DOI: 10.5061/dryad.4f4qrfjg6
title: Data for: Liverworts show a globally consistent mid-elevation richness peak

description: The study of elevational gradients allows to draw conclusions on factors
 and mechanisms determining patterns in species richness distribution.
 Several earlier studies investigated liverwort diversity on single or few
 elevational transects. However, a comprehensive survey on elevational
 distribution patterns of liverwort richness and their underlying factors
 is lacking so far. This study’s purpose was to fill this gap by compiling
 an extensive dataset of liverwort elevational patterns encompassing a
 broad diversity of mountains and mountain ranges around the world. Using
 polynomial regression analyses, we found a prevalence of hump-shaped
 richness patterns (19 of 25 gradients), where liverwort species richness
 peaked at mid-elevation and decreased towards both ends of the gradient.
 Against our expectation and unlike in other plant groups, in liverworts,
 this pattern also applies to elevational gradients at mid-latitudes in
 temperate climates. Indeed, relative elevation, calculated as the
 percentage of the elevational range potentially inhabited by liverworts,
 was the most powerful predictor for the distribution of liverwort species
 richness. We conclude from these results that admixture of low- and
 high-elevation liverwort floras, in combination with steep ecological
 gradients, leads to a floristic turnover shaping elevational distribution
 patterns of liverwort diversity. Our analyses further detected significant
 effects of climatic variables (temperature of the warmest month, potential
 evapotranspiration, and precipitation of the warmest month) in explaining
 elevational liverwort richness patterns. This indicates that montane
 liverwort diversity is restricted by high temperatures and subsequent low
 water availability, especially towards lower elevations, which presumably
 will lead to serious effects by temperature shifts associated with global
 warming.
description: # Data for: Liverworts show a globally consistent mid-elevation richness
 peak
 [https://doi.org/10.5061/dryad.4f4qrfjg6](https://doi.org/10.5061/dryad.4f4qrfjg6) ## Description of the data and file structure Files in this archive: \- Sheet 1: Dataset containing absolute species numbers, relative species numbers, elevation (m), relative elevation, and predicted climate data (Bio1, mean annual temperature (°C); Bio5, maximum temperature of the warmest month (°C); Bio6, minimum temperature of the coldest month (°C); Bio12, annual precipitation amount (mm); Bio13, precipitation amount of the wettest month (mm); Bio14, precipitation amount of the driest month (mm); Bio18, precipitation amount of the warmest quarter (mm); Bio19, precipitation amount of the coldest quarter (mm); maxPET, maximum potential evapotranspiration (mm)) per transect. \- Sheet 2: GPS coordinates used to obtain Bioclim data. ### Files and variables #### File: Coordinates\_to\_DataDryad.txt **Description:**  GPS coordinates used to obtain Bioclim data ##### Variables * Number of transect (refers to Table 1 in article) * Transect * Elevation (m) * Latitude * Longitude #### File: Dataset\_to\_DataDryad.txt **Description:** Dataset containing absolute species numbers, relative species numbers, elevation (m), relative elevation, and predicted climate data (Bio1, mean annual temperature (°C); Bio5, maximum temperature of the warmest month (°C); Bio6, minimum temperature of the coldest month (°C); Bio12, annual precipitation amount (mm); Bio13, precipitation amount of the wettest month (mm); Bio14, precipitation amount of the driest month (mm); Bio18, precipitation amount of the warmest quarter (mm); Bio19, precipitation amount of the coldest quarter (mm); maxPET, maximum potential evapotranspiration (mm)) per transect. ##### Variables * absolute species numbers * relative species numbers * elevation (m) * relative elevation * predicted climate data * Bio1, mean annual temperature (°C) * Bio5, maximum temperature of the warmest month (°C) * Bio6, minimum temperature of the coldest month (°C) * Bio12, annual precipitation amount (mm) * Bio13, precipitation amount of the wettest month (mm) * Bio14, precipitation amount of the driest month (mm) * Bio18, precipitation amount of the warmest quarter (mm) * Bio19, precipitation amount of the coldest quarter (mm) * maxPET, maximum potential evapotranspiration (mm) #### File: README.txt **Description:**  ##### Variables *