In today's landscape of zoonotic pathogen outbreaks, the dilution effect theory, i.e., the theory that greater biodiversity can help curb pathogen transmission among wildlife, has gained significant attention. However, the positive link between animal diversity and pathogen richness urges us to apply this concept with caution. It is crucial to explore how conservation biology can safeguard human health by preventing the emergence of zoonotic diseases. By investigating the implications of conservation strategies on animal communities and pathogen transmission as well as the adaptive capabilities of pathogens, we propose that biodiversity conservation based on small reserves can effectively reduce pathogen spread in wildlife, provided certain measurable conditions are met. Given the urgent need to tackle both zoonoses disease emergence and biodiversity loss, these interventions should be prioritized and implemented without delay.

Invasive species are significant contributors to global changes and constitute a severe threat to biodiversity. Yet invasions offer an incredible framework to understand how small and low-diverse introduced populations adapt to novel environmental conditions and succeed in colonizing large areas. However, due to the insufficient data on the origin of the first introduced propagule and the first stage of invasion, reconstructing a species’ invasion history is challenging. Here, we applied genetic clustering methods and explicit admixture tests combined with ABC models and Machine Learning algorithms to describe the phylogeography of native and invasive populations and infer the most probable demographic invasion scenarios of Pseudorasbora parva, a highly invasive freshwater fish and the healthy carrier of a novel lethal fungi-like pathogen (Sphaerothecum destruens), which is responsible for the decline of several fish species in Europe. We found that the current genetic structuring of the native P. parva range has been shaped by waves of gene flow originating from southern and northern Chinese populations. Furthermore, our results strongly suggest that the invasive genetic diversity is the outcome of past recurrent global invasion pathways of admixed native populations. Our study also illustrates how the combination of admixture tests, ABC, Machine Learning can be used to detect high-resolution demographic signatures and reconstruct an integrative biological invasion history.