Data for: Liverworts show a globally consistent mid-elevation richness peak
Data files
Feb 13, 2023 version files 136.32 KB
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Coordinates_to_DataDryad.txt
27.72 KB
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Dataset_to_DataDryad.txt
107.89 KB
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README.txt
712 B
Dec 17, 2024 version files 138.13 KB
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Coordinates_to_DataDryad.txt
27.72 KB
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Dataset_to_DataDryad.txt
107.89 KB
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README.md
2.52 KB
Abstract
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.
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
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