Optimal movement decisions in complex landscapes.

Area of Science:

• Ecology
• Movement Ecology
• Behavioral Ecology

Background:

• Predicting animal movement in dynamic landscapes is challenging, requiring more than simple energetic models.
• Human activities rapidly alter environments, impacting animal adaptability, movement patterns, and fitness.
• Existing models often overlook crucial non-energetic factors influencing movement decisions.

Purpose of the Study:

• To propose a structured movement framework integrating abiotic and biotic drivers of foraging decisions.
• To incorporate both energetic landscapes and non-energetic modifiers into movement predictions.
• To improve the identification of critical habitats and inform conservation strategies.

Main Methods:

• Developed a conceptual framework integrating net energyscape and optimal movement landscape.
• Included abiotic factors (net energy gains) and biotic factors (landscape of fear).
• Focused on foraging decisions as a key driver of movement.

Main Results:

• The proposed framework provides a more holistic view of movement decision-making.
• Integrating energetic and non-energetic factors enhances predictive accuracy.
• Highlights the importance of considering multiple drivers beyond simple cost-benefit analyses.

Conclusions:

• The structured movement framework offers improved avenues for understanding animal behavior.
• Accurate identification of critical habitats can be achieved by considering diverse environmental influences.
• Effective conservation strategies require a comprehensive understanding of movement mechanisms in transformed landscapes.