Interpretable by design : language model–derived ecological rules for species distribution modelling

Miok, Kristian (author), Škrlj, Blaž (author), Robnik Šikonja, Marko (author), Pârvulescu, Lucian (author)

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.

• Language: English
• Keywords: species distribution modelling, large language models, interpretable machine learning, decision trees, ecological rules, freshwater crayfish
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 9 str.
• Numbering: Vol. 97, art. 103883
• PID: 20.500.12556/RUL-190532
• UDC: 004.85:595.3
• ISSN on article: 1574-9541
• DOI: 10.1016/j.ecoinf.2026.103883
• COBISS.SI-ID: 302211587
• Publication date in RUL: 29.10.2026

Record is a part of a journal

• Title: Ecological informatics
• Publisher: Elsevier
• ISSN: 1574-9541
• COBISS.SI-ID: 23488295

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: modeliranje razširjenosti vrst, veliki jezikovni modeli, interpretabilno strojno učenje, odločitvena drevesa, ekološka pravila, sladkovodni raki