Bioregions as heuristics: Data-driven delineation reveals continuous gradients rather than discrete boundaries in national biodiversity

Bioregions as heuristics: Data-driven delineation reveals continuous gradients rather than discrete boundaries in national biodiversity | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 December 2025 V1 Latest version Share on Bioregions as heuristics: Data-driven delineation reveals continuous gradients rather than discrete boundaries in national biodiversity Author : Eric Wahlsteen 0000-0001-8283-1112 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176615126.67303993/v1 145 views 89 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The delineation of biogeographical regions is fundamental to conservation planning, yet it remains debated whether these regions represent discrete ecological entities or merely heuristic simplifications of continuous variation. While classical regionalization relies on expert judgment or abiotic proxies, the explosion of citizen-science data offers a unique opportunity to derive regions directly from empirical species turnover. In this study, a reproducible, data-driven workflow is developed to delineate biogeographical regions in Sweden using over 115 million species records. A spatially stratified focal-associate species filtering protocol was applied to enhance ecological signals and performed Leiden clustering on Hellinger-transformed community data across three ecological strata: grasslands, forests, and a general multi-taxon dataset. The analysis consistently recovered a robust latitudinal divide near 60°N, aligning closely with the established boreal–temperate ecotone. However, beyond this major transition, clustering solutions were unstable at higher resolutions, and community distinctiveness was low. Furthermore, beta-diversity was overwhelmingly driven by turnover rather than nestedness. These findings suggest that aside from the primary boreal transition, Swedish biodiversity is structured by continuous compositional gradients rather than sharp boundaries. While data-driven methods can validate macroecological patterns, the pursuit of fine-scale discrete bioregions is ecologically unsupported in this system. Instead, bioregions should be viewed as practical tools for conservation planning imposed upon a fundamentally Gleasonian continuum. Supplementary Material File (ms bioregions 2025-12-18.docx) Download 3.16 MB Information & Authors Information Version history V1 Version 1 19 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords beta-diversity biogeography bioregionalization citizen-science machine-learning Authors Affiliations Eric Wahlsteen 0000-0001-8283-1112 [email protected] Lund University Faculty of Science View all articles by this author Metrics & Citations Metrics Article Usage 145 views 89 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Eric Wahlsteen. Bioregions as heuristics: Data-driven delineation reveals continuous gradients rather than discrete boundaries in national biodiversity. Authorea . 19 December 2025. DOI: https://doi.org/10.22541/au.176615126.67303993/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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