Linking community and disease ecology: the impact of biodiversity on pathogen transmission

Abstract

The increasing number of zoonotic diseases spilling over from a range of wild animal species represents a particular concern for public health, especially in light of the current dramatic trend of biodiversity loss. To understand the ecology of these multi-host pathogens and their response to environmental degradation and species extinctions, it is necessary to develop a theoretical framework that takes into account realistic community assemblages. Here, we present a multi-host species epidemiological model that includes empirically determined patterns of diversity and composition derived from community ecology studies. We use this framework to study the interaction between wildlife diversity and directly transmitted pathogen dynamics. First, we demonstrate that variability in community composition does not affect significantly the intensity of pathogen transmission. We also show that the consequences of community diversity can differentially impact the prevalence of pathogens and the number of infectious individuals. Finally, we show that ecological interactions among host species have a weaker influence on pathogen circulation than inter-species transmission rates. We conclude that integration of a community perspective to study wildlife pathogens is crucial, especially in the context of understanding and predicting infectious disease emergence events.

Figure 1.

Relationship between susceptibility distribution (X-axis: mean, Y-axis: variance) and peak disease prevalence (Z-axis) within the whole community. Peak disease prevalence is defined here as the maximal number of infectious individuals observed during simulations (100 years). Parameters used: z = 0.1, Y₀ = 10, a = 0.6931, b = 0, days, , . k and ω are modified to explore different forms of susceptibilities distribution.

Figure 2.

Relationship between host species community characteristics (X-axis: Shannon's index, see the main text for mathematical formulation) and peak disease prevalence, in terms of abundance (left Y-axis; dark grey line and diamonds) and proportion (right Y-axis; light grey line and crosses) of infectious individuals. Both axes are rescaled between 0 and 1 for readability. Dark and light grey lines represent linear regressions and diamonds and crosses on these lines are the values predicted by linear regressions. Parameters used: a = 0.6931, b = 0, days, , , , . z and Y₀ are modified to explore different shapes of host community structures.

Figure 3.

Influence of contact patterns between host species on peak disease prevalence. The small cartoons represent an example with different values of c (number of species connected to a single species). Parameters used: z = 0.1, Y₀ = 10, a = 0.6931, b = 0, days, k = 0.1, .