Higgins Steven I, Larcombe Matthew J, Beeton Nicholas J, Conradi Timo
Plant Ecology University of Bayreuth Bayreuth Germany.
Department of Botany University of Otago Dunedin New Zealand.
Ecol Evol. 2021 Sep 6;11(19):13613-13617. doi: 10.1002/ece3.8081. eCollection 2021 Oct.
Here, we respond to Booth's criticism of our paper, "Predictive ability of a process-based versus a correlative species distribution model." Booth argues that our usage of the MaxEnt model was flawed and that the conclusions of our paper are by implication flawed. We respond by clarifying that the error Booth implies we made was not made in our analysis, and we repeat statements from the original manuscript which anticipated such criticisms. In addition, we illustrate that using BIOCLIM variables in a MaxEnt analysis as recommended by Booth does not change the conclusions of the original analysis. That is, high performance in the training data domain did not equate to reliable predictions in novel data domains, and the process model transferred into novel data domains better than the correlative model did. We conclude by discussing a hidden implication of our study, namely, that process-based SDMs negate the need for BIOCLIM-type variables and therefore reframe the variable selection problem in species distribution modeling.
在此,我们回应布思对我们论文《基于过程的物种分布模型与相关物种分布模型的预测能力》的批评。布思认为我们对MaxEnt模型的使用存在缺陷,进而暗示我们论文的结论也有缺陷。我们通过澄清布思所暗示的我们犯下的错误并非出现在我们的分析中进行回应,并重复原始手稿中预期此类批评的陈述。此外,我们还表明,按照布思的建议在MaxEnt分析中使用生物气候变量并不会改变原始分析的结论。也就是说,在训练数据域中的高性能并不等同于在新数据域中的可靠预测,并且基于过程的模型比相关模型能更好地转移到新数据域中。我们通过讨论我们研究的一个潜在含义来得出结论,即基于过程的物种分布模型无需生物气候类型的变量,因此重新构建了物种分布建模中的变量选择问题。