Decision-making under uncertainty for species introductions into ecological networks

Area of Science:

• Ecology
• Conservation Biology
• Network Theory

Background:

• Ecological communities face increasing species additions from climate change, conservation efforts, and human transport.
• Predicting the impacts of new species on ecosystems is challenging due to uncertainties in species traits, novel interactions, and recipient ecosystem structure.

Purpose of the Study:

• To develop a conceptual framework for species introduction decision-making in high-uncertainty ecological contexts.
• To integrate ecological network theory with conservation decision theory for managing introduced species.
• To apply the framework to a theoretical assisted migration scenario considering biodiversity and ecosystem services.

Main Methods:

• Synthesized literature from ecology and conservation decision theory.
• Developed a conceptual framework based on ecological networks.
• Applied the framework to a theoretical case study of assisted migration.

Main Results:

• The framework facilitates evaluation of trade-offs between different management outcomes.
• It enables prediction of worst-case scenarios for species introductions.
• The framework guides decisions on when to gather more data and how to improve system knowledge over time.

Conclusions:

• A network-based decision framework is crucial for managing species introductions under uncertainty.
• This approach aids in balancing biodiversity and ecosystem service objectives.
• The framework supports adaptive management by improving knowledge and decision-making iteratively.