1. Host–parasite dynamics are impacted by the relationship between host density and parasite transmission, and thus, all epidemiological models contain a central transmission–density function. Recent theoretical work demonstrates that this central parasite transmission function might be best represented by a nonlinear continuum from one linear extreme to another: density‐dependent transmission at low host densities to density‐independent transmission at high host densities. But how often are nonlinear transmission functions used, and when are they better at describing transmission in real host–parasite systems?
2. To quantify existing modelling practices, we systematically reviewed seven representative ecology journals, finding 262 studies containing host–parasite models that contained linear and/or nonlinear transmission functions. We also reviewed the literature to find 28 experimental and observational studies that compared multiple transmission functions in real host–parasite systems, and tallied which functions were best supported in those systems. Finally, we created a flexible model simulation tool to explore and quantify the bias in model parameter estimates that is created when using an inaccurate transmission function.
3. We found that most experimental and observational studies reported that nonlinear transmission–density functions outperformed simple linear transmission–density functions, supporting recent theoretical work. In contrast, most studies containing host–parasite models assumed that host density was constant and/or used a single, linear transmission function to explain how transmission rates changed with density. Using the wrong linear function and/or using a linear function when the underlying transmission–density relationship is even slightly nonlinear can substantially bias model parameter estimates, as demonstrated by our simulations over a broad parameter space.
4. Some modelling studies may be using linear functions in host–parasite systems where nonlinear functions are more appropriate. If true, these models would yield substantially biased parameter estimates. To avoid such biases that compromise ecological understanding and prediction, we recommend that future studies compare multiple transmission functions, including nonlinear options, whenever possible.

中文翻译：

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对建模假设和经验证据的系统评价：寄生虫传播是否随宿主密度非线性增加？

1. 寄主-寄生虫动力学受到寄主密度和寄生虫传播之间关系的影响，因此，所有流行病学模型都包含一个中心传播-密度函数。最近的理论研究表明，这种中心寄生虫的传播功能可能最好地表现为从一个线性极值到另一个线性极值的非线性连续体：从低宿主密度的密度依赖型传输到在高宿主密度的密度无关型传输。但是非线性传递函数多久使用一次，何时才能更好地描述真实宿主-寄生虫系统中的传递？
2. 为了量化现有的建模方法，我们系统地审查了七种代表性的生态学期刊，发现262项包含宿主-寄生虫模型的研究，这些模型包含线性和/或非线性传递函数。我们还回顾了文献，找到了28个实验和观察性研究，它们比较了真实宿主-寄生虫系统中的多种传播功能，并指出了那些系统中最能支持的功能。最后，我们创建了一个灵活的模型仿真工具，以探索和量化使用不准确的传递函数时创建的模型参数估计中的偏差。
3. 我们发现，大多数实验和观察性研究都报告说，非线性透射-密度函数优于简单的线性透射-密度函数，从而支持了最新的理论工作。相反，大多数包含宿主-寄生虫模型的研究都假设宿主密度是恒定的，并且/或者使用单个线性传递函数来解释传播速率如何随密度变化。正如我们在较宽的参数空间上的仿真所表明的那样，如果潜在的传输密度关系甚至略微为非线性，则使用错误的线性函数和/或使用线性函数会大大偏离模型参数估计值。
4. 一些建模研究可能在宿主-寄生虫系统中使用线性函数，而非线性函数更合适。如果为真，则这些模型将产生明显有偏差的参数估计。为了避免影响生态学理解和预测的偏见，我们建议将来的研究尽可能地比较多种传递函数，包括非线性选项。