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Bottom-up effects of fungicides on tadpoles of the European common frog (Rana temporaria).

Mirco BundschuhJochen P ZubrodTheo WernickeMarco KonschakLeon WernerCarsten A BrühlPatrick BaudyRalf Schulz
Published in: Ecology and evolution (2021)
Biodiversity is under pressure worldwide, with amphibians being particularly threatened. Stressors related to human activity, such as chemicals, are contributing to this decline. It remains, however, unclear whether chemicals exhibiting a fungicidal activity could indirectly affect tadpoles that depend on microbially conditioned leaf litter as food source. The indirect effect of fungicides (sum concentration of a fungicide mixture composed of azoxystrobin, carbendazim, cyprodinil, quinoxyfen, and tebuconazole: 100 µg/L) on tadpoles was assessed relative to leaf litter colonized by microbes in absence of fungicides (control) and a worst-case scenario, that is leached leaf litter without microbial colonization. The quality of leaf litter as food for tadpoles of the European common frog (Rana temporaria) was characterized through neutral lipid fatty acid profiles and microbial sum parameters and verified by sublethal responses in tadpoles (i.e., feeding rate, feces production, growth, and fatty acid composition). Fungicides changed the nutritious quality of leaf litter likely through alterations in leaves' neutral lipid fatty acid profiles (i.e., changes in some physiologically important highly unsaturated fatty acids reached more than 200%) in combination with a potential adsorption onto leaves during conditioning. These changes were reflected by differences in the development of tadpoles ultimately resulting in an earlier start of metamorphosis. Our data provide a first indication that fungicides potentially affect tadpole development indirectly through bottom-up effects. This pathway is so far not addressed in fungicide environmental risk assessment and merits further attention.
Keyphrases
  • fatty acid
  • human health
  • risk assessment
  • microbial community
  • endothelial cells
  • working memory
  • climate change
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  • heavy metals
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  • deep learning