Emerging technologies for assessing ecosystem services: A synthesis of opportunities and challenges

Ecosystem Services, Journal Year: 2023, Volume and Issue: 63, P. 101558 - 101558

Published: Sept. 4, 2023

Rapid technological development opens up new opportunities for assessing ecosystem services (ES), which may help to overcome current knowledge gaps and limitations in data availability. At the same time, emerging technologies, such as mobile devices, social media platforms, artificial intelligence, give rise a series of challenges limitations. This study provides comprehensive overview broad range technologies that are increasingly used collecting, analyzing, visualizing on ES, including Earth observation, science, modeling/simulation, immersive visualization, web-based tools. To identify challenges, we systematically reviewed literature ES last 10 years (2012–2022). We first describe state-of-the-art synthesizing their applicability, opportunities, Then, discuss open issues, future research needs, potential further applications research. Our findings indicate great increase thanks low costs, high availability, flexibility technologies. also find strong support decision-making, learning communication. However, related accuracy variables models, accessibility data, information well ethical concerns need be addressed by community assure an inclusive meaningful use suggest insights into achieved through better integration different future, e.g., stronger transdisciplinary collaboration advance broadening perspective developments other fields

Language: Английский

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3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking

International Journal of Computer Vision, Journal Year: 2024, Volume and Issue: 132(10), P. 4235 - 4252

Published: May 7, 2024

Abstract Markerless methods for animal posture tracking have been rapidly developing recently, but frameworks and benchmarks large groups in 3D are still lacking. To overcome this gap the literature, we present 3D-MuPPET, a framework to estimate track poses of up 10 pigeons at interactive speed using multiple camera views. We train pose estimator infer 2D keypoints bounding boxes pigeons, then triangulate 3D. For identity matching individuals all views, first dynamically match detections global identities frame, use tracker maintain IDs across views subsequent frames. achieve comparable accuracy state art terms median error Percentage Correct Keypoints. Additionally, benchmark inference with 9.45 fps 1.89 3D, perform quantitative evaluation, which yields encouraging results. Finally, showcase two novel applications 3D-MuPPET. First, model data single results estimation 5 pigeons. Second, show that 3D-MuPPET also works outdoors without additional annotations from natural environments. Both cases simplify domain shift new species environments, largely reducing annotation effort needed tracking. best our knowledge 2D/3D trajectory both indoor outdoor environments individuals. hope can open opportunities studying collective behaviour encourages further developments multi-animal

Language: Английский

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Time is of the essence: The importance of considering biological rhythms in an increasingly polluted world

PLoS Biology, Journal Year: 2024, Volume and Issue: 22(1), P. e3002478 - e3002478

Published: Jan. 30, 2024

Biological rhythms have a crucial role in shaping the biology and ecology of organisms. Light pollution is known to disrupt these rhythms, evidence emerging that chemical pollutants can cause similar disruption. Conversely, biological influence effects toxicity chemicals. Thus, by drawing insights from extensive study biomedical light research, we greatly improve our understanding pollution. This Essay advocates for integration rhythmicity into research gain more comprehensive how affect wildlife ecosystems. Despite historical barriers, recent experimental technological advancements now facilitate ecotoxicology, offering unprecedented, high-resolution data across spatiotemporal scales. Recognizing importance will be essential understanding, predicting, mitigating complex ecological repercussions

Language: Английский

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Leveraging AI to improve evidence synthesis in conservation

Trends in Ecology & Evolution, Journal Year: 2024, Volume and Issue: 39(6), P. 548 - 557

Published: May 24, 2024

Language: Английский

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YOLO‐Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos

Methods in Ecology and Evolution, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Abstract Manually coding behaviours from videos is essential to study animal behaviour but it labour‐intensive and susceptible inter‐rater bias reliability issues. Recent developments of computer vision tools enable the automatic quantification behaviours, supplementing or even replacing manual annotation. However, widespread adoption these methods still limited, due lack annotated training datasets domain‐specific knowledge required optimize models for research. Here, we present YOLO‐Behaviour, a flexible framework identifying visually distinct video recordings. The robust, easy implement, requires minimal annotations as data. We demonstrate flexibility with case studies event‐wise detection in house sparrow nestling provisioning, Siberian jay feeding, human eating frame‐wise detections various pigeons, zebras giraffes. Our results show that reliably detects accurately retrieve comparable accuracy metrics extracted were less correlated annotation, potential reasons discrepancy between annotation are discussed. To mitigate this problem, can be used hybrid approach first detecting events using pipeline then manually confirming detections, saving time. provide detailed documentation guidelines on how implement YOLO‐Behaviour framework, researchers readily train deploy new their own systems. anticipate another step towards lowering barrier entry applying behaviour.

Language: Английский

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Elephants and algorithms: a review of the current and future role of AI in elephant monitoring

Journal of The Royal Society Interface, Journal Year: 2023, Volume and Issue: 20(208)

Published: Nov. 1, 2023

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour conservation strategies. Using elephants, a crucial species in Africa Asia’s protected areas, as focal point, we delve into the role AI ML their conservation. Given increasing amounts data gathered from variety sensors like cameras, microphones, geophones, drones satellites, challenge lies managing interpreting this vast data. New techniques offer solutions streamline process, helping us extract vital information that might otherwise be overlooked. This paper focuses on different AI-driven monitoring methods potential for improving elephant Collaborative efforts between experts ecological researchers are essential leveraging these innovative technologies enhanced wildlife conservation, setting precedent numerous other species.

Language: Английский

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Collective incentives reduce over-exploitation of social information in unconstrained human groups

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 27, 2024

Abstract Collective dynamics emerge from countless individual decisions. Yet, we poorly understand the processes governing dynamically-interacting individuals in human collectives under realistic conditions. We present a naturalistic immersive-reality experiment where groups of participants searched for rewards different environments, studying how weigh personal and social information this shapes collective outcomes. Capturing high-resolution visual-spatial data, behavioral analyses revealed individual-level gains—but group-level losses—of high use spatial proximity environments with concentrated (vs. distributed) resources. Incentivizing at group individual) level facilitated adaptation to buffering apparently excessive scrounging. To infer discrete choices unconstrained interactions uncover underlying decision mechanisms, developed an unsupervised Social Hidden Markov Decision model. Computational results showed that were more sensitive frequently switching relocation state they approach successful members. Group-level incentives reduced participants’ overall responsiveness promoted higher selectivity over time. Finally, mapping spatio-temporal through time-lagged regressions exploration-exploitation trade-off across timescales. Our study unravels linking strategies emerging dynamics, provides tools investigate decision-making freely-interacting collectives.

Language: Английский

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Individual activity levels and presence of conspecifics affect fish passage rates over an in‐flume barrier

Ecology Of Freshwater Fish, Journal Year: 2024, Volume and Issue: 33(4)

Published: May 2, 2024

Abstract Dams and other in‐stream obstacles disrupt longitudinal connectivity hinder fish from moving between habitats. Fishways passage solutions are used to pass over these artificial migration barriers. Fish functionality, however, varies greatly with design environmental conditions depends on species characteristics. In particular, swimming performance behaviour considered key characteristics predict performance. It is also well known, but not quantified, that the presence of conspecifics affects behaviour. this study, we quantified individual rates PIT‐tagged gudgeons ( Gobio gobio ) a scaled deep side notch weir in an hydraulic flume. We then capability (time fatigue) activity level (distance moved open field test) for same tested potential effects rate. To check group effects, repeated experiment individually or groups five. More active displayed higher compared less fish, passed obstacle at five alone. No effect was detected. This result highlights need take both variation as into account studies evaluations. Doing so has improve understanding behaviour, end, solutions. Future should explore results free ranging relation in‐situ

Language: Английский

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Mechanisms of group‐hunting in vertebrates

Biological reviews/Biological reviews of the Cambridge Philosophical Society, Journal Year: 2023, Volume and Issue: 98(5), P. 1687 - 1711

Published: May 18, 2023

ABSTRACT Group‐hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less known about mechanisms by which grouping predators hunt their prey. This primarily due to a lack experimental manipulation alongside logistical difficulties quantifying behaviour multiple at high spatiotemporal resolution as they search, select, capture wild However, use new remote‐sensing technologies broadening focal beyond apex provides researchers with great opportunity discern accurately how together not just whether doing so hunters per capita benefit. We incorporate many ideas from collective locomotion throughout this review make testable predictions for future pay particular role that computer simulation can play feedback loop empirical data collection. Our literature showed breadth predator:prey size ratios among be considered group very large (<10 0 >10 2 ). therefore synthesised respect these found promoted different hunting mechanisms. Additionally, are also related stages (search, selection, capture) thus we structure our accordance two factors (stage ratio). identify several novel group‐hunting largely untested, particularly under field conditions, highlight range potential study organisms amenable testing connection tracking technology. believe combination hypotheses, systems methodological approaches should help push directions.

Language: Английский

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Beyond observation: Deep learning for animal behavior and ecological conservation

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102893 - 102893

Published: Nov. 1, 2024

Language: Английский

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