Use of state-space population dynamics models in hypothesis testing: advantages over simple log-linear regressions for modeling survival, illustrated with application to longfin smelt (Spirinchus thaleichthys)

Abstract

a b s t r a c tFactors 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 longfin smelt in the San Francisco Bay-Delta.

Population Dynamics of an Estuarine Forage Fish: Disaggregating Forces Driving Long‐Term Decline of Longfin Smelt in California's San Francisco Estuary

“…Stock assessments are usually applied to harvested fishes, particularly those in marine ecosystems. Although the Longfin Smelt is not targeted for harvest in the SFE, it is nonetheless useful to construct explicit spawnerrecruit relationships so as to evaluate different conceptual models of Longfin Smelt recruitment (see also Maunder et al 2015).…”

“…The Longfin Smelt is also one of several SFE fishes that have shown a strong and persistent association between juvenile production and the freshwater flow variation experienced early in the life cycle (Stevens and Miller 1983;Jassby et al 1995;Kimmerer 2002b;Rosenfield and Baxter 2007;Thomson et al 2010;Maunder et al 2015). However, little attention has been given to whether and how freshwater flow rates might affect production of Longfin Smelt beyond the first year of life.…”

“…However, little attention has been given to whether and how freshwater flow rates might affect production of Longfin Smelt beyond the first year of life. It is well established that Longfin Smelt production per unit of flow has declined (Kimmerer 2002b;Rosenfield and Baxter 2007;Thomson et al 2010), but with only one very recent exception (Maunder et al 2015), researchers have not attempted to evaluate the population dynamics of SFE Longfin Smelt in a classical spawner-recruit framework.…”

Local and population-level responses of Greater sage-grouse to oil and gas development and climatic variation in Wyoming

“…Preliminary analyses considered Gompertz State-Space Population Models (Dennis et al, 2006;Knape and de Valpine, 2012) which estimate both process and observer error (Maunder et al, 2015). However, the models were unable to reliably estimate both error terms.…”

“…However, the models were unable to reliably estimate both error terms. As the observer error was estimated to be smaller than the process error and because ignoring process error can bias Akaike's Information Criterion 4/43 based tests towards incorrectly accepting covariates (Maunder et al, 2015), we followed Garton et al (2011) in assuming no observer error. The preliminary analyses indicated that fixing the observer error at zero had little effect on the results.…”