Modelling of Potential Distribution of Helichrysum nuratavicum Krasch (Asteraceae) in Uzbekistan

Khujanov, Alisher Nuralievich

doi:10.5958/2320-3196.2021.00008.2

Cited by 1 publication

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“…Using SDMs for studying rare plant species in Uzbekistan is in the initial stage now. Only a few studies were carried out (Baikov et al 2021; Khujanov, 2021; Mavlanov et al 2021) and most of them were conducted using MaxEnt (Philips et al 2006), which is considered the most powerful machine learning algorithm with an uncomplicated graphical user interface, which made MaxEnt the most comfortable and popular technique for researchers in last years (Philips et al 2017). However, numerous studies demonstrated that Random Forest and other machine learning techniques in some cases are more efficient than MaxEnt (Williams et al 2009; Duan et al 2014; Mi et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison of different models in predicting habitat suitability of rare species in Uzbekistan: 8 rare Tulipa species case-study

Rustamov

2022

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Species distribution models (SDMs) have become an essential tool in conservational biology, biogeography and ecology. But there is no consequence in what SDM method is the most efficient in predicting suitable habitat distribution of rare species. To explore this issue, we chose 8 rare Tulipa species in Uzbekistan as case study to test 8 common Machine Learning (GLM, GBM, MARS, CTA, SRE, FDA, RF, MaxEnt) and Deep Neural Network (DNN) SDM models, using three different methods of pseudo-absence data generation (random sampling, random sampling with exclusion buffer, random sampling with environmental profiling). To compare the effectiveness of each model 3 common metrics (Area under ROC (AUC), True skill statistics (TSS) and Cohen Kappa (K)) were used. We have found that RF and GBM combined with RSEP strategy are superior to other modeling methods.

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Section: Introductionmentioning

confidence: 99%

Comparison of different models in predicting habitat suitability of rare species in Uzbekistan: 8 rare Tulipa species case-study

Rustamov

2022

Preprint

View full text Add to dashboard Cite

show abstract

Modelling of Potential Distribution of Helichrysum nuratavicum Krasch (Asteraceae) in Uzbekistan

Cited by 1 publication

References 0 publications

Comparison of different models in predicting habitat suitability of rare species in Uzbekistan: 8 rare Tulipa species case-study

Comparison of different models in predicting habitat suitability of rare species in Uzbekistan: 8 rare Tulipa species case-study

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