Quantile normalisation enables interpretable spatial prioritisation of persistence-related mechanistic suitability under uncertainty for tomato red spider mite (Tetranychus evansi) in Africa

Mechanistic trait models are increasingly considered to estimate persistence-related suitability for invasive species, although translating reproduction metrics into operational indicators for spatial decision-making remains challenging. Branching-process transformations are widely applied to convert mechanistic reproduction numbers into probabilities of establishment; however, such transformations may compress spatial gradients when persistence is broadly feasible. Using tomato red spider mite ( Tetranychus evansi ) as a case study across Africa, this study developed an uncertainty-aware, physiologically-based model and generated spatially explicit estimates of expected reproduction (R 0 ). We compared three representations: raw mechanistic suitability, branching-process persistence probability, and quantile-normalised suitability. Although all representations preserved rank ordering, branching probability showed strong spatial saturation (>93% of pixels have P = 1 − 1 R 0 close to 1), reducing effective resolution for prioritisation. In contrast, quantile normalisation retained uniform spatial contrast and stable hotspot delineation under fixed-effort targeting. Our findings demonstrate that indicator choice fundamentally affects interpretability, and that rank-based transformations may provide more robust ecological indicators for spatial and spatiotemporal decision support under uncertainty.