Host community composition, community assembly pattern, and disease transmission mode jointly determine the direction and strength of the diversity-disease relationship

Abstract

Rapid global biodiversity loss and increasing emerging infectious diseases underscore the significance of identifying the diversity-disease relationship. Although experimental evidence supports the existence of dilution effects in several natural ecosystems, we still know very little about the conditions under which a dilution effect will occur. Using a multi-host Susceptible-Infected-Recovered model, we found when disease transmission was density-dependent, the diversity-disease relationship could exhibit an increasing, decreasing, or non-monotonic trend, which mainly depended on the patterns of community assembly. However, the combined effects of the host competence-abundance relationship and species extinction order may reverse or weaken this trend. In contrast, when disease transmission was frequency-dependent, the diversity-disease relationship only showed a decreasing trend, the host competence-abundance relationship and species extinction order did not alter this decreasing trend, but it could reduce the detectability of the dilution effect and affect disease prevalence. Overall, a combination of disease transmission mode, community assembly pattern, and host community composition determines the direction or strength of the diversity-disease relationship. Our work helps explain why previous studies came to different conclusions about the diversity-disease relationship and provides a deeper understanding of the pathogen transmission dynamics in actual communities.

The Influence of Cultural Factors, Social Norms, and Social Support on the Spread of Infectious Diseases in Communities in Indonesia

“…Understanding the effects of sociocultural processes on disease transmission is essential for effective interventions [25]. Additionally, local contexts and sociocultural factors should be integrated into studies on infectious diseases to gain meaningful insights [26]. By combining measures of local context with microbiological data, researchers can identify valuable insights for disease prevention and control [27].…”