TAILIEUCHUNG - A comparison of logistic regression and maximum entropy for distribution modeling of range plant species (a case study in rangelands of western Taftan, southeastern Iran)

This study aimed to compare the efficiency of logistic regression and maximum entropy models for distribution modelling of plant species habitats in the rangelands of western Taftan, southeastern Iran. | Turkish Journal of Botany Turk J Bot (2018) 42: 28-37 © TÜBİTAK doi: Research Article A comparison of logistic regression and maximum entropy for distribution modeling of range plant species (a case study in rangelands of western Taftan, southeastern Iran) Hossein PIRI SAHRAGARD*, Majid AJORLO Department of Range and Watershed Management, Faculty of Water and Soil, University of Zabol, Zabol, Iran Received: Accepted/Published Online: Final Version: Abstract: This study aimed to compare the efficiency of logistic regression and maximum entropy models for distribution modelling of plant species habitats in the rangelands of western Taftan, southeastern Iran. Vegetation cover was sampled using a systematicrandomized method. Soils were sampled at 0–30 and 30–60 cm depths through digging of eight soil profiles. The agreement between predictive maps generated by models with documented maps of habitats indicated that logistic regression was able to predict the distribution of Artemisia aucheri and Artemisia sieberi habitats at excellent (kappa value = ) and weak (kappa value = ) levels, respectively. On the other hand, the agreement between predicted maps generated by maximum entropy with documented maps was very good for Amygdalus scoparia and Artemisia aucheri habitats (kappa value = and , respectively), and weak for Artemisi aucheri (kappa value = ). This study indicates that logistic regression and maximum entropy methods had the same efficiency in distribution modelling of plant species with a limited ecological niche. However, the maximum entropy model can receive priority in distribution prediction of plant species with a limited ecological niche because it uses only presence data of plants and a small dataset. Key words: Predictive modelling, logistic regression, maximum entropy, ecological niche, plant habitats 1. Introduction Availability of .

• Nông nghiệp
• Predictive modelling
• Logistic regression
• Maximum entropy
• Ecological niche
• Plant habitats
• BMC Bioinformatics
• Pathway scores
• Pathway collections
• Demonstrate robustness
• Predictive methods
• Percutaneous absorption
• Estimating permeability across artificial membranes
• Modelling percutaneous absorption
• Squiggly lines
• Model development
• Báo cáo lâm nghiệp hay
• cách trình bày báo cáo
• báo cáo lâm nghiệp
• công trình nghiên cứu lâm nghiệp
• tài liệu về lâm nghiệp
• BMC Cancer
• Ovarian cancer
• Predictive model
• Statistical modelling
• Chemotherapy treatment
• Gene expression
• Histone modifications
• Predictive gene
• Karyotype invariant
• Bolted connection
• Structural identification
• Piezoelectric interface
• Impedance method
• Model updating
• Ecology habitats
• Soil plant relations
• Annual Gypsophila
• G
• antari
• Organic matter
• Plant Biology
• Rheum tanguticum
• Non target metabolism
• Chinese medicine plant
• Plants occupying
• Reproductive success
• Plant density
• Viable embryo
• Orchid species
• BMC Genomics
• Plant biomass
• Environments including marine
• Terrestrial habitats
• Especially plant biomass
• Horizontal transfers
• Host shift
• Host adaptation
• Transcriptional variation
• RNA Seq
• Plant–insect interactions
• Journal of Zoology
• Bipartite network
• Multivariate ordination
• Disturbance theory
• Species interaction
• Trophic specialization
• Journal of Botany
• Ferula sinkiangensis
• Genetic assessment
• Small population size
• Medicinal plant
• Plant’s natural habitats
• Sacred grove
• Plant diversity
• Four life forms
• Sacred groves of Dapoli
• Ecosystem service providing habitats