Aimara Planillo, Lena Fiechter, Ulrike Sturm, Silke Voigt-Heucke & Stephanie Kramer-Schadt (2021), Citizen science data for urban planning: Comparing different sampling schemes for modelling urban bird distribution. Landscape and Urban Planning 211(September 2020), 104098, DOI: j.landurbplan.2021.104098
- CS standardized data outperform opportunistic data for SDMs.
- Opportunistic data fail to identify key areas for nightingale distribution.
- Citizen Science projects aimed at SDM should be designed to avoid data bias.
- Environmental gradients should be considered in CS project designs.
- Recording species absences could improve opportunistic data quality.
There are three scripts and a source file with the R packages required to run the code. The scripts load and save data to specific folders that were not included in github due to size limitations.
Scripts: 1_Data_Preparation_Exploration: This script loads, cleans and prepares the data in the correct format for the analysis. It also explores some summaries and information of the raw data.
2_SDMs_Nightingale_Berlin: This script runs the species distribution models for all the datasets and compare the results.
3_Additional_Plots: Here is the code for the figure of the study area.
Additionally, there is a Folder with extra functions to deal with eBird data "R_functions_Johnston", more info in Johnston, A, Hochachka, WM, Strimas-Mackey, ME, et al. Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions. Divers Distrib. 2021; 27: 1265– 1277. https://doi.org/10.1111/ddi.13271