Data from: Drivers of amphibian richness in European ponds
Data files
Dec 16, 2024 version files 38.58 KB
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Drivers_of_amphibian_richness_in_European_ponds.csv
32.11 KB
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README.md
6.47 KB
Abstract
Amphibians are commonly occurring inhabitants of most lentic freshwater ecosystems, yet their global populations are in alarming decline. Ponds in particular play a crucial role in supporting amphibian biodiversity. In this study, we identified the main drivers influencing amphibian species richness by conducting a comprehensive ecological characterization in 201 ponds across seven European countries spanning a large latitudinal and longitudinal gradient. The amphibian species richness in each of these ponds was assessed using environmental DNA metabarcoding on water samples. The relative influence of climatic, local abiotic and biotic, and land use variables on variation in species richness across ponds was quantified using Boosted Regression Trees. Our results suggest that local factors, particularly chlorophyll-a concentration, but also pond area and depth, are the main drivers of amphibian richness, together with climatic variables such as annual mean precipitation and temperature. The highest richness was observed in low-nutrient, fishless, intermediate-sized, shallow ponds, located in warmer regions with higher precipitation rates. These potential drivers of amphibian richness should be considered in the planning and implementation of amphibian conservation and management actions.
README: Drivers of amphibian richness in European ponds
https://doi.org/10.5061/dryad.hdr7sqvtq
Description of the data and file structure
Project Description
This dataset explores the drivers of amphibian species richness across 201 European ponds in seven countries. It includes ecological characterizations derived from environmental DNA metabarcoding and environmental variables (climatic, land use, abiotic, and biotic). The aim is to identify key factors influencing amphibian biodiversity to inform conservation and management practices.
This dataset is associated with manuscript ECOG-07347 from the Ecography journal (DOI: 10.1111/ecog.07347).
Dataset Overview
- File Name:
Drivers_of_amphibian_richness_in_European_ponds.csv
- Sheet Structure: The dataset includes columns for pond identifiers, country, environmental variables, amphibian species richness, and metadata on sampling conditions.
- Variable Descriptions:
Latitude
andLongitude
: Coordinates of ponds.Annual Temperature
,Precipitation
: Climatic data.Pond Area
,Depth
: Abiotic measurements.Chlorophyll-a
,Fish Richness
: Local biotic factors.
For more details, see the documentation in the dataset or the publication.
Usage Notes
- Researchers should cite the original manuscript when using this data.
- Ensure proper acknowledgment of the authors and institutions.
Contact Information
For inquiries about the dataset, please contact:
Alejandro López-de Sancha
Aquatic Ecology Group
University of Vic - Central University of Catalonia
Email: 93lopez.alejandro@gmai.com
Files and variables
Variables Included in the Dataset
The dataset contains the following variables, their descriptions, units, and summary statistics:
Variable Name | Description | Units/Categories | Minimum | Maximum | Average | Standard Deviation |
---|---|---|---|---|---|---|
Latitude | Distance of the pond from the Equator | Degrees (°) | 40.13 | 56.28 | 48.68 | 5.48 |
Longitude | Distance of the pond from the Greenwich Meridian | Degrees (°) | -2.92 | 32.73 | 9.85 | 10.07 |
Mean annual temperature | Annual mean of the monthly mean temperature | Celsius degrees (°C) | 7.74 | 15.47 | 10.62 | 1.81 |
Mean annual precipitation | Annual mean precipitation rate (both liquid and solid) | Meters per second (m/s) | 1.38e-8 | 5.12e-8 | 2.83e-8 | 1.04e-8 |
Aridity index | Monthly potential evaporation divided by precipitation | Dimensionless | 0.08 | 0.51 | 0.22 | 0.13 |
Precipitation seasonality | Annual coefficient of variation of precipitation sums | Percentage (%) | 0.39 | 0.82 | 0.50 | 0.11 |
Pond area | Area of the pond | Square meters (m²) | 12.9 | 14689 | 1526 | 2463 |
Pond depth | Mean depth of the pond | Meters (m) | 0.12 | 5.30 | 1.02 | 0.90 |
Turbidity | Turbidity of the pond water | Nephelometric Turbidity Units (NTU) | 0.00 | 2359 | 66.98 | 267.26 |
Total nitrogen | Total nitrogen concentration in the pond water | Milligrams per liter (mg/L) | 0.33 | 10.10 | 1.83 | 1.48 |
Total phosphorus | Total phosphorus concentration in the pond water | Milligrams per liter (mg/L) | 0.01 | 3.94 | 0.36 | 0.61 |
Chlorophyll-a concentration | Proxy for phytoplankton biomass | Milligrams per liter (mg/L) | 0.05 | 314.04 | 33.91 | 52.46 |
Fish species richness | Number of fish species detected | Number of species | 0.00 | 12 | 1.86 | 2.52 |
Emersed vegetation (pond) | Coverage of the pond by emersed vegetation | Percentage (%) | 0.00 | 100 | 38.93 | 29.05 |
Emersed vegetation (perimeter) | Coverage of the pond perimeter by emersed vegetation | Percentage (%) | 0.00 | 100 | 60.61 | 36.16 |
Open water | Percentage of open water within the pond | Percentage (%) | 0.00 | 100 | 54.30 | 33.93 |
Arable land use (0-100m) | Percentage of arable land within a 100 m radius | Percentage (%) | 0.00 | 100 | 26.38 | 32.95 |
Urban land use (0-100m) | Percentage of urban land use within a 100 m radius | Percentage (%) | 0.00 | 50 | 2.48 | 6.93 |
Missing Values
- Missing values are indicated as blank cells.
- No other special characters (e.g., "NA") are used to represent missing data.
Code/software
To assess the effect of climatic, land use, and local abiotic and biotic variables on amphibian richness we used Boosted Regression Trees (BRTs). BRTs were implemented using the gbm package (Ridgeway 2007) in R, with additional functions outlined in Elith et al. (2008).
Elith, J., Leathwick, J.R. and Hastie, T. 2008. A working guide to bossted regression trees. – J. Anim. Ecol. 77: 802 – 813.
Methods
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Sampling Sites: 201 ponds were sampled across Belgium, Denmark, Germany, Spain, Switzerland, the United Kingdom, and Turkey.
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eDNA Collection: Water samples were collected and analyzed using environmental DNA metabarcoding techniques.
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Variables Measured: Climatic (e.g., temperature, precipitation), land use, abiotic (e.g., pond depth, nutrient concentrations), and biotic factors (e.g., vegetation, fish richness).
Detailed methods are provided in the associated manuscript.