Amiri, M; Tarkesh, M; Jafari, R; Jetschke, G (2020). Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: Which side can perform better in species distribution modeling?. ECOLOGICAL INFORMATICS, 57, 101060.

Bioclimatic variables are considered as an indispensable data type in species distribution modeling. Such variables are available from the WorldClim database for the entire earth surface and at various spatial resolutions. Moreover, convenient access to real-time satellite data and their products has recently created a new way to produce environmental variables. Therefore, in the present study, it was attempted to compare the performance of bioclimatic variables derived from precipitation and temperature instrumental records (scenario I) and variables derived from remote sensing data (scenario II) where both scenarios were from 2001 to 2017. The variables were employed to predict the distribution of Artemisia sieberi in central Iran through five Species Distribution Models (SDMs) such as Generalized Linear Model (GLM), Random Forest (RF), Classification Tree Analysis (CTA), Multivariate Adaptive Regression Splines (MARS), and Maximum Entropy (Maxent). The DEM layer was derived from 90-m Shuttle Radar Topography Mission (SRTM), 1-km MODIS land surface temperature and vegetation indices products, and downscaled PERSIANN-CDR precipitation data were employed as derivations of temperature and precipitation to produce bioclimatic variables for scenario II. The results obtained from independent sample t-test on AUC(ratio) values derived from the correlative models showed that it had more satisfactory results when they were getting from the data of scenario II than the scenario I (p < .01). RF scored the highest partial AUC values (AUC(ratio)) among the single models, and based on both scenarios, ensemble map was able to provide the most accurate predictions. There were also non-significant differences among the performance of RF, CTA and ensemble models under two scenarios (p < .01). Results emphasized the importance of bioclimatic variables derived from remote sensing to produce more up-to-date information and also to improve the predictive performance of SDMs. Finally, it was suggested that convenient access to reliable and up-to-date information can assist modelers to outline management practices well.