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Chronic contamination decreases disease spread: a Daphnia –fungus–copper case study
Author(s) -
David J. Civitello,
Philip Forys,
Adam P. Johnson,
Spencer R. Hall
Publication year - 2012
Publication title -
proceedings of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.342
H-Index - 253
eISSN - 1471-2954
pISSN - 0962-8452
DOI - 10.1098/rspb.2012.0684
Subject(s) - contamination , copper , fungus , daphnia , biology , disease , microbiology and biotechnology , medicine , ecology , botany , materials science , metallurgy , zooplankton
Chemical contamination and disease outbreaks have increased in many ecosystems. However, connecting pollution to disease spread remains difficult, in part, because contaminants can simultaneously exert direct and multi-generational effects on several host and parasite traits. To address these challenges, we parametrized a model using a zooplankton–fungus–copper system. In individual-level assays, we considered three sublethal contamination scenarios: no contamination, single-generation contamination (hosts and parasites exposed only during the assays) and multi-generational contamination (hosts and parasites exposed for several generations prior to and during the assays). Contamination boosted transmission by increasing contact of hosts with parasites. However, it diminished parasite reproduction by reducing the size and lifespan of infected hosts. Multi-generational contamination further reduced parasite reproduction. The parametrized model predicted that a single generation of contamination would enhance disease spread (via enhanced transmission), whereas multi-generational contamination would inhibit epidemics relative to unpolluted conditions (through greatly depressed parasite reproduction). In a population-level experiment, multi-generational contamination reduced the size of experimental epidemics but did not affectDaphnia populations without disease. This result highlights the importance of multi-generational effects for disease dynamics. Such integration of models with experiments can provide predictive power for disease problems in contaminated environments.

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