Harnessing online digital data in biodiversity monitoring
Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
38358955
PubMed Central
PMC10868793
DOI
10.1371/journal.pbio.3002497
PII: PBIOLOGY-D-23-03304
Knihovny.cz E-zdroje
- MeSH
- biodiverzita * MeSH
- zachování přírodních zdrojů * MeSH
- Publikační typ
- časopisecké články MeSH
Online digital data from media platforms have the potential to complement biodiversity monitoring efforts. We propose a strategy for integrating these data into current biodiversity datasets in light of the Kunming-Montreal Global Biodiversity Framework.
Biodiversity Unit University of Turku Turku Finland
BIOPOLIS Program in Genomics Biodiversity and Land Planning CIBIO Campus de Vairão Vairão Portugal
BirdLife International Cambridge United Kingdom
Departamento de Biologia Faculdade de Ciências Universidade do Porto Porto Portugal
Department of Zoology University of Cambridge Cambridge United Kingdom
Helsinki Institute of Sustainability Science University of Helsinki Helsinki Finland
Institute for Marine and Antarctic Studies University of Tasmania Hobart Tasmania Australia
Instituto de Ciências Biológicas e da Saúde Universidade Federal de Alagoas Maceió Brazil
International Union for Conservation of Nature Gland Switzerland
School of Life Sciences University of KwaZulu Natal Durban South Africa
World Agroforestry Center University of the Philippines Los Baños Laguna Philippines
Zobrazit více v PubMed
Díaz S, Settele J, Brondízio ES, Ngo HT, Agard J, Arneth A, et al.. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science. 2019;366:eaax3100. doi: 10.1126/science.aax3100 PubMed DOI
Moussy C, Burfield IJ, Stephenson PJ, Newton AFE, Butchart SHM, Sutherland WJ, et al.. A quantitative global review of species population monitoring. Conserv Biol. 2022;36:e13721. doi: 10.1111/cobi.13721 PubMed DOI
Jarić I, Correia RA, Brook BW, Buettel JC, Courchamp F, Di Minin E, et al.. iEcology: Harnessing Large Online Resources to Generate Ecological Insights. Trends Ecol Evol. 2020;35:630–639. doi: 10.1016/j.tree.2020.03.003 PubMed DOI
Ladle RJ, Correia RA, Do Y, Joo G-J, Malhado AC, Proulx R, et al.. Conservation culturomics. Front Ecol Environ. 2016;14:269–275. doi: 10.1002/fee.1260 DOI
Correia RA, Ladle R, Jarić I, Malhado ACM, Mittermeier JC, Roll U, et al.. Digital data sources and methods for conservation culturomics. Conserv Biol. 2021;35:398–411. doi: 10.1111/cobi.13706 PubMed DOI
Keith DA, Ferrer-Paris JR, Nicholson E, Bishop MJ, Polidoro BA, Ramirez-Llodra E, et al.. A function-based typology for Earth’s ecosystems. Nature. 2022;610:513–518. doi: 10.1038/s41586-022-05318-4 PubMed DOI PMC
Díaz S, Pascual U, Stenseke M, Martín-López B, Watson RT, Molnár Z, et al.. Assessing nature’s contributions to people. Science. 2018;359:270–272. doi: 10.1126/science.aap8826 PubMed DOI
Salafsky N, Salzer D, Stattersfield AJ, Hilton-Taylor C, Neugarten R, Butchart SHM, et al.. A Standard Lexicon for Biodiversity Conservation: Unified Classifications of Threats and Actions. Conserv Biol. 2008;22:897–911. doi: 10.1111/j.1523-1739.2008.00937.x PubMed DOI
Kulkarni R, Di Minin E. Automated retrieval of information on threatened species from online sources using machine learning. Methods Ecol Evol. 2021;12:1226–1239. doi: 10.1111/2041-210X.13608 DOI
Kulkarni R, Di Minin E. Towards automatic detection of wildlife trade using machine vision models. Biol Conserv. 2023;279:109924. doi: 10.1016/j.biocon.2023.109924 DOI
Di Minin E, Fink C, Hausmann A, Kremer J, Kulkarni R. How to address data privacy concerns when using social media data in conservation science. Conserv Biol. 2021;35:437–446. doi: 10.1111/cobi.13708 PubMed DOI
Chapman AD. Current Best Practices for Generalizing Sensitive Species Occurrence Data. Copenhagen: GBIF Secretariat; 2020. Available from: 10.15468/doc-5jp4-5g10. DOI
Leveraging social media and other online data to study animal behavior