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Enregistrement W6976965339 · doi:10.60692/r0aar-mmr35

COVID‐19 and biodiversity: The paradox of cleaner rivers and elevated extinction risk to iconic fish species

2020· article· en· W6976965339 sur OpenAlexaffabout

Notice bibliographique

RevueGreater South Information System · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueCOVID-19 impact on air quality
Établissements canadiensCarleton University
Organismes subventionnairesnon disponible
Mots-clésFreshwater fishFish <Actinopterygii>Resource (disambiguation)Fisheries scienceExtinction (optical mineralogy)Hydrilla

Résumé

récupéré en direct d'OpenAlex

Aquatic Conservation: Marine and Freshwater EcosystemsVolume 30, Issue 6 p. 1061-1062 COMMENTARY AND CORRESPONDENCEOpen Access COVID-19 and biodiversity: The paradox of cleaner rivers and elevated extinction risk to iconic fish species Adrian C. Pinder, Corresponding Author Adrian C. Pinder [email protected] orcid.org/0000-0002-2175-9337 Faculty of Science and Technology, Bournemouth University, Dorset, UK Mahseer Trust, c/o Freshwater Biological Association, Wareham, Dorset, UK Correspondence Adrian Pinder, Faculty of Science and Technology, Bournemouth University, Fern Barrow, Poole. Dorset, BH12 5BB, UK. Email: [email protected]Search for more papers by this authorRajeev Raghavan, Rajeev Raghavan orcid.org/0000-0002-0610-261X Mahseer Trust, c/o Freshwater Biological Association, Wareham, Dorset, UK Department of Fisheries Resource Management, Kerala University of Fisheries and Ocean Studies (KUFOS), Kochi, India South Asia Office, IUCN SSC/WI Freshwater Fish Specialist Group (FFSG), Coimbatore, IndiaSearch for more papers by this authorJ. Robert Britton, J. Robert Britton orcid.org/0000-0003-1853-3086 Faculty of Science and Technology, Bournemouth University, Dorset, UKSearch for more papers by this authorSteven J. Cooke, Steven J. Cooke orcid.org/0000-0002-5407-0659 Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, Ottawa, ON, CanadaSearch for more papers by this author Adrian C. Pinder, Corresponding Author Adrian C. Pinder [email protected] orcid.org/0000-0002-2175-9337 Faculty of Science and Technology, Bournemouth University, Dorset, UK Mahseer Trust, c/o Freshwater Biological Association, Wareham, Dorset, UK Correspondence Adrian Pinder, Faculty of Science and Technology, Bournemouth University, Fern Barrow, Poole. Dorset, BH12 5BB, UK. Email: [email protected]Search for more papers by this authorRajeev Raghavan, Rajeev Raghavan orcid.org/0000-0002-0610-261X Mahseer Trust, c/o Freshwater Biological Association, Wareham, Dorset, UK Department of Fisheries Resource Management, Kerala University of Fisheries and Ocean Studies (KUFOS), Kochi, India South Asia Office, IUCN SSC/WI Freshwater Fish Specialist Group (FFSG), Coimbatore, IndiaSearch for more papers by this authorJ. Robert Britton, J. Robert Britton orcid.org/0000-0003-1853-3086 Faculty of Science and Technology, Bournemouth University, Dorset, UKSearch for more papers by this authorSteven J. Cooke, Steven J. Cooke orcid.org/0000-0002-5407-0659 Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, Ottawa, ON, CanadaSearch for more papers by this author First published: 19 June 2020 https://doi.org/10.1002/aqc.3416Citations: 9AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Notwithstanding the human suffering caused by COVID-19, the response (e.g. shelter-in-place orders) has yielded some tangible environmental benefits such as substantial improvements in air and water quality (Corlett et al., 2020). In India, this has manifested as heavily polluted rivers now running clear for the first time in decades with, for example, reports suggesting that the quality of the River Ganges has improved sufficiently to support safe bathing. Hidden beneath these brighter stories however, COVID-19 is also intensifying pressure on India's aquatic wildlife. In an already poverty-stricken country, an additional ~12 million are predicted to face extreme poverty as a result of COVID-19 (World Bank, 2020). Lacking social security, 90% of India's workforce are entirely dependent on daily wages, and are heavily reliant on food supply chains (Reardon et al., 2019) that have been severely disrupted across rural India. With fish (farmed as well as marine-sourced) and meat forming a primary source of protein for many, its sudden unavailability has resulted in local communities exploiting wild populations, especially freshwater fish. As most newly recruited fishers lack knowledge on responsible and regulated capture techniques, illegal, indiscriminate and destructive methods are being used that have impacts on all aquatic fauna (e.g. dynamite, poisons). This also includes harvesting species of high extinction risk, exemplified by the endemic hump-backed mahseer (Tor remadevii, Figure 1), an iconic and critically endangered member of the freshwater megafauna (Pinder, Raghavan, & Britton, in press) symbolic of India's extraordinarily diverse aquatic life. There is increasing evidence that their last remaining giant specimens are being removed from South India's River Cauvery by illegal fishers using a variety of capture methods (Deccan Herald, 2020), pushing them a step closer to extinction. This demonstrates that to understand fully the longer-term environmental impacts of COVID-19, there is always a need to look beneath the surface. FIGURE 1Open in figure viewerPowerPoint Youths with a Critically Endangered hump-backed mahseer, Tor remadevii, caught from the Harangi Reservoir in Kodagu, Karnataka, India [Photo Credit: Star of Mysore] REFERENCES Corlett, R. T., Primack, R. B., Devictor, V., Maas, B., Goswami, V. R., Bates, A. E., … Cumming, G. S. (2020). Impacts of the coronavirus pandemic on biodiversity conservation. Biological Conservation, 246, 108571. https://doi.org/10.1016/j.biocon.2020.108571 Deccan Herald. (2020). Youth in a fix for catching Mahseer fish, equipment seized. Retrieved from https://www.deccanherald.com/state/mangaluru/youth-in-a-fix-for-catching-mahseer-fish-equipment-seized-832067.html Pinder, A. C., Raghavan, R., & Britton, J. R. (in press). From scientific obscurity to conservation priority: Research on angler catch rates is the catalyst for saving the hump-backed mahseer Tor remadevii from extinction. Aquatic Conservation: Marine and Freshwater Ecosystems. Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations. Agricultural Systems, 172, 47– 59. https://doi.org/10.1016/j.agsy.2018.01.022 World Bank. (2020). The impact of COVID-19 (Coronavirus) on global poverty: Why Sub-Saharan Africa might be the region hardest hit. Retrieved from https://blogs.worldbank.org/opendata/impact-covid-19-coronavirus-global-poverty-why-sub-saharan-africa-might-be-region-hardest Citing Literature Volume30, Issue6June 2020Pages 1061-1062 FiguresReferencesRelatedInformation

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,050
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,074

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,050
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0040,010
Communication savante0,0050,010
Science ouverte0,0040,004
Intégrité de la recherche0,0240,033
Charge utile insuffisante (le modèle a refusé de juger)0,0210,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,051
Tête enseignante GPT0,225
Écart entre enseignants0,174 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission2
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