Determining the areas prone to the growth of Rhume ribes L. species in Razavi Khorasan Province using machine learning models

Document Type : Research/Original/Regular Article

Authors

1 Former Ph.D. Student, Department of Natural Resources Engineering, Faculty of Agriculture & Natural Resources, University of Hormozgan, Bandar Abbas, Iran

2 Associate Professor, Range and Watershed Management, Faculty of Natural Resource, University of Birjand, Birjand, Iran

3 Ph.D. Student, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran

4 Former M.Sc. Student, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran

Abstract

Introduction
In recent years, advancements in computer technologies, remote sensing systems, software, and various models have enabled the prediction of ecological niches for diverse plant and animal species. Over the past decades, alterations in human lifestyles, industrialization, and production processes have resulted in increased atmospheric pollutants, leading to severe climate change. Global climate change has induced shifts in plant growth ranges, with an expansion of warm-weather-adapted plants and a decline in cold-weather-adapted ones. These changes consequently modify the structure and ecosystems of the entire planet, directly and indirectly impacting ecosystem services crucial for human well-being and economic prosperity. Consequently, predicting the effects of climate change on plant distribution has emerged as a pivotal research area to inform conservation strategies and programs. Species distribution models primarily predict the impact of climate change on plant growth ranges. Accurate predictions of species distribution are essential for effective conservation planning and sustaining forest ecosystem services in the face of climate change. Given the significance of this issue, this research aimed to identify the most critical climatic and environmental factors influencing the distribution of Rhume ribes L. species and ascertain its current geographical range within Razavi Khorasan Province, located in northeastern Iran.
 
Materials and Methods
For this purpose, 68 bioclimatic variables including soil characteristics (45 cases), topographical factors (four cases), and climatic factors (19 cases) were first subjected to correlation analysis as predictive variables and variables with high correlation (above 80%) were removed. Due to the large size of the studied area, sampling of presence points was done with field visits during the period of 1400-1401 of the introduced areas, and a total of 232 presence points from eight regions were registered as presence points using the global positioning system (GPS). Then all the environmental data and presence points in R software using Biomed 2 package models which include GLM, GBM, GAM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt models in determining the relationship between vegetation and environmental factors in pastures Razavi Khorasan Province was predicted in the present time. The accuracy of the models was evaluated using the values of KAPPA, TSS, and ROC indices, which are prominent and widely used indices for determining and identifying the potential areas.
 
Results and Discussion
The results of this research showed that according to the accuracy evaluation index, the best modeling for the current time is the random forest model with an accuracy of 95.5%, which indicates the accuracy of the modeling at an excellent level. Also, the relative importance of the selected models and the variables that have had the greatest impact at present include digital elevation model (DEM), Average monthly (BIO2), This is the sum of all total monthly precipitation values (BIO12), The average temperatures experienced during the wettest quarter (BIO 8) and the amount of sand at a depth of 15-30 cm from the soil surface (Sand 15-30), which indicates the great influence of climatic factors on the distribution of this species, and in the next stage, the height above sea level and finally the soil factors have the greatest influence. The most distribution of Rhume ribes L. species at present is in the east of Razavi Khorasan Province including the cities of Bakharz, Torbat Jam, Taibad, Zaveh, Khaf, and Rashtkhwar in the form of a strip on their border and in the west of the Province on the border of Koh Sorkh and Neishabur cities and the north of the Province on the border Binaloud, Zabarkhan and Mashhad cities and the south of the Province in Gonabad city has spread in a strip and limited way.
 
Conclusion
The results of this research can be used to improve, protect, and economically exploit and expand the habitat of the Rhume ribes L. species. Destructive human activities, such as livestock grazing and the corrupt exploitation of rhubarb, combined with climate change, have endangered the current habitats of this species in Razavi Khorasan Province. These unprincipled exploitations, disregarding environmental capacities in natural resource management, are a significant problem in Razavi Khorasan Province and the country, gradually leading to water, soil, and plant loss in the region. While this study sufficiently examined current climatic and soil factors to identify areas suitable for rhubarb species, a deeper understanding is required to effectively restore damaged areas, preserve those at risk, and enhance the predictive capabilities of ecological models. In addition to climatic and soil factors, the potential habitats of plant species are influenced by various factors, including human activities, exploitation methods, livestock grazing, wildlife, economic and social conditions, and other direct and indirect impacts on distribution. Numerous studies have been conducted on different plant species. This research evaluated various machine learning-based species distribution models, selecting random forests as the most suitable. Species distribution models are valuable, cost-effective tools for natural resource managers, increasing their awareness and decision-making abilities regarding the effects of climate change on species.

Keywords

Main Subjects

References

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Water and Soil Management and Modelling
Volume 4, Issue 3
September 2024
Pages 75-94

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History

  • Receive Date: 26 April 2023
  • Revise Date: 08 May 2023
  • Accept Date: 12 May 2023

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