A machine learning algorithm-based approach (MaxEnt) for predicting invasive potential of Trioza erytreae on a global scale-Reference-Cited by-同舟云学术

A machine learning algorithm-based approach (MaxEnt) for predicting invasive potential of Trioza erytreae on a global scale

Published:2022-11 Issue: Volume:71 Page:101792
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Aidoo Owusu Fordjour,Souza Philipe Guilherme Corcino,da Silva Ricardo Siqueira,Júnior Paulo Antonio Santana,Picanço Marcelo Coutinho,Osei-Owusu Jonathan,Sétamou Mamoudou,Ekesi Sunday,Borgemeister Christian

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Ecological Modeling,Modeling and Simulation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference101 articles.

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2. Distribution, degree of damage and risk of spread of Trioza erytreae (Hemiptera: Triozidae) in Kenya;Aidoo;J. Appl. Entomol.,2019

3. The African citrus triozid Trioza erytreae (Del Guercio) (Hemiptera: Triozidae): temporal dynamics and susceptibility to entomopathogenic fungi in East Africa;Aidoo;Int. J. Trop. Insect. Sci.,2021

4. Climate-induced range shifts of invasive species (Diaphorina citri Kuwayama);Aidoo;Pest Manag. Sci.,2022

5. Temperature-based phenology model of African citrus triozid (Trioza erytreae Del Guercio): vector of citrus greening disease;Aidoo;J. Appl. Entomol.,2022

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