The Caucasus Endemic Carabus Exaratus In The Maxent Environment

Abstract

The research, titled "The Caucasus Endemic Carabus exaratus in the MaxEnt Environment," focuses on forecasting the geographic range of the ground beetle species Carabus exaratus, which is native to the Caucasus region. The primary objective of the study is to employ MaxEnt modeling, a robust ecological niche modeling technique, to predict the potential distribution of this endemic species based on various environmental variables. To execute the research, presence data for Carabus exaratus were meticulously collected from field surveys and museum records. Concurrently, relevant environmental variables encompassing climate, topography, and land cover were sourced from reputable databases and literature. The MaxEnt algorithm, a machine learning approach, was then utilized to construct a predictive model that estimates the probability of the species' occurrence across the defined study area. The accuracy and reliability of the MaxEnt model were subsequently evaluated through rigorous assessments, including the calculation of the area under the receiver operating characteristic curve (AUC-ROC) and other pertinent metrics. These measures serve to validate the precision of the model in capturing the intricate distribution patterns of Carabus exaratus. The results of the MaxEnt modeling effort yielded a comprehensive predictive map delineating potential habitats for Carabus exaratus throughout the Caucasus region. The accuracy assessments, particularly the AUC-ROC analysis, affirmed the dependability of the model in effectively representing the species' distribution dynamics. In conclusion, this research contributes valuable insights into understanding and forecasting the potential distribution of the Caucasus endemic Carabus exaratus.