1. Detailed spatio‐temporal information about geographic distributions of species is critical for biodiversity analyses in conservation and planning. Traditional correlative modelling approaches use species observational data in model calibration and testing in a time‐averaged framework. This method averages environmental values through time to yield a single environmental value for each location. Although valuable for exploring distributions of species at a broad level, this averaging is one of myriad factors impacting model quality and reliability.
2. We sought to optimize traditional correlative niche model performance in distributional ecology contexts by incorporating time specificity into the existing modelling framework. We modified the existing framework to account for temporal dynamics in species' distributions to produce more robust, temporally explicit models. Using the Wood Thrush Hylostichla mustelina as our study species, we introduce a method of (a) deriving a temporally explicit pseudo‐absence dataset using kernel density estimates to replicate relative sampling of sites through time, and (b) incorporating temporally explicit covariates in model calibration.
3. By accounting for location, and month and year of primary data collection, the time‐specific models successfully yielded dynamic predictions reflecting known distributional shifts in Hylocichla mustelina's annual movement pattern.
4. The modified data preparation steps that we present incorporate temporal dimensions into traditional correlational modelling approaches improving predictive capacity and overall utility of these models for highly mobile, short‐lived or behaviourally complex species. With the ability to estimate species' niches in greater detail, time‐specific models will be able to address specific concerns of species‐level management and policy development for highly mobile and/or migratory species, as well as disease vectors of public health interest.

中文翻译：

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将时间整合到传统的相关分布建模框架中：使用Wood Thrush Hylocichla mustelina的概念验证

1. 有关物种地理分布的详细时空信息对于保护和规划中的生物多样性分析至关重要。传统的相关建模方法在时间平均框架内使用物种观测数据进行模型校准和测试。此方法对时间平均环境值，以对每个位置产生单个环境值。尽管这种平均值对于探索广泛的物种分布很有价值，但这种平均值是影响模型质量和可靠性的众多因素之一。
2. 我们试图通过将时间特异性结合到现有的建模框架中来优化分布生态环境中的传统相关生态位模型性能。我们修改了现有框架，以解决物种分布中的时间动态问题，以产生更健壮的，时间明确的模型。我们以Wood Thrush Hylostichla mustelina作为我们的研究物种，引入了一种方法（a）使用核密度估计来推导时间上明确的伪缺席数据集，以复制时间上站点的相对采样，以及（b）在模型中纳入时间上明确的协变量校准。
3. 通过考虑位置，原始数据收集的月份和年份，针对特定时间的模型成功产生了动态预测，反映了Hylocichla mustelina年度运动模式中的已知分布变化。
4. 我们目前提出的修改后的数据准备步骤将时间维度纳入了传统的相关建模方法中，从而提高了这些模型对高度移动，短命或行为复杂物种的预测能力和整体效用。通过更详细地估计物种的生态位的能力，特定时间的模型将能够解决高度流动和/或迁徙物种的物种级管理和政策制定的特定问题，以及关注公共卫生的疾病媒介。