“…Researchers have recently turned to habitat suitability modeling as a reliable and practical method for managing the habitats of various pests, insects, and plant species 77 . Researchers compared the effectiveness of seven data analysis strategies for forecasting the spread of China berry (Melia azedarach L.) in a study using three standard metrics for evaluating model accuracy.…”
Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.
“…Researchers have recently turned to habitat suitability modeling as a reliable and practical method for managing the habitats of various pests, insects, and plant species 77 . Researchers compared the effectiveness of seven data analysis strategies for forecasting the spread of China berry (Melia azedarach L.) in a study using three standard metrics for evaluating model accuracy.…”
Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.
“…Figure 2 shows conditioning factors maps of gully erosion in the Shazand watershed. Random Forest (RF) is a non-parametric, supervised learning algorithm that uses an ensemble learning method for regression, and is one of the best methods that use several regression trees [43]. Some of the advantages of the RF method are: (1) it can combine predictions of multiple separate algorithms, (2) different case studies have identi ed that the RF algorithm is very accurate for simulation and modeling, and (3) this algorithm is a very applicable tool to determine the priority of the conditioning factors compared to other statistical modeling and data mining methods [44].…”
Soil, as a valuable natural resource, provides a large number of services and plays an important role in the environment and world economy. Soil degradation and erosion reduce the quality and quantity the soil and are important natural and anthropogenic processes that affect many countries. Water erosion is the most common type of soil degradation in the world and Asia has about fty percent of the total water erosion area of the world. Gullies are a typical erosion type and gully formation is an important process of soil erosion and degradation in semi-arid and arid areas, especially areas impacted by human activities and land uses. Because of arid and semi-arid climate, piping and gully erosion is an active phenomenon in the agricultural lands, bare land and rangeland areas of the Shazand watershed, Markazi Province, central Iran. The goal of this research was to identify the priority conditioning factors of gully erosion, map the susceptibility of the Shazand watershed to gully erosion and compare some of the applied machine learning techniques based on their accuracy. Prioritization of conditioning factors using a random forest (RF) algorithm demonstrated that distance from the roads, altitude, and rainfall have the greatest impact on gully occurrence in the Shazand watershed. The RF, boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), and mixture discriminant analysis (MDA) algorithms were applied to create gully erosion susceptibility maps in the study area. The receiver operating characteristic curve (ROC) and area under the curve (AUC) performance metrics were used to validate susceptibility maps. The AUC values of 0.850, 0.831, 0.760, 0.751, and 0.758 were achieved for the RF, BRT, FDA, GLM, and MDA algorithms, respectively. Due to the negative and destructive effects of gully erosion, its management and control is a critical component in the management of natural resources and land uses. The susceptibility maps of gully erosion prepared in this study are a substantial information resource for decision makers, planners, and engineers concerned with human impacts on natural resources and land uses. The areas identi ed with high and very high erosion susceptibility in the Shazand watershed need more care to mitigate the consequences of gully erosion and soil degradation.
“…Although SDMs have been widely used in Iran for various purposes e.g. investigating the future distribution of white mangroves 38 , mapping the habitat suitability of endemic and sub-endemic almond species in Iran 39 , modeling and predicting habitat suitability for Ferula gummosa 40 , mapping the current and future distributions of Onosma species endemic to Iran 41 , the effect of climate change on the ecological niches of Bromus tomentellus 42 , modeling the distribution of some medicinal plant species 43 , and modeling climate change effects on Zagros forests in Iran 44 , no previous study has investigated the impact of climate change on the future distribution of the three valuable Thymus species within Iran so far. Figure 1 Photographs of T. fedtschenkoi ( A ), T. pubescens ( B ) and T. transcaucasicus ( C ) in Iran.…”
Over the course of a few decades, climate change has caused a rapid and alarming reshaping of species habitats, resulting in mass extinction, particularly among sensitive species. In order to investigate the effects of climate change on species distribution and assess habitat suitability, researchers have developed species distribution models (SDMs) that estimate present and future species distribution. In West Asia, thyme species such as T. fedtschenkoi, T. pubescens, and T. transcaucasicus are rich in thymol and carvacrol, and are commonly used as herbal tea, spice, flavoring agents, and medicinal plants. This study aims to model the distribution of these Thymus species in Iran using the MaxEnt model under two representative concentration pathways (RCP 4.5 and RCP 8.5) for the years 2050 and 2070. The objective is to identify the crucial bioclimatic (n = 5), edaphic (n = 1), and topographic (n = 3) variables that influence their distribution and predict how their distribution might change under various climate scenarios. The findings reveal that the most significant variable affecting T. fedtschenkoi and T. pubescens is altitude, while soil organic carbon content is the primary factor influencing the distribution of T. transcaucasicus. The MaxEnt modeling demonstrates excellent performance, as indicated by all the area under the curve (AUC) values exceeding 0.9. Based on the projections, it is expected that these three thyme species will experience negative area changes in the coming years. These results can serve as a valuable tool for developing adaptive management strategies aimed at enhancing protection and sustainable utilization in the context of global climate change. Special attention should be given to conserving T. fedtschenkoi, T. pubescens, and T. transcaucasicus due to their significant habitat loss in the future.
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