Species distribution modelling (SDM) is a valuable tool for predicting the potential distribution of invasive species across space and time. Maximum entropy modelling (MaxEnt) is a popular choice for SDM, but questions have been raised about how these models are developed. Without biologically informed baseline assumptions, complex default SDM models could be selected, even though alternative settings may be more appropriate. Here we explored the effects of various SDM design strategies on distribution mapping of four forest invasive species (FIS) in Canada. We found that if we ignored the underlying FIS biology such as use of biologically relevant predictors, appropriate feature selection and inclusion of dispersal and biotic interactions when we developed our SDMs, we obtained complex SDMs (default) that provided an incomplete picture of the potential FIS invasion. We recommend simplifying SDM complexity and including biologically informed assumptions to achieve more accurate dispersal predictions, particularly when projecting FIS spread across time. We strongly encourage SDM users to perform species-specific tuning when modeling FIS distributions with MaxEnt to determine the best SDM design.

物种分布模型（SDM）是预测入侵物种在空间和时间上的潜在分布的宝贵工具。最大熵建模（MaxEnt）是SDM的一种流行选择，但是有关如何开发这些模型的问题已经提出。如果没有生物学依据的基线假设，则可以选择复杂的默认SDM模型，即使替代设置可能更合适。在这里，我们探讨了各种SDM设计策略对加拿大四种森林入侵物种（FIS）分布图的影响。我们发现，如果在开发SDM时忽略了基本的FIS生物学，例如使用生物学相关的预测因子，进行适当的特征选择以及分散和生物相互作用，我们获得了复杂的SDM（默认），这些SDM无法提供有关潜在FIS入侵的完整图片。我们建议简化SDM的复杂性，并包括生物学上合理的假设，以实现更准确的分散预测，尤其是在预测FIS跨时间分布时。我们强烈鼓励SDM用户在使用MaxEnt对FIS分布进行建模时确定特定于SDM的设计，以执行特定于物种的调整。