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Enregistrement W1498872209 · doi:10.1111/conl.12019

Response to Stevens and Jenkins’ pesticide impacts on bumblebees: a missing piece

2013· article· en· W1498872209 sur OpenAlexaffabout
Sheila R. Colla, Nora D. Szabo, David L. Wagner, Lawrence F. Gall, Jeremy T. Kerr

Notice bibliographique

RevueConservation Letters · 2013
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueInsect and Pesticide Research
Établissements canadiensUniversity of OttawaYork University
Organismes subventionnairesnon disponible
Mots-clésNeonicotinoidBumblebeeWildlifePesticidePopulationBiologyGeographyToxicologyImidaclopridEcologyPollinatorPollinationDemography

Résumé

récupéré en direct d'OpenAlex

Stevens & Jenkins (2012) highlight neonicotinoids as an important potential threat to wild bumblebees. Experiments on the effects of neonicotinoids on bumblebees demonstrate negative impacts on colonies exposed in field experimental (Whitehorn et al. 2012) or lab conditions (reviewed in Blacquière et al. 2012; Hopwood et al. 2012). Hence, neonicotinoid use may threaten wild bee populations at or near sites where these pesticides are used. We agree that better tracking of neonicotinoid input from various treatments is needed (as described by Stevens & Jenkins 2012) to assess their wildlife impacts. However, the data available indicate that neonicotinoid use does not explain broad-scale declines among the three eastern North American bumblebee species we studied (Szabo et al. 2012). This is supported by recent evidence that these species began exhibiting declines prior to the registration and widespread use of neonicotinoids in North America (Colla et al. 2012). Stevens & Jenkins (2012) correctly point out that our data exclude seed application of pesticides. However, our data do include other neonicotinoid treatments, which can lead to higher neonicotinoid residues (see Figure S1). Although none of the relationships were statistically significant, for two of the three species studied insecticide use was actually positively related to population persistence. As noted by Stevens & Jenkins (2012), most corn seed planted in North America is treated with neonicotinoids. In the region we considered, corn is a commonly produced crop (USDA 2011). To examine the potential role of corn treatment in declines, we carried out a new analysis testing for relationships between declines and corn production density. If neonicotinoid corn treatment was a significant cause of decline, species studied should persist to a greater extent in areas with little corn production. Yet, there are no significant relationships in the direction predicted (see Table S1). The lack of suitable data for pesticide concentration/amount used on corn crops as well as for additional seed-treated crops (e.g., potato, wheat, canola, sunflower) prevents further analyses. Thus, we cannot completely rule out the possibility that neonicotinoids may explain declines. Single factor explanations for the rapid decline of North American pollinators remain elusive. At most, our analyses support prior suggestions that pathogen spillover may be contributing to the loss of wild populations (e.g., Colla et al. 2006). However, bumblebees differ in their susceptibility to environmental change (Williams et al. 2009), landscapes are complex and threats interact differently throughout species’ ranges. With such complex interactions at play, leapfrogging the evidence to find “silver bullet” explanations for pollinator declines is likely to yield ineffective interventions. Yet, we conclude on a note of caution: our results do not disprove previous research, which clearly demonstrates that bumblebees can be harmed by proximate pesticide, and especially neonicotinoid, application at local scales. Our results suggest that field-level effects do not scale up to cause range-wide declines known to have occurred among the species we have considered here but better data are required for more thorough study. Until better data exist, strong limitations on pesticide use around at-risk pollinator populations are recommended. Figure S1: Scatterplots and logistic regression results of losses of B. affinis, B. terricola, and B. pensylvanicus in American counties or Canada census divisions against corn density. Corn density is in units of km2 of corn per km2 land area. Coefficients, P-values, and Nagelkerke R2 values from logistic regressions are shown. N = 45, 49, and 95 for B. affinis, B. terricola, and B. pensylvanicus, respectively. Table S1: Scientific studies that have quantified residues for seed-treated and soil-drenched crops as reviewed in Hopwood et al. (2012). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut 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: Empirique
Score de désaccord entre enseignants0,960
Score d'incertitude au seuil0,569

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,035
Tête enseignante GPT0,263
Écart entre enseignants0,227 · 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 tête enseignante, 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

Citations2
Publié2013
Routes d'admission2
Résumé présentoui

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