|Document Type:||Journal Article|
|Title:||Giants' shoulders 15 years later: Lessons, challenges, and guidelines in fisheries meta-analysis|
|Author:||James T. Thorson, Jason Cope, Kristin Kleisner, J. F. Samhouri, Andrew O. Shelton, E. J. Ward|
|Journal:||Fish and Fisheries|
|Keywords:||hierarchical models,research synthesis,meta-analysis|
Meta-analysis has been an integral tool for fisheries researchers since the late 1990s. However, there remain few guidelines for the design, implementation, or interpretation of meta-analyses in the field of fisheries. Here we provide the necessary background for readers, authors, and reviewers, including a brief history of the use of meta-analysis in fisheries, an overview of common model types and distinctions, and an illustration of different study goals. We outline the primary challenges in implementing meta-analyses, including difficulties in discriminating between alternative hypotheses that can both explain the data, the importance of validating results using multiple lines of evidence, the trade-off between complexity and sample size, and problems associated with the use of model output. For each of these challenges, we also provide suggestions, such as the use of propensity scores for dealing with selection bias and the use of covariates to control for confounding effects. These challenges are then illustrated with examples from diverse sub-fields of fisheries, including (1) the analysis of the stock-recruit relationship, (2) fisheries management, rebuilding, and population viability, (3) habitat-specific vital rates, (4) life history theory, and (5) the evaluation of marine reserves. We conclude with our reasons for believing that meta-analysis will continue to grow in importance for these and many other research goals in fisheries science, and argue that standards of practice are therefore essential.
|Full Text URL:||http://onlinelibrary.wiley.com/doi/10.1111/faf.12061/abstract|
|Theme:||Recovery, Rebuilding and Sustainability of Marine and Anadromous Species|
Develop methods to use physiological and biological information to predict population-level processes.
Characterize vital rates and other demographic parameters for key species, and develop and improve methods for predicting risk and viability/sustainability from population dynamics and demographic information.