Chen Shirley, McFarlane S Eryn
Department of Biology York University Toronto Canada.
Ecol Evol. 2025 Aug 12;15(8):e71969. doi: 10.1002/ece3.71969. eCollection 2025 Aug.
The role of biodiversity in regulating zoonotic disease in ecological communities has been broadly referred to as the biodiversity-disease relationship in disease ecology. Whether biodiversity decreases or increases disease risk, known as a dilution or amplification effect respectively, remains unclear. The literature has focused on the strength, generality, nature, and context dependencies that could explain contradictory evidence. We suggest that a continued focus on this approach to resolving the biodiversity-disease debate detracts from a more foundational problem with testing these dilution and amplification hypotheses, in that these hypotheses are not falsifiable as proposed. When tested and interpreted as net effects in a system, these hypotheses do not possess a true null outcome; they are vulnerable to explanations. Specifically, that an empirical null outcome can be explained by multiple processes (i.e., a true null vs. a canceling out of amplification and dilution effects) means that process cannot be inferred from pattern. To remedy this problem, we propose that biodiversity and disease risk can be modeled as latent variables in multivariate causal models to reframe how we understand them and test the relationship between them. We present a case study on Lyme disease (LD) through a systematic review, concluding that testing these net effect hypotheses falls short of providing robust evidence for its underlying mechanisms. While these hypotheses have previously been helpful in conceptualizing this idea of biodiversity as a potentially protective factor for human health, they require further specificity moving forward in order to appropriately test the relationship.
生物多样性在调节生态群落中动物源性疾病方面的作用，在疾病生态学中被广泛称为生物多样性与疾病的关系。生物多样性是降低还是增加疾病风险，分别称为稀释效应或放大效应，目前尚不清楚。文献聚焦于能够解释相互矛盾证据的强度、普遍性、本质和背景依赖性。我们认为，持续关注这种解决生物多样性与疾病争论的方法，会偏离检验这些稀释和放大假说时一个更根本的问题，即这些假说并非如所提出的那样可证伪。当在一个系统中作为净效应进行检验和解释时，这些假说没有真正的零结果；它们容易受到多种解释的影响。具体而言，一个实证性的零结果可以由多个过程来解释（即真正的零结果与放大和稀释效应的相互抵消），这意味着无法从模式推断过程。为解决这个问题，我们建议在多变量因果模型中将生物多样性和疾病风险建模为潜在变量，以重新构建我们对它们的理解方式，并检验它们之间的关系。我们通过系统综述展示了一个关于莱姆病（LD）的案例研究，得出结论：检验这些净效应假说不足以提供关于其潜在机制的有力证据。虽然这些假说此前有助于将生物多样性这一概念概念化为对人类健康的潜在保护因素，但为了恰当地检验这种关系，它们需要在未来进一步细化。
