“ZAMONAVIY BIOLOGIYANING DOLZARB MUAMMOLARI VA
RIVOJLANISH ISTIQBOLLARI”
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BIOCLIMATIC MODELING RESULTS FOR INULA GRANDIS
Ermatova Gulzoda Zakirdjanovna
PhD student Andijan state university.
gulzoda1712@mail.ru
https://doi.org/10.5281/zenodo.15572670
Abstract.
In this article Based on
MaxEnt
modeling, a species bioclimatic model for the
Inula
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 asosida
Inula 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 research geoforensic data reflecting the distribution of
I. grandis
species 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 downloaded from 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 < 0.7 indicates low accuracy of the model, the
prediction results are acceptable when AUC is 0.7-0.9, and 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 ).
“ZAMONAVIY BIOLOGIYANING DOLZARB MUAMMOLARI VA
RIVOJLANISH ISTIQBOLLARI”
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322
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
month
°
С
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
variation
1
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, calculated on the predictive performance independent test
data set, revealed four bioclimatic variables with high multivariate significance for the Yellow
Elecampane ( Figure 1 ).
1. Bio5 (maximum temperature of the hottest month)
2. Bio10 (average temperature of the warmest quarter)
3. Bio13 (precipitation in the warmest month)
4. Bio16 (Wet Quarter Precipitation)
“ZAMONAVIY BIOLOGIYANING DOLZARB MUAMMOLARI VA
RIVOJLANISH ISTIQBOLLARI”
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adu.uz
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323
Figure 1 .
Jackknife test results significance of bioclimatic factors for the yellow elecampane.
When each of these four variables was excluded from the model individually, the resulting
AUC scores were significantly lower than in models where the other variables were excluded. This
shows that the maximum and average temperatures in the warmest periods, and the precipitation in
the warmest and wettest intervals provide informative signals about the climatic boundaries of the
yellow elecampane. Most notably, these important variables describe temperature and humidity
conditions during the extremes of the warm season. Yellow Elecampane refers to its physiological
tolerance and habitat requirements, which depend on high heat tolerance and sufficient rainfall during
active growth. The identification of these four bioclimatic factors as key drivers of the potential
distribution of the yellow elecampane confirms their ecological importance and provides insight into
the climatic controls that shape its range limits. Further research is planned based on the above results
to understand and potentially project how yellow elecampane will respond to future climate change.
References:
1. Phillips, SJ, RP Anderson, and REJE m. Schapire. Maximum entropy modeling of species
geographic distributions. 2006 . - No. 190(3-4) . - P.231-259.
2. Phillips, SJ and MJE Dudík. Modeling of species distributions with Maxent: new extensions
and a comprehensive evaluation. 2008 . - No. 31(2) . - P. 161-175.
