Beyond niche overlap : axis-specific reorganisation of environmental space in freshwater crayfish invasions

Miok, Kristian (author), Petko, Olga N. (author), Robnik Šikonja, Marko (author), Pârvulescu, Lucian (author)

Aim: Understanding whether invasive species retain or shift their ecological niches has relied on scalar overlap metrics that quantify the magnitude of niche change but not its structure. Here, we test whether biological invasions involve a reorganisation of the environmental axes along which native and invasive ranges are differentiated, and whether the dominant axis of this reorganisation is associated with invasion pathway type. Location: Global (North America, Europe, Africa, Asia, Australasia). Taxon: Freshwater crayfish (Decapoda: Astacidea): Procambarus clarkii, Faxonius limosus, F. virilis, F. rusticus, Pacifastacus leniusculus. Methods: We analysed native and invasive occurrences for five crayfish invaders representing intercontinental and within-continent pathways, using ~400 hydrologically resolved environmental variables (long-term averages, 1980–2021) from the Global Crayfish Database of Geospatial Traits. Decision tree and random forest classifiers quantified environmental differentiation between ranges, and feature contributions were aggregated by domain (climate, topography, soil, land cover). Robustness was assessed using spatially blocked cross-validation, grouped-permutation importance (which is reduces sensitivity to within-domain collinearity), exact permutation tests, sample-size sensitivity analysis and comparison with classical niche overlap metrics. Results: Native and invasive occurrences were consistently distinguishable (random forest accuracy 98.2%–100.0%). Intercontinental invaders were differentiated predominantly along climatic axes (57%–76% of model importance), with topography contributing negligibly, whereas within-continent invaders retained climate as the dominant axis (~42%) but showed a substantial secondary topographic contribution (~33%), including river network position. This contrast was stable across cross-validation folds (SD < 1.3%), and the observed pathway grouping was the most extreme of the 10 possible species partitions (exact one-sided p = 0.10, the minimum attainable at this sample size). Classical overlap metrics (Schoener's D = 0.30–0.62) did not capture this distinction. Main Conclusions: Biological invasions involve not only changes in niche position but a reorganisation of the environmental axes that distinguish species' distributions. The dominant axis of reorganisation differs with invasion pathway, reflecting whether species encounter novel climatic regimes or shift within existing climatic space along topographic and network-position gradients. This axis-specific approach complements scalar overlap metrics and provides a basis for more mechanistic interpretations of invasion processes.

• Language: English
• Keywords: biological invasions, decision trees, dendritic networks, ecological niche shift, freshwater crayfish, Hydrography90m, machine learning, niche conservatism, niche reorganisation, species distribution modelling
• 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: 14 str.
• Numbering: Vol. 53, iss. 8, art. e70332
• PID: 20.500.12556/RUL-190519
• UDC: 595.3:004.94
• ISSN on article: 0305-0270
• DOI: 10.1111/jbi.70332
• COBISS.SI-ID: 302194947
• Publication date in RUL: 29.10.2026

Record is a part of a journal

• Title: Journal of biogeography
• Shortened title: J. biogeogr.
• Publisher: Wiley
• ISSN: 0305-0270
• COBISS.SI-ID: 35095040

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: biološke invazije, odločitvena drevesa, dendritična omrežja, premik ekološke niše, sladkovodni raki, Hydrography90m, strojno učenje, konservativnost niše, reorganizacija niše, modeliranje razširjenosti vrst

Projects

• Funder: EC - European Commission
• Project number: 101081355
• Name: Machine learning for Sciences and Humanities
• Acronym: SMASH