Abstract

Paeonia (Paeoniaceae), a culturally and economically important plant genus, has an isolated taxonomy while the evolution of this genus is unclear. A plant species endemic to southwest China, Paeonia mairei is precious germplasm for evolution-related research and cultivar improvement, and its conservation is urgent. However, little is known about its patterns of habitat distribution and responses to climate change. Using 98 occurrence sites and data of 19 bioclimatic variables, we conducted principal component analysis and hierarchical cluster analysis to delineate different climatic populations. Maximum entropy algorithm (MaxEnt) was applied to each population to evaluate the importance of environmental variables in shaping their distribution, and to identify distribution shifts under different climate change scenarios. We also applied MaxEnt to all of the P. mairei presence sites (P_Whole) to evaluate the need to construct separate species distribution models for separate populations rather than a common approach by treating them as a whole. Our results show that local adaptation exists within the distribution range of P. mairei and that all presence sites were clustered into a western population (P_West) and an eastern population (P_East). Two variables (precipitation of the driest month and temperature seasonality) are important when shaping the distribution of P_West, and another two variables (mean diurnal range and mean temperature of the wettest quarter) are important for P_East. Both populations are likely to shift upward under climate change, while P_East may lose most current suitable areas while P_West may not. P_Whole produced a narrower area compared to the combination of P_West and P_East but a suitable area (south Chongqing) may have been missed in the prediction. Accordingly, a population-based approach in constructing a species distribution model is needed to provide a detailed appreciation of the distribution of P. mairei, allowing for a population-based conservation strategy. In this case, it could include assisted migration to new and suitable distribution areas for P_West and in situ conservation in high elevation regions for P_East. The results of our study could be a useful reference for implementing the long-term conservation and further research of P. mairei.

Highlights

  • Over the past 100 years, anthropogenic greenhouse gas emissions have increased and resulted in unequivocal global warming, increasing temperatures by approximately 1.0°C more than pre-industrial levels, which are likely to increase 3.2°C by 2100 if emissions continue to increase at the current rate (HoeghGuldberg et al, 2018)

  • Considering that south Chongqing was lost from P_Whole, we suggest that a separate-species distribution models (SDMs) approach that can correctly evaluate the suitability of a species in regions where presence sites are disproportional, and provide more detail about species distribution, is a more reasonable strategy

  • It is of vital importance to estimate how climate change will affect the distribution of rare species for specific conservation purposes

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Summary

Introduction

Over the past 100 years, anthropogenic greenhouse gas emissions have increased and resulted in unequivocal global warming, increasing temperatures by approximately 1.0°C more than pre-industrial levels, which are likely to increase 3.2°C by 2100 if emissions continue to increase at the current rate (HoeghGuldberg et al, 2018). The only genus in the Paeoniaceae, has an isolated taxonomy and ancient origin (Pan, 1995; Zhou et al, 2018). Paeonia mairei Lévl., assumed to originate from two ancient populations (Zhou et al, 2018), is an endemic species to southwestern China (Pan, 1995). This germplasm is important for further studies on the evolution of the genus Paeonia and for breeding research for medicinal and horticultural industries. As demonstrated by our field survey from 2017-2019 in southwest China (Supplementary File 1), most populations show a limited number of individuals, while it is difficult to find individuals in some documented distribution regions, such as Nanchuan and Zhaojue counties

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