The Dilution Effect hypothesis (DEH) posits that high biodiversity communities can reduce zoonotic disease risk, offering alluring opportunities to utilize conservation initiatives that can reduce human disease risk. However, there are concerns about the generality and applicability of the dilution effect hypothesis due to the limited ability to perform manipulative experiments in this context. Here, we use a spatially explicit individual-based modeling approach to experimentally test three mechanisms (vector regulation, encounter reduction, and transmission reduction) underlying the dilution effect as it relates to Lyme disease risk in humans. By incorporating species-specific host permissiveness and reservoir competency in a previously calibrated model. Our overall objective was to explore how changes in biodiversity regulate tick vector populations; tick encounters with wildlife host reservoirs and pathogen transmission. We developed several general linear regression models to relate biodiversity metrics including Shannon diversity, species richness, and functional diversity, as well as host count to the simulated density of questing nymphs (DON) and density of infected nymphs (DIN), the life-stage that poses the greatest human Lyme disease risk. All models were compared using Akaike's Information Criterion.
Results/Conclusions
We found support for the vector regulation mechanism of the DEH where increases in species richness reduced the density of nymphs. This result is reinforced by the encounter reduction mechanism, where increased Shannon diversity reduces the proportion of nymphs fed by mice. When the number of low permissive wildlife hosts increased, fewer larval ticks fed on mice and consequently became infected with the Lyme disease pathogen. When accounting for increases in mouse populations, increased number of hosts in the community reduced the proportion of nymphs fed by mice as larvae. Finally, increased functional biodiversity results in a decreased DIN, although the proportion of infected nymphs increases. With our modeling approach, we identified specific mechanisms through which wildlife hosts maintain the Lyme disease pathogen. While it may be impractical (and potentially unethical) to experiment on disease systems in the wild, an individual-based modeling approach can help delineate the specific roles individual host species play in amplifying or diluting the Lyme disease pathogen. We now have a better understanding of influence biodiversity plays which can facilitate more informed decisions by epidemiologists and ecologists in the development of interventions for reducing human Lyme disease risk.