Gjini Erida, Madec Sten
Instituto Gulbenkian de Ciência Oeiras Portugal.
Center for Computational and Stochastic Mathematics Instituto Superior Técnico University of Lisbon Lisbon Portugal.
Ecol Evol. 2021 Jun 6;11(13):8456-8474. doi: 10.1002/ece3.7259. eCollection 2021 Jul.
The high number and diversity of microbial strains circulating in host populations have motivated extensive research on the mechanisms that maintain biodiversity. However, much of this work focuses on strain-specific and cross-immunity interactions. Another less explored mode of pairwise interaction is via altered susceptibilities to co-colonization in hosts already colonized by one strain. Diversity in such interaction coefficients enables strains to create dynamically their niches for growth and persistence, and "engineer" their common environment. How such a network of interactions with others mediates collective coexistence remains puzzling analytically and computationally difficult to simulate. Furthermore, the gradients modulating stability-complexity regimes in such multi-player endemic systems remain poorly understood. In a recent study (Madec & Gjini, , 82), we obtained an analytic representation for -type coexistence in an SIS epidemiological model with co-colonization. We mapped multi-strain dynamics to a replicator equation using timescale separation. Here, we examine what drives coexistence regimes in such co-colonization system. We find the ratio of single to co-colonization, , critically determines the type of equilibrium and number of coexisting strains, and encodes a trade-off between overall transmission intensity and mean interaction coefficient in strain space, . Preserving a given coexistence regime, under fixed trait variation, requires balancing between higher mean competition in favorable environments, and higher cooperation in harsher environments, and is consistent with the stress gradient hypothesis. Multi-strain coexistence tends to steady-state attractors for small , whereas as increases, dynamics tend to more complex attractors. Following strain frequencies, evolutionary dynamics in the system also display contrasting patterns with , interpolating between multi-stable and fluctuating selection for cooperation and mean invasion fitness, in the two extremes. This co-colonization framework could be applied more generally, to study invariant principles in collective coexistence, and to quantify how critical shifts in community dynamics get potentiated by mean-field and environmental gradients.
宿主群体中循环的微生物菌株数量众多且种类多样，这推动了对维持生物多样性机制的广泛研究。然而，这项工作大多集中在菌株特异性和交叉免疫相互作用上。另一种较少被探索的成对相互作用模式是通过改变对已被一种菌株定殖的宿主进行共同定殖的易感性。这种相互作用系数的多样性使菌株能够动态地创造它们生长和持续存在的生态位，并“设计”它们的共同环境。这样一个与其他生物的相互作用网络如何介导集体共存，在分析上仍然令人困惑，并且在计算上难以模拟。此外，在这种多主体地方病系统中调节稳定性 - 复杂性状态的梯度仍然知之甚少。在最近的一项研究（马德克和吉尼，，82）中，我们在具有共同定殖的SIS流行病学模型中获得了 - 型共存的解析表示。我们使用时间尺度分离将多菌株动态映射到一个复制方程。在这里，我们研究在这种共同定殖系统中驱动共存状态的因素。我们发现单一定殖与共同定殖的比率，，关键地决定了平衡的类型和共存菌株的数量，并编码了总体传播强度 与菌株空间中平均相互作用系数，之间的权衡。在固定性状变异的情况下，保持给定的共存状态需要在有利环境中更高的平均竞争与更恶劣环境中更高的合作之间取得平衡，这与压力梯度假说一致。对于小的，多菌株共存倾向于稳态吸引子，而随着 的增加，动态倾向于更复杂的吸引子。跟踪菌株频率，系统中的进化动态在 的两个极端情况下，对于合作和平均入侵适合度也表现出介于多稳定和波动选择之间的对比模式。这种共同定殖框架可以更广泛地应用，以研究集体共存中的不变原理，并量化平均场和环境梯度如何增强群落动态中的关键转变。
