Social media data for conservation science: A methodological overview

Biological Conservation, Journal Year: 2019, Volume and Issue: 233, P. 298 - 315

Published: April 8, 2019

Improved understanding of human-nature interactions is crucial to conservation science and practice, but collecting relevant data remains challenging. Recently, social media have become an increasingly important source information on interactions. However, the use advanced methods for analysing still limited, are not used their full potential. In this article, we present available sources approaches mining these science. Specifically, (i) describe what kind can be retrieved from platforms, (ii) provide a detailed overview spatio-temporal, content network analyses, (iii) exemplify potential real-world challenges, (iv) discuss limitations analysis in Combined with other carefully considering biases ethical issues, complementary cost-efficient addressing grand challenges biodiversity Anthropocene epoch.

Language: Английский

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Passive crowdsourcing of social media in environmental research: A systematic map

Global Environmental Change, Journal Year: 2019, Volume and Issue: 55, P. 36 - 47

Published: Feb. 11, 2019

The analysis of data from social media and networking sites may be instrumental in achieving a better understanding human-environment interactions shaping future conservation environmental management. In this study, we systematically map the application research. quantitative review 169 studies reveals that most focus on people's behavior perceptions environment, followed by monitoring applications planning governance. literature testifies to very rapid growth field, with Twitter (52 studies) Flickr (34 being frequently used as sources. A growing number combine multiple jointly investigates types media. broader, more qualitative insights provided investigated suggests while offer unprecedented opportunities terms volume, scale analysis, real-time monitoring, researchers are only starting cope challenges data's heterogeneity noise levels, potential biases, ethics acquisition use, uncertainty about availability. Critical areas for development field include integration different information mashups, quality assurance procedures ethical codes, improved existing methods, long-term, free easy-to-access provision public researchers.

Language: Английский

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iEcology: Harnessing Large Online Resources to Generate Ecological Insights

Trends in Ecology & Evolution, Journal Year: 2020, Volume and Issue: 35(7), P. 630 - 639

Published: April 10, 2020

iEcology is a new research approach that seeks to quantify patterns and processes in the natural world using data accumulated digital sources collected for other purposes.iEcology studies have provided insights into species occurrences, traits, phenology, functional roles, behavior, abiotic environmental features.iEcology expanding, will be able provide valuable support ongoing efforts, as comparatively low-cost based on freely available data.We expect experience rapid development over coming years become one of major approaches ecology, enhanced by emerging technologies such automated content analysis, apps, internet things, ecoacoustics, web scraping, open source hardware. Digital are accumulating at unprecedented rates. These contain lot information about world, some which can used answer key ecological questions. Here, we introduce (i.e., ecology), an uses diverse online methods generate distribution space time, interactions dynamics organisms their environment, anthropogenic impacts. We review methods, examples potential applications. also outline reduce biases improve reliability applicability. As expertise improve, costs diminish, increasingly important means gain novel world. The age characterized accumulation myriad types [1.Castells M. Information Age: Economy. Blackwell, Society Culture1996Google Scholar]. Central this revolution Internet, amounts readily accessible data, via webpages, social media, various platforms. constantly created stored realm form omnipresent part modern They opportunities scientific community only beginning explore. describe – define study generated purposes digitally (Figure 1). address fundamental questions analyze range spatiotemporal scales across contexts. such, has understandings mechanisms, complementing more traditional obtaining data. While considered fit within wider scope informatics (see Glossary), it distinct from Big Data biological sciences not specifically intentionally [2.Hampton S.E. et al.Big future ecology.Front. Ecol. Environ. 2013; 11: 156-162Crossref Scopus (516) Google Scholar, 3.LaDeau S.L. al.The next decade big ecosystem science.Ecosystems. 2017; 20: 274-283Crossref (48) 4.Michener W.K. Jones M.B. Ecoinformatics: supporting ecology data-intensive science.Trends Evol. 2012; 27: 85-93Abstract Full Text PDF PubMed (276) Moreover, expands with dedicated them. predominantly focused collecting, collating, exploring human society, either passively or unintentionally (e.g., Internet search activity, media interactions, uploaded media), process referred passive crowdsourcing [5.Ghermandi A. Sinclair Passive research: systematic map.Glob. Chang. 2019; 55: 36-47Crossref (127) access, handle, these manner akin techniques fields sociology, culture studies, biomedical sciences, computer economics [6.Ekman Litton J.E. New times, needs; e-epidemiology.Eur. J. Epidemiol. 2007; 22: 285-292Crossref (98) Scholar,7.Bohannon Books, Wikipedia, culturomics.Science. 2011; 331e6395Crossref (20) shares its toolbox conservation culturomics area science [8.Ladle R.J. al.Conservation culturomics.Front. 2016; 14: 269-275Crossref (134) 9.Di Minin E. al.Prospects challenges science.Front. Sci. 2015; 3: 63Crossref (165) 10.Sutherland W.J. al.A 2018 horizon scan issues global diversity.Trends 2018; 33: 47-58Abstract (87) Scholar] albeit different focus. Specifically, while interested understanding engagement nature, focus knowledge gained human–nature realm. give rise correlative similar large-scale explorations much macroecology [11.Gaston K.J. Blackburn T.M. Pattern Process Macroecology. Blackwell Science, 2000Crossref (176) Scholar], should viewed such. present broad overview description iEcology, including scope, types, well current caveats prospects approach. Several recent highlighted 2). most common applications been explore occurrences trends 3). For example, comparing real-world encounter rates bird USA Trends found good agreement between two 2A) [12.Schuetz J.G. Johnston Characterizing cultural niches North American birds.Proc. Natl. Acad. U. S. 116: 10868-10873Crossref (21) This showcases voluminous engine distributions many regions. Others explored sources, Flickr, news articles, Twitter, YouTube, Facebook, [13.Barve V. Discovering developing primary biodiversity networking sites: approach.Ecol. Inform. 2014; 24: 194-199Crossref (49) 14.Daume Mining Twitter monitor invasive alien analytical framework sample topologies.Ecol. 31: 70-82Crossref (56) 15.Dylewski Ł. al.Social complementary –YouTube shrikes case study.Sci. Nat. 104: 48Crossref (30) 16.ElQadi M.M. al.Mapping geo-tagged images: bees flowering plants Australia.Ecol. 39: 23-31Crossref (39) 17.Hong activities Eurasian otter (Lutra lutra) South Korea traced newspapers during 1962–2010.Biol. Conserv. 210: 157-162Crossref (11) 18.Jeawak S.S. al.Using Flickr characterizing environment: exploratory analysis.in: 13th International Conference Spatial Theory (COSIT 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017Google 19.Jeawak wildlife media: Augmenting text classification names.in: Winter 10th Geographic Science (GIScience 2018). 2018: 45:1-45:6Google 20.Hart A.G. al.Testing mining acquisition: Evaluating multiple taxa.Methods 9: 2194-2205Crossref (16) 21.Allain S.J. Flickr: method expanding known species.Herpetol. Bull. 148: 11-14Crossref (10) 22.Fukano Y. Soga Spatio-temporal drivers public interest species.Biol. Invas. 21: 3521-3532Crossref (6) 23.Pace D.S. al.An integrated cetacean central Mediterranean Sea sources.Aquat. 29: 1302-1323Crossref (26) 24.Giovos I. service conservation: dolphins Hellenic seas.Aquat. Mamm. 42: 12-19Crossref (13) 25.Jiménez-Valverde al.Photo-sharing platforms characterising niche poorly studied taxa.Insect Divers. 12: 389-403Crossref (12) population phenology [14.Daume Scholar,20.Hart Scholar,23.Pace Scholar,26.Hentati-Sundberg Olsson O. Amateur photographs reveal history colonial seabird.Curr. Biol. 26: R226-R228Abstract 27.De Frenne P. archived television video footage responses climate change.Methods 1874-1882Crossref 28.Foglio al.Animal estimation images collections.arXiv preprint. 1908.01875Google 29.Francis F.T. al.Shifting headlines? Size newsworthy fishes.PeerJ. 7e6395Crossref (8) 30.Jiménez-Alvarado D. al.Historical captures recreational fishers indicate overexploitation nearshore resources oceanic island.J. Fish 94: 857-864PubMed 31.Breckheimer I.K. al.Crowd-sourced social–ecological mismatches driven climate.Front. 2020; 18: 76-82Crossref A particular illustration comes assessing seasonal migration sockeye salmon (Oncorhynchus nerka) Atlantic (Salmo salar) Wikipedia pageview frequencies 2B) [32.Mittermeier J.C. season all things: phenological imprints usage relevance conservation.PLoS 17e3000146Crossref (27) In addition mapping species, identify [33.Gonella P.M. al.Drosera magnifica (Droseraceae): largest World sundew, discovered Facebook.Phytotaxa. 220: 257-267Crossref (44) Scholar,34.Rahayu Rodda Hoya amicabilis sp. nov. (Apocynaceae, Asclepiadoideae), Java Facebook.Nord. Bot. 37e02563Crossref (3) Trait dynamics, evolutionary trends, biogeographic methods. instance, Images were presence hybrid zones hooded (Corvus cornix) carrion corone) crows Europe 2C) [35.Leighton G.R. al.Just it: use geographical variation visible traits organisms.Methods 7: 1060-1070Crossref (38) Furthermore, biophysical environments, solar radiation climatic parameters tags [18.Jeawak Scholar].Figure 3Overview Studied Taxa, Sources Used, Knowledge Categories Addressed Studies Cited Article.Show full captionColors represent taxa, width lines represents relative number publications connecting categories.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Colors categories. tools, biotic environments. feeding yellow anaconda (Eunectes notaeus) green murinus) videos [36.Miranda E.B. human-anaconda conflict: videos.Trop. 43-77Crossref (25) simultaneously depicted African birds herbivorous mammals construct associations groups 2D) [37.Mikula al.Large-scale assessment commensalistic–mutualistic photos.PeerJ. 6e4520Crossref provides animal behavior [15.Dylewski YouTube compare red (Sciurus vulgaris) grey squirrels carolinensis) habitats 2E) [38.Jagiello Z.A. al.What learn behaviour YouTube?.Ecol. 51: 52-60Crossref (9) sheer volume coverage could prove fertile ground identifying tracking spread behaviors [39.Fisher Hinde R.A. opening milk bottles birds.Br. Birds. 1949; 347-357Google 40.Gil M.A. iformation links individual dynamics.Trends 535-548Abstract (79) 41.Firth J.A. Considering complexity: networks behavioural contagions.Trends 35: 100-104Abstract (23) Disease occurrence, distribution, prevalence, severity diseases, recently benefited [42.Elmer F. al.Black spot syndrome reef fishes: archival imagery field surveys characterize spatial temporal Caribbean.Coral Reefs. 38: 1303-1315Crossref (7) investigate habitat response increasing Tour Flanders cycling race 35 track changes vegetation change 2F) [27.De corals tweets referring both evaluate state coral reefs areas, suffering impacts [43.Haas A.F. al.Can measure beauty? Computational evaluation aesthetics.PeerJ. 3e1390Crossref (33) Scholar,44.Becken born: integrating collective sensing, citizen professional monitoring environment.Ecol. 52: 35-45Crossref Aspects invasion Scholar,45.Proulx R. al.Googling biology.Conserv. 28: 44-51Crossref (80) fish [29.Francis Scholar,30.Jiménez-Alvarado tweets, articles. same way, behavioral animals [46.Snijders L. help conservation.Trends 32: 567-577Abstract (74) 47.Brakes cultures matter conservation.Science. 363: 1032-1034Crossref (62) 48.Sullivan resource monkseal One. 14e0222627Crossref (15) tracked inherently varied conceivably benefit tools macroecology, landscape urban ecology. At core, fall categories: (i) users purposes; (ii) access usage. Types first category comprise text, images, videos, sounds second aggregated exploration times term was searched webpage visited, but include likes). Both categories associated metadata particularly locality, timestamp, user identity, differ availability, ease metadata, therefore utility research. Potential Flickr) [49.Chamberlain Using biomonitoring: how data.Adv. Res. 59: 133-168Crossref engines Google, Baidu, Bing), encyclopedias Encyclopedia Britannica online), repositories (blogs, discussion forums, popular books, etc.). Many accessed through engines. differs coverage, linguistic breadth, resolution, degree multimedia composition video) per source. availability: available, may restrict availability limiting collection limits volume, time frame, queries) privileged paywall restrictions). simple embedded webpage), application programming interfaces (APIs) scripts Flickr), APIs restricted Facebook). However, time. analysis faces solutions Scholar,50.Bollier Firestone C.M. Promise Peril Data. Aspen Institute, 2010Google rely high levels automation, frequently adopting machine-learning [51.Christin al.Applications deep learning ecology.Methods 10: 1632-1644Crossref (135) There aid each stage downloading, handling, extraction, storage, pattern identification recognition, visualization. constant evolution, illustrated developments neural network (Box 1).Box 1Emerging Technologies Relevant iEcologyFuture rapidly technologies:Apps GamesApps mobile devices ubiquitous, often person's reach 24-7. augmented reality interface detailed real-time diagnostics [64.Jepson Ladle Nature apps: waiting revolution.Ambio. 44: 827-832Crossref (36) 65.Buettel Brook B.W. Egress! How technophilia reinforce biophilia restoration.Restor. 843-847Crossref 66.Dorward L.J. al.Pokémon Go: benefits, costs, lessons movement.Conserv. Lett. 160-165Crossref (71) addition, apps 'gamify' nature motivate interact thus environment. Overall, games transform humans (both positive negative) cause shift quantity quality data.Automated Content AnalysisThe algorithms analyzing visual, textual, audio sources. allowed, automatic identification, counting, individuals [67.Di al.Machine illegal trade media.Nat. 2: 406-407Crossref Scholar,68.Norouzzadeh M.S. al.Automatically identifying, describing wild camera-trap learning.Proc. 115: E5716-E5725Crossref (392) extraction [69.Kaur K.M. text-mined trait test cooperate-and-radiate co-evolution ants plants.PLoS Comput. 15e1007323Crossref Further allow combining large volumes [70.Di investigating machine learning.Conserv. 210-213Crossref (53) All carefully consider ethical concerns [71.Wearn O.R. al.Responsible AI conservation.Nat. Mach. Intell. 1: 72-73Crossref (37) Scholar].Bioacoustics EcoacousticsThe recording produced entities entire Increase sonic publicized soundscapes untapped [72.Aide al.Real-time bioacoustics identification.PeerJ. 1e103Crossref (218) 73.Harris S.A. al.Ecoacoustic indices proxies temperate reefs.Methods 713-724Crossref (106) 74.Linke al.Freshwater ecoacoustics tool continuous monitoring.Front. 16: 231-238Crossref (57) 75.Rajan S.C. al.Rapid acoustic indices.Biodivers. 2371-2383Crossref Scholar].BlockchainCryptographically linked growing lists. blockchains plug-ins creation immutable complex formats permanently recorded decentralized platform moment creation. would increase security, traceability, decrease errors entries, imprinting technical details generator [76.Firdaus "blockchain": bibliometric blockchain study.Scientometrics. 120: 1289-1331Crossref (67) Scholar].Internet ThingsA computers, machines, objects share interact. greatly amount pertaining actions [77.Atzori survey.Comput. Netw. 2010; 54: 2787-2805Crossref (9681) Scholar].Open Source HardwarePhysical design specifications them widely studied, modified, created, distributed. construction sensors shared, larger high-quality specialized [78.Berger-Tal Lahoz-Monfort J.J. Conservation technology: generation.Conserv. 11e12458Crossref (35) Scholar,79.Hill A.P. al.Leveraging action open-source hardware.Conserv. 12e12661Crossref (5) Scholar].Web ScrapingThe fetching relevant content, mostly done automatically. enable better quicker potentially [80.Galaz crawlers revolutionize monitoring?.Front. 8: 99-104Crossref Future technologies: Apps Games Automated Analysis 2

Language: Английский

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Social media data for environmental sustainability: A critical review of opportunities, threats, and ethical use

One Earth, Journal Year: 2023, Volume and Issue: 6(3), P. 236 - 250

Published: March 1, 2023

Language: Английский

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An empirical analysis of knowledge co-construction in YouTube comments

Computers & Education, Journal Year: 2020, Volume and Issue: 156, P. 103939 - 103939

Published: June 3, 2020

Language: Английский

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Drones as a Threat to Wildlife: YouTube Complements Science in Providing Evidence about Their Effect

Environmental Conservation, Journal Year: 2019, Volume and Issue: 46(3), P. 205 - 210

Published: June 14, 2019

Summary Although drones are becoming very common in the skies, most concerns about their use not focused on possible impact wildlife. We used information available from scientific literature effects of wildlife and complement it with Internet (YouTube) to evaluate whether recreational activities using produce behavioural responses Scientific papers specifically evaluating scarce but increasing. Nonetheless, we found abundant videos which many species different taxonomic groups multiple countries presented drone overflights. Furthermore, 26% that were disturbed included one International Union for Conservation Nature categories threat. aerial terrestrial habitats more likely show a response than those occupying aquatic habitats. The is source evidence disturbances should be considered, particularly activities. advocate technology, argue funding effort devoted impacts call educational programmes laypeople who recreation research regulations sensitive areas.

Language: Английский

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Evaluating the benefits and risks of social media for wildlife conservation

FACETS, Journal Year: 2022, Volume and Issue: 7, P. 360 - 397

Published: Jan. 1, 2022

Given its extensive volume and reach, social media has the potential to widely spread conservation messaging be a powerful tool mobilize change for conserving biodiversity. We synthesized gray primary academic literature investigate effects of on wildlife conservation, revealing several overarching benefits risks. found that can increase pro-conservation behaviours among public, funding, incite policy changes. Conversely, contribute species exploitation illegal trade, cause unprecedented increases in tourism protected areas, perpetuate anti-conservation via misinformation. In most cases, we content sharing did not result detectable impact conservation; this paper, however, focus providing examples where was achieved. relate these positive negative outcomes psychological phenomena may influence efforts discuss limitations our findings. conclude with recommendations best practices administrators, public users, nongovernmental organizations, governing agencies minimize risks while maximizing beneficial outcomes. By improving messaging, policing online misconduct, guidance action, help achieve goals.

Language: Английский

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Unusual intra-specific aggression in striped hyena

acta ethologica, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Language: Английский

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Possible changes in spatial distribution of walnut (Juglans regia L.) in Europe under warming climate

Regional Environmental Change, Journal Year: 2021, Volume and Issue: 21(1)

Published: Feb. 1, 2021

Abstract Juglans regia L. is a species of great importance for environmental management due to attractive wood and nutritious fruits, but also high invasive potential. Thus, uncertainties connected with its range shift are essential management. We aimed predict the future climatic optimum J. in Europe under changing climate, assess most important factors that determine potential distribution, compare results obtained among three different global circulation models (GCMs). used distribution data from Global Biodiversity Information Facility completed it literature. Using MaxEnt algorithm, we prepared model years 2061–2080 using 19 bioclimatic variables. applied emission scenarios, expressed by representative concentration pathways (RCPs): RCP2.6, RCP4.5, RCP8.5 GCMs: HadGEM2-ES, IPSL-CM5A-LR, MPI-SM-LR. Our study predicted northward species, simultaneous loss at southern edge current range, driven increasing climate seasonality. Temperature seasonality temperature annual were predictors highest importance. General trends common projections presented, variability our GCMs or RCPs (predicted will contract 17.4 84.6% area) shows caution should be maintained while managing populations. Adaptive measures focus on maintaining genetic resources assisted migration edge, contraction. Simultaneously, northern turns into an which may need risk assessments control unintended spread.

Language: Английский

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Citizen science helps predictions of climate change impact on flowering phenology: A study on Anemone nemorosa

Agricultural and Forest Meteorology, Journal Year: 2022, Volume and Issue: 325, P. 109133 - 109133

Published: Aug. 19, 2022

Language: Английский

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