**ABSTRACT
NOT FOR CITATION WITHOUT AUTHOR PERMISSION. The title, authors, and
abstract for this completion report are provided below. For a copy
of the full completion report, please contact the author via e-mail at yjiao@vt.edu. Questions? Contact the GLFC
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Jiao,
Y. and C. Zhou
1 Department of Fish and Wildlife Conservation, Virginia Tech University,
540-231-5749
June 2018
ABSTRACT:
Natural mortality (M)
has been found to be one of the most uncertain and most influential parameters
in walleye fisheries stock assessment which needs to be addressed. The
difficulty to estimate M arises from model structure, data uncertainty and the
confounding effect from other parameters in the model. How to treat M in stock
assessment models is a controversial topic involving considerations on both the
degree of realism in the model structure and the amount of information
available on M in the data. In our study, we integrate tagging, catch, and
survey data to estimate Lake Erie walleye M through a Bayesian approach. Catch
and abundance indices were modeled following the basic structure of the current
assessment model, tag-recapture was modeled using the traditional Brownie
process, and the age-length distributions of tagged fish were inferred through
a concurrent biological survey. Four types of hypotheses on the variation of M were
integrated in the SCA modeling framework of walleye and these hypotheses on M
are: a) M is constant over time and age but is unknown; b) M changes over age;
c) M changes over time; d) M changes over both age and time. We first analyzed
M based on tagging data and based on catch-at-age data separately, which both
suggested temporal and age-specific variations in M. Then, we integrated these
two sources of data, and the integrated model yielded consistent parameter
estimates with reduced uncertainty for M and other primary population
quantities, such as abundances. This study provides evidence on the temporal
and age class variations in Lake Erie walleye M. Factors that may cause the
variation of M over age and/or time were related to climate changes and/or
predator abundance, and were further tested. Walleye natural mortality
variation tends to be influence by multiple factors and may vary over time
because of the variations in the ecosystem characteristics in Lake Erie. The
incorporation of extra tagging information facilitates improvement of
estimation of critical population parameters in the assessment model. Extra
simulation studies were conducted to verify the robustness of the supported
hypothesis and illustrate the possible misunderstanding of walleye population
dynamics and management. Our study also provided a framework for modeling M
within an integrated assessment context for Lake Erie walleye.