Global potential distribution of Paeonia lactiflora and its climate-driven shifts: insights from an enhanced MaxEnt model integrating soil and solar radiation variables

<i>Paeonia lactiflora</i> Pall. is a globally important medicinal perennial whose habitat suitability remains poorly known beyond China. Using an enhanced MaxEnt model integrating 45 climatic, soil, and solar radiation variables, we predicted its current and future global distribution based on 833 spatially thinned occurrence records and 12 low-collinearity predictors. The model performed excellently (test AUC = 0.945 ± 0.001; TSS = 0.762 ± 0.018). Precipitation of the warmest quarter (bio18), mean temperature of the coldest quarter (bio11), temperature seasonality (bio4), and November solar radiation (srad11) were the dominant drivers. Currently, total suitable habitat is centered in East Asia, central Europe, and northeastern/midwestern USA. All future scenarios (SSP2-4.5 and SSP5-8.5, 2041-2060 and 2061-2080) project about 25-45% expansion of total suitable area, accompanied by a consistent northeastward centroid shift of highly suitable habitat (up to ~1,234 km under SSP5-8.5 2061-2080). Late-century high-emission conditions cause localized contraction of core habitat in southern margins. <i>P. lactiflora</i> is likely to benefit from moderate warming, but high-emission pathways will drive major reorganization and degradation of traditional production areas, necessitating strengthened conservation in current strongholds and proactive planning in emerging northern regions.

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Journal
Frontiers in Plant Science
Published
2026-02-13
DOI
https://doi.org/10.3389/fpls.2026.1756429
Primary Topic
Species Distribution and Climate Change
Type
article
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article

Global potential distribution of Paeonia lactiflora and its climate-driven shifts: insights from an enhanced MaxEnt model integrating soil and solar radiation variables

Shu Wang, Rongchun Han, Xiaohui Tong, Jinyu Cai et al.
Frontiers in Plant Science
Species Distribution and Climate Change
article

Global potential distribution of Paeonia lactiflora and its climate-driven shifts: insights from an enhanced MaxEnt model integrating soil and solar radiation variables

Shu Wang, Rongchun Han, Xiaohui Tong, Jinyu Cai, Zheng Fan
article en

Abstract

<i>Paeonia lactiflora</i> Pall. is a globally important medicinal perennial whose habitat suitability remains poorly known beyond China. Using an enhanced MaxEnt model integrating 45 climatic, soil, and solar radiation variables, we predicted its current and future global distribution based on 833 spatially thinned occurrence records and 12 low-collinearity predictors. The model performed excellently (test AUC = 0.945 ± 0.001; TSS = 0.762 ± 0.018). Precipitation of the warmest quarter (bio18), mean temperature of the coldest quarter (bio11), temperature seasonality (bio4), and November solar radiation (srad11) were the dominant drivers. Currently, total suitable habitat is centered in East Asia, central Europe, and northeastern/midwestern USA. All future scenarios (SSP2-4.5 and SSP5-8.5, 2041-2060 and 2061-2080) project about 25-45% expansion of total suitable area, accompanied by a consistent northeastward centroid shift of highly suitable habitat (up to ~1,234 km under SSP5-8.5 2061-2080). Late-century high-emission conditions cause localized contraction of core habitat in southern margins. <i>P. lactiflora</i> is likely to benefit from moderate warming, but high-emission pathways will drive major reorganization and degradation of traditional production areas, necessitating strengthened conservation in current strongholds and proactive planning in emerging northern regions.

Frontiers in Plant ScienceVol. 17
Anhui University of Traditional Chinese Medicine (CN)
Life in Land
Openalex Percentile: Top 57%
Species Distribution and Climate Change
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Global potential distribution of Paeonia lactiflora and its climate-driven shifts: insights from an enhanced MaxEnt model integrating soil and solar radiation variables — Shu Wang, Rongchun Han, et al. · Frontiers in Plant Science (2026) | TGRS Research Map | TGRS