Disease-impact models may rely on incorrect assumptionsEven when we know how a disease affects individual animals, it is challenging to predict what impact it will have on the whole population, and yet predicting how disease affects a population is a primary concern for wildlife conservation and even public health. In a new study from the May issue of American Naturalist, Anna E. Jolles (Princeton University and University of Groningen), Rampal S. Etienne (University of Groningen), and Han Olff (University of Groningen), contest two assumptions commonly present in models that try to predict how individual disease will impact populations.
Many models assume that disease acts independently of other causes of death. However, the researchers point out that it is possible for disease to kill those that are already doing poorly and would have died of starvation or been killed by predators, for example. Second, disease models usually assume constant environments, free from changes in food availability or catastrophic disturbances, such as drought or wildfires.
The researchers tested the assumptions by gathering field data on a herd of African buffalo struck by tuberculosis and following them through different conditions, including a devastating drought. They found that tuberculosis does explain increased mortality and decreased fecundity in some prime-aged buffalo, but older buffalo reflect the competing risks, and the increased survival and greater fecundity of buffalo in the herd without tuberculosis may compensate for some disease-related losses to the herd.
"Pathogens and parasites can have drastic effects, reducing survival or reproduction in infected hosts," write the authors. But they warn against drawing potentially misleading conclusions that may incorrectly influence how we think about epidemics--particularly chronic infections of long-lived hosts.
Founded in 1867, The American Naturalist is one of the world's most renowned, peer-reviewed publications in ecology, evolution, and population and integrative biology research. AN emphasizes sophisticated methodologies and innovative theoretical syntheses--all in an effort to advance the knowledge of organic evolution and other broad biological principles.
Anna E. Jolles, Rampal S. Etienne, and Han Olff. "Independent and competing disease risks: implications for host populations in variable environments," The American Naturalist 167:5.
Last reviewed: By John M. Grohol, Psy.D. on 14 Apr 2016
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