Sampling bias and autocorrelation can lead to erroneous estimates of habitat selection, model overfitting and elevated omission rates. We developed a multi-scale habitat suitability model of the flammulated owl (Psiloscops flammeolus) in the Northern Rocky Mountains based on extensive but spatially clustered survey data, and then used simulations to evaluate the effects of spatially non-representative and spatially representative sampling strategies on model performance and predictions. Our hypothesis was that models trained with spatially non-representative simulated datasets would suffer from bias in parameter estimates, and would show lower predictive performance. The models trained with the spatially representative simulated datasets greatly outperformed the models trained with the spatially non-representative simulated datasets judged on standard metrics of model performance. However, the spatially non-representative models produced superior predictions based on their ability to identify the correct spatial scales, covariates, signs and magnitudes of the species-environment relationships, when compared to the spatially representative models. Thus, it is likely that representative spatial sampling across a broad range of environmental gradients also resulted in over-dispersion of sampling data, with a higher proportion of samples falling in areas of low probability of presence, leading to lower ability to resolve the relationships between species presence-absence and environmental covariates. In contrast, the spatially non-representative sampling, by concentrating sampling along environmental gradients that are characterized by higher probability of presence of the modelled species, produced predictions that, while seeming to be weaker based on standard measures of model performance (e.g., AUC, Kappa, PCC), greatly outperformed the spatially representative models based on measures of true model prediction (e.g., correctly describing the actual spatial scales, direction and strength of species-environment relationships). Further work using simulation approaches is warranted to more fully evaluate the ability of species distribution modelling techniques to correctly identify scales, driving covariates, signs and magnitudes of relationships between species presence-absence patterns, and environmental covariates.

抽样偏差和自相关可能导致对栖息地选择，模型拟合过度和遗漏率升高的估计错误。我们开发了多层次的易燃猫头鹰（Psiloscops flammeolus）栖息地适应性模型）在北部落基山脉上基于大量但在空间上聚集的调查数据，然后使用模拟来评估空间非代表性和空间代表性采样策略对模型性能和预测的影响。我们的假设是，使用空间上不具有代表性的模拟数据集训练的模型会遭受参数估计的偏差，并且显示出较低的预测性能。用具有空间代表性的模拟数据集训练的模型大大优于用基于模型性能的标准度量判断的空间无代表性的模拟数据集训练的模型。但是，基于空间的非代表性模型可以识别正确的空间比例，协变量，与空间代表性模型相比，物种与环境关系的符号和大小。因此，跨大范围环境梯度的代表性空间采样也可能导致采样数据的过度分散，较高比例的样本落入存在概率较低的区域，导致解决以下问题之间关系的能力较低物种的存在与否与环境的协变量。相比之下，空间上不具代表性的采样是通过沿环境梯度集中采样来实现的，这些环境梯度的特征在于存在较高的建模物种概率，从而得出预测，尽管基于模型性能的标准度量（例如AUC， Kappa，PCC），基于真实模型预测的度量（例如正确描述实际的空间规模，物种与环境关系的方向和强度），其性能大大优于具有空间代表性的模型。有必要使用模拟方法进行进一步的工作，以更充分地评估物种分布建模技术正确识别规模，驱动协变量，物种存在与缺失模式与环境协变量之间的关系的符号和大小的能力。