Tropical niche conservatism and dispersal limitation jointly determine taxonomic and phylogenetic β-diversities of Odonata in Eastern China
Data files
Apr 15, 2025 version files 2.04 MB
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Odonata_climate-niche_data.xlsx
99.09 KB
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Odonata_raw_bio_data.xlsx
1.70 MB
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Odonata_raw_env_data.xlsx
144.65 KB
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Odonata_β-diversities__data.xlsx
53.18 KB
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R_Code-calculation_of_climate-niche.rtf
42.44 KB
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README.md
4.21 KB
Abstract
AQ4Tropical niche conservatism (TNC) and dispersal limitation (DL) are major ecological and evolutionary mechanisms in shaping taxonomic and phylogenetic β‐diversities. While these mechanisms have been studied in plants and vertebrates, their roles in freshwater taxa remain unclear. We leveraged Odonata species distribution and phylogenetic data to map geographical patterns of taxonomic and phylogenetic β‐diversities, and to determine whether Odonata β‐diversity is primarily shaped by TNC or DL and whether temperature seasonality is a key driver determining TNC.
A moving window containing nine grids of 50 × 50 km was employed to quantify taxonomic and phylogenetic β‐diversities, including their turnover and nestedness components. A null model was utilised to calculate randomly expected phylogenetic β‐diversity based on observed taxonomic β‐diversity and site‐specific regional species pools. The generalised dissimilarity model was used to assess the roles of climatic and geographic distances shaping β‐diversity and to identify the key climatic factors.
Taxonomic total β‐diversity and its turnover component were generally higher than phylogenetic β‐diversity in most Odonata communities, with phylogenetic β‐diversity being relatively higher mainly in tropical regions. Current climatic factors independently explained slightly more of the variation in total β‐diversity than geographic distance alone, while geographic distance independently explained slightly greater proportions of deviance in turnover components. However, their joint effects accounted for an even larger part of the variation in β‐diversity. The key climatic predictors were temperature seasonality.
Current climatic factors, particularly temperature seasonality, largely shape taxonomic and phylogenetic β‐diversities of Odonata communities. Spatial turnover along the climatic gradient tends to involve phylogenetically related taxa, resulting in overall higher taxonomic than phylogenetic β‐diversity, supporting the TNC. The joint effects of climatic and geographic distances highlight the roles of climate, interacting with topographic complexity, shaping taxonomic and phylogenetic β‐diversities of Odonata in eastern China.
Tropical niche conservatism and dispersal limitation jointly determine taxonomic and phylogenetic β-diversities of Odonata in Eastern China
Zhenyuan Liu1,2, Bo-Ping Han1, *, Janne Soininen2
Description of the data and file structure
1) We include two datasets that used to statistical analysis:
Odonata_raw_bio_data: includes Odonata presence/absence data of 891 grid cells were used to calculate taxonomic and phylogenetic β-diversities.
Odonata_raw_env_data: include 10 environmental variables of 891 grid cells. See below:
Human: the human modification index, range from 0 to 1, with higher values indicating stronger human pressures.
Ele_Range: the elevation difference between the maximum and minimum values within each grid cell (m).
LGM_Bio1: the difference between the temperature of Last Glacial Maximum (LGM, ~21 Ka) and present- day values (1970-2000) within each grid cell (℃).
LGM_Bio12: the difference between the precipitation of Last Glacial Maximum (LGM, ~21 Ka) and present- day values (1970-2000) within each grid cell (mm).
Bio1: Annual mean temperature (℃).
Bio4: Temperature seasonality (standard deviation ×100).
Bio6: Min temperature of the coldest month (℃).
Bio12: Annual precipitation (mm).
Bio14: Precipitation of the driest month (mm).
Bio15: Precipitation seasonality (standard deviation ×100).
2) We provide two output tables summarizing the key statistical analyses:
Odonata_climate-niche_data: includes 682 Odonata’s climate niche.
Odonata_β-diversities _data: includes each cell grid’s β-diversities.
3) We provide R-Code to calculate climate niche.
R_Code-calculation of climate-niche
Data-1 Community presence/absence data of Odonata in eastern China (.xlsx).
**NOTE: **A total of 891 grid cells were utilized for the calculation of taxonomic and phylogenetic β-diversities, including their turnover and nestedness components. In the moving-window approach, only grid cells with a minimum of four neighboring cells were considered as focal cells for β-diversity calculations, resulting in the inclusion of 760 grids. These 760 grids were subsequently employed to compute pair-wise taxonomic and phylogenetic β-diversities.
Data-2 Environmental data at the grid-cell level in eastern China (.xlsx).
**NOTE: **A total of ten factors were incorporated, encompassing paleo-climate anomaly factors (2), current climatic factors (6), habitat heterogeneity (1), and human disturbance (1). Specifically, the two paleo-climate change variables included temperature (LGM_Bio1) and precipitation changes (LGM_Bio12) since the Last Glacial Maximum (LGM), calculated as the absolute differences between LGM and contemporary temperature and precipitation values. The six current climatic factors comprised: mean annual temperature (Bio1), mean annual precipitation (Bio12), minimum temperature of the coldest month (Bio6), precipitation during the driest month (Bio14), temperature seasonality (Bio4), and precipitation seasonality (Bio15). Habitat heterogeneity was represented by the elevation range (Ele_range) within each grid cell, while human disturbance was derived from the global human modification index. These ten explanatory variable layers were sourced from PaleoClim, WorldClim, and the global human modification database. Subsequently, average values of all variables were calculated for each cell (50 × 50 km). To align scale of β-diversity with environmental variables, the environmental factors for each cell were further computed as the average of the focal cell and its eight neighboring cells.
Reference:
J. L. Brown, D. J. Hill, A. M. Dolan, A. C. Carnaval, A. M. Haywood, PaleoClim, high spatial
resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).
M. Kennedy, J. R. Oakleaf, D. M. Theobald, S. Baruch-Mordo, J. Kiesecker, Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology. 25, 811–826 (2019)
Data-3 R-script for the statistical analysis (.rtf).