Predictive modeling of the rate of occurrence favorable to Cola attiensis Aubrév. & Pellegr., in a context of climate change using a nomogram

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Research Paper 10/07/2023
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Predictive modeling of the rate of occurrence favorable to Cola attiensis Aubrév. & Pellegr., in a context of climate change using a nomogram

Akotto Odi Faustin, Kouadio Konan Kan Hippolyte, Yoboue Kouadio Emile, Assamoi Kocola Christ Williams Siracide
Int. J. Biosci.23( 1), 150-161, July 2023.
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Climate change poses a pressing challenge to the distribution patterns of plant species, necessitating innovative approaches to predict occurrence rates in affected ecosystems. In this study, we focus on Cola attiensis, an economically and ecologically important plant species endemic to Côte d’Ivoire. Our novel methodology employs a nomogram, traditionally used for intricate mathematical equations, to model the intricate relationships between topographic, climatic variables, and the presence of C. attiensis. By integrating field occurrence data and bioclimatic variables, we delineate the potential habitat of this species. Utilizing logistic regression-based nomogram predictions, we estimate the probability of occurrence, with the minimum temperature of the coldest month emerging as the most influential variable. Calibration curve analysis convincingly demonstrates the validity of our model by accurately predicting occurrence probabilities. The implementation of this nomogram holds significant implications for effective ecosystem management, enabling the estimation of C. attiensis survival probabilities in its potential habitat. The total vulnerability score of C. attiensis in its potential habitat was higher than 80%. These findings provide crucial insights for the formulation of conservation and management strategies aimed at protecting this endemic species in Côte d’Ivoire. However, future research endeavors should address complex biological interactions, uncertainties in climate projections, and incorporate additional variables to advance our understanding of species dynamics.


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