20.500.12556/RUL-183939 Environmentally grounded pseudo-absence sampling for species distribution models a language guided framework Aim: Species Distribution Models (SDMs) are widely used in conservation planning, invasive species management and global change assessments. Their reliability depends on both presence and absence data, yet biodiversity databases are dominated by presence records, while true absences are rarely collected and geographically restricted. We introduce a framework that integrates large language models (LLMs) to sample ecologically realistic pseudo-absences under the constraints of dendritic river networks. Innovation: We present the Language-Grounded Multivariate Pseudo-Absence Sampling (LGMPAS) framework, which converts environmental predictor profiles into natural-language eco-narratives that an LLM uses to score and rank pre-filtered candidate locations drawn exclusively from the river network and restricted to unlabelled sites. Retrieval-Augmented Generation (RAG) further anchors eco-narratives in published ecological knowledge. We tested LGMPAS on two ecologically contrasting crayfish species in the Danube basin, the widespread invasive Faxonius limosus and the narrowly endemic Austropotamobius bihariensis, validating outputs against independent field-collected true-absence data using Random Forest predictive performance, spatial overlap of high-suitability areas and predictor-space distances. LLM-derived pseudo-absences closely reproduced true-absence model outputs and consistently outperformed random sampling across both species. Main Conclusions: LGMPAS demonstrates that LLMs can reliably sample pseudo-absences that reproduce the ecological signal of true-absence data, even under complex freshwater network constraints. By reducing dependence on costly absence surveys in contexts where true-absence data are unavailable or spatially restricted, and by avoiding the biases inherent to random sampling, the framework strengthens the robustness of SDMs for conservation applications. Its reproducibility and adaptability across taxa and ecosystems offer particular value for biodiversity monitoring, invasive species management and conservation planning under global change. species distribution models pseudo-absence sampling large language models ecological modelling spatial ecology freshwater ecosystems crayfish retrieval-augmented generation modeliranje razširjenosti vrst psevdo-odsotnosti veliki jezikovni modeli ekološko modeliranje prostorska ekologija sladkovodni ekosistemi raki priklicno obogatena generacija true false true Angleški jezik Slovenski jezik Članek v reviji 2026-06-22 14:07:00 2026-06-22 14:07:04 2026-06-23 08:46:08 0000-00-00 00:00:00 2026 0 0 13 str. iss. 5, art. e70199 Vol. 32 May 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8:574 1366-9516 10.1111/ddi.70199 277720323 RAZ_Miok_Kristian_2026.pdf RAZ_Miok_Kristian_2026.pdf 1 353EE713B22F5B866BF7203226D70812 875425bdddae177b56756baecf16927e5fe2b76f3b98c32fe2047c7928467a81 5ab6179f-6e34-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=236267 https://onlinelibrary.wiley.com/doi/10.1111/ddi.70199 1 d04050e2-6e32-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=236264 Fakulteta za računalništvo in informatiko 0 0 0