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.

在物种分布模型中，生物气候变量被认为是必不可少的数据类型。此类变量可从WorldClim数据库中以整个地球表面和各种空间分辨率获得。而且，方便地访问实时卫星数据及其产品最近创建了一种产生环境变量的新方法。因此，在本研究中，尝试比较从降水和温度仪器记录（场景I）得出的生物气候变量与从遥感数据（场景II）得出的变量（两种场景都是2001年至2017年）的性能。被用来预测西伯利亚蒿的分布在伊朗中部通过五个物种分布模型（SDM），例如广义线性模型（GLM），随机森林（RF），分类树分析（CTA），多元自适应回归样条（MARS）和最大熵（Maxent）。DEM层来自90米的航天飞机雷达地形任务（SRTM），1公里的MODIS地表温度和植被指数产品，并采用了降比例的PERSIANN-CDR降水数据作为温度和降水的推导，以产生情景的生物气候变量二。独立样本t检验对相关模型得出的AUC_比率值的结果表明，从情景II的数据获得的结果要比情景I（p <.01）。RF在单个模型中获得了最高的部分AUC值（AUC_比率），并且基于这两种方案，集成图能够提供最准确的预测。在两种情况下，RF，CTA和集成模型的性能之间也没有显着差异（p  <.01）。结果强调了源自遥感的生物气候变量对于产生更多最新信息以及改善SDM的预测性能的重要性。最后，有人建议方便地访问可靠的最新信息可以帮助建模人员很好地概述管理实践。