Data from: Asymmetric species turnover underpins the latitudinal diversity gradient in the Western Ghats biodiversity hotspot
Data files
Jul 20, 2026 version files 19.49 MB
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Beta_data_updated.zip
19.48 MB
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README.md
14.63 KB
Abstract
Latitudinal diversity gradients (LDGs), namely an increase in species richness from higher to lower latitudes, are one of ecology’s best documented patterns, yet how the fundamental components of compositional mismatches (i.e., species gains and losses along a gradient) explain these patterns remains poorly understood. As we argue here, one of two alternative turnover scenarios must apply to a typical latitudinal richness gradient. Such gradients can be the product of either a scenario where both species gains and losses are correlated and increase with geographical distance, or of uncorrelated change where species gains increase with distance, but losses remain constant. We distinguish between these alternatives using the freshwater fish communities of the Western Ghats of India, a biodiversity hotspot with a strong latitudinal species richness gradient. We find that the uncorrelated change scenario prevails for both taxonomic and functional diversity, and on both the western and eastern slopes of the mountain range, albeit with subtle differences. Our results suggest that these uncorrelated changes result from high nestedness among communities and reflect the dominance of dispersal processes in the region. As the observed pattern arises due to an increase in range-restricted species at lower latitudes, it is consistent with Rapoport’s rule. Partitioning species turnover into its gain and loss components thus helps explain the mechanisms underlying latitudinal gradients of diversity, and informs our understanding of biodiversity change.
Dataset DOI: 10.5061/dryad.nk98sf86r
Description of the data and file structure
The folder contains R scripts and data files used to investigate the latitudinal richness gradient by measuring the underlying directional compositional changes in freshwater fish communities in the Western Ghats.
Main directory: Beta_data_updated.zip
T_F_beta_diversity1.R
This script is used to perform null model analysis, and calculate the directional measures of taxonomic and functional beta-diversity. We calculated both the rough and standardized effect size values of directional taxonomic and functional beta-diversity. We also calculated standardized Jaccard replacement and nestedness components by performing a null model analysis. Using this script, we plotted the replacement and nestedness components across east- and west-flowing basins. This script is also used to calculate geodesic distance among basins using ‘geodist’ package. We used ‘adespatial’, ‘mFD’, and ‘betapart’ packages to perform the analysis.
CSV files used in the analysis:
beta_data_2_5_23.csv
This file contains basin by species community data that is used to calculate the compositional distances among communities.
Variables:
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Basin: basins included in the study
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Column (2:326): Species presence (1) and absence (0) in basins
env_15_7_24.csv
This file contains the basin-wise values for environmental variables.
Variables:
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Basin: 59 river basins included in the study
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flow.dir: The direction of the water flow- east (1) & west (0)
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position: Basins situated north of Palghat gap in east (NPE) and west (NPW), and basins that are situated south of Palghat gap in eastern (SPE) and western (SPW) side of the Escarpment.
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flow: Basins categorized based on whether a basin flows towards east or west
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gap: Basins categorized based on whether they are situated north (NP) or south (SP) of Palghat gap
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SpRich: Observed species richness in each basin
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EndRich: Observed endemic species richness in each basin
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area: Total area of a basin in km2
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str.dens: Stream density
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soc: Soil organic carbon in tonnes/hectare
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dis.pyr : Annual average water discharge (cubic meters/second)
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soil.div: Soil diversity (Shannon index)
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cover.div: Land cover diversity
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classarea: Percentage of basin area above 1000-meter altitude
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alt.mean: Mean elevation (meter)
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alt.min: Minimum elevation (meter)
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alt.max: Maximum elevation (meter)
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alt.range: elevation range (meter)
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HI.index: Hypsometric index
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slope.mean: Mean slope (in degrees)
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rad.cv: Solar radiation annual variability
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rad.sum: Overall annual sum of solar radiation (KJ/m2/day)
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aet.sum: Overall annual sum of actual evapotranspiration (mm)
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aet.cv: Overall annual variability of actual evapotranspiration
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prec.bio12: Precipitation (annual mean; in millimetres)
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prec.cv.bio15: Precipitation annual variability (seasonality)
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t.mean.bio1: Temperature (annual mean; in degree celsius)
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t.range.bio7: Temperature (annual range)
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varbio1: Quaternary climatic stability (annual mean temperature; in degree censius)
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varbio7: Quaternary climatic stability (temperature variability)
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varbio12: Quaternary climatic stability (annual mean precipitation; in millimeters)
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varbio15: Quaternary climatic stability (precipitation variability)
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dor.pva: Degree of regulation (in %)
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ppd.sav: Population density (people per km2)
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rdd.sav: Road density (meters per km2)
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urb.sse: Urban extent (in %)
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pac.sse: Protected area (in %)
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xcoord: Centroid location of a basin (longitude in ˚E).
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ycoord: Centroid location of a basin (latitude in ˚N).
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Perc_800: Percentage of basin area above 800-meter altitude
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Column logArea to column arcsinHI: Transformed variables (log or arcsine)
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Column logAreaS to column slope.meanS: Standardized variables (z score; mean=0, standard deviation=1)
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clim1, clim2, energy1, energy2, iso1, iso2, history1, history2: Variables derived from PCA analysis conducted on all climate, all energy, all isolation and all Quaternary history variables.
traitafterimpute_group1.csv
This file contains functional traits for 325 species that are found in the Western Ghats.
Variables:
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Species: Species found in the Western Ghats region.
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Endemism: Assigned endemic status to each species based on Dahanukar et al. (2004).
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genus: Genus of the species
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family: Family of the species
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West: Number of basins a species occupies in west
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East: Number of basins a species occupies in east
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Presence: Presence of species in east, west or in both sides of the escarpment
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Presence_end_status: Presence of endemic (end) and non-endemic (ne) species in east, west or in both sides of the escarpment.
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body_form: Species are categorized according to their body forms (leaf fish, carp, loach, carp-minnow, glassfish, snakehead, etc.).
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body_form_major: Species are categorized according to their major body forms (e.g., carp, carp-minnow, catfish, sucker catfish, snakehead, leaf fish, loach, etc.)
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MBL: Maximum body length (in cm)
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MBL_log: log transformed maximum body length
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BEL: Body elongation
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VEP: Vertical eye position
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RES: Relative eye size
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OGP: Oral gap position
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BLS: Body lateral shape
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PFV: Pectoral fin vertical position
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PFS: Pectoral fin size
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CPT: Caudal peduncle throttling
nullresults_function.rds
This datafile contains the basin-wise functional beta-diversity components (i.e. gains and losses) that were generated by permuting the species×basin matrix for 99 times using the ‘independent swap’ algorithm.
nullresults_taxonomic.rds
This datafile contains the basin-wise taxonomic beta-diversity components (i.e. gains and losses) that were generated by permuting the species×basin matrix for 99 times using the ‘independent swap’ algorithm.
nullresults_partition.rds
This datafile contains the basin-wise Jaccard turnover and nestedness diversity components that were generated by permuting the species×basin matrix for 999 times using the ‘frequency’ algorithm.
distance_decay1.R
This script is used to perform the distance-decay modelling. We fitted distance-gain/loss models along with distance-dissimilarity models using the decay.model function from ‘betapart’ package. These models are plotted using plot.decay function and two models were compared using the zdep function of the ‘betapart’ package. This script was also used to perform the Mantel tests. Packages ‘Ternary’ and ‘ggplot2’ are used to plot the ternary plots.
CSV files used in the analysis:
dist_a.ee.csv
This square matrix (24 * 24) of geodesic distances (in meters) among all east-flowing basins is used to fit distance-overlap, distance-gains, distance-loss, and distance-β-diversity models.
dist_a.ww.csv
This square matrix (35 * 35) of geodesic distances (in meters) among all west-flowing basins is used to fit distance-overlap, distance-gains, distance-loss and distance-β-diversity models.
dist_a.e_w.csv
This square matrix (59 * 59) of geodesic distances (in meters) among all east-to-west-flowing basins is used to fit distance-overlap, distance-gains, distance-loss, and distance-β-diversity models.
dist_a.w_e.csv
This square matrix (59 * 59) of geodesic distances (in meters) among all west- to east-flowing basins is used to fit distance-overlap, distance-gains, distance-loss and distance-β-diversity models.
over.a.ee.csv
This square matrix (24 * 24) of species overlap among all east-flowing basins is used to fit distance-overlap, and distance-β-diversity models.
over.a.ww.csv
This square matrix (35 * 35) of species overlap among all west-flowing basins is used to fit distance-overlap, and distance-β-diversity models.
over_a.e_w.csv
This square matrix (59 * 59) of species overlap among all east-to-west-flowing basins is used to fit distance-overlap, and distance-β-diversity models.
over_a.w_e.csv
This square matrix (59 * 59) of species overlap among all west- to east-flowing basins is used to fit distance-overlap, and distance-β-diversity models.
fe.over.a.ee.csv
This square matrix (24 * 24) of functional overlap among all east-flowing basins is used to fit distance-overlap, and distance-β-diversity models.
fe.over.a.ww.csv
This square matrix (35 * 35) of functional overlap among all west-flowing basins is used to fit distance-overlap, and distance-β-diversity models.
fe.over.a.e_w.csv
This square matrix (59 * 59) of functional overlap among all east to west-flowing basins is used to fit distance-overlap, and distance-β-diversity models.
fe.over.a.w_e.csv
This square matrix (59 * 59) of functional overlap among all west- to east-flowing basins is used to fit distance-overlap, and distance-β-diversity models.
gain.a.ee.csv
This square matrix (24 * 24) of species gains among all east-flowing basins is used to fit distance-gains models.
gain.a.ww.csv
This square matrix (35 * 35) of species gains among all west-flowing basins is used to fit distance-gains models.
gain.a.e_w.csv
This square matrix (59 * 59) of species gains among all east-to-west-flowing basins is used to fit distance-gains models.
gain.a.w_e.csv
This square matrix (59 * 59) of species gains among all west- to east-flowing basins is used to fit distance-gains models.
#### fe.gain.a.ee.csv
This square matrix (24 * 24) of functional gains among all east-flowing basins is used to fit distance-gains models.
fe.gain.a.ww.csv
This square matrix (35 * 35) of functional gains among all west-flowing basins is used to fit distance-gains models.
fe.gain.a.e_w.csv
This square matrix (59 * 59) of functional gains among all east-to-west-flowing basins is used to fit distance-gains models.
fe.gain.a.w_e.csv
This square matrix (59 * 59) of functional gains among all west- to east-flowing basins is used to fit distance-gains models.
loss.a.ee.csv
This square matrix (24 * 24) of species losses among all east-flowing basins is used to fit distance-loss models.
loss.a.ww.csv
This square matrix (35 * 35) of species losses among all west-flowing basins is used to fit distance-loss models.
loss.a.e_w.csv
This square matrix (59 * 59) of species losses among all east-to-west-flowing basins is used to fit distance-loss models.
loss.a.w_e.csv
This square matrix (59 * 59) of species losses among all west-to-east-flowing basins is used to fit distance-loss models.
fe.loss.a.ee.csv
This square matrix (24 * 24) of functional losses among all east-flowing basins is used to fit distance-loss models.
fe.loss.a.ww.csv
This square matrix (35 * 35) of functional losses among all west-flowing basins is used to fit distance-loss models.
fe.loss.a.e_w.csv
This square matrix (59 * 59) of functional losses among all east-to-west-flowing basins is used to fit distance-loss models.
fe.loss.a.w_e.csv
This square matrix (59 * 59) of functional losses among all west- to east-flowing basins is used to fit distance-loss models.
taxo_SES_beta.ee._1_4_26.csv
This square matrix (24 * 24) of standardised taxonomic dissimilarity among all east-flowing basins is used to fit distance-dissimilarity models.
taxo_SES_beta.ww._1_4_26.csv
This square matrix (35 * 35) of standardised taxonomic dissimilarity among all west-flowing basins is used to fit distance-dissimilarity models.
SES_beta.ee._1_4_26.csv
This square matrix (24 * 24) of standardised functional dissimilarity among all east-flowing basins is used to fit distance-dissimilarity models
SES_beta.ww.1_4_26.csv
This square matrix (35 * 35) of standardised functional dissimilarity among all west-flowing basins is used to fit distance-dissmilarity models.
Range_analysis.R
This script is used to perform range size analysis in freshwater fishes of the Western Ghats. In first steps, latitudinal range sizes (latitude in ˚N) were calculated for each species. Then, the decline in species range size was assessed by constructing the 3 degrees bins from northern to southern WG. ‘dplyr’, ‘ggplot2’, and ‘ggbeeswarm’ packages were used to perform the range-size analysis and plotting.
functional_alpha.R
This script is used to calculate the functional α-diversity and plot the relationship between latitude vs taxonomic and functional richness. ‘mFD’ package was used to calculate the functional α-diversity. ‘ggplot2’ is used for plotting.
CSV files used in the analysis:
TDFD.csv
This file is used to run the linear models between latitude and taxonomic and functional α-diversity.
Variables:
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flow.dir: The direction of the water flow- east (1) & west (0)
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position: Basins situated north of Palghat gap in east (NPE) and west (NPW), and basins that are situated south of Palghat gap in eastern (SPE) and western (SPW) side of the Escarpment.
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flow: Basins categorized based on whether a basin flows towards east or west
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gap: Basins categorized based on whether they are situated north (NP) or south (SP) of Palghat gap
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SpRich_new: Observed species richness in each basin
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EndRich_new: Observed endemic species richness in each basin
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xcoord: Centroid location of a basin (longitude in ˚E).
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ycoord: Centroid location of a basin (latitude in ˚N).
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fun.richness: Observed functional α-diversity
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fun_rich_ses: Standardized functional alpha diversity
Code/software
All analyses were done in R (R version 4.4.2)
