РЕЗУЛЬТАТЫ БИОКЛИМАТИЧЕСКОГО МОДЕЛИРОВАНИЯ ДЛЯ INULA GRANDIS

Аннотация

In this article Based on MaxEnt modeling, a species bioclimatic model for theInula grandis Schrenk ex Fisch. & C.A.Mey. was developed for the using random algorithms with 19 bioclimatic variables. Key words. Inula grandis, area, climate factors, MaxEnt modeling, plantation. Аннотация. В данной статье на основе моделирования MaxEnt была разработана биоклиматическая модель вида Inula grandis Schrenk ex Fisch. & C.A.Mey. с использованием случайных алгоритмов с 19 биоклиматическими переменными. Ключевые слова. Inula grandis, площадь, климатические факторы, моделирование MaxEnt, плантация. Аннотация. Ushbu maqolada MaxEnt modellashtirish asosidaInula grandis Schrenk ex Fisch & C.A.Mey. turi uchun bioiqlim modeli va 19 ta bioiqlim o‘zgaruvchi parametrlar bilan tasodifiy algoritmlar yordamida ishlab chiqilgan. Kalit so‘zlar.Inula grandis, hudud, iqlim omillari, MaxEnt modellashtirish, plantatsiya, qulay ekologik muhit. I. grandis are multifaceted and comprehensive. They include various aspects of assessing the species' distribution, understanding its ecological requirements, and developing strategies for its sustainable management. This researchgeoforensic data reflecting the distribution ofI. grandisspecies in the preparation of the work, field research an), Plants and lichens of Russia and neighboring countries: open online galleries and plant identification guide (https:/ /www.plantarium.ru ) online databases were used. In general 20 geoforensics data of I. grandis (Yellow elecampane) have been downloaded. Bioclimatic data downloadedfrom the World Clim database ( http://www.worldclim.org ) for species distribution modeling included nineteen climate factors ( Table 1 ). Data from the World Soil Database (82 soil parameters) were combined with bioclimatic data (19 climate factors) to better represent the potential distribution of I. grandis. ArcGIS 10.8 software using the layer tool was used to crop and extract the climate data data, maintain the desired data range, and finally convert it to ASCII format. Geoforensic data were loaded into MaxEnt 3.4.4 [1,2] and evaluated using the Jackknife test with bioclimate, soil and elevation data. T he resulting models were calibrated using 75% of the available data for each species as training (calibration) data, and the remaining 25% were used for model validation as test data. The bootstrap method was performed with 10 replicates, a maximum of 5000 replicates, and default selected parameters. Model performance was evaluated by calculating the area under the receiver operator curve (AUC), where models with an AUC value greater than 0.7 were considered satisfactory for our study [1,2]. MaxEnt is a multivariate approach that evaluates species distribution by finding the probability distribution of maximum entropy, taking into account the constraints that represent our incomplete information about distribution. In general, AUC 0.9 indicates that the prediction results are very accurate, for further analysis can be used. Future climate scenarios. In its fifth report (AR5), the Intergovernmental Panel on Climate Change (IPCC, 2013) presented two Representative Concentration Pathway (RCP) climate scenarios (RCP 8.5) for 2050, downloaded from the WorldClim online database ( www .worldclim.org ). adu.uz universaljurnal.uz 321 “ZAMONAVIY BIOLOGIYANING DOLZARB MUAMMOLARI VA RIVOJLANISH ISTIQBOLLARI” xalqaro ilmiy-amaliy anjuman materiallari Table 1. Bioclimatic factors Code Trends in bioclimatic variability Notes Unity BIO1 Average annual temperature °С BIO2 Average interval of the day °С BIO3 Isothermal BIO1 / BIO7 * 100 % BIO4 Seasonality of temperature Coefficient of variation BIO5 Maximum temperature of the hottest month °С BIO6 The minimum temperature of the coldest °С month BIO7 Annual temperature range BIO5 – BIO6 °С BIO8 Average temperature of the warm quarter °С BIO9 Average temperature of the dry quarter °С BIO10 Average temperature of the warmest quarter °С BIO11 Average temperature of the cold quarter °С BIO12 Annual precipitation mm BIO13 Precipitation in the hottest month . mm BIO14 Rainfall in a dry month mm BIO15 Seasonality of precipitation Coefficient of 1 variation BIO16 Precipitation in the Wet Quarter mm BIO17 Precipitation in the dry quarter mm BIO18 Precipitation in the warmest quarter mm BIO19 Precipitation in the coldest quarter mm A species bioclimatic model was conducted for the Yellow Elecampane using random algorithms with 19 bioclimatic variables . To determine the main climatic factors determining the range of the yellow elecampane, we used a jackknife test with variable significance in a trained random model. The Jackknife test systematically evaluates the contribution of each variable to the predictive performance of the model by repeatedly training each variable with and without each variable at random. The results of the Jacknife test, c