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Distribution models of estuarine fish species: The effect of sampling bias, species ecology and threshold selection on models' accuracy

机译:河口鱼类的分布模型:采样偏差,物种生态学和阈值选择对模型的准确性的影响

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Species distribution models (SDMs) relate presence/absence data to environmental variables, allowing to predict species environmental requirements and potential distribution. They have been increasingly used in fields such as ecology, biogeography and evolution, and often support conservation priorities and strategies. Thus, it becomes crucial to understand how trustworthy and reliable their predictions are. Different approaches, such as using ensemble methods (combining forecasts of different single models), or applying the most suitable threshold to transform continuous probability maps into species presences or absences, have been used to reduce model-based uncertainty. Taking into account the influence of biased sampling imprecision in species location, small datasets and species ecological characteristics, may also help to detect and compensate for uncertainty in the model building process. To investigate the effect of applying an ensemble approach, several threshold selection criteria and different datasets representing seasonal and spatial sampling bias, on models' accuracy, SDMs were built for four estuarine fish species with distinct use of the estuarine systems. Overall, predictions obtained with the ensemble approach were more accurate. Variability in accuracy metrics obtained with the nine threshold selection criteria applied was more pronounced for species with low prevalence and when sensitivity was calculated. Higher values of accuracy measures were registered with the threshold that maximizes the sum of sensitivity and specificity, and the threshold where the predicted prevalence equals the observed, whereas the 0.5 cut-off was unreliable, originating the lowest values for these metrics. Accuracy of models created from a spatially biased sampling was overall higher than accuracy of models created with a seasonally biased sampling or with the multi-year database created and this pattern was consistently obtained for marine migrant species, which use e
机译:物种分布模型(SDMS)将存在/缺位数据与环境变量相关联,允许预测物种环境要求和潜在分布。他们越来越多地用于生态,生物地理和演化等领域,并且经常支持保护优先事项和策略。因此,了解他们如何值得信赖和可靠的预测是至关重要的。不同的方法,例如使用集合方法(组合不同单模型的预测),或应用最合适的阈值将连续概率图转换为物种存在或缺位,以减少基于模型的不确定性。考虑到偏置采样不精确在物种位置,小型数据集和物种生态特征的影响,也可能有助于检测和弥补模型建设过程中的不确定性。为了调查应用集合方法的效果,几种阈值选择标准和不同数据集代表季节性和空间采样偏置的不同数据集,为型号的四种偏卤素鱼类而构建了SDMS,具有脱硝酸盐系统的不同用途。总体而言,通过集合方法获得的预测更准确。使用九个阈值选择标准获得的精度度量的可变性更加明显,普及率低,并且计算灵敏度时。在最大化灵敏度和特异性的阈值的阈值中注册了更高的精度测量值,以及预测普遍性等于观察到的阈值,而0.5截止是不可靠的,始致这些度量的最低值。从空间偏见的采样创建的模型的准确性总体上高于使用季节性偏见的采样或创造的多年数据库创建的模型的准确性,并且该模式始终如一地获得海洋移民物种,其使用e

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