Prioritizing removals of a highly cryptic invasive species using machine learning and demographic weighting.

Invasive species present substantial challenges to biodiversity conservation, particularly when low detectability and demographic heterogeneity obscure the population-level consequences of management actions. Invasive Burmese pythons (Python bivittatus) in South Florida exemplify this challenge, as metrics based on raw removals or body size alone may not reflect demographic impacts. To address this, we introduce the Weighted Removal Index (WRI), a demographic weighting framework that prioritizes removals based on expected contributions to survival and reproduction. Using survey data from the South Florida Water Management District's Python Elimination Program, we quantified operational and environmental conditions associated with higher survey WRI, defined as the summed WRI of all pythons captured on a single survey, and evaluated three forecasting approaches: Generalized Additive Models (GAMs), Boosted Regression Trees (BRT), and Neural Networks (NN). Survey WRI was highest under waning lunar phases, when surveys were assisted, and under warmer and more humid conditions. Additionally, survey WRI was elevated on warm days early in the wet season and cool dry-season days. In held-out test evaluation, the NN achieved the highest balanced accuracy for predicting survey success (0.703), and the lowest error for predicting survey WRI (root mean squared error [RMSE] = 5.50). Contractor performance was highly heterogeneous, with contractor-specific effects positively associated with mean survey WRI (R