Skip to main content

Do the predicted suitability scores from species distribution models correlate with species performance on-ground?

Cite this dataset

Jha, Ashish; Nameer, Paingamadathil Ommer; Praveen, Jayadevan (2021). Do the predicted suitability scores from species distribution models correlate with species performance on-ground? [Dataset]. Dryad.


Species distribution models are a very popular statistical tool for inferring potential distribution range of species across space and time and are thought to be a good predictor for habitat suitability. Some studies have suggested that if these models are reliable, predicted habitat suitability (PHS) should relate to species traits visualization, growth potential, body size, abundance. We validated this hypothesis by estimating association between the PHS and species abundance for 17 avian species endemic to the Western Ghats - Sri Lanka biodiversity hotspot. Additionally, we compared the PHS of sites where species were detected in both seasons (wet and dry) against sites where they were detected in the dry season alone. As a proxy for abundance, we estimated single-season occupancy estimates (ψ) using detection/non-detection data from multiple visits to the survey sites. We report significant and positive PHS-ψ correlation, though the strength of this association varied across species and models. Half of the species showed higher suitability scores for the sites where they were detected year round. The results presented here suggest that the predictive models can be used as a proxy for habitat quality, in addition to inferring the potential distribution.


The present work utilises the Kerala Bird Atlas dataset available at ( Species of interest were selected, presence and absence files were created and five different SDM algorithms were run in the R platform. Input files for occupancy analysis in PRESENCE software were generated. Correlation between the predicted habitat suitability and species occupancy estimates (a proxy for abundance) was estimated.

Usage notes

This folder consists of four input files (.RDS), three raster stacks files (.grd) and one R script file (.R) for the analyses performed using the Kerala Bird Atlas: 2015-2020 dataset. 
These files are part of the publication "Do the predicted suitability scores from Species Distribution Models correlate with species performance on-ground?".

1) Download all the files provided here as well as the Kerala Bird Atlas files available at
2) Create following folders in the working directory: 'INPUT', 'OCCUPANCY', 'PREDICTORS' and 'RESULTS'
3) All raster stacks will be in the PREDICTORS folder.
4) Transfer all other files to the INPUT folder except the 'R script.R'
5) Run the R script. All input files will be called from the folder 'INPUT'. Raster layers will be called from the folder 'PREDICTORS'.
6) Files for Occupancy analysis in the program 'PRESENCE' will be saved in the folder 'OCCUPANCY'.
7) SDM results will be saved as ASCII files in the folder 'RESULTS'.
8) Result tables will be save as csv files in the working directory.

The figures generated in the R platform were exported in svg format and annotated in Inkscape software.