Incorporating Local Adaptation Into the Forecasts of Paeonia Veitchii Distribution

BackgroundSpecies distribution models(SDMs) have been widely used to forecast the distribution of medicinal plants under climate change at the species level. However, most predictions of this distribution have ignored local adaptation.MethodsHere, we used Paeonia veitchii, a Daodi herb from Sichuan Province, China, as an example. The 226 occurrence sites of P. veitchii and 31 bioclimatic variables were used to delineate different climatic populations based on principal component analysis and hierarchical cluster analysis. Maximum entropy algorithm was developed to project the distribution of the diverse population under Last Glacial Maximum community climate (ca. 22 ka), the Mid-Holocene (ca. 6 ka), and current and various future climate scenarios (representative concentration pathways [RCPs] 2.6, 6.0, and 8.5). Critical environmental variables that shape separate population distributions and distribution shifts under multiple climate change scenarios were also identified. ResultsThe results showed that local adaptation exists within the distribution range of P. veitchii and all presence sites were clustered into P. veitchii from Daodi (PV_Daodi) and non-Daodi (PV_non-Daodi). Precipitation of the wettest month, mean global UV radiation in January (UV_JAN), and mean global UV radiation in July are essential when shaping the distribution of PV_Daodi, and UV_JAN, annual mean air temperature, and isothermality are vital for PV_non-Daodi. The PV_Daodi population was predicted to have an expansion in potential distribution in 2050 and a slight contraction in 2070 under RCP 2.6 and 6.0 scenarios. The PV_non-Daodi population will have a consistent expansion under RCP 6.0 and 8.5 scenarios. Both populations tend to shift northward under climate change. ConclusionThe results of our study could be a useful reference to implement the long-term conservation of high-quality P. veitchii.

• Publication year

  2020

• Venue

  Unknown venue

• Publication date

  2020-11-03

• Fields of study

  Not labeled

• Identifiers
  DOI 10.21203/rs.3.rs-98582/v1
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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