Utilising affordable smartphones and open-source time-lapse photography for pollinator image collection and annotation

Journal of Pollination Ecology, Journal Year: 2025, Volume and Issue: 37, P. 1 - 21

Published: Jan. 10, 2025

Monitoring plant-pollinator interactions is crucial for understanding the factors influencing these relationships across space and time. Traditional methods in pollination ecology are resource-intensive, while time-lapse photography offers potential non-destructive automated complementary techniques. However, accurate identification of pollinators at finer taxonomic levels (i.e., genus or species) requires high enough image quality. This study assessed feasibility using a smartphone setup to capture images arthropods visiting flowers evaluated whether offered sufficient resolution arthropod by taxonomists. Smartphones were positioned above target from various plant species urban green areas around Leipzig Halle, Germany. We present proportions identifications (instances) different (order, family, genus, based on visible features as interpreted document limitations stem (e.g., fixed positioning preventing distinguishing despite resolution) low Recommendations provided address challenges. Our results indicate that 89.81% all Hymenoptera instances identified family level, 84.56% pollinator only 25.35% level. less able identify Dipterans levels, with nearly 50% not identifiable 26.18% 15.19% levels. was due their small size more challenging needed wing veins). Advancing technology, along accessibility, affordability, user-friendliness, promising option coarse-level monitoring.

Language: Английский

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Emerging technologies for pollinator monitoring

Current Opinion in Insect Science, Journal Year: 2025, Volume and Issue: unknown, P. 101367 - 101367

Published: March 1, 2025

Language: Английский

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From buzzes to bytes: A systematic review of automated bioacoustics models used to detect, classify and monitor insects

Journal of Applied Ecology, Journal Year: 2024, Volume and Issue: 61(6), P. 1199 - 1211

Published: April 3, 2024

Abstract Insects play vital ecological roles; many provide essential ecosystem services while others are economically devastating pests and disease vectors. Concerns over insect population declines expansion have generated a pressing need to effectively monitor insects across broad spatial temporal scales. A promising approach is bioacoustics, which uses sound study communities. Despite recent increases in machine learning technologies, the status of emerging automated bioacoustics methods for monitoring not well known, limiting potential applications. To address this gap, we systematically review effectiveness models past four decades, analysing 176 studies that met our inclusion criteria. We describe their strengths limitations compared traditional propose productive avenues forward. found 302 species distributed nine Orders. Studies used intentional calls (e.g. grasshopper stridulation), by‐products flight bee wingbeats) indirectly produced sounds grain movement) identification. Pests were most common focus, driven largely by weevils borers moving dried food wood. All vector focused on mosquitoes. quarter multiple families. Our illustrates learning, deep particular, becoming gold standard modelling approaches. identified could classify hundreds with 90% accuracy. Bioacoustics can be useful reducing lethal sampling, phenological patterns within days working locations or conditions where less effective shady, shrubby remote areas). However, it important note all taxa emit easily detectable sounds, pollution may impede recordings some environmental contexts. Synthesis applications : Automated tool addressing societal questions. Successful include assessing biodiversity, distribution behaviour, as evaluating restoration pest control efforts. recommend collaborations among ecologists experts increase model use researchers practitioners.

Language: Английский

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Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists

Biological reviews/Biological reviews of the Cambridge Philosophical Society, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 17, 2024

ABSTRACT Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, corresponding increase volume data generated. However, sets are often becoming so sizable that analysing them manually is increasingly burdensome unrealistic. Fortunately, we also computing power capability machine learning algorithms, which offer possibility performing some analysis required PAM automatically. Nonetheless, field automatic detection events still its infancy biology ecology. In this review, examine trends bioacoustic their implications burgeoning amount needs to be analysed. We explore different methods other tools scanning, analysing, extracting automatically from large volumes recordings. then provide step‐by‐step practical guide using bioacoustics. One biggest challenges greater bioacoustics there gulf expertise between sciences computer science. Therefore, review first presents an overview requirements bioacoustics, intended familiarise those science background with community, followed by introduction key elements artificial intelligence biologist understand incorporate into research. building pipeline data, conclude discussion possible future directions field.

Language: Английский

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Fragmentation in patchy ecosystems: a call for a functional approach

Trends in Ecology & Evolution, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

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Early Prediction of Harmful Environmental Changes Based on Bioacoustics Analysis

Advances in environmental engineering and green technologies book series, Journal Year: 2024, Volume and Issue: unknown, P. 129 - 148

Published: Nov. 22, 2024

Environmental conditions of ocean are crucial for predicting harmful events, which gives an impact on marine ecosystems and human well-being. Various threats, such as acidification, algal blooms, coral bleaching, need early monitoring variables, including temperature, acidity, pollution levels, biodiversity, to mitigate the adverse effects. This proactive approach enables conservation efforts that safeguard life, fisheries, coastal communities while promoting sustainable stewardship. Deep learning techniques play a pivotal role in this endeavour by processing underwater acoustic recordings. It accurate results identify species, detect changes their behavior, predict environmental based sound patterns. The proposed work suggests that, combination Long Short Term Memory(LSTM) Graphical Neural Network(GNN) is used over ocean. LSTM GNN architectures establish indirect relationship, shedding light emerging trends potential threats.

Language: Английский

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Setting-Up the Audiomoth Recorder for Wildlife Monitoring in the Rainforest

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 200 - 212

Published: Oct. 17, 2024

Language: Английский

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