Supplemental data from: Contraction and expansion: global geographical variation in reproductive systems of Primula is driven by different mechanisms
Data files
Nov 04, 2024 version files 8.20 MB
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Appendix.rar
8.19 MB
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README.md
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Abstract
Aim: Reproductive systems strongly influence plant’s evolution and adaption, and the biogeographic pattern of its variation has intrigued biologists and ecologist. Here, to test the impacts of paleoglaciation on plant’s reproductive system variation, we compared the geographical pattern and environmental drivers in the proportions of different reproductive systems in Primula in regions affected and unaffected by paleoglaciation.
Location: Global
Time period: Since the Last Glacial Maximum (LGM)
Major taxa studied: Primula
Methods: Using data on reproductive systems and distributions of 604 Primula taxa around the world and 221 along the elevational gradient of the Himalayas, we demonstrated the global pattern and the elevational pattern in the proportions of homostylous taxa. We employed general linear models to established the relationship between the proportion of homostylous taxa and environmental variables and hierarchical partitioning to assess the relative contributions of these variables in both regions affected and unaffected by paleoglaciation, respectively.
Results: We found a higher proportion of homostylous taxa in regions glaciated during the LGM than those unglaciated, with different latitudinal patterns and climate drivers. The proportion of homostylous taxa showed varying trends across different regions: increasing with latitude and temperature anomaly in glaciated regions, while concentrating at lower latitudes with higher winter temperatures in unglaciated regions. Additionally, homostylous taxa were more prevalent at lower elevations in the Himalayas.
Main conclusions: Our study provides the first quantitative evidence for the hypothesis that selfers are more prevailing in regions affected by paleoglaciation facilitated by recolonization via comparing the geographic pattern and drivers in regions affected and unaffected by paleoglaciation. Our findings also reveal the concentrated distribution of homostylous taxa in Primula at low latitudes, which may be the result of population shrinkage caused by heat stress, facing a more severe survival crisis under the circumstances of global warming and increasing human activities.
https://doi.org/10.5061/dryad.tb2rbp08x
This dataset contains the original data sources used in the study, the datasets collected for analysis, and the analysis results, which presents the distribution pattern in reproductive systems (heterostyly and homostyly) of the genus Primula.
Description of the data and file structure
The dataset contains 4 MS Word documents and 4 MS Excel sheets.
Appendix S1 (Word document) contains data sources used in the research for global Primula distributions.
Appendix S2 (Word document) contains the map of global geographical units used in the analyses.
Appendix S3 (Excel sheet) is the distributional database which contains records for 611 Primula taxa. In the sheet, rows represent taxa, columns represent regions. If the value of a taxon in a region is 0, it means that the taxon is not distributed in the region, and 1 means that the taxon is distributed in the region.
Appendix S4 (Excel sheet) is the dataset of elevational distribution which contains 221 Primula taxa in the Himalayas.
Appendix S5 (Excel sheet) is the dataset of breeding systems and life history traits of 604 Primula taxa.
Appendix S6 (Word document) contains phylogenetic analyses about the traits of heterostyly and homostyly in the genus Primula.
Appendix S7 (Excel sheet) presents the complete results of the analysis by every sensitivity criteria in both types of regions (affected or unaffected by paleoglaciation). The sheet “correlation” shows the Spearman Correlation Index between all the variables used in the analyses. The sheet “Mann-Whitney U test” shows the results of Mann-Whitney U test to test whether there was a significant difference in the proportion of homostylous taxa between the two types of regions at different latitudinal zones. The sheet “univariable GLM” shows the results of univariate GLM models between the proportion of homostylous taxa and each variables. The sheet “model selection” shows the results of selecting the best combinations of variables used to construct the multivariate models, which was aimed to reduce the impact of multicollinearity. The sheet “multivariate GLM” shows the results of multivariate GLM models between the proportion of homostylous taxa and the selected best combinations of variables, and the independent contribution of each variable to the models analyzed by hierarchical partitioning.
Abbreviations: absolute latitude of regional centroid (LAT), mean annual temperature (MAT), mean temperature of coldest quarter (MTCQ), mean temperature of warmest quarter (MTWQ), annual potential evapotranspiration (PET), annual precipitation (MAP), precipitation of coldest quarter (PCQ), precipitation of warmest quarter (PWQ), aridity index (AI), changes of mean annual temperature and precipitation (MATano and MAPano) and climate-change velocity (Velocity) since the Last Glacial Maximum, regional standard deviation of elevation (ESD), regional proportion of land covered by glaciers and ice sheets during the LGM period (GLA), species richness of Primula in region(SPR).
For climate-change velocity, please see: Sandel B., Arge L., Dalsgaard B., Davies R. G., Gaston K. J., Sutherland W. J., & Svenning J. C. (2011). The Influence of Late Quaternary Climate-Change Velocity on Species Endemism. Science, 334(6056), 660-664. doi:10.1126/science.1210173
Appendix S8 (Word document) contains the global distribution of annual/biennial taxa of Primula, including their species richness and regional proportion.
Folder (Supplemental Information) contains figure files and phylogenetic tree file used in MS word documents above.
Sharing/Access information
The climate data used in the study was downloaded from from WorldClim V1.4 (https://worldclim.org/), and the topographic data was obtained from GLOBE Digital Elevation Model (https://ngdc.noaa.gov/mgg/topo/globe.html)).
Code/Software
All analyses were performed in R 3.5.2.