DOI: 10.5061/dryad.z8w9ghxqg
title: Tropical niche conservatism and dispersal limitation jointly determine taxonomic and phylogenetic β-diversities of Odonata in Eastern China

description: Aim: 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.
 Location: Eastern China   Time Period: Present
   Major Taxa Studied: Odonata   Methods 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. Results
 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. Main Conclusions 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.
description: # Tropical niche conservatism and dispersal limitation jointly determine
 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).**