Data from: 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，绝大多数物种分布模型均基于“与环境达到平衡”的假设，而新发病原体按照定义尚未达成这一平衡状态。为探究部分物种分布模型方法是否更适用于模拟非平衡新发病原体的传播过程，我们以蛙壶菌（Batrachochytrium dendrobatidis）——一种具有毁灭性危害的两栖类感染真菌——为研究对象，采用基于可用数据的时间递增子集训练的多种方法，分析了物种分布模型的时效性预测性能。我们将研究数据划分为基于时间线的训练集与测试集，并采用包括受试者工作特征曲线下面积（Area Under Curve, AUC）、卡帕系数（kappa）、假阴性率（false negative rate）以及博伊斯指数（Boyce index）在内的标准性能评价指标，对各数据集上的模型开展性能评估。在所测试的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，模型训练所用的时间序列长度越长，整体预测性能越优异。本研究结果明确了可在多快的时间内确定新发疾病的潜在传播范围，并指明了在病原体扩张早期阶段能够提供有效预测信息的建模框架。