Accuracy of climate-based forecasts of pathogen spread

Species distribution models (SDMs) are a tool for predicting the eventual geographical range of an emerging pathogen. Most SDMs, however, rely on an assumption of equilibrium with the environment, which an emerging pathogen, by definition, has not reached. To determine if some SDM approaches work better than others for modelling the spread of emerging, non-equilibrium pathogens, we studied time-sensitive predictive performance of SDMs for Batrachochytrium dendrobatidis, a devastating infectious fungus of amphibians, using multiple methods trained on time-incremented subsets of the available data. We split our data into timeline-based training and testing sets, and evaluated models on each set using standard performance criteria, including AUC, kappa, false negative rate and the Boyce index. Of eight models examined, we found that boosted regression trees and random forests performed best, closely followed by MaxEnt. As expected, predictive performance generally improved with the length of time series used for model training. These results provide information on how quickly the potential extent of an emerging disease may be determined, and identify which modelling frameworks are likely to provide useful information during the early phases of pathogen expansion.

物种分布模型（Species Distribution Models，SDMs）是一类用于预测新发病原体最终地理分布范围的工具。然而，大多数物种分布模型均依赖于与环境达到平衡的假设，而根据定义，新发病原体尚未达成这一平衡状态。为探究部分物种分布模型方法在模拟新发非平衡病原体传播时是否优于其他方法，本研究以蛙壶菌（*Batrachochytrium dendrobatidis*，一种极具破坏性的两栖类感染真菌）为研究对象，采用基于时序递增的可用数据子集训练得到的多种模型，分析其时序敏感预测性能。我们将数据按时间线划分为训练集与测试集，并采用包括受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve，AUC）、科恩kappa系数、假阴性率以及博伊斯指数（Boyce index）在内的标准性能指标，对各数据集上的模型进行评估。在本次研究考察的8种模型中，提升回归树与随机森林表现最优，紧随其后的是最大熵模型（MaxEnt）。正如预期，模型的预测性能通常会随着训练所用时间序列长度的增加而提升。本研究结果揭示了可在多快的时间内确定新发疾病的潜在传播范围，并甄别出在病原体扩张早期阶段能够提供有效信息的建模框架。