Literature DB >> 33670998

Assessing the Impact of Climate Change on the Distribution of Lime (16srii-B) and Alfalfa (16srii-D) Phytoplasma Disease Using MaxEnt.

Amna M Al Ruheili1, Alaba Boluwade2, Ali M Al Subhi1.   

Abstract

Witches' broom disease has led to major losses in lime and alfalfa production in Oman. This paper identifies bioclimatic variables that contribute to the prediction of distribution of witches' broom disease in current and future climatic scenarios. It also explores the expansion, reduction, or shift in the climatic niche of the distribution of the disease across the different geographical areas of the entire country (309,501 km²). The maximum entropy model (MaxEnt) and geographical information system were used to investigate the potential suitability of habitats for the phytoplasma disease. This study used current (1970-2000) and future projected climatic scenarios (2021-2040, 2041-2060, 2061-2080, and 2081-2100) to model the distribution of phytoplasma for lime trees and alfalfa in Oman. Bioclimatic variables were downloaded from WorldClim with ± 60 occurrence points for lime trees and alfalfa. The area under the curve (AUC) was used to evaluate the model's performance. Quantitatively, the results showed that the mean of the AUC values for lime (16SrII-B) and alfalfa (16SrII-D) future distribution for the periods of 2021-2040, 2041-2060, 2061-2080, and 2081-2100 were rated as "excellent", with the values for the specified time periods being 0.859, 0.900, 0.931, and 0.913 for 16SrII-B; and 0.826, 0.837, 08.58, and 0.894 for 16SrII-D respectively. In addition, this study identified the hotspots and proportions of the areas that are vulnerable under the projected climate-change scenarios. The area of current (2021-2040) highly suitable distribution within the entire country for 16SrII-D was 19474.2 km2 (7.1%), while for 16SrII-B, an area of 8835 km2 (3.2%) was also highly suitable for the disease distribution. The proportions of these suitable areas are very significant from the available arable land standpoint. Therefore, the results from this study will be of immense benefit and will also bring significant contributions in mapping the areas of witches' broom diseases in Oman. The results will equally aid the development of new strategies and the formulation of agricultural policies and practices in controlling the spread of the disease across Oman.

Entities:  

Keywords:  bioclimatic variables; distribution model; future projection; witches’ broom disease

Year:  2021        PMID: 33670998      PMCID: PMC7997136          DOI: 10.3390/plants10030460

Source DB:  PubMed          Journal:  Plants (Basel)        ISSN: 2223-7747


  16 in total

1.  Phytoplasmas: bacteria that manipulate plants and insects.

Authors:  Saskia A Hogenhout; Kenro Oshima; El-Desouky Ammar; Shigeyuki Kakizawa; Heather N Kingdom; Shigetou Namba
Journal:  Mol Plant Pathol       Date:  2008-07       Impact factor: 5.663

2.  Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.

Authors:  Steven J Phillips; Miroslav Dudík; Jane Elith; Catherine H Graham; Anthony Lehmann; John Leathwick; Simon Ferrier
Journal:  Ecol Appl       Date:  2009-01       Impact factor: 4.657

3.  Detection, Identification, and Molecular Characterization of the 16SrII-D Phytoplasmas Infecting Vegetable and Field Crops in Oman.

Authors:  Ali M Al-Subhi; Saskia A Hogenhout; Rashid A Al-Yahyai; Abdullah M Al-Sadi
Journal:  Plant Dis       Date:  2018-01-17       Impact factor: 4.438

4.  First Report of Alfalfa Witches Broom Disease in Oman Caused by a Phytoplasma of the 16Sr II Group.

Authors:  A J Khan; K M Azam; M L Deadman; A M Al-Subhi; P Jones
Journal:  Plant Dis       Date:  2001-12       Impact factor: 4.438

5.  Molecular identification of a new phytoplasma associated with alfalfa witches'-broom in oman.

Authors:  A J Khan; S Botti; A M Al-Subhi; D E Gundersen-Rindal; A F Bertaccini
Journal:  Phytopathology       Date:  2002-10       Impact factor: 4.025

6.  SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses.

Authors:  Jason L Brown; Joseph R Bennett; Connor M French
Journal:  PeerJ       Date:  2017-12-05       Impact factor: 2.984

7.  Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

Authors:  Yoan Fourcade; Jan O Engler; Dennis Rödder; Jean Secondi
Journal:  PLoS One       Date:  2014-05-12       Impact factor: 3.240

8.  Increased sodium and fluctuations in minerals in acid limes expressing witches' broom symptoms.

Authors:  Aisha G Al-Ghaithi; Muhammad Asif Hanif; Walid M Al-Busaidi; Abdullah M Al-Sadi
Journal:  Springerplus       Date:  2016-04-06

9.  Mapping the climatic suitable habitat of oriental arborvitae (Platycladus orientalis) for introduction and cultivation at a global scale.

Authors:  Guoqing Li; Sheng Du; Zhongming Wen
Journal:  Sci Rep       Date:  2016-07-21       Impact factor: 4.379

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  2 in total

1.  Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables.

Authors:  Owusu Fordjour Aidoo; Fangyu Ding; Tian Ma; Dong Jiang; Di Wang; Mengmeng Hao; Elizabeth Tettey; Sebastian Andoh-Mensah; Kodwo Dadzie Ninsin; Christian Borgemeister
Journal:  Sci Rep       Date:  2022-10-19       Impact factor: 4.996

2.  Predicting the Potential Suitable Climate for Coconut (Cocos nucifera L.) Cultivation in India under Climate Change Scenarios Using the MaxEnt Model.

Authors:  Kukkehalli Balachandra Hebbar; Pulloott Sukumar Abhin; Veliyathukudy Sanjo Jose; Poonchalikundil Neethu; Arya Santhosh; Sandip Shil; P V Vara Prasad
Journal:  Plants (Basel)       Date:  2022-03-09
  2 in total

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