Multimodel inference and adaptive management.

Area of Science:

• Ecology
• Ecological modeling
• Conservation biology

Background:

• Ecological complexity, including correlated variables and nonlinear interactions, complicates experimental inference.
• Natural resource managers require timely scientific recommendations, but ecosystem interactions often take longer to understand than management decisions allow.
• Multimodel inference (MMI) is a technique used to handle complexity and assess uncertainty among competing hypotheses.

Purpose of the Study:

• To quantify the prevalence of multimodel inference (MMI) approaches in ecological journals.
• To assess the strength of inference (weak vs. strong) resulting from MMI in published ecological studies.
• To examine how authors address model selection uncertainty when providing management recommendations.

Main Methods:

• A review of articles published in the Journal of Wildlife Management (JWM) and Conservation Biology (CB).
• Quantification of the use of MMI approaches.
• Categorization of study inference as weak or strong.
• Analysis of how management recommendations were presented in relation to inference strength and uncertainty.

Main Results:

• MMI was used in 38% of JWM articles and 14% of CB articles.
• Strong inference was rare, achieved in only 7% of JWM and 20% of CB studies.
• The majority of studies with weak inference (59%) provided specific management recommendations, often ignoring model selection uncertainty.

Conclusions:

• Ecological research frequently results in weak inference, necessitating careful consideration of uncertainty in management recommendations.
• Adaptive management is proposed as a suitable framework for addressing uncertainty when research yields weak inference.
• There is a need for clearer communication of uncertainty in ecological studies to inform effective natural resource management.