Understanding and predicting animal movements and distributions in the Anthropocene

Area of Science:

• Movement ecology
• Ecological processes
• Conservation science

Background:

• Predicting animal movements and distributions is vital for ecological understanding and conservation.
• Developing robust predictions in rapidly changing, human-modified environments remains a significant challenge.
• Understanding how anthropogenic changes affect animal movement drivers is essential.

Purpose of the Study:

• To review and discuss features of human-modified environments within the movement ecology framework.
• To explore the transition from correlative to dynamical systems approaches for robust predictions.
• To highlight the integration of theory, empirical data, and mechanistic modeling for predictive accuracy.

Main Methods:

• Reviewing movement ecology framework, including external environment, internal state, navigation, and motion capacity.
• Advocating for mechanistic modeling based on first principles of animal movement and decision-making.
• Emphasizing the integration of theory and empirical data through experimental approaches and diverse datasets.

Main Results:

• Robust predictions require moving beyond correlative models to a dynamical systems perspective with mechanistic modeling.
• A targeted, supervised data collection approach is needed, considering various taxa, scales, and biases.
• Caution is advised against indiscriminate use of citizen science, AI, and machine learning without supervision.

Conclusions:

• Movement ecology is transitioning to a predictive science, essential for evidence-based management in changing environments.
• Adaptive management frameworks offer opportunities for mutual benefit between movement ecology and conservation.
• Developing models with reliable predictive ability in novel situations is the primary goal for future research.