Distributional regression models are powerful “input-output” frameworks in which the response variable is assumed to follow a well-defined theoretical distribution. Unlike traditional regression models that focus solely on the mean, distributional regression models offer a more comprehensive perspective by capturing multiple aspects of the response distribution. This includes not only location parameters but also variability, skewness, kurtosis, quantiles, and exceedance probabilities. Such flexibility is especially valuable in wildlife research, where understanding the full shape of a distribution can provide critical insights into species abundance, habitat suitability, and the likelihood of extreme ecological events. As such, they serve as valuable tools for translating complex ecological data into actionable insights for wildlife management and conservation planning.