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Potential distribution modeling based on machine learning of Sechium edule (Jacq.) Sw. in Japan

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Abstract

Species distribution models identify regions with ideal environmental characteristics for the establishment and proliferation of species. The chayote (Sechium edule) is a crop that originated and domesticated in Mexico; however, it is cultivated in different parts of the world due to its nutritional and pharmaceutical importance. The objective of this research was to locate the potential distribution of S. edule in Japan supported on seven machine learning models, to also determine which bioclimatic variables influence its distribution, and which are the most suitable regions for its establishment. Thirty-one occurrence points, elevation, and the bioclimatic variables bio1, bio3, bio4, bio7, bio8, bio12, bio14, bio15, and bio17 were used to infer the models. Hundred percent of the occurrence points coincided with the Cfa climate distributed in Acrisol (60.9%), Andosol (17.4%), Cambisol (13%), Fluvisol (4.35%), and Gleysol (4.35%) soil. The maximum entropy model (Maxent) model reported the highest area under the curve (AUC) value (0.93), while the generalized linear model (GLM) obtained the best true skills statistics (TSS) value (0.84); the super vector machine (SVM) model reported the largest suitability area ≥ 0.5 with 100,394.4 km2. Temperature-related variables were the major contributors to the models and the ones explaining the distribution limits of S. edule in Japan. The coastal eastern prefectures of Kantō, Chūbu, Kinki, Chūgoku, Kyūshū, and Shikoku regions showed a suitability ≥ 0.5.

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Acknowledgements

The Research was initiated with a SATREPS project by JST-JICA, "Diversity Assessment and Development of Sustainable Use of Mexican Genetic Resources" . The research was supported in part by the Plant Transgenic Design Initiative (P-TraD) at Tsukuba Plant Innovation Research Center, University of Tsukuba, Japan.

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Authors and Affiliations

  1. Colegio de Postgraduados Campus San Luis Potosí, Salinas de Hidalgo, 78600, San Luis Potosí, México

    Jorge Cadena Iñiguez

  2. Universidad del Valle de Puebla, Calle 3 A Sur 5759, El Cerrito, 72440, Puebla, México

    Luis Ángel Barrera Guzmán

  3. Colegio de Postgraduados Campus Montecillo, Km 36.5 Carr. México-Texcoco, 56230, Montecillo, México

    Ma. de Lourdes C. Arévalo Galarza

  4. Instituto de Ecología A.C, Carretera antigua a Coatepec, El Haya, 351, Xalapa, Veracruz, México

    Daniel Alejandro Cadena Zamudio

  5. Tsukuba Plant Innovation Research Center, University of Tsukuba, 1‑1‑1 Tennodai, Tsukuba, Ibaraki Prefecture, 305‑8571, Japan

    Kazuo N. Watanabe

  6. Universidad Autónoma Chapingo 38.5 Carretera México – Texcoco Chapingo, 56230, Texcoco, Estado de México, México

    Gustavo Antonio Arévalo Galarza

  7. Facultad de Ciencias Agrícolas, Universidad Autónoma de Chiapas, 30660, Huehuetán, Chiapas, México

    Juan Francisco Aguirre Medina

  8. Interdisciplinary Research Group of Sechium Edule in Mexico (GISeM) 56160, Texcoco, Estado de México, México

    Jorge Cadena Iñiguez, Luis Ángel Barrera Guzmán, Ma. de Lourdes C. Arévalo Galarza, Daniel Alejandro Cadena Zamudio, Kazuo N. Watanabe & Juan Francisco Aguirre Medina

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Correspondence to Daniel Alejandro Cadena Zamudio.

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Iñiguez, J.C., Barrera Guzmán, L.Á., Arévalo Galarza, M.d.L.C. et al. Potential distribution modeling based on machine learning of Sechium edule (Jacq.) Sw. in Japan. Genet Resour Crop Evol 71, 2105–2115 (2024). https://doi.org/10.1007/s10722-023-01739-w

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