Emerging predictable features of replicated biological invasion fronts.

Area of Science:

• Ecology
• Population Dynamics
• Theoretical Biology

Background:

• Biological dispersal is crucial for species distribution, coexistence, and range shifts due to environmental changes.
• Predicting biological dispersal has been challenging due to limitations in experimental data and inherent stochasticity.
• Understanding dispersal dynamics is vital for managing invasive species and pathogen spread.

Purpose of the Study:

• To investigate the predictability of biological dispersal using replicated experimentation.
• To establish a causal link between individual-level movement and population-level spread patterns.
• To develop a predictive framework for biological invasions.

Main Methods:

• Replicated experiments on the spread of the ciliate Tetrahymena sp. in linear landscapes.
• Utilizing data on local unconstrained movement and reproduction rates.
• Applying a theoretical approach based on the Fisher-Kolmogorov framework, incorporating demographic stochasticity.

Main Results:

• Local movement and reproduction data reliably predict the existence and speed of traveling invasion waves.
• The theoretical model accurately captures fluctuations observed in range expansions.
• Demonstrated predictability of key dispersal features despite biological stochasticity.

Conclusions:

• Predictability of biological dispersal can be achieved by integrating local-scale data.
• A causal link exists from individual behavior to large-scale population patterns.
• The findings offer a potential general framework for predicting biological invasions in natural settings.