Towards the fully automated monitoring of ecological communities

Ecology Letters, Год журнала: 2022, Номер 25(12), С. 2753 - 2775

Опубликована: Окт. 20, 2022

Abstract High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real‐time automated abiotic components has been possible for some time, biotic components—for example, individual behaviours traits, species abundance distribution—is far more challenging. Recent technological advancements offer potential solutions achieve this through: (i) increasingly affordable high‐throughput recording hardware, which can collect rich multidimensional data, (ii) accessible artificial intelligence approaches, extract ecological knowledge from large datasets. However, automating the facets communities via such technologies primarily achieved at low spatiotemporal resolutions within limited steps workflow. Here, we review existing data processing that enable communities. We then present novel frameworks combine technologies, forming fully pipelines detect, track, classify count multiple species, record behavioural morphological have previously impossible achieve. Based on these rapidly developing illustrate a solution one greatest challenges ecology: ability generate high‐resolution, standardised across complex ecologies.

Язык: Английский

---

Accurate detection and identification of insects from camera trap images with deep learning

PLOS Sustainability and Transformation, Год журнала: 2023, Номер 2(3), С. e0000051 - e0000051

Опубликована: Март 15, 2023

Reported insect declines have dramatically increased the global demand for standardized monitoring data. Image-based can generate such data cost-efficiently and non-invasively. However, extracting ecological from images is more challenging insects than vertebrates because of their small size great diversity. Deep learning facilitates fast accurate detection identification, but lack training coveted deep models a major obstacle application. We present large annotated image dataset functionally important taxa. The primary consists 29,960 representing nine taxa including bees, hoverflies, butterflies beetles across two million recorded with ten time-lapse cameras mounted over flowers during summer 2019. was extracted using an iterative approach: First, preliminary model identified candidate insects. Second, were manually screened by users online citizen science platform. Finally, all annotations quality checked experts. used to train compare performance selected You Only Look Once (YOLO) algorithms. show that these detect classify in complex scenes unprecedented accuracy. best performing YOLOv5 consistently identifies dominant species play roles pollination pest control Europe. reached average precision 92.7% recall 93.8% classification species. Importantly, when presented uncommon or unclear not seen training, our detects 80% individuals usually interprets them as closely related This useful property (1) rare which are absent, (2) new correctly identify those future. Our camera system, framework promising results non-destructive Furthermore, resulting quantify phenology, abundance, foraging behaviour flower-visiting Above all, this represents critical first benchmark future development evaluation identification.

Язык: Английский

---

Challenges in remote sensing based climate and crop monitoring: navigating the complexities using AI

Journal of Cloud Computing Advances Systems and Applications, Год журнала: 2024, Номер 13(1)

Опубликована: Фев. 6, 2024

Abstract The fast human climate change we are witnessing in the early twenty-first century is inextricably linked to health and function of biosphere. Climate affecting ecosystems through changes mean conditions variability, as well other related such increased ocean acidification atmospheric CO 2 concentrations. It also interacts with ecological stresses like degradation, defaunation, fragmentation.Ecology monitoring critical understanding complicated interactions between changing trends. This review paper dives into issues monitoring, emphasizing complications caused by technical limits, data integration, scale differences, requirement for accurate timely information. Understanding dynamics these climatic impacts, identifying hotspots susceptibility resistance, management measures that may aid biosphere resilience all necessary. At same time, can help mitigation adaptation. processes, possibilities, constraints nature-based solutions must be investigated assessed. Addressing developing successful policies strategies mitigating effects promoting sustainable ecosystem management. Human actions inscribe their stamp big narrative our planet’s story, very substance global atmosphere. transformation goes beyond chemistry, casting a spell on physical characteristics choreograph Earth’s brilliant dance. These qualities, heavenly notes, create song echoes deep We go journey via recorded tales they respond ever-shifting environment this text. peek rich fabric change, drawing insight from interconnected observatories. Nonetheless, growing symphony set unleash additional transformational stories - narratives natural riches rhythms both economically environmentally essential. essential navigating epic. A roadmap development necessitates ability comprehend stories, problem resonates across breadth programs, particularly infancy integrated sites.

Язык: Английский

---

Extinction of experience among ecologists

Trends in Ecology & Evolution, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

---

A predictive timeline of wildlife population collapse

Nature Ecology & Evolution, Год журнала: 2023, Номер 7(3), С. 320 - 331

Опубликована: Янв. 26, 2023

Язык: Английский

---

Novel community data in ecology-properties and prospects

Trends in Ecology & Evolution, Год журнала: 2023, Номер 39(3), С. 280 - 293

Опубликована: Ноя. 8, 2023

Язык: Английский

---

Networking the forest infrastructure towards near real-time monitoring – A white paper

The Science of The Total Environment, Год журнала: 2023, Номер 872, С. 162167 - 162167

Опубликована: Фев. 11, 2023

Язык: Английский

---

Insect detect: An open-source DIY camera trap for automated insect monitoring

PLoS ONE, Год журнала: 2024, Номер 19(4), С. e0295474 - e0295474

Опубликована: Апрель 3, 2024

Insect monitoring is essential to design effective conservation strategies, which are indispensable mitigate worldwide declines and biodiversity loss. For this purpose, traditional methods widely established can provide data with a high taxonomic resolution. However, processing of captured insect samples often time-consuming expensive, limits the number potential replicates. Automated facilitate collection at higher spatiotemporal resolution comparatively lower effort cost. Here, we present Detect DIY (do-it-yourself) camera trap for non-invasive automated flower-visiting insects, based on low-cost off-the-shelf hardware components combined open-source software. Custom trained deep learning models detect track insects landing an artificial flower platform in real time on-device subsequently classify cropped detections local computer. Field deployment solar-powered confirmed its resistance temperatures humidity, enables autonomous during whole season. On-device detection tracking estimate activity/abundance after metadata post-processing. Our classification model achieved top-1 accuracy test dataset generalized well real-world images. The software highly customizable be adapted different use cases. With custom models, as accessible programming, many possible applications surpassing our proposed method realized.

Язык: Английский

---

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

Philosophical Transactions of the Royal Society B Biological Sciences, Год журнала: 2024, Номер 379(1904)

Опубликована: Май 5, 2024

Automated sensors have potential to standardize and expand the monitoring of insects across globe. As one most scalable fastest developing sensor technologies, we describe a framework for automated, image-based nocturnal insects—from development field deployment workflows data processing publishing. Sensors comprise light attract insects, camera collecting images computer scheduling, storage processing. Metadata is important sampling schedules that balance capture relevant ecological information against power limitations. Large volumes from automated systems necessitate effective We vision approaches detection, tracking classification including models built existing aggregations labelled insect images. Data account inherent biases. advocate explicitly correct bias in species occurrence or abundance estimates resulting imperfect detection individuals present during occasions. propose ten priorities towards step-change vital task face rapid biodiversity loss global threats. This article part theme issue ‘Towards toolkit monitoring’.

Язык: Английский

---

Overcoming biodiversity blindness: Secondary data in primary citizen science observations

Ecological Solutions and Evidence, Год журнала: 2024, Номер 5(1)

Опубликована: Янв. 1, 2024

Abstract In the face of global biodiversity crisis, collecting comprehensive data and making best use existing are becoming increasingly important to understand patterns drivers environmental biological phenomena at different scales. Here we address concept secondary data, which refers additional information unintentionally captured in species records, especially multimedia‐based citizen science reports. We argue that can provide a wealth ecologically relevant information, utilisation enhance our understanding traits interactions among individual organisms, populations dynamics general. explore possibilities offered by describe their main types sources. An overview research this field provides synthesis results already achieved using approaches extraction. Finally, discuss challenges widespread such as biases, licensing issues, metadata lack awareness trove due missing common terminology, well possible solutions overcome these barriers. Although exploration is only emerging, many opportunities identified show how enrich monitoring.

Язык: Английский

---