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Incorporating time into the traditional correlational distributional modelling framework: A proof‐of‐concept using the Wood Thrush Hylocichla mustelina

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. 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. 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. 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.