Multiple viral infection is an important issue in health and agriculture with strong impacts on society and the economy. Several investigations have dealt with the population dynamics of viruses with different dynamic properties, focusing on strain competition during multiple infections and the effects on viruses’ hosts. Recent interest has been on how multiple infections respond to abiotic factors such as temperature ($T$). This is especially important in the case of plant pathogens, whose dynamics could be affected significantly by global warming. However, few mathematical models incorporate the effect of $T$ on parasite fitness, especially in mixed infections. Here, we investigate simple mathematical models incorporating thermal reaction norms (TRNs), which allow for quantitative analysis. A logistic model is considered for single infections, which is extended to a Lotka-Volterra competition model for mixed infections. The dynamics of these two models are investigated, focusing on the roles of $T$-dependent replication and competitive interactions in both transient and asymptotic dynamics. We determine the scenarios of co-existence and competitive exclusion, which are separated by a transcritical bifurcation. To illustrate the applicability of these models, we ran single- and mixed-infection experiments in plants growing at 20 ${}^{\circ }$C and 30 ${}^{\circ }$C using two strains of the plant RNA virus Pepino mosaic virus. Using a macroevolutionary algorithm, we fitted the models to the data by estimating the TRNs for both strains in single infections. Then, we used these TRNs to feed the mixed-infection model estimating the strength of competition. We found an asymmetrical pattern in which each strain dominated at different $T$ values due to differences in their TRNs. We also identified that $T$ can modify competition interference greatly for both isolates. The models proposed here can be useful for investigating the outcomes of multiple-infection dynamics under abiotic changes and have implications for the understanding of viral responses to global warming.

多重病毒感染是健康和农业领域的一个重要问题，对社会和经济产生重大影响。一些研究涉及具有不同动态特性的病毒的种群动态，重点关注多次感染期间的菌株竞争以及对病毒宿主的影响。最近的兴趣是多重感染如何对温度等非生物因素做出反应。$吨$）。这对于植物病原体尤其重要，其动态可能会受到全球变暖的显着影响。然而，很少有数学模型包含$吨$寄生虫适应性，尤其是在混合感染中。在这里，我们研究了包含热反应规范 (TRN) 的简单数学模型，这些模型允许进行定量分析。考虑单一感染的逻辑模型，将其扩展到混合感染的 Lotka-Volterra 竞争模型。研究了这两个模型的动力学，重点是$吨$瞬态和渐近动态中的依赖复制和竞争相互作用。我们确定共存和竞争排斥的场景，它们被跨临界分叉分开。为了说明这些模型的适用性，我们对生长在 20 ${}^{\circ }$C 和 30 ${}^{\circ }$C使用两株植物RNA病毒Pepino花叶病毒。使用宏观进化算法，我们通过估计单一感染中两种菌株的 TRN 将模型拟合到数据中。然后，我们使用这些 TRN 为混合感染模型提供数据，以评估竞争强度。我们发现了一种不对称的模式，其中每个菌株在不同的情况下占主导地位$吨$由于其 TRN 的差异而产生的值。我们还发现$吨$可以极大地改变两个分离株的竞争干扰。这里提出的模型可用于研究非生物变化下多重感染动力学的结果，并有助于理解病毒对全球变暖的反应。