Effects of climate change on the distribution of wild Akebia trifoliata

Abstract Understanding the impacts and constraints of climate change on the geographical distribution of wild Akebia trifoliata is crucial for its sustainable management and economic development as a medicinal material or fruit. In this study, according to the first‐hand information obtained from field investigation, the distribution and response to climate change of A. trifoliata were studied by the MaxEnt model and ArcGIS. The genetic diversity and population structure of 21 natural populations of A. trifoliata were studied by simple sequence repeat (SSR) markers. The results showed that the most important bioclimatic variable limiting the distribution of A. trifoliata was the Mean Temperature of Coldest Quarter (bio11). Under the scenarios SSP1‐2.6 and SSP2‐4.5, the suitable area of A. trifoliata in the world will remain stable, and the suitable area will increase significantly under the scenarios of SSP3‐7.0 and SSP5‐8.5. Under the current climate scenario, the suitable growth regions of A. trifoliata in China were 79.9–122.7°E and 21.5–37.5°N. Under the four emission scenarios in the future, the geometric center of the suitable distribution regions of Akebia trifoliata in China will move to the north. The clustering results of 21 populations of A. trifoliata analyzed by SSR markers showed that they had a trend of evolution from south to north.

[1]  Xiangyong Peng,et al.  Effects of Climate Change on the Distribution of Akebia quinata , 2021, Frontiers in Ecology and Evolution.

[2]  Xin-Qiao Xu,et al.  Current Situation, Global Potential Distribution and Evolution of Six Almond Species in China , 2021, Frontiers in Plant Science.

[3]  Neftalí Sillero,et al.  Common mistakes in ecological niche models , 2020, Int. J. Geogr. Inf. Sci..

[4]  H. Xiong,et al.  Akebia trifoliata pericarp extract ameliorates inflammation through NF-κB/MAPK signaling pathways and modifies gut microbiota. , 2020, Food & function.

[5]  Chao-kui Li,et al.  Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China , 2020, Forests.

[6]  M. Acevedo,et al.  Local extinction risk under climate change in a neotropical asymmetrically dispersed epiphyte , 2020, Journal of Ecology.

[7]  Gang Fan,et al.  Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. , 2020, The Science of the total environment.

[8]  Tao Yang,et al.  Akebia trifoliata (Thunb.) Koidz Seed Extract inhibits human hepatocellular carcinoma cell migration and invasion in vitro. , 2019, Journal of ethnopharmacology.

[9]  Mauro Fois,et al.  Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt , 2019, Ecol. Informatics.

[10]  Jun Tao,et al.  Impact of climate factors on future distributions of Paeonia ostii across China estimated by MaxEnt , 2019, Ecol. Informatics.

[11]  Hongwen Huang,et al.  Effectiveness of recurrent selection in Akebia trifoliata (Lardizabalaceae) breeding , 2019, Scientia Horticulturae.

[12]  J. Gasca-Pineda,et al.  Effects of global warming on the potential distribution ranges of six Quercus species (Fagaceae) , 2019, Flora.

[13]  Hongwen Huang,et al.  Genetic analysis of fruit traits and selection of superior clonal lines in Akebia trifoliate (Lardizabalaceae) , 2018, Euphytica.

[14]  J. Tao,et al.  Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. , 2018, The Science of the total environment.

[15]  Stephen E. Fick,et al.  WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas , 2017 .

[16]  Hai-yan Wei,et al.  Prediction of the potential geographic distribution of the ectomycorrhizal mushroom Tricholoma matsutake under multiple climate change scenarios , 2017, Scientific Reports.

[17]  Yujun Yi,et al.  Evaluating the ecological influence of hydraulic projects: A review of aquatic habitat suitability models , 2017 .

[18]  Janneke HilleRisLambers,et al.  Climate Warming and Seasonal Precipitation Change Interact to Limit Species Distribution Shifts across Western North America , 2016, PloS one.

[19]  Yujun Yi,et al.  Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China , 2016 .

[20]  J. Lendemer,et al.  Climate change impacts on endemic, high-elevation lichens in a biodiversity hotspot , 2016, Biodiversity and Conservation.

[21]  M. Wisz,et al.  Forecasted coral reef decline in marine biodiversity hotspots under climate change , 2015, Global change biology.

[22]  Yong Cao,et al.  Antibacterial oleanane-type triterpenoids from pericarps of Akebia trifoliata. , 2015, Food chemistry.

[23]  A. Wróblewska.  Genetic Diversity and Spatial Genetic Structure of Chamaedaphne calyculata (Ericaceae) at the Western Periphery in Relation to its Main Continuous Range in Eurasia , 2014, Folia Geobotanica.

[24]  P. Roy,et al.  Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills , 2013 .

[25]  T. Miyase,et al.  Triterpene saponins from the pericarps of Akebia trifoliata. , 2012, Chemical and pharmaceutical bulletin.

[26]  D. Neale,et al.  Patterns of Population Structure and Environmental Associations to Aridity Across the Range of Loblolly Pine (Pinus taeda L., Pinaceae) , 2010, Genetics.

[27]  J. Elith,et al.  Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models , 2009 .

[28]  Likun Gong,et al.  Comparative 28-day repeated oral toxicity of Longdan Xieganwan, Akebia trifoliate (Thunb.) koidz., Akebia quinata (Thunb.) Decne. and Caulis aristolochiae manshuriensis in mice. , 2008, Journal of ethnopharmacology.

[29]  P. Marquet,et al.  A Significant Upward Shift in Plant Species Optimum Elevation During the 20th Century , 2008, Science.

[30]  Sassan Saatchi,et al.  Modeling distribution of Amazonian tree species and diversity using remote sensing measurements , 2008 .

[31]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[32]  D. Guo,et al.  Structure elucidation and NMR spectral assignments of three new lignan glycosides from Akebia trifoliata , 2008, Magnetic resonance in chemistry : MRC.

[33]  C. Perrings,et al.  Future challenges , 2007, Proceedings of the National Academy of Sciences.

[34]  Zhimin Wang,et al.  Triterpenoid saponins and phenylethanoid glycosides from stem of Akebia trifoliata var. australis. , 2006, Phytochemistry.

[35]  R. Ceulemans,et al.  End-of-season effects of elevated temperature on ecophysiological processes of grassland species at different species richness levels , 2006 .

[36]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[37]  R. Petit,et al.  Conserving biodiversity under climate change: the rear edge matters. , 2005, Ecology letters.

[38]  H. Nybom.  Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants , 2004, Molecular ecology.

[39]  D. Neale,et al.  From genotype to phenotype: unraveling the complexities of cold adaptation in forest trees , 2003 .

[40]  G. Yohe,et al.  A globally coherent fingerprint of climate change impacts across natural systems , 2003, Nature.

[41]  E. Nevo,et al.  Microsatellite diversity associated with ecological factors in Hordeum spontaneum populations in Israel , 2001, Molecular ecology.

[42]  M. K. Hughes,et al.  Influence of snowfall and melt timing on tree growth in subarctic Eurasia , 1999, Nature.