Knowledge of the habitat associations and spatial–temporal distributions of wild animals is essential for successful ecosystem management, and effective analytical approaches are key to develop accurate models of these relationships. We explore the influence of several modeling techniques, tuning parameters, and assignment thresholds on a variety of model fit metrics to characterize habitat associations and make spatial–temporal predictions of species distribution based on a nine-year acoustic telemetry fish tracking dataset from a freshwater system. Unweighted generalized linear mixed models (GLMM) and random forests (RF) had the highest prediction accuracy of fish occupancy (> 84%) and precision (positive predictive value accuracy), but because the data were imbalanced (> 70% absences), predictions had low sensitivity (accuracy of true presences, < 45%), and therefore, low accuracy balance. Model weighting to prioritize presences and lowered presence probability thresholds both produced more balanced models, but RF exhibited low sensitivity to alterations in probability thresholds. Model weighting presents a straightforward approach to balance class accuracy in imbalanced datasets, which are common in species distribution samples. However, there is a wide range of weighting options and an important trade-off between model sensitivity and precision, either of which may be favoured depending on the research question or management application.

了解野生动物的栖息地关联和时空分布对于成功地进行生态系统管理至关重要，有效的分析方法对于建立这些关系的精确模型至关重要。我们探索了几种建模技术，调整参数和分配阈值对各种模型拟合度量的影响，以表征栖息地关联并根据淡水系统的九年声遥测鱼跟踪数据集对物种分布进行时空预测。未加权广义线性混合模型（GLMM）和随机森林（RF）的鱼类占有率预测精度最高（> 84％），精度最高（正预测值精度），但是由于数据不平衡（> 70％缺失），预测的敏感性较低（真实存在的准确性为<45％），因此准确度平衡较低。优先权存在的模型加权和较低的存在概率阈值都产生了更平衡的模型，但是RF对概率阈值的更改表现出较低的敏感性。模型权重提出了一种在不平衡数据集中平衡类准确性的简单方法，这在物种分布样本中很常见。但是，存在多种加权选项，并且在模型灵敏度和精度之间存在重要的权衡取舍，取决于研究问题或管理应用，可能会偏爱其中任何一种。但是RF对概率阈值的变化表现出较低的敏感性。模型权重提出了一种在不平衡数据集中平衡类准确性的简单方法，这在物种分布样本中很常见。但是，存在多种加权选项，并且在模型灵敏度和精度之间存在重要的权衡取舍，取决于研究问题或管理应用，可能会偏爱其中任何一种。但是RF对概率阈值的变化表现出较低的敏感性。模型权重提出了一种在不平衡数据集中平衡类准确性的简单方法，这在物种分布样本中很常见。但是，存在多种加权选项，并且在模型灵敏度和精度之间存在重要的权衡取舍，取决于研究问题或管理应用，可能会偏爱其中任何一种。