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Theoretical and Applied Climatology

, Volume 129, Issue 3–4, pp 801–814 | Cite as

Variances in the projections, resulting from CLIMEX, Boosted Regression Trees and Random Forests techniques

  • Farzin ShabaniEmail author
  • Lalit Kumar
  • Samaneh Solhjouy-fard
Original Paper

Abstract

The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm (Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations, especially in correlative models such as MX, BRT, and RF. Intersections between different techniques may decrease uncertainty in future distribution projections. However, readers should not miss the fact that the uncertainties are mostly because the future GHG emission scenarios are unknowable with sufficient precision. Suggestions towards methodology and processing for improving projections are included.

Keywords

Random Forest Date Palm Middle Eastern Country Boost Regression Tree Phoenix Dactylifera 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Farzin Shabani
    • 1
    Email author
  • Lalit Kumar
    • 1
  • Samaneh Solhjouy-fard
    • 2
  1. 1.Ecosystem Management, School of Environmental and Rural ScienceUniversity of New EnglandArmidaleAustralia
  2. 2.Department of Entomology, Science and Research BranchIslamic Azad UniversityTehranIran

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