title: Fitting state-space integral projection models to size-structured time series data to estimate unknown parameters.
abstract: Integral projection models (IPMs) have a number of advantages over matrix-model approaches for analyzing size-structured population dynamics, because the latter require parameter estimates for each age or stage transition. However, IPMs still require appropriate data. Typically they are parameterized using individual-scale relationships between body size and demographic rates, but these are not always available. We present an alternative approach for estimating demographic parameters from time series of size-structured survey data using a Bayesian state-space IPM (SSIPM). By fitting an IPM in a state-space framework, we estimate unknown parameters and explicitly account for process and measurement error in a dataset to estimate the underlying process model dynamics. We tested our method by fitting SSIPMs to simulated data; the model fit the simulated size distributions well and estimated unknown demographic parameters accurately. We then illustrated our method using nine years of annual surveys of the density and size distribution of two fish species (blue rockfish, Sebastes mystinus, and gopher rockfish, S. carnatus) at seven kelp forest sites in California. The SSIPM produced reasonable fits to the data, and estimated fishing rates for both species that were higher than our Bayesian prior estimates based on coast-wide stock assessment estimates of harvest. That improvement reinforces the value of being able to estimate demographic parameters from local-scale monitoring data. We highlight a number of key decision points in SSIPM development (e.g., open vs. closed demography, number of particles in the state-space filter) so that users can apply the method to their own datasets.
authors.affiliation: Department of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, North Carolina, 28043, USA.
authors.forename: J Wilson
authors.initials: JW
authors.lastname: White
authors.affiliation: Division of Science and Environmental Policy, California State University Monterey Bay, Seaside, California, 93955, USA.
authors.forename: Kerry J
authors.initials: KJ
authors.lastname: Nickols
authors.affiliation: Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, California, 95060, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Malone
authors.affiliation: Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, California, 95060, USA.
authors.forename: Mark H
authors.initials: MH
authors.lastname: Carr
authors.affiliation: California Sea Grant Extension Program, Moss Landing Marine Laboratories, Moss Landing, California, 95039, USA.
authors.forename: Richard M
authors.initials: RM
authors.lastname: Starr
authors.affiliation: Department of Wildlife, Fish, and Conservation Biology, University of California Davis, Davis, California, 95616, USA.
authors.forename: Flora
authors.initials: F
authors.lastname: Cordoleani
authors.affiliation: Department of Environmental Science and Policy, University of California Davis, Davis, California, 95616, USA.
authors.forename: Marissa L
authors.initials: ML
authors.lastname: Baskett
authors.affiliation: Department of Environmental Science and Policy, University of California Davis, Davis, California, 95616, USA.
authors.forename: Alan
authors.initials: A
authors.lastname: Hastings
authors.affiliation: Department of Wildlife, Fish, and Conservation Biology, University of California Davis, Davis, California, 95616, USA.
authors.forename: Louis W
authors.initials: LW
authors.lastname: Botsford
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002140:California; D003710:Demography; D008954:Models, Biological; D011157:Population Dynamics
keywords: 
  Sebastes carnatus
  ; 
  Sebastes mystinus
  ; fishing rate; integral projection model; particle filter; state-space model