A model-driven approach to quantify migration patterns: individual, regional and yearly differences.

Area of Science:

• Ecology
• Movement ecology
• Statistical modeling

Background:

• Animal migration is crucial for species survival but threatened by human activities.
• Differentiating migratory from non-migratory behaviors and quantifying migration parameters are essential for effective wildlife management.
• Existing methods often rely on arbitrary criteria, limiting objective analysis.

Purpose of the Study:

• To introduce a unified statistical framework for analyzing animal movement behaviors.
• To objectively separate migratory from non-migratory movements without arbitrary cut-offs.
• To quantify migration timing, duration, and distance, and assess predictive power.

Main Methods:

• Developed a statistical framework using nonlinear models of squared displacement patterns.
• Validated the approach with simulated data representing various movement behaviors.
• Empirically tested the framework on 108 GPS-collared moose (Alces alces) in Scandinavia.

Main Results:

• The framework successfully distinguished migratory from other movement types.
• Classified 87% of Swedish and 67% of Norwegian moose subpopulations as migratory.
• Nonlinear mixed effects models revealed significant differences in migration distance, timing, and duration between sexes and years, with individual variation playing a key role.

Conclusions:

• The proposed framework offers a robust and objective method for analyzing animal migration.
• The model demonstrated high explanatory power (92%) and predictive ability (69-74%) for migratory movements.
• This approach can enhance understanding of migration drivers and inform conservation strategies for migratory species.