Stop guessing thinning distances: a data-driven framework for spatial thinning parametrization for ecological modelling

Spatially heterogeneous sampling and uneven publication effort in biodiversity repositories limit ecological niche characterization and habitat suitability modelling, affecting downstream inferences at regional and global scales. Spatial thinning offers a widely used strategy to mitigate these biases due to its simplicity and scalability. However, researchers typically select thinning parameters using heuristic or arbitrary criteria, often independent of species-specific characteristics, which introduces an additional, largely unquantified source of potential error. Here, we propose a data-driven framework for spatial thinning parameterization that replaces heuristic decision-making with principled, reproducible criteria. We define optimality as the stability of thermal niche estimates derived from empirical data, rather than as an agreement with a known true distribution. The framework integrates two complementary approaches. The Last Stable Thinning Parameterization (LS-TP) identifies the largest thinning intensity that preserves the stability of species-specific niche descriptors. Biogeographic-size Dependent Thinning Parameterization (BD-TP) links thinning intensity to species' geographic range size and scales spatial filtering across taxa with different distributional properties. Using seagrasses as a model system, we evaluated the framework across a global compilation of more than 100,000 occurrence records. Our results showed that optimal thinning parameters varied systematically with species range size and that uniform thinning approaches led to either excessive information loss or reduction of spatial clustering. Furthermore, the framework identified species for which available data did not support stable niche characterization, as these species failed to meet stability criteria even at the smallest thinning parameters. We recommend explicitly acknowledging this limitation in future studies and prioritizing increased sampling effort for such undersampled taxa. Our approaches provide a first step toward explicit, data-driven spatial thinning parameterization, with direct implications for ecological niche modelling, biodiversity assessment, and climate-driven distribution analyses.