Record Details

Ecological Niche Modelling of an Industrially Important Mushroom - Ganoderma lucidum (Leys.) Karsten: A Machine Learning Global Appraisal

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Ecological Niche Modelling of an Industrially Important Mushroom - Ganoderma lucidum (Leys.) Karsten: A Machine Learning Global Appraisal
 
Creator Mathur, Manish
Mathur, Preet
 
Subject Bioclimatic variables
Ecosystem rooting depth
Ensemble machine learning
Random forest algorithm
Representative concentration pathways
 
Description 1231-1249
Species Distribution Modelling (SDM) involves utilizing observations of a given species and its surrounding
environment to produce a sound approximation of the species' potential distribution. The intricate relationships between
organisms and their surroundings, coupled with the profusion of data, have captured the attention of ecologists and
statisticians alike. Consequently, they have directed their efforts towards exploring the potential of machine learning
techniques. Our study employs an ensemble machine learning approach to simulate the global ecological niche modelling of
Ganoderma lucidum fungus. This involves the utilization of various environmental predictors and the averaging of multiple
algorithms to achieve a comprehensive analysis. 563 spatially thinned presence points of G. lucidum were projected with
three bio-climatic time frames, namely current, 2050, and 2070, and four Representative Concentration Pathways (RCPs),
namely 2.6, 4.5, 6.0, and 8.5, as well as non-climatic variables (surface soil features, land use, rooting depth and water
storage capacity at rooting zone). We observed excellent model qualities as the Area Under the receiver operating
Curve (AUC) approached 0.90. Random Forest was identified as the best individual algorithm, while the Maxent entropy
was identified as the least effective for Ecological Niche Modelling (ENM) of G. lucidum. Globally, under the current
bio-climatic and non-bioclimatic projection, optimum habitat for this fungus covers 12510876.3 km2 area while, maximum
area (13248546.9 Sq. km.) under this habitat class with future projections was recorded with RCP of 8.5 in 2070.
The primary determinants of its current global distribution were ecosystem rooting depth, water storage capacity, and
precipitation seasonality. While, with two future bioclimatic time frames and RCPs, Isothermality was identified as the
most influential predictor. Based on our assessment, it has been determined that this particular fungus is exhibiting a
persistent pattern of proliferation across the regions of Europe, America, and certain areas of India. The present investigation
sought to underscore the importance of discerning the native habitats of this species, taking into account both current and
anticipated climatic shifts. This knowledge is essential for effectively coordinating the artificial cultivation and natural
harvesting of G. lucidum, which is necessary to meet the ever-increasing industrial demands.
 
Date 2023-12-29T12:22:54Z
2023-12-29T12:22:54Z
2023-12
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63138
https://doi.org/10.56042/jsir.v82i12.1973
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(11) [November 2023]