Effects of input data sources on species distribution model predictions across species with different distributional ranges

Species distribution models (SDMs) are a popular tool in theoretical and quantitative ecology and constitute the most widely used modelling framework in global change science and biodiversity conservation. As main data sources, SDMs require georeferenced biodiversity observations as a response or dependent variable (e.g. species occurrence, species richness, etc) and geographic layers of environmental information as predictors or independent variables (e.g. climate, land cover, vegetation indices derived from remote sensing, etc). However, although SDMs have become one of the most important quantitative tools for addressing regular and timely biodiversity assessments worldwide, these techniques are still subject to different sources of uncertainty that have been unequally assessed. Thus, despite the uncertainty related to niche-based or distribution-based models has been addressed at different stages in the modelling process, an analysis of the effect of uncertainty coming from alternat..., Methods for processing the data: 
1) The Iberian bird species occurrences dataset (10km): Bird occurrence data were collected from two different data sources of biodiversity: i) a standardized dataset based on national Bird Atlases (Atlas); and ii) a citizen science (i.e. non-standardized) dataset based on the EOD - eBird Observation Dataset from the Global Biodiversity Information Facility – GBIF (eBird). To reduce the potential geographical errors in species records that can strongly influence the results of models, we filtered the original dataset removing duplicates and positional/spatial errors such as outliers using R and QGIS programs, and nomenclature errors and taxa misidentification supported by expert knowledge. In addition, we harmonized the species records in grid cells with a resolution higher than 10 km before the modelling procedures. We adjusted eBird data to the spatial resolution of both Atlases (10-km UTM square) to standardize input data and make both datasets compa..., , # Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges

[https://doi.org/10.5061/dryad.qfttdz0jm](https://doi.org/10.5061/dryad.qfttdz0jm)

To perform and replicate this study, this dataset provides all needed files (as tables) to fit SDMs: i) the Iberian bird species occurrences at 10km UTM square as a response or dependent variable;  ii) the geographic layers of environmental information at 10km UTM square for the Iberian Peninsula as predictors or independent variables, such as climate data, ecosystem functioning attributes (EFAs) and the combined climate and EFA data. The dataset is provided by four **.csv* files named as:

*1) The_Iberian_bird_species_occurrences_dataset_10km.csv*

*2) CHELSA_bioclimate_variables_IP10km.csv*

*3) MODIS_EVI-based_EFAs_IP10km.csv*

*4) Combined_bioclimate_EFA_dataset_IP10km.csv*

Recommended citation for this dataset: Arenas-Castro, S. et al. (2024), Data from: Effects ...