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Models used to investigate the relationship between biodiversity change and vector-borne disease risk often do not explicitly include the vector; they instead rely on a frequency-dependent transmission function to represent vector dynamics. However, differences between classes of vector (e.g., ticks and insects) can cause discrepancies in epidemiological responses to environmental change. Using a pair of disease models (mosquito- and tick-borne), we simulated substitutive and additive biodiversity change (where noncompetent hosts replaced or were added to competent hosts, respectively), while considering different relationships between vector and host densities. We found important differences between classes of vector, including an increased likelihood of amplified disease risk under additive biodiversity change in mosquito models, driven by higher vector biting rates. We also draw attention to more general phenomena, such as a negative relationship between initial infection prevalence in vectors and likelihood of dilution, and the potential for a rise in density of infected vectors to occur simultaneously with a decline in proportion of infected hosts. This has important implications; the density of infected vectors is the most valid metric for primarily zoonotic infections, while the proportion of infected hosts is more relevant for infections where humans are a primary host. [ABSTRACT FROM AUTHOR]

Although accumulated evidence has shown that biodiversity can play an important role in disease transmission and prevalence, it remains unclear how different measures of diversity based on taxonomy or function perform in predicting disease risk. In this article, we assess the relative ability of species richness, Shannon's evenness index, single functional traits, and several functional diversity metrics and their interactions to predict disease risk in both non-equilibrium and equilibrium communities simulated by a multihost epidemiological model. On the basis of generalized linear models and Akaike's information criterion, we found that Shannon's evenness index outperforms species richness as a single variable in explaining variation in disease risk, while the best combination consists of Shannon's evenness index and functional diversity. This study is, to our knowledge, the first to demonstrate the important role played by species evenness and functional diversity in accounting for variation in disease risk in multihost communities. [ABSTRACT FROM AUTHOR]