Journal
FISHERIES RESEARCH
Volume 164, Issue -, Pages 102-111Publisher
ELSEVIER
DOI: 10.1016/j.fishres.2014.10.017
Keywords
Data assimilation; Longfin smelt; Population dynamics; Random effects; State-space model; Survival
Categories
Funding
- San Luis & Delta-Mendota Water Authority
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Factors impacting the survival of individuals between two life stages have traditionally been evaluated using log-linear regression of the ratio of abundance estimates for the two stages. These analyses require simplifying assumptions that may impact the results of hypothesis tests and subsequent conclusions about the factors impacting survival. Modern statistical methods can reduce the dependence of analyses on these simplifying assumptions. State-space models and the related concept of random effects allow the modeling of both process and observation error. Nonlinear models and associated estimation techniques allow for flexibility in the system model, including density dependence, and in error structure. Population dynamics models link information from one stage to the next and over multiple time periods and automatically accommodate missing observations. We investigate the impact of observation error, density dependence, population dynamics, and data for multiple stages on hypothesis testing using data for longfln smelt in the San Francisco Bay-Delta. (C) 2014 The Authors. Published by Elsevier B.V.
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