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Cited by 48Environmental Toxicology and EcotoxicologyPesticide and Herbicide Environmental StudiesFreshwater macroinvertebrate diversity and ecology

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Combined impact of pesticides and other environmental stressors on animal diversity in irrigation ponds

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Ito, Hiroshi C., Shiraishi, Hiroaki, Nakagawa, Megumi, Takamura, Noriko·2020·PLOS ONE
DOI 10.1371/journal.pone.0229052PMC PMC7332035

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Abstract

Rice paddy irrigation ponds can sustain surprisingly high taxonomic richness and make significant contributions to regional biodiversity. We evaluated the impacts of pesticides and other environmental stressors (including eutrophication, decreased macrophyte coverage, physical habitat destruction, and invasive alien species) on the taxonomic richness of freshwater animals in 21 irrigation ponds in Japan. We sampled a wide range of freshwater animals (reptiles, amphibians, fishes, mollusks, crustaceans, insects, annelids, bryozoans, and sponges) and surveyed environmental variables related to pesticide contamination and other stressors listed above. Statistical analyses comprised contraction of highly correlated environmental variables, best-subset model selection, stepwise model selection, and permutation tests. Results showed that: (i) probenazole (fungicide) was a significant stressor on fish (i.e., contamination with this compound had a significantly negative correlation with fish taxonomic richness), (ii) the interaction of BPMC (insecticide; also known as fenobucarb) and bluegill (invasive alien fish) was a significant stressor on a "large insect" category (Coleoptera, Ephemeroptera, Hemiptera, Lepidoptera, Odonata, and Trichoptera), (iii) the interaction of BPMC and concrete bank protection was a significant stressor on an "invertebrate" category, (iv) the combined impacts of BPMC and the other stressors on the invertebrate and large insect categories resulted in an estimated mean loss of taxonomic richness by 15% and 77%, respectively, in comparison with a hypothetical pond with preferable conditions.

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Fig 1 Taxonomic richness of freshwater animals sampled in the study ponds. The numbers atop bars are pond IDs assigned according to taxonomic richness. The large insect category consists of Coleoptera, Ephemeroptera, Hemiptera, Lepidoptera, Odonata, and Trichoptera. The small insect category consists of Diptera. The annelid category consists mainly of annelids and contains small fractions of bryozoans and sponges.
Fig 1 Taxonomic richness of freshwater animals sampled in the study ponds. The numbers atop bars are pond IDs assigned according to taxonomic richness. The large insect category consists of Coleoptera, Ephemeroptera, Hemiptera, Lepidoptera, Odonata, and Trichoptera. The small insect category consists of Diptera. The annelid category consists mainly of annelids and contains small fractions of bryozoans and sponges.CC BY · PubMed Central
Fig 2 Statistically significant stressors on taxonomic richness of all-sampled category and its subcategories: Large insects (Coleoptera, Ephemeroptera, Hemiptera, Lepidoptera, Odonata, and Trichoptera), fishes, small insects (Diptera), large animals (reptiles, fishes, mollusks, crustaceans, and large insects), small animals (small insects and annelids), and invertebrates (mollusks, crustaceans, large insects, small insects, and annelids). In each panel, the white bar indicates the expected taxonomic richness of the focal animal category in the absence of all statistically contributive stressors (R in Eq. (S2.10) in S2 Appendix 8). The light gray (or dark gray) bar indicates the expected taxonomic richness in the presence of only the focal stressor denoted by xj at its mean intensity x-j (or maximum intensity, scaled to 1.0) among the studied ponds, given by Rexp(βjx-j) (or R exp(βj)) with its regression coefficient βj in the contracted best model. The value labeled with “mean” (or “max”) shows the mean (or maximum) impact of the focal stressor among ponds, given by the height ratio of the white bar to the light gray bar (or dark gray bar). Specifically, the mean (or maximum) impact was calculated as R/(Rexp(βjx¯j))=exp(−βjx¯j) (or exp(−βj)). (See S2 Appendix 8 for details). The estimation errors were calculated as Wald 95% confidence intervals, indicated in the format of (lower bound—upper bound). The solid curve indicates the expected taxonomic richness as a function R exp(βjxj) of the focal stressor’s intensity xj. The scatter plots indicate R exp(βjxj,i) + εi, where xj,i is the intensity of the focal stressor at the ith pond, and εi is the fitting residual of the contracted best model for the ith pond.
Fig 2 Statistically significant stressors on taxonomic richness of all-sampled category and its subcategories: Large insects (Coleoptera, Ephemeroptera, Hemiptera, Lepidoptera, Odonata, and Trichoptera), fishes, small insects (Diptera), large animals (reptiles, fishes, mollusks, crustaceans, and large insects), small animals (small insects and annelids), and invertebrates (mollusks, crustaceans, large insects, small insects, and annelids). In each panel, the white bar indicates the expected taxonomic richness of the focal animal category in the absence of all statistically contributive stressors (R in Eq. (S2.10) in S2 Appendix 8). The light gray (or dark gray) bar indicates the expected taxonomic richness in the presence of only the focal stressor denoted by xj at its mean intensity x-j (or maximum intensity, scaled to 1.0) among the studied ponds, given by Rexp(βjx-j) (or R exp(βj)) with its regression coefficient βj in the contracted best model. The value labeled with “mean” (or “max”) shows the mean (or maximum) impact of the focal stressor among ponds, given by the height ratio of the white bar to the light gray bar (or dark gray bar). Specifically, the mean (or maximum) impact was calculated as R/(Rexp(βjx¯j))=exp(−βjx¯j) (or exp(−βj)). (See S2 Appendix 8 for details). The estimation errors were calculated as Wald 95% confidence intervals, indicated in the format of (lower bound—upper bound). The solid curve indicates the expected taxonomic richness as a function R exp(βjxj) of the focal stressor’s intensity xj. The scatter plots indicate R exp(βjxj,i) + εi, where xj,i is the intensity of the focal stressor at the ith pond, and εi is the fitting residual of the contracted best model for the ith pond.CC BY · PubMed Central
Fig 3 Statistically significant interactions among stressors on taxonomic richness of categories of large insects (Coleoptera, Ephemeroptera, Hemiptera, Lepidoptera, Odonata, and Trichoptera), small animals (small insects (Diptera) and annelids), and invertebrates (mollusks, crustaceans, large insects, small insects, and annelids). Result of analysis for detecting interactions among statistically contributive stressors (S2 Appendix 6) is shown. The plotting was done as in Fig 2.
Fig 3 Statistically significant interactions among stressors on taxonomic richness of categories of large insects (Coleoptera, Ephemeroptera, Hemiptera, Lepidoptera, Odonata, and Trichoptera), small animals (small insects (Diptera) and annelids), and invertebrates (mollusks, crustaceans, large insects, small insects, and annelids). Result of analysis for detecting interactions among statistically contributive stressors (S2 Appendix 6) is shown. The plotting was done as in Fig 2.CC BY · PubMed Central
Fig 4 Estimation of combined impacts of statistically significant stressors. For each animal category that has multiple statistically significant stressors in Figs 2 and 3, the combined impact of those stressors in each pond is plotted as the reciprocal of the diminishing ratio of the taxonomic richness (S2 Appendix 8), by using the contracted best model (S2 Appendix 7). The numbers atop bars indicate pond IDs shown in Fig 1.
Fig 4 Estimation of combined impacts of statistically significant stressors. For each animal category that has multiple statistically significant stressors in Figs 2 and 3, the combined impact of those stressors in each pond is plotted as the reciprocal of the diminishing ratio of the taxonomic richness (S2 Appendix 8), by using the contracted best model (S2 Appendix 7). The numbers atop bars indicate pond IDs shown in Fig 1.CC BY · PubMed Central

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Citations

Cites

Show all 56 references

Research works printed but not linked: 10

  1. AP Covich, 2004, Sustaining Biodiversity and Ecosystem Services in Soil and Sediments, 45
  2. K Kawano, 2006, Aquatic insects in the ponds of Hirata Area (Izumo City) in Shimane Prefecture (in Japanese), Japanese Bulletin of Hoshizaki Green Foundation, 9, 13
  3. A Iitomi, 2002, Dominant pests and their damage to water shield in Akita (in Japanese), Annual Report of the Society of Plant Protection of North Japan, 53, 256
  4. N Takamura, 2007, Biodiversity monitoring: collaboration to build capacity for ecosystem management (in Japanese), 49
  5. H. Tsunoda, 2017, Ecological impacts of pond losses and abandonments on regional aquatic biodiversity: What will happen in the depopulating Japan? (in Japanese), Wildlife and Human Society, 5, 5
  6. P Kennedy, 2003, A Guide to Econometrics
  7. M Kitagawa, 1983, Entropy statistics (in Japanese)
  8. N Choudhury, 2018, Ecotoxicology of aquatic system: a review on fungicide-induced toxicity in fishes, Progress in Aqua Farming and Marine Biology, 1, 180001
  9. T Nakashima, 1996, Fate of pesticides aerially sprayed on paddy fields in river water (in Japanese), Journal of Environmental Laboratories Association, 21, 81
  10. M Sakai, 2013, Investigation of pesticides in the Tsurumi River (April 2011–March 2012) (in Japanese), Annual Reports of the Institute of Yokohama-city Environmental Science, 37, 13

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Metadata

Authors
Ito, H. C., Shiraishi, H., Nakagawa, M., & Takamura, N.
Affiliations
National Institute for Environmental Studies (JP)
Corresponding author
Ito, Hiroshi C.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2020-07-02
Volume
15
Issue
7
Pages
e0229052
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0229052
PMC
PMC7332035
OpenAlex
W3040065177
Cited by
42
References
56
Citations by year
2026: 6 · 2025: 11 · 2024: 5 · 2023: 8 · 2022: 5 · 2021: 7
FWCI
2.00
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
  • Physical Sciences › Environmental Science › Pollution › Pesticide and Herbicide Environmental Studies
  • Physical Sciences › Environmental Science › Ecology › Freshwater macroinvertebrate diversity and ecology
Keywords
pesticide contamination, irrigation ponds, paddy fields, freshwater biodiversity, taxonomic richness, probenazole, fenobucarb, bluegill, invasive alien fish, eutrophication, habitat loss, combined stress, insecticides, fungicides, invertebrates, freshwater fauna
MeSH terms
Animals, Carbamates, Environmental Monitoring, Invertebrates, Pesticides, Thiazoles, Vertebrates, Ecosystem, Biodiversity, Introduced Species, Ponds

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