Stochastic differential equation modeling of climate-driven ecological population dynamics

Shalin, L. V. Arun; Ali, Hayder M.; Alomari, Saleh Ali; Sri, T. Santhi; Sivakumar, G.; Smerat, Aseel; Sengan, Sudhakar; Dadheech, Pankaj

doi:10.47974/JIM-2574

Open Access Research Article

Stochastic differential equation modeling of climate-driven ecological population dynamics

^*L. V. Arun ShalinCorresponding authorarunshalin7@gmail.comDepartment of Computer Science and EngineeringSchool of Computing(Deemed to be University), AvadiVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyChennai, Tamil Nadu, 600062, IndiaView full profile → , Hayder M. Alihayder@uowa.edu.iqDepartment of Information TechnologyCollege of ScienceUniversity of Warith Al-AnbiyaaKarbala, 56001, IraqView full profile → , Saleh Ali Alomariomari08@jadara.edu.joDepartment of Computer ScienceFaculty of Information TechnologyJadara UniversityIrbid, 21110, JordanView full profile → , T. Santhi Srisri_santhi2003@yahoo.comDepartment of Computer Science and Engineering(Deemed to be University), VaddeswaramKoneru Lakshmaiah Education FoundationGuntur, Andhra Pradesh, 522302, IndiaView full profile → , G. Sivakumarsivakumarganapathi@gmail.comDepartment of Computer Science and EngineeringPerunduraiErode Sengunthar Engineering CollegeErode, Tamil Nadu, 638057, IndiaView full profile → , Aseel Smeratsmerat.2020@gmail.comHourani Center for Applied Scientific ResearchAl-Ahliyya Amman UniversityAmman, 19328, JordanView full profile → , Sudhakar Sengansudhasengan@gmail.comDepartment of Computer Science & EngineeringPSN College of Engineering and Technology (Autonomous)Tirunelveli, Tamil Nadu, 627152, IndiaView full profile → , Pankaj Dadheechpankajdadheech777@gmail.comDepartment of Computer Science and EngineeringSwami Keshvanand Institute of Technology, Management & Gramothan (SKIT)Jaipur, Rajasthan, 302017, IndiaView full profile →

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pp. 1045–1055Vol. 29Issue 4April 2026DOI: 10.47974/JIM-2574XML

Received:: 01 Sep 2025
Published Online:: 09 Apr 2026
Article type:: Research Article
Language:: EN
Article no.:: JIM-2574
Pages:: 1045–1055

Abstract

Complex, non-linear human connections contribute to the significant impact that the inherently unpredictable nature of environmental factors (EF) has on the behavior of environmental populations. To forecast climate-driven population changes, this study proposes a hybrid model combining Statistical Learning (SL) with Stochastic Differential Equations (SDE). For the aim of Feature Selection (FS) and the detection of temperature anomalies, this investigation applies Mutual Information and Bayesian Data Augmentation (MI + BDA). The Earth Solar System (ENSO) index has been identified as the primary predictor. This work implements stochastic forcing variables that characterize ecological risk in the climate-coupled SDE. Forecasting methods have been reported to have lower predictive accuracy (R² = 0.92, RMSE = 0.12) than the proposed model, as tested with the North Atlantic cod and Adélie penguin datasets. The environment faces significant temperature constraints, according to this investigation and other studies on sustainable development. The empirical evidence indicates that a 78% increase in the Risk of Death (RoD) over 52 years is associated with temperatures exceeding 3.5°C. Maintain awareness that this study proves the value of non-linear climate-population correlations, which are vital to the maintenance of biodiversity.

Keywords

Stochastic differential equations Statistical learning Climate change Population dynamics Feature selection Bayesian inference Ecological modeling Extinction risk

Subject Classifications

Primary 60H10Secondary 62M2092D25

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