected in Yellowstone National Park. Our results demonstrate the effectiveness of the snowmobile detection model in identifying snowmobile noise and reveal an association between snowmobile passage and changes in avian vocalization patterns. Snowmobile noise led to a decrease in the frequency of bird vocalizations during mornings and evenings, potentially affecting winter and pre-breeding behaviors such as foraging, predator avoidance and successfully finding a mate. However, we observed a recovery in avian vocalizations after detection of snowmobiles during mornings and afternoons, indicating some resilience to sporadic noise events. These findings emphasize the need to consider noise impacts in the non-breeding season and provide valuable insights for natural resource managers to minimize disturbance and protect critical avian habitats. The deep learning approach presented in this study offers an efficient and accurate means of analyzing large-scale acoustic monitoring data and contributes to a comprehensive understanding of the cumulative impacts of multiple stressors on avian communities. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/548680v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1deda15org.highwire.dtl.DTLVardef@1bfa2e4org.highwire.dtl.DTLVardef@5f2cc2org.highwire.dtl.DTLVardef@739ecd_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2023

Modeling the Spatiotemporal Influence of Climate on American Avian Migration

Birds have adapted to climatic and ecological cycles to inform their Spring and Fall migration timings, but anthropogenic global warming has affected these long-establish cycles. Understanding these dynamics is critical for conservation during a changing climate. Here, we employ a modeling approach to explore how climate spatiotemporally affects bird occurrence on eBird surveys. Specifically, we train an ensemble of multivariate and multi-response random forest models on North and South American climate data, then predict eBird survey occurrence rates for 41 migrating passerine bird species in a Northeastern American ecoregion from 2008-2018. In October, when many passerines have begun their southward winter migration, we achieve more accurate predictions of bird occurrence using lagged climate features alone to predict occurrence. These results suggest that analyses of machine learning model metrics may be useful for identifying spatiotemporal climatic cues that affect migratory behavior. Lastly, we explore the application and limitations of random forests for prediction of future bird occurrence using 2021-2040 climate projections.

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

Automated speech detection in eco-acoustic data enables privacy protection and human disturbance quantification

Eco-acoustic monitoring is increasingly being used to map biodiversity across huge scales, yet little thought is given to the privacy concerns and potential scientific value of inadvertently recorded human speech. Automated speech detection is possible using Voice Activity Detection (VAD) models, but existing approaches have been developed for indoor or urban use cases, rather than diverse natural soundscapes. In this study we used a data augmentation approach to create ecoVAD, a convolutional neural network designed for robust voice detection in eco-acoustic data. We performed playback experiments using speech samples from a woman, man, and child in two ecosystems in Borsa, Norway, and showed that ecoVAD was able to accurately detect voices at distances of up to 20m - at which point the speech was unintelligible. We compared ecoVAD with two existing VAD models and found that ecoVAD consistently outperformed the state-of-the-art (mean F1 scores: ecoVAD, 0.917; pyannote, 0.890; WebRTC VAD, 0.876). Using long-term passive recordings from a popular hiking location in Bymarka, Norway, we found that the frequency of speech detections was linked closely to peak traffic hours (using bus timings) demonstrating how VAD models can be used to quantify human activity with a fine temporal resolution. Anonymising audio data effectively using VAD models will allow eco-acoustic monitoring to continue to deliver invaluable ecological insight at scale, whilst minimising the risk of data misuse. Furthermore, using speech detections as a fine scale measure of human disturbance opens new possibilities for studying subtle human-wildlife interactions on the vast scales made possible by eco-acoustic monitoring technology. Author SummaryEco-acoustic monitoring (i.e., recording and analysing the sounds of an ecosystem) allows us to monitor biodiversity and ecological health on vast spatial and temporal scales. However, as networks of audio recorders are increasingly being deployed across the world by scientists and land managers, little thought has been given to the implications of inadvertently recorded human speech in these datasets. We developed a neural-network model to detect and remove speech from audio data, and showed it performed more accurately than existing state-of-the-art models when evaluated on eco-acoustic datasets. Furthermore, we showed frequency of voice detections could be used to monitor human pressure on an ecosystem. Our work paves the way for ethical and responsible processing of eco-acoustic data and provides a novel approach to studying human wildlife interactions on a large scale with high temporal resolution.

bioinformatics↗

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↗

bioRxiv2019

catch22: CAnonical Time-series CHaracteristics

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147 000 time series, including biomedical datasets) and using a filtered version of the hctsa feature library (4791 features), we introduce a generically useful set of 22 CAnonical Time-series CHaracteristics, catch22. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.

bioinformatics↗

bioRxiv2018

CCL3 promotes germinal center B cells sampling by follicular regulatory T cells and ensures optimal humoral response

Previous studies and our findings suggest upregulated expression of proinflammatory chemokines CCL3/4 in germinal center (GC) centrocytes. However, the role of CCL3/4 for centrocyte interactions with follicular T cells and regulation of humoral immunity is poorly understood. We found that CCL3 promotes chemotaxis of Tfr cells ex vivo. In vivo CCL3 is not required for Tfr cells recruitment of into the GC light zone. However, B cells-intrinsic production of CCL3 promotes their direct interactions and negative regulation by follicular regulatory T cells (Tfr) within GCs.

immunology↗

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

ecology↗