Citizen science in environmental and ecological sciences

Nature Reviews Methods Primers, Journal Year: 2022, Volume and Issue: 2(1)

Published: Aug. 25, 2022

Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental ecological sciences, which non-professional participants contribute to data collection advance research. We present contributory citizen as a valuable method scientists practitioners focusing on full life cycle of practice, from design implementation, evaluation management. highlight key issues how address them, such participant engagement retention, quality assurance bias correction, well ethical considerations regarding sharing. also provide range examples illustrate diversity applications, biodiversity research land cover assessment forest health monitoring marine pollution. The aspects reproducibility sharing are considered, placing encompassing open perspective. Finally, we discuss its limitations challenges outlook for application multiple domains. Contributory whole or part This Primer outlines use discussing engagement, correction.

Language: Английский

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A systematic literature review of citizen science in urban studies and regional urban planning: policy, practical, and research implications

Urban Ecosystems, Journal Year: 2025, Volume and Issue: 28(2)

Published: Feb. 21, 2025

Language: Английский

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Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation

Frontiers in Ecology and Evolution, Journal Year: 2021, Volume and Issue: 9

Published: Feb. 26, 2021

Camera trapping is an effective non-invasive method for collecting data on wildlife species to address questions of ecological and conservation interest. We reviewed 2,167 camera trap (CT) articles from 1994 2020. Through the lens technological diffusion, we assessed trends in: (1) CT adoption measured by published research output, (2) topic, taxonomic, geographic diversification composition applications, (3) sampling effort, spatial extent, temporal duration studies. Annual publications have grown 81-fold since 1994, increasing at a rate 1.26 (SE = 0.068) per year 2005, but with decelerating growth 2017. Topic, richness studies increased encompass 100% topics, 59.4% ecoregions, 6.4% terrestrial vertebrates. However, declines in article rates accretion plateaus Shannon's H topics major taxa studied suggest upper limits further as currently practiced. Notable compositional changes included decrease capture-recapture, recent spatial-capture-recapture, increases occupancy, interspecific interactions, automated image classification. Mammals were dominant taxon studied; within mammalian orders carnivores exhibited unimodal peak whereas primates, rodents lagomorphs steadily increased. Among biogeographic realms observed decreases Oceania Nearctic, Afrotropic Palearctic, peaks Indomalayan Neotropic. days, area sampled increased, much greater 0.90 quantile compared median. Next-generation are poised expand knowledge valuable ecology posing previously infeasible unprecedented spatiotemporal scales, array species, wider variety environments. Converting potential into broad-based application will require transferable models classification, sharing among users across multiple platforms coordinated manner. Further taxonomic likely modifications that permit more efficient smaller improvements modeling unmarked populations. Environmental can benefit engineering solutions ease traditionally challenging sites.

Language: Английский

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Technological advances in biodiversity monitoring: applicability, opportunities and challenges

Current Opinion in Environmental Sustainability, Journal Year: 2020, Volume and Issue: 45, P. 36 - 41

Published: Aug. 1, 2020

Language: Английский

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The Impact of Imperfect XAI on Human-AI Decision-Making

Proceedings of the ACM on Human-Computer Interaction, Journal Year: 2024, Volume and Issue: 8(CSCW1), P. 1 - 39

Published: April 17, 2024

Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating appropriate reliance and task performance the aim of designing more human-centered computer-supported collaborative tools. Several explainable (XAI) have been proposed in hopes improving decision-makers' collaboration AI; however, these grounded findings from studies that primarily focus on impact incorrect advice. Few acknowledge possibility explanations even if advice is correct. Thus, it crucial understand XAI affects decision-making. In this work, we contribute a robust, mixed-methods user study 136 participants evaluate influence humans' behavior bird species identification task, taking into account their level expertise an explanation's assertiveness. Our reveal team performance. We also discuss can deceive during collaboration. Hence, shed light impacts field provide guidelines for designers systems.

Language: Английский

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Postdigital Citizen Science and Humanities: A Theoretical Kaleidoscope

Postdigital Science and Education, Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Abstract This collective article presents a theoretical kaleidoscope, the multiple lenses of which are used to examine and critique citizen science humanities in postdigital contexts from perspectives. It brings together 19 short experiential contributions, organised into six loose groups explore areas perspectives including Indigenous local knowledge, technology, children young people as researchers. suggests that this approach is appropriate because both research founded on committed collaboration, dialogue, co-creation, well challenging tenets approaches traditional academic research. In particular, it transformations contemporary societies changing making more important.

Language: Английский

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Large‐scale and long‐term wildlife research and monitoring using camera traps: a continental synthesis

Biological reviews/Biological reviews of the Cambridge Philosophical Society, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

ABSTRACT Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, potential for increasing impact. We investigated a decade of use cameras (2012–2022) with case study on Australian terrestrial vertebrates using multifaceted approach. ( i ) synthesised information from literature review; ii conducted an online questionnaire 132 professionals; iii hosted in‐person workshop 28 leading experts representing academia, non‐governmental organisations (NGOs), government; iv mapped camera trap usage based all sources. predicted that the last would have shown: exponentially sampling effort, continuation trends up 2012; analytics shifted naive presence/absence capture rates towards hierarchical modelling accounts imperfect detection, thereby improving quality outputs inferences occupancy, abundance, density; broader scales terms multi‐species, multi‐site multi‐year studies. However, results showed effort has reached plateau, publication only modestly. Users reported reaching saturation point images could be processed by humans time complex analyses academic writing. There were strong taxonomic geographic biases medium–large mammals (>500 g) forests along Australia's southeastern coastlines, reflecting proximity major cities. Regarding analytical choices, bias‐prone indices still accounted ~50% this was consistent across user groups. Multi‐species, multiple‐year studies rare, largely driven hesitancy around collaboration data sharing. no repository Atlas Living Australia (ALA) dominant sharing tabular occurrence records. ALA presence‐only thus unsuitable creating detection histories absences, inhibiting modelling. Workshop discussions identified pressing need enhance efficiency, scale management outcomes, proposal Wildlife Observatory (WildObs). To encourage standards sharing, WildObs should promote metadata collection app; create tagged image facilitate artificial intelligence/machine learning (AI/ML) computer vision space; address identification bottleneck via AI/ML‐powered image‐processing platforms; commons suitable modelling; v provide capacity building tools Our review highlights while investments monitoring biodiversity position global leader context, realising requires paradigm shift best practices collecting, curating, analysing ‘Big Data’. findings framework broad applicability outside meet conservation objectives ranging local scales. This articulates country/continental observatory approach also international collaborative networks.

Language: Английский

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Research on the Integration and Innovation of Artificial Intelligence in Intangible Cultural Heritage Illustration Creation

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: March 8, 2025

Incorporation of AI into the developmental process illustrations ICH is not only a great advancement in utilizing technology to put practice ICH, but also shows shift from static use traditional cultural factors representations ICH. In this research context, references shall be made how information science and AI, particularly connection with computer technologies, can used for better visualization sharing intangible heritage generations come. This paper discusses computational methods, especially deep learning generative models mine replicate historical data generate new, relevant, culturally authentic heritage. will establish tools recreate reimagine signifiers belonging by using image recognitions, natural language processing, adversarial networks (GANs). Unlike arts that have copied conform current standards, these technologies replicate, they bring new approaches providing novel interpretations while at same time conserving their originality as discussed below. important because it now due relevant are created, which shared through digital platforms making more accessible. The results help determine whether an instrument effective sphere conservation, well open up possibility further creation provide reference point artists, historians organizations, who want repurposing or asset modern socio-technological context.

Language: Английский

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The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality

Sustainability, Journal Year: 2021, Volume and Issue: 13(14), P. 8087 - 8087

Published: July 20, 2021

Advances in artificial intelligence (AI) and the extension of citizen science to various scientific areas, as well generation big data, are resulting AI being good partners, their combination benefits both fields. The integration has mostly been used biodiversity projects, with primary focus on using data train machine learning (ML) algorithms for automatic species identification. In this article, we will look at how ML techniques can be they influence volunteer engagement, collection, validation. We reviewed several use cases from domains categorized them according technique impact each project. Furthermore, risks integrating explored, some recommendations provided enhance while mitigating integration. Finally, because is still its early phases, have proposed potential ideas challenges that implemented future leverage power AI, key emphasis article.

Language: Английский

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eDNA metabarcoding for biodiversity assessment, generalist predators as sampling assistants

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: March 25, 2021

With an accelerating negative impact of anthropogenic actions on natural ecosystems, non-invasive biodiversity assessments are becoming increasingly crucial. As a consequence, the interest in application environmental DNA (eDNA) survey techniques has increased. The use eDNA extracted from faeces generalist predators, have recently been described as "biodiversity capsules" and suggested complementary tool for improving current assessments. In this study, using faecal samples two omnivore species, Eurasian badger red fox, we evaluated applicability metabarcoding determining dietary composition, compared to macroscopic diet identification techniques. Subsequently, used information obtained assess its contribution Compared classic techniques, found that detected more taxa, at higher taxonomic resolution, proved be important technique verify species predator field collected faeces. Furthermore, showed how analyses complemented observations describing by identifying consumed flora fauna went unnoticed during observations. While analysis approaches could not substitute entirely, suggest their integration with other methods might overcome intrinsic limitations single future surveys.

Language: Английский

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