Data from: Population structure and historical demography of Dipteronia dyeriana (Sapindaceae), an extremely narrow palaeoendemic plant from China: implications for conservation in a biodiversity hot spot

Inferring past demography is a central question in evolutionary and conservation biology. It is, however, sometimes challenging to disentangle their roles of contemporary versus historical processes in shaping the current patterns of genetic variation in endangered species. In this study, we used both chloroplast microsatellite (cpSSR) loci and nuclear microsatellite (nSSR) loci to assess the levels of genetic differentiation, genetic effective population size, contemporary/historical levels of gene flow and demographic history for five populations sampled across the range of Dipteronia dyeriana, an endangered palaeoendemism from Southwestern China. We found that D. dyeriana had a mixed pattern of moderate genetic diversity and high inbreeding. Bayesian clustering divided D. dyeriana populations into two nSSR genetic clusters. Coalescent-based approximate Bayesian computation analyses suggest the western and eastern groups of D. dyeriana likely persisted in a long-term refuge in Southern China since the beginning of the last glacial period, whereas increasingly colder and arid climates at the onset of the last glacial maximum might have fostered the fragmentation of D. dyeriana within refugia. Following their divergence, the western group kept relatively stable effective population size, whereas the eastern group had experienced 500-fold population expansion during the Holocene. Although clear loss of genetic diversity by human activities was not suggested, recent habitat fragmentation has led to a reduction of population connectivity and increased genetic differentiation by ongoing genetic drift in isolated populations, possibly owing to decreased population size in recent dozen years. Finally, we discussed the implications of these results on conservation policies

program_codes_of_abc_for_Dipteronia161207

program_codes_of_abc_for_Dipteronia16120

Dipteronia dyeriana-nSSRs

Dipteronia dyeriana-nSSR

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation

Histograms and Gaussian distribution characteristics of the crop canopy reflectance data at different spatial scales before and after data analysis and correction with the new method in Labudalin farm of Hailaer Farming Cultivate Bureau in Inner Mongolia, China.

<p>Histograms and Gaussian distribution characteristics of the crop canopy reflectance data at different spatial scales before and after data analysis and correction with the new method in Labudalin farm of Hailaer Farming Cultivate Bureau in Inner Mongolia, China.</p

Histograms and Gaussian distribution characteristics of the crop canopy reflectance data at different spatial scales before and after data analysis and correction with the new method in Shunyi District and Changping District of Beijing, China.

<p>Histograms and Gaussian distribution characteristics of the crop canopy reflectance data at different spatial scales before and after data analysis and correction with the new method in Shunyi District and Changping District of Beijing, China.</p

Location of the experimental fields.

<p>(A): Location of the experimental field in Labudalin farm of Hailaer Farming Cultivate Bureau in Inner Mongolia, China. (B): Location of the experimental field in Shunyi District and Changping District of Beijing, China.</p