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Original Research Article

Integrating Multi-Scale Environmental Structures and Physiological Dynamics in Animal Movement Modeling: Evidence from Gashaka Gumti National Park

Owan Raphael Asu, Dooga, Murphy Y., Ojua Doris N., Asu Dorcas N.

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Owan Raphael Asu Corresponding Author

Department of Statistics, Faculty of physical sciences, University of Calabar, Calabar, Nigeria

ORCID: 0009-0007-8667-176X

Correspondence: owanraphael@unical.edu.ng

Received
30 Jun 2026
Published
14 Jul 2026

Abstract

This study examined the integration of multi-scale environmental structures and physiological dynamics in modeling animal movement within Gashaka Gumti National Park. The research was motivated by the need to improve traditional movement models that often consider environmental or physiological factors in isolation, thereby limiting predictive accuracy in complex ecological systems. The study adopted a quantitative modeling approach using four complementary frameworks: Step-Selection Function (SSF), Hidden Markov Model (HMM), State-Space Model (SSM), and a Mechanistic Energy-Based Model. Data used in the study included simulated and ecologically grounded movement variables such as step length, turning angle, vegetation index, distance to water, and physiological energy levels. The SSF results showed that vegetation density (β = 1.85, p = 0.001) and proximity to water (β = −1.22, p = 0.003) significantly influenced movement decisions, indicating strong habitat selection patterns. The HMM classified movement into three behavioral states—feeding (50%), traveling (35%), and resting (15%)—with high state persistence, particularly in foraging behavior. The SSM reduced mean positional error from 35 m to 12 m, demonstrating improved accuracy in movement trajectory reconstruction. The energy-based model revealed a clear inverse relationship between energy levels and movement probability, with low energy states corresponding to higher movement activity (P = 0.85 at low energy levels and P = 0.25 at high levels). Furthermore, the integrated modeling framework outperformed individual models, achieving 88% predictive accuracy compared to 72% (SSF) and 75% (HMM). The study concludes that animal movement is driven by the combined effects of environmental heterogeneity and physiological state, and that integrated modeling significantly enhances predictive performance. The findings highlight the importance of multi-scale and mechanistic approaches in ecological modeling and provide valuable insights for wildlife conservation and habitat management in dynamic ecosystems.

Keywords: Animal Movement, Multi-Scale Environment, Physiological Dynamics, Ssf, Hmm, State-Space Model, Energy-Based Model, Gashaka Gumti National Park.

How to Cite

APA

Asu, O. R., Dooga, M. Y., N., O. D., & N., A. D. (2026). Integrating Multi-Scale Environmental Structures and Physiological Dynamics in Animal Movement Modeling: Evidence from Gashaka Gumti National Park. Journal of Contemporary Academic Research and Methodologies, 1(5). https://doi.org/10.5281/zenodo.21357768

MLA

Asu, Owan Raphael, Murphy Y. Dooga, Ojua Doris N., and Asu Dorcas N.. "Integrating Multi-Scale Environmental Structures and Physiological Dynamics in Animal Movement Modeling: Evidence from Gashaka Gumti National Park." Journal of Contemporary Academic Research and Methodologies, vol. 1, no. 5, 2026. DOI: https://doi.org/10.5281/zenodo.21357768

Chicago

Asu, Owan Raphael, Murphy Y. Dooga, Ojua Doris N., and Asu Dorcas N.. "Integrating Multi-Scale Environmental Structures and Physiological Dynamics in Animal Movement Modeling: Evidence from Gashaka Gumti National Park." Journal of Contemporary Academic Research and Methodologies 1, no. 5 (2026). https://doi.org/10.5281/zenodo.21357768

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ISSN 3139-7247
Tracking ID JCARM_JUN_26_172
Article No. 051
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Article Info
Journal JCARM
Volume Vol 1, No 5
Year 2026
Type Original Research Article
Licence CC BY-NC-SA 4.0
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