T-115-14
Incorporation of Environmental Factors in Stock Assessments: Just Because We Can, When Should We?

John F. Walter III , Sustainable Fisheries Division, NOAA Fisheries Southeast Fisheries Science Center, Miami, FL
Skyler Sagarese , Cooperative Institute for Marine and Atmospheric Sciences, Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, FL
Michael J. Schirripa , Sustainable Fisheries, NOAA Fisheries, Southeast Fisheries Science Center, Miami, FL
Arnaud Gruss , Marine Biology and Ecology, Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, FL
William Harford , Cooperative Institute of Marine and Atmospheric Sciences, Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, FL
Mandy Karnauskas , Sustainable Fisheries Division, NOAA Fisheries, Southeast Fisheries Science Center, Miami, FL
Matthew V. Lauretta , Sustainable Fisheries Division, NOAA Fisheries, Southeast Fisheries Science Center, Miami, FL
The convergence of relatively long-term environmental and ecological data series and the ability of the integrated assessment framework to handle these factors has made the incorporation of environmental processes in stock assessments very possible. This increased capacity, coupled with a rekindling of the Thompson-Burkenroad (fishing vs environment) debate further increases the call for such considerations in stock assessments. Yet, there remains little formal consideration of when and how these factors should be considered. From a practical standpoint it is not possible to evaluate every possible environmental factor nor is it reasonable to do so from the standpoint of parsimony. In this paper we evaluate some of the key issues for environmental data in stock assessments including use and misuse, linkage of environmental factors with nuisance (i.e. catchability) versus leading factors (i.e. recruitment or mortality), statistical versus weight of evidence approaches for inclusion criteria and projection and benchmark implications.  We show several examples of successful and unsuccessful attempts at including environmental factors and propose a set of minimum standards for their inclusion that include: a) clear development of the mechanism b) statistical, weight of evidence and effect size criteria for inclusion and c) support for both historical and forecasting capability.