Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: Which side can perform better in species distribution modeling?

Abstract 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 AUCratio 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

[1]  S. Dobrowski,et al.  Evaluating ensemble forecasts of plant species distributions under climate change , 2013 .

[2]  G. Jetschke,et al.  Comparison of six correlative models in predictive vegetation mapping on a local scale , 2012, Environmental and Ecological Statistics.

[3]  Sameer Saran,et al.  Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills , 2013 .

[4]  Predicting Shifts in the Suitable Climatic Distribution of Walnut (Juglans regia L.) in China: Maximum Entropy Model Paves the Way to Forest Management , 2018 .

[5]  Steven J. Phillips,et al.  Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. , 2009, Ecological applications : a publication of the Ecological Society of America.

[6]  Conor D. Wilson,et al.  Applying species distribution modelling to identify areas of high conservation value for endangered species: A case study using Margaritifera margaritifera (L.) , 2011 .

[7]  Sunil Kumar,et al.  Caveats for correlative species distribution modeling , 2015, Ecol. Informatics.

[8]  Wim G.M. Bastiaanssen,et al.  First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling–calibration procedure , 2013 .

[9]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[10]  Mark New,et al.  Ensemble forecasting of species distributions. , 2007, Trends in ecology & evolution.

[11]  Zhou Shi,et al.  A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai–Tibet Plateau with the effects of systematic anomalies removed , 2017 .

[12]  Arthur P. Cracknell,et al.  Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia , 2015, Remote. Sens..

[13]  Omri Allouche,et al.  Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) , 2006 .

[14]  Jennifer A. Miller,et al.  Mapping Species Distributions: Spatial Inference and Prediction , 2010 .

[15]  M. Araújo,et al.  Reducing uncertainty in projections of extinction risk from climate change , 2005 .

[16]  A. Townsend Peterson,et al.  Rethinking receiver operating characteristic analysis applications in ecological niche modeling , 2008 .

[17]  Mohammad Reza Keshavarz,et al.  Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data , 2014 .

[18]  Gégout Jean-Claude,et al.  Comparison of indicator values of forest understory plant species in Western Carpathians (Slovakia) and Vosges Mountains (France) , 2003 .

[19]  J. Drake,et al.  Modelling ecological niches with support vector machines , 2006 .

[20]  Mathieu Marmion,et al.  Evaluation of consensus methods in predictive species distribution modelling , 2009 .

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  Sunil Kumar,et al.  Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling , 2013 .

[23]  J. Gutiérrez,et al.  Predicting plant species distribution across an alpine rangeland in northern Spain. A comparison of probabilistic methods , 2011 .

[24]  Sunil Kumar,et al.  Assessing Forest Vulnerability and the Potential Distribution of Pine Beetles Under Current and Future Climate Scenarios in the Interior West of the US , 2011 .

[25]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[26]  R. Jafari,et al.  Discriminating and monitoring rangeland condition classes with MODIS NDVI and EVI indices in Iranian arid and semi-arid lands , 2017 .

[27]  Yuli Shi,et al.  Spatial Downscaling of Monthly TRMM Precipitation Based on EVI and Other Geospatial Variables Over the Tibetan Plateau From 2001 to 2012 , 2015 .

[28]  S. Dobrowski,et al.  Modeling plant ranges over 75 years of climate change in California, USA: temporal transferability and species traits , 2011 .

[29]  A. Šimundić.  Bias in research , 2013, Biochemia medica.

[30]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[31]  Munehisa K. Yamamoto,et al.  Comparison of Satellite Precipitation Products with Rain Gauge Data for the Khumb Region, Nepal Himalayas , 2011 .

[32]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[33]  M. Perry,et al.  The generation of monthly gridded datasets for a range of climatic variables over the UK , 2005 .

[34]  A. Jarvis,et al.  Using species distributions models for designing conservation strategies of Tropical Andean biodiversity under climate change , 2014 .

[35]  K. Walker,et al.  Climatic Associations of British Species Distributions Show Good Transferability in Time but Low Predictive Accuracy for Range Change , 2012, PloS one.

[36]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[37]  Martin Kappas,et al.  Modelling potential habitats for Artemisia sieberi and Artemisia aucheri in Poshtkouh area, central Iran using the maximum entropy model and geostatistics , 2013, Ecol. Informatics.

[38]  Catherine S. Jarnevich,et al.  Ensemble Habitat Mapping of Invasive Plant Species , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[39]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[40]  Sunil Kumar,et al.  Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US , 2009 .

[41]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[42]  No-Wook Park,et al.  Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables , 2013 .

[43]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[44]  Yujun Yi,et al.  Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China , 2016 .

[45]  S. Soltani,et al.  Bioclimatic classification of Isfahan province using multivariate statistical methods , 2009 .

[46]  M. Hill,et al.  Data analysis in community and landscape ecology , 1987 .