ECOLOGICAL NICHE MODELING FOR CHEMICAL PROSPECTING OF FOREST SPECIES: A CASE STUDY IN Nothapodytes nimmoniana (Graham) Mabb.
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Date
2010-01-12
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University of Agricultural Sciences, Bangalore
Abstract
Ecological Niche Modeling (ENM) has become powerful tool in predicting the
geographic distribution of a given species. Being modelling process, it maps the similar
habitat conditions corresponding to the habitat type of the places of known occurrence
of the species and stratifies the area from most suitable to least suitable habitat for the
species occurrences. However, in this study an attempt has been made to test the
potentiality of ENM in prospecting for the populations that yield high level of seconday
metabolites in particular, CPT. The underlying assumption of ENM here is, the plants in
most suitable area are expected to show higher fitness , higher regeneration status, and
hence the traits that confirms the fitness would be expected to be high in most suitable
area compared to least suitable area. There are hardly any attempts made to this
important assumption, however, in this study we provide evidence towards this
assumption in specific metabolite, CPT. Therefore we proposed to use ENM to predict
the habitat suitability for Nothapodytes nimmoniana (Graham), which produce an anticancer
drug, Comptothecin (CPT).
Firstly we developed niche map for N. nimmoniana using DIVA- GIS and
Maxent. However in this study to validate the ENM assumption, density, regeneration,
girth of trees, fluctuating leaf asymmetry, and specific leaf weight were considered as
fitness measures. Result indicated that, mean % CPT content is significantly higher in
predicted most suitable habitat compared to least suitable habitat wherein population
showed higher regeneration status and higher fitness. Therefore, we concluding that,
production of CPT in N. nimmoniana could be purely a spin-off of growth and
reproduction. Thus, results from this study validated the assumption of ENM accordingly
suggested that, ENM could be potentially used to predict the niches of high fitness and
area with high level of secondary metabolites.
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