Conserving nature’s chorus: local and landscape features promoting frog species richness in farm dams.
This dataset contains all data, statistics, and R codes used for the study. Corresponding publication: Malerba, Martino; Rowley, Jodi J. L.; Macreadie, Peter; Frazer, James; Zaidi, Nayyar; Nazari, Asef; Thiruvady, Dhananjay; Driscoll, Don (2023), “Conserving nature’s chorus: local and landscape features promoting frog species richness in farm dams.”, Biological Conservation, https://doi.org/10.1016/j.biocon.2023.110270. Contact Information: * Name: Martino E. Malerba * Affiliations: School of Life and Environmental Sciences, Deakin University, Melbourne, Victoria, Australia * ORCID ID: https://orcid.org/0000-0002-7480-4779 * Email: email@example.com Abstract: Habitat loss is a key factor in the ongoing freshwater biodiversity crisis. A promising way to help tackle the rapid decline in freshwater biodiversity is to improve the potential for artificial wetlands to provide habitat for aquatic wildlife. Farm dams are among the most abundant waterbodies in agricultural landscapes and can act as “oases” against droughts for many species. Despite their prominent role in agriculture, predictive models to evaluate their ecological potential are yet to emerge. Here we use a continental-scale data set of 104,013 audio recordings from citizen scientists to identify and locate 107 species of frogs near 8,800 Australian farm dams. Frog species are among the most threatened taxa on earth and we asked: What characteristics promote higher frog species richness at farm dams? We found that the highest values of frog species richness were at old (>20 years) farm dams of intermediate size (0.1 ha in surface area), with small or medium rainfall catchments (<10 ha), and situated near other freshwater systems or conservation sites. The relationships shown here are highly generalisable and applicable on a continental scale. By identifying quantifiable features improving the ecological value of farm dams, we help identify “win-win” outcomes for agricultural productivity and conservation. In the future, “biodiversity credit” policies could incentivise large-scale ecological restoration by rewarding individuals who invest in enhancing their farm dams to support and promote local biodiversity.
Steps to reproduce
The .zip file contains three folders: - Data (where all data are) - R code (all analyses in R) - Results (plots and tables from R) We used the statistical software R version 4.0.3 (R Core Team, 2020) running on a Mac with the packages nlme (Pinheiro et al., 2020) and effects (Fox & Weisberg, 2018, 2019) for the statistical analyses, and dplyr (Wickham et al., 2018), plyr (Wickham, 2011), and ggplot2 (Wickham, 2009) for data manipulation and plotting. Within the script file: (1) check that all packages are installed in the computer (otherwise install before running the script)(e.g., “install.packages(“nlme”)”), and (2) set the directory where the "Main script.R" file is in the computer. After having done that, everything else should work by simply running the file in R. The script will read all data, calculate the statistics, plot th e images, and save the images in the same folder where the Main script file is. The file is designed to run on a Mac computer.