Keywords
Multi-Scale Environment
Physiological Dynamics
Ssf
Hmm
State-Space Model
Energy-Based Model
Gashaka Gumti National Park.
Article Number
051Abstract
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.References
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