Habitat suitability study for green gram production under present and future climatic scenarios in Kibwezi East Kenya

Authors

  • Zipporah Maluvu Department of Earth and Climate Sciences, University of Nairobi, Nairobi, Kenya
  • Oludhe Christopher Department of Earth and Climate Sciences, University of Nairobi, Nairobi, Kenya
  • Kisangau Daniel Department of Earth and Climate Sciences, University of Nairobi, Nairobi, Kenya, Department of Life Sciences, South Eastern Kenya University (SEKU), Kitui, Kenya
  • Maweu Jacinta Mwende Department of Journalism & Communication, University of Nairobi, Nairobi, Kenya

DOI:

https://doi.org/10.25081/jsa.2024.v8.8903

Keywords:

Adaptation, Climate Change, Green gram, Habitat suitability, Kibwezi East Sub-County, Species Distribution Modelling

Abstract

The species distribution model was used to predict the suitability of green gram production under the present, RCP 4.5 and 8.5 climate scenarios. An ensemble of a species distribution model comprising six models was developed. Validation of these models revealed that all models were robust with the best model being random forest (RF) with Area Under the Curve (AUC) = 0.98 and Deviance = 0.29 while the least was the generalized linear model (GLM) with AUC = 0.87 and Deviance = 0.71. The green gram habitat suitability greatly decreased under RCP 8.5 climate scenario prediction whereby about half of the agricultural land in the Kibwezi East Sub County was highly unsuitable for green gram production. The Habitat suitability predictions showed that Thange ward out of the four wards in the location was the most suitable for green gram production. However, as per the predictions its suitability for green gram production may be affected by climate change under all climate scenarios. Results from this study give decision-makers a foundational understanding of the likely effects of climate change in the 2050s compared to the present scenario on habitat suitability for green gram production and a basis for creating strategies and policies to enhance adaptation and create resilience to its effects.

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References

Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223-1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x

Buisson, L., Thuiller, W., Casajus, N., Lek, S., & Grenouillet, G. (2010). Uncertainty in ensemble forecasting of species distribution. Global Change Biology, 16(4), 1145-1157. https://doi.org/10.1111/j.1365-2486.2009.02000.x

Chan, J. Y.-L., Leow, S. M. H., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z.-W., & Chen, Y.-L. (2022). Mitigating the multicollinearity problem and its machine learning approach : A review. Mathematics, 10(8), 1283. https://doi.org/10.3390/math10081283

Dastres, E., Bijani, F., Naderi, R., Zamani, A., & Edalat, M. (2023). Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques. Research Square. https://doi.org/10.21203/rs.3.rs-2441475/v1

del Río, S., Canas, R., Cano, E., Cano-Ortiz, A., Musarella, C., Pinto-Gomes, A., & Penas, A. (2021). Modelling the impacts of climate change on habitat suitability and vulnerability in deciduous forests in Spain. Ecological Indicators, 131, 108202. https://doi.org/10.1016/j.ecolind.2021.108202

Di Mari, R., Ingrassia, S., & Punzo, A. (2023). Local and Overall Deviance R-Squared Measures for Mixtures of Generalized Linear Models. Journal of Classification, 40, 233-266. https://doi.org/10.1007/s00357-023-09432-4

Fick, S. E., & Hijmans R. J. (2017). WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. https://doi.org/10.1002/joc.5086

Freeman, E. A., & Moisen, G. G. (2008). A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological Modelling, 217(1-2), 48-58. https://doi.org/10.1016/j.ecolmodel.2008.05.015

Gajowniczek, K., Ząbkowski, T., & Szupiluk, R. (2014). Estimating the Roc Curve and Its Significance for Classification Models’ Assessment. Quantitative Methods in Economics, XV(2), 382-391.

GoK. (2013). Makueni County First County Integrated Development Plan 2013-2017. Government of Kenya, Nairobi.

Gould, S. F., Nicholas, J. B., Harris, R. M. B., Michael, F. H., Lechner, A. M., Porfirio, L. L., & Mackey, B. G. (2014). A tool for simulating and communicating uncertainty when modelling species distributions under future climates. Ecology and Evolution, 4(24), 4798-4811. https://doi.org/10.1002/ece3.1319

Halder, J. (2013). Land Suitability Assessment for Crop Cultivation by Using Remote Sensing and GIS. Journal of Geography and Geology, 5(3), 65-74. https://doi.org/10.5539/jgg.v5n3p65

Han, F., & Liu, H. (2017). Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution. Bernoulli, 23(1), 23-57. https://doi.org/10.3150/15-BEJ702

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. New York, US: Springer. https://doi.org/10.1007/978-0-387-84858-7

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression. (3rd ed). New York, US: John Wiley & Sons, Inc. https://doi.org/10.1002/9781118548387

Inglis, A., Parnell, A., & Hurley, C. B. (2022). Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models. Journal of Computational and Graphical Statistics, 31(3), 766-778. https://doi.org/10.1080/10618600.2021.2007935

IPCC. (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Retrieved from https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-FrontMatterA_FINAL.pdf

James, W. J., Antle, J. M., Basso, B., Boote, K. J., Conant. F. I., Foster, I., Godfray, H. C. J., Herrero, M., Howitt, R. E., Janssen, S., Keating, B. A., Munoz-Carpena, R., Porter, C. H., Rosenzweig, C., & Wheeler, T. R. (2017). Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agricultural Systems, 155, 269-288. https://doi.org/10.1016/j.agsy.2016.09.021

Jarvie, S., & Svenning J.-C. (2018). Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change. Philosophical Transactions of the Royal Society B Biological Sciences, 373(1761), 20170446. https://doi.org/10.1098/rstb.2017.0446

Kufa, C. A., Bekele, A., & Atickem, A. (2022). Impacts of climate change on predicted habitat suitability and distribution of Djaffa Mountains Guereza (Colobus guereza gallarum, Neumann 1902) using MaxEnt algorithm in Eastern Ethiopian Highland. Global Ecology and Conservation, 35, e02094. https://doi.org/10.1016/j.gecco.2022.e02094

Kumar, R., & Indrayan, A. (2011). Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatrics, 48, 277-287. https://doi.org/10.1007/s13312-011-0055-4

Latif, Q. S., Saab, V. A., Dudley, J. G., & Hollenbeck, J. P. (2013). Ensemble modeling to predict habitat suitability for a large-scale disturbance specialist. Ecology and Evolution, 3(13), 4348-4364. https://doi.org/10.1002/ece3.790

Lobo, J. M., Jiménez-Valverde, A., & Real, R. (2008). AUC: A misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17(2), 145-151. https://doi.org/10.1111/j.1466-8238.2007.00358.x

Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Edenhofer, O., Stocker, T. F., Field, C. B., Ebi, K. L., & Matschoss, P. R. (2011). The IPCC AR5 guidance note on consistent treatment of uncertainties: A common approach across the working groups. Climatic Change, 108(675), https://doi.org/10.1007/s10584-011-0178-6

Mugo, J. W., Kariuki, P. C., & Musembi, D. K. (2016). Identification of Suitable Land for Green Gram Production Using GIS Based Analytical Hierarchy Process in Kitui County, Kenya. Journal of Remote Sensing & GIS, 5, 1000170.

Mugo, J. W., Opijah, F. J., Ngaina, J., Karanja, F., & Mburu, M. (2020). Suitability of Green Gram Production in Kenya under Present and Future Climate Scenarios Using Bias-Corrected Cordex RCA4 Models. Agricultural Sciences, 11(10), 882-896. https://doi.org/10.4236/as.2020.1110057

Nair, R. M., Schafleitner, R., Kenyon, L., Srinivasan, R., Easdown, W., Ebert, A. W., & Hanson, P. (2012). Genetic improvement of mungbean. Sabrao Journal of Breeding and Genetics, 44(2), 177-190.

Noce, S., Caporaso, L., & Santini, M. (2019). Climate change and geographic ranges: The implications for Russian forests. Frontiers in Ecology and Evolution, 7, 57. https://doi.org/10.3389/fevo.2019.00057

Noce, S., Collalti, A., & Santini, M. (2017). Likelihood of changes in forest species suitability, distribution, and diversity under future climate: the case of Southern Europe. Ecology and Evolution, 7(22), 9358-9375. https://doi.org/10.1002/ece3.3427

O’Donnell, M. S., & Ignizio, D. A. (2012). Bioclimatic predictors for supporting ecological applications in the conterminous. United States: U.S. Geological Survey Data Series. Retrieved from https://pubs.usgs.gov/ds/691/ds691.pdf

Smith, A. B., & Santos, M. J. (2020). Testing the ability of species distribution models to infer variable importance. Ecography, 43(12), 1801–1813. https://doi.org/10.1111/ecog.05317

Taleshi, H., Jalali, S. G., Alavi, S. J., Hosseini, S. M., Naimi, B., & Zimmermann, N. E. (2019). Climate change impacts on the distribution and diversity of major tree species in the temperate forests of Northern Iran. Regional Environmental Change, 19, 2711-2728. https://doi.org/10.1007/s10113-019-01578-5

Urban, M. C., Bocedi, G., Hendry, A. P., Mihoub, J.-B., Pe'er, G., Singer, A., Bridle, J. R., Crozier, L. G., De Meester, L., Godsoe, W., Gonzalez, A., Hellmann, J. J., Holt, R. D., Huth, A., Johst, K., Krug, C. B., Leadley, P. W., Palmer, S. C. F., Pantel, J. H., ... Travis, J. M. (2016). Improving the forecast for biodiversity under climate change. Science, 353(6304), aad8466. https://doi.org/10.1126/science.aad8466

Vieilledent, G., Cornu, C., Sanchez, A. C., Pock-Tsy, J.-M. L., & Danthu, P. (2013).Vulnerability of baobab species to climate change and effectiveness of the protected area network in Madagascar: Towards new conservation priorities. Biological Conservation, 166, 11-22. https://doi.org/10.1016/j.biocon.2013.06.007

Xie, S., & Zhang, J. (2023). TOPSIS-based comprehensive measure of variable importance in predictive modelling. Expert Systems with Applications, 232, 120682. https://doi.org/10.1016/j.eswa.2023.120682

Yoon, S., & Lee, W.-H. (2023). Application of true skill statistics as a practical method for quantitatively assessing CLIMEX performance. Ecological Indicators, 146, 109830. https://doi.org/10.1016/j.ecolind.2022.109830

Zhang, L., Liu, S., Sun, S., Wang, T., Wang, G., Zhang, X., & Wang, L. (2015). Consensus forecasting of species distributions: The effects of niche model performance and niche properties Plos One, 10(3), e0120056. https://doi.org/10.1371/journal.pone.0120056

Zurell, D. (2017). Integrating demography, dispersal and interspecific interactions into bird distribution models. Journal of Avian Biology, 48(12), 1505-1516. https://doi.org/10.1111/jav.01225

Zurell, D. (2020). Introduction to Species Distribution Modelling (SDR) in R. Retrieved from https://damariszurell.github.io/SDM-Intro/

Published

04-07-2024

How to Cite

Maluvu, Z., Christopher, O., Daniel, K., & Mwende, M. J. (2024). Habitat suitability study for green gram production under present and future climatic scenarios in Kibwezi East Kenya. Journal of Scientific Agriculture, 8, 31–37. https://doi.org/10.25081/jsa.2024.v8.8903

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