Integrating learning into animal range dynamics under rapid human-induced environmental change

Area of Science:

• Ecology
• Evolutionary Biology
• Environmental Science

Background:

• Human-induced rapid environmental change (HIREC) creates novel environments, pushing species outside their evolved habitats.
• Climate warming further exacerbates this by shifting species' climatic envelopes to unfamiliar ecological conditions.
• Current range shift models often use static habitat-fitness relationships, potentially underestimating HIREC impacts.

Purpose of the Study:

• To enhance the ecological realism of species range shift predictions under HIREC.
• To integrate concepts of ecological fitting and behavioral plasticity into range shift modeling.
• To highlight the role of learning ability in species' adaptation to novel environments.

Main Methods:

• Developed a conceptual model integrating ecological fitting and behavioral plasticity (learning).
• Focused on dynamic species-resource relationships rather than static ones.
• Used the model as a proof of principle for range expansion under HIREC.

Main Results:

• Accounting for dynamic species-resource relationships increases ecological realism in range shift predictions.
• Ecological fitting and behavioral plasticity are crucial for species persistence in novel environments.
• Learning ability plays a potentially substantial role in enabling species range shifts under HIREC.

Conclusions:

• Traditional range shift models may be insufficient for predicting species responses to HIREC.
• Dynamic processes like ecological fitting and learning are vital for understanding species' adaptive capacity.
• Future range shift models should incorporate behavioral plasticity to improve accuracy in a changing world.