Gajewski Zachary, Stevenson Lisa A, Pike David A, Roznik Elizabeth A, Alford Ross A, Johnson Leah R
Department of Biological Science Virginia Tech Blacksburg Virginia USA.
Department of Statistics Virginia Tech Blacksburg Virginia USA.
Ecol Evol. 2021 Dec 17;11(24):17920-17931. doi: 10.1002/ece3.8379. eCollection 2021 Dec.
Environmental temperature is a crucial abiotic factor that influences the success of ectothermic organisms, including hosts and pathogens in disease systems. One example is the amphibian chytrid fungus, (), which has led to widespread amphibian population declines. Understanding its thermal ecology is essential to effectively predict outbreaks. Studies that examine the impact of temperature on hosts and pathogens often do so in controlled constant temperatures. Although varying temperature experiments are becoming increasingly common, it is unrealistic to test every temperature scenario. Thus, reliable methods that use constant temperature data to predict performance in varying temperatures are needed. In this study, we tested whether we could accurately predict growth in three varying temperature regimes, using a Bayesian hierarchical model fit with constant temperature growth data. We fit the Bayesian hierarchical model five times, each time changing the thermal performance curve (TPC) used to constrain the logistic growth rate to determine how TPCs influence the predictions. We then validated the model predictions using growth data collected from the three tested varying temperature regimes. Although all TPCs overpredicted growth in the varying temperature regimes, some functional forms performed better than others. Varying temperature impacts on disease systems are still not well understood and improving our understanding and methodologies to predict these effects could provide insights into disease systems and help conservation efforts.
环境温度是一个关键的非生物因素，它影响着变温生物的生存繁衍，包括疾病系统中的宿主和病原体。一个例子是两栖类壶菌（），它已导致两栖动物种群数量普遍下降。了解其热生态学对于有效预测疫情爆发至关重要。研究温度对宿主和病原体影响的实验通常在可控的恒定温度下进行。尽管变温实验越来越普遍，但测试每一种温度情况是不现实的。因此，需要可靠的方法利用恒温数据来预测变温环境下的表现。在本研究中，我们使用一个基于恒温生长数据拟合的贝叶斯层次模型，测试能否准确预测在三种变温条件下的生长情况。我们五次拟合贝叶斯层次模型，每次改变用于约束逻辑增长率的热性能曲线（TPC），以确定TPC如何影响预测结果。然后，我们使用从三种测试变温条件下收集的生长数据来验证模型预测。尽管所有的TPC都高估了变温条件下的生长情况，但某些函数形式的表现优于其他形式。变温对疾病系统影响仍未得到充分理解，改进我们对这些影响的理解和预测方法，可为疾病系统研究提供见解，并有助于保护工作。
