Predictive modeling of the rate of occurrence favorable to Cola attiensis Aubrév. & Pellegr., in a context of climate change using a nomogram
Paper Details
Predictive modeling of the rate of occurrence favorable to Cola attiensis Aubrév. & Pellegr., in a context of climate change using a nomogram
Abstract
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.
Akotto OF, Alui KA, Malan DF, Kouakou KJ, Yao-Kouamé A, Kagoyiré K. 2014. Soil landscape and stand conditions in Cola attiensis in Côte d’Ivoire 4(5), 102-113. DOI: 10.12692/ijb/4.5.102-11.
Alimonti G, Mariani L, Prodi F, Ricci RA. 2022. A critical assessment of extreme events trends in times of global warming. Eur. Phys. J. Plus 137, 112. https://doi.org/10.1140/epjp/s13360-021-
Ankrah J, Monteiro A, Madureira H. 2023. Extreme Temperature and Rainfall Events and Future Climate Change Projections in the Coastal Savannah Agroecological Zone of Ghana. Atmosphere 14, 386.
Austin PC, Steyerberg EW. 2012. The Integrated Calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models. Statistics in Medicine 31(29), 3842-3855.
Borumandnia N, Doosti H, Jalali A, Khodakarim S, Charati YJ, Pourhoseingholi AM, Talebi Agah SA. 2021. Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study. Int. J. Environ. Res. Public Health 18, 7734.
Bouquet A, Debray M. 1974. Les plantes médicinales de la Côte d’Ivoire. Travaux et documents de l’O. RSTOM., Editions ORSTOM. Paris 230 p.
Couch C, Haba PM. 2021. Tipa assessment : diecke classifed forest, Yomou Prefecture http://www. Herbierguinee.org/ Uploads/2/6/3/0 /26303479/2. _Tipas_Report_Diecke_ En_New_Final.Pdf
Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129-151.
Filleron T, Chaltie L, Jouve E, Cabarrou B, Gilhodes J, Lusque A, Mery E, Dalenc F, Martinez A. 2017. Nomograms in routine clinical practice : Methodology, interest and limitations. Bulletin du cancer 105(1), 10-24.
GIEC. 2020. Le rapport spécial du GIEC sur le changement climatique et les terres émergées : Quels impacts pour l’Afrique. Genève : GIEC. 40 p.
Gillet A, Brostaux Y, Palm R. 2011. Principaux modèles utilisés en régression logistique. Biotechnol. Agron. Soc. Environ. 15(3), 425-433.
Guisan A, Thuiller W, Zimmermann NE. 2017. Habitat Suitability and Distribution Models: With Applications in R. Cambridge University Press.
Harrell FEJr. 2022. Rms: Regression Modeling Strategies. R package version 6, 3-0. URL https://hbiostat.org/R/rms/, https://github.com.
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25(15), 1965-1978.
Iasonos A, Schrag D, Raj GV, Panageas KS. 2008. How to build and interpret a nomogram for cancer prognosis. Journal of Clinical Oncology 26(8), 1364-1370.
IPCC. 2021. Summary for Policymakers. In : Climate Change. 2021. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, Zhai VP, Pirani A, Connors SL, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis MI, Huang M, Leitzell K, Lonnoy E, Matthews JBR, Maycock TK, Waterfield T, Yelekçi O, Yu R, Zhou B, (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA pp. 3-32, DOI: 10.1017/9781 009157896.001.
IUCN. 2012a. IUCN Red List Categories and Criteria: Version 3.1. Deuxième édition. Gland, Switzerland and Cambridge, UK: UICN.
IUCN. 2012b. Guidelines for Application of IUCN Red List Criteria at Regional and National Levels: Version 4.0. Gland, Suisse et Cambridge, UK: UICN.
IUCN. 2015. Guidelines for the Application of IUCN Red List of Ecosystems Categories and Criteria. Version 1.0. Bland, L.M., Keith, D.A., Murray, N.J., and Rodríguez, J.P. (Eds.) Gland, Suisse: UICN. ix + 93 pp.
IUCN. 2016. Standard mondial pour l’identification des Zones Clés pour la Biodiversité, Version 1.0. Première édition. Gland, Suisse: UICN.
Iwu MM, Jackson JE, Tally JD, Klayman DL. 1992. Evaluation of plant extracts for antileishmanial activity using a mechanism-based radiorespirometric microtechnique (RAM). Planta-Med (Thieme Medical Publishers) 58(5), 436-441.
Jalali A, Alvarez-Iglesias A, Roshan D, Newell J. 2019. Visualising statistical models using dynamic nomograms. PLoS One. 14(11), e0225253. DOI: 10.1371/journal.pone.0225253. PMID: 31730633; PMCID: PMC6857916.
Jiang H, Tang E, Xu D, Chen Y, Zhang Y, Tang M, Xiao Y, Zhang Z, Deng X, Li H. 2017. Development and validation of nomograms for predicting survival in patients with non-metastatic colorectal cancer. Oncotarget 8, 29857.
Kouamé FN, Kouamé PN, Bongui JB, N’Guessan KE, Koffi KA, Kouadio AK, Zoro Bi IA, Koudou J, Djè Y, N’Guessan K. 2018. Inventory of Cola attiensis, Cola lizae and Cola acuminata (Malvaceae) populations in Côte d’Ivoire. International Journal of Biological and Chemical Sciences 12(4), 1987-2001.
Legendre P. 2018. Spatial autocorrelation: Trouble or new paradigm. Ecology 99(2), 343-356.
Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W. 2009. Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib 15, 59-69.
Meng X, Hao F, Ju Z, Chang X, Guo Y. 2022. Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer : Analysis of population-based cohort with external validation. Front Public Health 31(10), 953992. DOI: 10.3389/fpubh.2022.953992. PMID: 36388300; PMCID: PMC9659596.
Moeslund JE, Arge L, Bøche RP, Dalgaard T, Odgaard MV, Nygaard B, Svenning JC. 2013. Topographically controlled soil moisture is the primary driver of local vegetation patterns across a lowland region. Ecosphère 4(7), 1-26.
Nduche MU, Magos Brehm J, Parra-Quijano M, Maxted N. 2023. In situ and ex situ conservation gap analyses of West African priority crop wild relatives. Genet Resour Crop Evol. 70, 333-351. https://doi.org/10.1007/s10722-022-01507-2
Pearce JL, Ferrier S. 2000a. An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecological Modelling 128, 127-147.
Pearce JL, Ferrier S. 2000b. Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling 133, 225-245.
Pearson RG, Dawson TP. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful. Global Ecology and Biogeography 12(5), 361-371.
Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S. 2009. Sample selection bias and presence-only distribution models : implications for background and pseudo-absence data. Ecol Appl 19(1), 181-97. DOI: https://doi.org/10.1890/07-2153.1. PMID: 19323182.
R Core Team. 2023. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/
Seo JH, Kim HJ, Lee JY. 2020. Nomogram construction to predict dyslipidemia based on a logistic regression analysis. Journal of Applied Statistics 47(5), 914-926 https://doi.org/10.1080 /02664763.2019.1660760.
Seoane J, Bustamante J, Díaz-Delgado R. 2005. Effect of Expert Opinion on the Predictive Ability of Environmental Models of Bird Distribution. Conservation Biology 19(2), 512-522. http://www.jstor.org/stable/3591263.
Toffa Y, Idohou R, Fandohan BA. 2022. Species distribution modelling in Africa : state of the art and prospects. Physio-Geo. 17(1), 45-64. Consulté le 28 juin 2023. URL : http://journals.openedition. org/physio-geo/13738 DOI : https://doi.org /10.40 00 /physio-geo.13738.
Tournès D. 2016. Abaques et nomogrammes. Ffhal-01484563, 101 p.
Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, Elith J, Dudík M, Ferrier S, Huettmann F, Leathwick JR, Lehmann A, Lohmann L, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JMC, Phillips SJ, Richardson KS, Scachetti-Pereira R, Schapire RE, Soberón J, Williams SE, Zimmermann NE. 2008. Effects of sample size on the performance of species distribution models. Divers Distrib 14, 763-773.
Wu J, Zhang H, Li L, Hu M, Chen L, Wu S, Xu B, Song Q. 2020. Prognostic nomogram for predicting survival in patients with high grade endometrial stromal sarcoma: A Surveillance Epidemiology, and End Results database analysis. Int J Gynecol Cancer. 30(10), 1520-1527. DOI: 10.1136/ijgc-2020-001409. Epub 2020 Aug 23. PMID: 32839227.
Xiang BY, Huang W, Zhou GQ, Hu N, Chen H, Chen C. 2017. Body mass index and the risk of low bone mass-related fractures in women compared with men: A PRISMA-compliant meta-analysis of prospective cohort studies. Medicine (Baltimore). 96(12), e5290. DOI: 10.1097/MD.00000000 00005290.
Yu H, Cooper RA, Infante MD. 2020. Improving species distribution model predictive accuracy using species abundance: Application with boosted regression trees, Ecological Modelling 432, 2020, 109202, ISSN 0304-3800, https://doi.org/10.1016/ j.ecolmodel.2020.109202. https://www.scien cedirect. com/science/article/pii/S030 438002036
Zhang QP, Fang RY, Deng CY, Zhao HJ, Shen MH, Wang Q. 2022. Slope aspect effects on plant community characteristics and soil properties of alpine meadows on Eastern Qinghai-Tibetan plateau. Ecol. Ind 143 (2022), 10.1016/j.ecolind.2022 .1400.
Akotto Odi Faustin, Kouadio Konan Kan Hippolyte, Yoboue Kouadio Emile, Assamoi Kocola Christ Williams Siracide (2023), Predictive modeling of the rate of occurrence favorable to Cola attiensis Aubrév. & Pellegr., in a context of climate change using a nomogram; IJB, V23, N1, July, P150-161
https://innspub.net/predictive-modeling-of-the-rate-of-occurrence-favorable-to-cola-attiensis-aubrev-pellegr-in-a-context-of-climate-change-using-a-nomogram/
Copyright © 2023
By Authors and International
Network for Natural Sciences
(INNSPUB) https://innspub.net
This article is published under the terms of the
Creative Commons Attribution License 4.0