Data from: Latitudinal beta-diversity gradient of riverine macroinvertebrate: Interplays of climatic and land use factors
Data files
Dec 08, 2025 version files 88.85 KB
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Raw_Data.zip
85.23 KB
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README.md
3.63 KB
Abstract
Climate change and land use have major effects on macroinvertebrate biodiversity, but it is unclear how they shape spatial variations, particularly at large scales. This study explored the latitudinal patterns of beta diversity in riverine macroinvertebrate assemblages and examined how they are shaped by climate and land use. Location: China (18 ºN–48 ºN) from 2006 to 2021. The major taxa studied were riverine macroinvertebrates. This is a comprehensive dataset covering macroinvertebrate taxa from 42 watersheds across China was compiled to explore the latitudinal patterns of taxonomic, functional, and phylogenetic beta diversities, including turnover and nestedness components. Generalized additive models were used to assess the impacts of climatic and land-use variables, selected from 20 climatic and 10 land-use candidates, on latitudinal patterns of beta diversity. The study revealed that taxonomic and functional beta diversities shared a non-monotonic latitudinal pattern with a minimum in regions located between 24 ºN and 30 ºN, in contrast to the monotonic pattern observed in phylogenetic beta diversity. Climatic stability and land use were shown to significantly modulate the taxonomic beta diversity of macroinvertebrates, particularly by diminishing spatial turnover in high-latitude regions (24–48 ºN) and augmenting nestedness in low-latitude regions (18–30 ºN) with latitude decreasing. Land use was identified as a key factor altering the nestedness component of functional beta diversity, while climatic factors predominantly influenced phylogenetic beta diversity by reducing spatial turnover with latitude decreasing. This study uncovers three facets of macroinvertebrate beta diversity with different latitudinal patterns, highlighting the variable influences of climate and land use on these spatial differences and the distinct mediation by turnover and nestedness processes. These insights provide a foundation for latitude-specific conservation strategies that aim at preserving the intricate tapestry of macroinvertebrate diversity in rivers.
https://doi.org/10.5061/dryad.m0cfxppdz
Description of the data and file structure
Raw_Data.zip includes beta data file, land use data file, environment variables file, and R code.
Beta Data File: Contains data for calculating taxonomic, functional traits, and phylogenetic beta diversity across five latitudinal zones, as well as their trait miss file.
Land Use Data File: Provides land use percentage data, transformed using arcsine square root, across five latitudinal regions, used to calculate land use heterogeneity indicators.
Environment variables File: Contains climate and land use variables relevant to the study.
R code: R code for this analysis
See the file "Description of the data and file structure.txt" for folder layout and file naming.
Files and variables
File: Raw_Data.zip
Description:
1.Abbreviations of study area
For zone A:
MDJ = Mudan River
MLH = Muling River
EEQSH = Irtysh River
YLH = Yili River
CEH = Chaor River
NLH = Naoli River
TMJ = Tumen River
For zone B:
BHH = Buha River
LH = Luanhe River
BSH = Beisanhe River
NSH = Nansanhe River
HMH = Tuhaimajia River
HSH = Huangshui River
HH = Hei River
HHSY = Upper Yellow River
HTH = Hun-Tai River
For zone C:
FTH = Futuan River
CHLY = Chaohu River
NJSY = Upper Nu River
HJ = Han River
HH = Huai River
HHY = Ruoergai
SH = Sihe River
WH = Wei River
For zone D:
GJ = Ganjiang River
QTJ = Qiantang River
LCJZ = Middle Lancang River
NJZ = Middle Nu River
CSH = Chishui River
JSJ = Jinshajiang River
WJ = Wujiang River
DJ = Diaojiang River
LJ = Lijiang River
For zone E:
XJ = The West River
XSBN = Xishuangbanna
DJZL = Shima and Danshui River
LXH = Liuxi River
NXH = Nanxi River
STSK = Songtao Reservoir River
TJ = Tanjiang River
WQH = Wanquan River
DJGL = Dongjiang River
2.Abbreviations of Bioclimatic Variables
The following is a list of the abbreviations used for bioclimatic variables in the dataset, which correspond to commonly used climate parameters in ecological and climate modeling studies. Each variable is defined according to the WorldClim dataset.
They are coded as follows:
bio1 = Annual Mean Temperature
bio2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
bio3 = Isothermality (BIO2/BIO7) (×100)
bio4 = Temperature Seasonality (standard deviation ×100)
bio5 = Max Temperature of Warmest Month
bio6 = Min Temperature of Coldest Month
bio7 = Temperature Annual Range (BIO5-BIO6)
bio8 = Mean Temperature of Wettest Quarter
bio9 = Mean Temperature of Driest Quarter
bio10 = Mean Temperature of Warmest Quarter
bio11 = Mean Temperature of Coldest Quarter
bio12 = Annual Precipitation
bio13 = Precipitation of Wettest Month
bio14 = Precipitation of Driest Month
bio15 = Precipitation Seasonality (Coefficient of Variation)
bio16 = Precipitation of Wettest Quarter
bio17 = Precipitation of Driest Quarter
bio18 = Precipitation of Warmest Quarter
bio19 = Precipitation of Coldest Quarter
elev = Elevation
3.Transformation of Land Use Variables
To ensure proper processing and normalization of land use variables for statistical modeling and analysis, all land use variables, including Cropland, Forest, Shrub, Grassland, Water, Snow/Ice, Barren, Impervious, and Wetland, have been transformed using the following mathematical procedure:
Transformation formula: Land use = arcsin(sqrt(x))
Code/software
Files can be accessed using Microsoft Excel and R.
We compiled a dataset of macroinvertebrate taxa of 42 riverine catchments across China, covering a broad inland region from 48 ° and 18 ° north latitude. Using China’s climate zoning (https://www.resdc.cn/data.aspx?DATAID=243) as a reference, we characterized China into five latitude zones: Zone A (42-48 °N), Zone B (36-42 °N), Zone C (30-36 °N), Zone D (24-30 °N), and Zone E (18-24 °N). Taxonomic data were primarily obtained from peer-reviewed studies published between 2006 and 2021. Macroinvertebrate traits were quantified using a binary coding approach that consisted of assigning each taxon to specific trait categories based on its predominant characteristics. Taxonomic distances based on the path lengths in Linnean taxonomic trees were employed as a proxy for the actual phylogeny. For climatic variables, average elevation, long-term temperature, and long-term precipitation data for the 1970–2000 period at a resolution of 1 km were retrieved from the WorldClim database. For land use variables, the land use percentage within each watershed was calculated using a dataset from 2015 at a spatial resolution of 30 m, including nine land use types, i.e., cropland, forest, shrubland, grassland, water, snow/ice, barren land, impervious land, and wetland, compiled by Yang & Huang (2022).
