20.500.12556/RUL-190532 Interpretable by design language model–derived ecological rules for species distribution modelling Predictive models in biodiversity research face a persistent trade-off between accuracy and interpretability. Complex machine learning approaches often achieve high predictive power, yet their opacity limits ecological understanding and undermines trust in conservation decision-making. Here we present a transparent framework that uses large language models (LLMs) to generate shallow, human-auditable decision-tree ensembles for predicting species distributions. Using three freshwater crayfish with contrasting ecological contexts, Austropotamobius torrentium (well-studied native), A. bihariensis (data-poor endemic), and Faxonius limosus (invasive generalist), we show that LLM-derived rule ensembles are competitive with both shallow decision trees and random forests (Macro-F1: 0.708 for A. torrentium, 0.689 for A. bihariensis, 0.927 for F. limosus), and provide the best performance among all tested models for the data-poor endemic A. bihariensis, where random forests exhibited the highest variance across folds (SD = 0.142). On three synthetic datasets with known ground-truth ecological rules, the LLM recovers true thresholds within 1% accuracy, providing methodological validation that the generated rules reflect genuine ecological structure rather than statistical noise. Beyond performance, the framework yields human-auditable explanations for every prediction, including explicit decision paths, supporting rules, and stable ecological rationales across cross-validation folds. These results suggest that, for structured ecological problems, interpretable models built from LLM-generated rules can approach black-box accuracy, particularly under data scarcity, while offering a practical pathway toward trustworthy biodiversity modelling in conservation applications. species distribution modelling large language models interpretable machine learning decision trees ecological rules freshwater crayfish modeliranje razširjenosti vrst veliki jezikovni modeli interpretabilno strojno učenje odločitvena drevesa ekološka pravila sladkovodni raki true false true Angleški jezik Slovenski jezik Članek v reviji 2026-10-29 14:41:40 2026-10-29 14:41:40 2026-10-30 04:14:42 0000-00-00 00:00:00 2026 0 0 9 str. art. 103883 Vol. 97 Aug. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:595.3 1574-9541 10.1016/j.ecoinf.2026.103883 302211587 Interpretable_by_design_Miok_et_al_2026.pdf Interpretable_by_design_Miok_et_al_2026.pdf 1 DC8717DC3B5BDE497E93D048D4C7C13A 27b4ced44355d3db0792135ae7460533ea3aad19e695674f92e1bc6a127f16c4 152730ab-d39f-11f1-aea2-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=252001 https://www.sciencedirect.com/science/article/pii/S1574954126002906 1 71c709de-d39e-11f1-aea2-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=251994 Fakulteta za računalništvo in informatiko 0 0 0