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Ecological divergence of Paracoccidioides brasiliensis lineages and P. lutzii with SDG-relevant implications.

Trájer, Attila J. and Vehovszky, Katalin (2026) Ecological divergence of Paracoccidioides brasiliensis lineages and P. lutzii with SDG-relevant implications. Acta Tropica.

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Abstract

Abstract The ecological differentiation of Paracoccidioides brasiliensis lineages (S1, PS3, S1a/b) and Paracoccidioides lutzii across South America was analysed using environmental, climatic, edaphic, and anthropogenic data. Occurrences were linked to elevation, urbanisation, Köppen–Geiger climate classes, and bioclimatic variables. S1 exhibited the broadest ecological range, spanning mid-elevation uplands to lowland Amazonia and multiple climate zones (Af, Am, Aw, Cfa). PS2, PS3 and P. lutzii were mainly restricted to mid-elevation environments. PS2 was associated with slightly higher elevations; PS3 overlapped broadly with S1, and P. lutzii showed a stronger association with ustult ultisols and tropical savannah (Aw) climates. Machine learning models separated P. brasiliensis from P. lutzii with high accuracy, whereas lineage-level discrimination was more variable, particularly for PS2 versus P. lutzii. Temperature variables (bio11, bio6, bio1), elevation, and soil type were the strongest predictors, with precipitation and aridity playing secondary roles. Ordination and niche analyses revealed strong overlap among lineages. S1 and PS3 lineages showed the greatest overlap; PS2 was more distinct, and P. lutzii occupied an intermediate position. High niche similarity with the xenarthran host Dasypus novemcinctus was also observed. Overall, ecological differentiation within Paracoccidioides reflects gradient-driven structuring rather than discrete niches, with implications for spatial risk assessment, particularly for S1-associated regions, and targeted environmental management aligned with SDGs 3, 13, and 15.

Item Type: Article
Uncontrolled Keywords: Paracoccidioidomycosis, Ecological niche modelling, Bioclimatic variables, Environmental clustering, South America, Machine learning
Subjects: R Medicine / orvostudomány > RZ Other systems of medicine / orvostudomány egyéb területei
Depositing User: Dr. Attila János Trájer
Date Deposited: 04 Sep 2026 08:28
Last Modified: 04 Sep 2026 08:28
URI: https://real.mtak.hu/id/eprint/245514

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