DOI: 10.5061/dryad.m0cfxppdz
title: Data from: Latitudinal beta-diversity gradient of riverine macroinvertebrate: Interplays of climatic and land use factors

description: 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.
description: 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).
description: # Data for: Latitudinal beta-diversity gradient of riverine
 macroinvertebrate: Interplays of climatic and land use factors
 [https://doi.org/10.5061/dryad.m0cfxppdz](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.