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Global distribution modelling of macrophomina phaseolina (tassi) goid: a comparative assessment using ensemble machine learning tools

Global distribution modelling of macrophomina phaseolina (tassi) goid: a comparative assessment... Macrophomina phaseolina, a soil saprophytic plant pathogen of global distribution and wide host range, was studied in relation to current and future (2050 and 2070) climate change scenarios, soil variables, and habitat heterogeneity indices (HHI). On 285 geographically thinned, presence-only data, we used R program-based Ensemble Species Distribution Modelling (ESDM) and eight individual algorithms to do ensemble modelling. When compared to other algorithms and ensemble outcomes, our study demonstrated that Random Forest (RF) was the best predictive individual algorithm. As a consequence, we utilized RF to assess this species’ habitat appropriateness, niche width, niche overlap, and area occupied within pre-defined habitat classes. In the present and 2050 Bio-Climatic (BC) periods, isothermality was recognized as the most significant element, whereas annual mean temperature was indicated as the most important regulating factor during BC-2070. According to HHI, the population of this species drops monotonically as the coefficient of variation increases. With the exception of 15 to 30 cm, depth soil predictor demonstrated that sand percentage had the least influence on the pathogen’s habitat at all examined depths. Silt played a vital function at varied depths. The findings of ESDM with the combined current data set demonstrated that climatic factors outperformed HHI and soil variables in terms of dispersion. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Australasian Plant Pathology Springer Journals

Global distribution modelling of macrophomina phaseolina (tassi) goid: a comparative assessment using ensemble machine learning tools

Mathur, Manish; Mathur, Preet
Australasian Plant Pathology , Volume 52 (4) – Jul 1, 2023
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References (120)

Publisher
Springer Journals
Copyright
Copyright © The Author(s) under exclusive licence to Australasian Plant Pathology Society Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0815-3191
eISSN
1448-6032
DOI
10.1007/s13313-023-00927-7
Publisher site
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Abstract

Macrophomina phaseolina, a soil saprophytic plant pathogen of global distribution and wide host range, was studied in relation to current and future (2050 and 2070) climate change scenarios, soil variables, and habitat heterogeneity indices (HHI). On 285 geographically thinned, presence-only data, we used R program-based Ensemble Species Distribution Modelling (ESDM) and eight individual algorithms to do ensemble modelling. When compared to other algorithms and ensemble outcomes, our study demonstrated that Random Forest (RF) was the best predictive individual algorithm. As a consequence, we utilized RF to assess this species’ habitat appropriateness, niche width, niche overlap, and area occupied within pre-defined habitat classes. In the present and 2050 Bio-Climatic (BC) periods, isothermality was recognized as the most significant element, whereas annual mean temperature was indicated as the most important regulating factor during BC-2070. According to HHI, the population of this species drops monotonically as the coefficient of variation increases. With the exception of 15 to 30 cm, depth soil predictor demonstrated that sand percentage had the least influence on the pathogen’s habitat at all examined depths. Silt played a vital function at varied depths. The findings of ESDM with the combined current data set demonstrated that climatic factors outperformed HHI and soil variables in terms of dispersion.

Journal

Australasian Plant Pathology – Springer Journals

Published: Jul 1, 2023

Keywords: Climatic variables; Habitat heterogenity indices; Isothermality; Niche overlap and niche range; Random Forest; Species distribution modelling

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