1. Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multispecies distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to bias in parameter estimates. 2. Here we present a general multispecies distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from GBIF. 3. Our simulation study showed that accounting for heterogeneity in the classification process increased precision by 30% and reduced accuracy and recall by 6%. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models due to the smaller misclassified sample sizes. However, when machine learning predictive scores are used as weights to inform the species distribution models about the classification process, the precision increases by 70%. 4. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine prediction scores should be used when the data contains relatively smaller misclassified samples.

翻译：1. 基于大规模生物多样性数据的物种分布模型与地图对保护管理至关重要。当前问题之一是生物多样性数据易出现分类学错误。现有处理多物种分布模型中此类错误的方法假设分类概率在研究区域内恒定。但实际上，分类概率可能随多种协变量变化。忽略此类异质性可能导致参数估计偏差。2. 本文提出一种考虑分类过程异质性的通用多物种分布模型。该模型采用多项式广义线性模型对分类混淆矩阵进行建模。通过评估均质与异质分类模型在参数估计与留出样本预测性能上的差异，比较两者的表现。我们将该模型应用于来自GBIF的挪威、丹麦及芬兰鸥类数据。3. 模拟研究表明，考虑分类过程异质性可使精确度提升30%，但准确率与召回率下降6%。由于错误分类样本量较小，将模型框架应用于鸥类数据集时，均质与异质模型的预测性能未显著改善。然而，当以机器学习预测分数作为权重告知物种分布模型分类过程时，精确度提升70%。4. 我们建议：当数据包含相对较多的错误分类样本时，采用多元多项式回归建模分类过程的异质性；当数据包含相对较少的错误分类样本时，则应使用机器预测分数。