scholarly journals Evaluation of methods for spawner–recruit analysis in mixed-stock Pacific salmon fisheries

2020 ◽  
Vol 77 (7) ◽  
pp. 1149-1162 ◽  
Author(s):  
Benjamin A. Staton ◽  
Matthew J. Catalano ◽  
Brendan M. Connors ◽  
Lewis G. Coggins ◽  
Michael L. Jones ◽  
...  
Keyword(s):  
State Space ◽  
Oncorhynchus Tshawytscha ◽  
Pacific Salmon ◽  
State Space Model ◽  
Reference Points ◽  
State Space Models ◽  
Space Model ◽  
Trade Offs ◽  
Salmon Fisheries ◽  
Mixed Stock

Salmon populations harvested in mixed-stock fisheries can exhibit genotypic, behavioral, and life history diversity that can lead to heterogeneity in population productivity and size. Methods to quantify this heterogeneity among populations in mixed-stock fisheries are not well-established but are critical to assessing harvest–biodiversity trade-offs when setting harvest policies. We developed an integrated, age-structured, state-space model that allows for more complete use of available data and sharing of information than simpler methods. We compared a suite of state-space models of varying structural complexity to simpler regression-based approaches and, as an example case, fitted them to data from 13 Chinook salmon (Oncorhynchus tshawytscha) populations in the Kuskokwim drainage in western Alaska. We found biological and policy conclusions were largely consistent among state-space models but differed strongly from regression-based approaches. Simulation trials illustrated our state-space models were largely unbiased with respect to spawner–recruit parameters, abundance states, and derived biological reference points, whereas the regression-based approaches showed substantial bias. These findings suggest our state-space model shows promise for informing harvest policy evaluations of harvest–biodiversity trade-offs in mixed-stock salmon fisheries.

scholarly journals A review of Bayesian state-space modelling of capture–recapture–recovery data

2012 ◽  
Vol 2 (2) ◽  
pp. 190-204 ◽  
Author(s):  
Ruth King
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Fitting ◽  
The State ◽  
State Space Models ◽  
Observation Error ◽  
Space Model ◽  
System Process ◽  
Observation Process ◽  
Capture Recapture

Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture–recapture–recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture–recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS.

Responses comparison of the two discrete-time linear fractional state-space models

2016 ◽  
Vol 19 (4) ◽  
Author(s):  
Tadeusz Kaczorek ◽  
Piotr Ostalczyk
Keyword(s):  
State Space ◽  
State Space Model ◽  
State Space Models ◽  
Discrete State ◽  
Numerical Examples ◽  
Space Model ◽  
The Difference ◽  
Time Linear ◽  
Discrete State Space ◽  
Fundamental Matrices

AbstractIn this survey we consider two fractional-order discrete state-space models of linear systems. In both cases the crucial elements are the fundamental matrices. The difference between them is analyzed. A fundamental condition for the first state-space model is given. The investigations are illustrated by the numerical examples.

Synthetic seismograms using the state‐space approach

Geophysics ◽  
1979 ◽  
Vol 44 (5) ◽  
pp. 880-895 ◽  
Author(s):  
J. M. Mendel ◽  
N. E. Nahi ◽  
M. Chan
Keyword(s):  
State Space ◽  
Time Domain ◽  
State Space Model ◽  
Layered Media ◽  
State Space Models ◽  
State Equations ◽  
Space Model ◽  
Domain State ◽  
The Time Domain ◽  
Treat All

We develop time‐domain state‐space models for lossless layered media which are described by the wave equation and boundary conditions. We develop state‐space models for two cases: (1) source and sensor at the surface, and (2) source and sensor in the first layer. Our models are for nonequal one‐way traveltimes; hence, they are more general than most existing models of layered media which are usually for layers of equal one‐way traveltimes. A notable exception to this is the work of Wuenschel (1960); however, most of the useful results even in his paper are developed only for the uniform traveltime case. Our state‐space model treat all of the equations that describe a layered‐media system together in the time domain. Earlier approaches (e.g., Wuenschel, 1960; Robinson, 1968) recursively connect adjacent layers by means of frequency‐domain relationships. We refer to our state equations as “causal functional equations.” They actually represent a new class of equations. Why are we interested in a different class of models for what appears to be a well‐studied system? As is well known, there is a vast literature associated with systems which are described by time‐domain state‐space models. Most recent results in estimation and identification theories, for example, require a state‐space model. These time‐domain techniques have proven very beneficial outside of the geophysics field and we feel should also be beneficial in the geophysics field. In fact, our ultimate objective is to apply those theories to the layered‐media problem; but, to do so, of course, requires state‐space models—hence, this paper.

A Bayesian state-space model for mixed-stock migrations, with application to Northeast Atlantic mackerelScomber scombrus

2007 ◽  
Vol 29 (3) ◽  
pp. 347-367 ◽  
Author(s):  
CL Cunningham ◽  
DG Reid ◽  
MK McAllister ◽  
GP Kirkwood ◽  
CD Darby
Keyword(s):  
State Space ◽  
State Space Model ◽  
Northeast Atlantic ◽  
Space Model ◽  
Mixed Stock

Modal Analysis of a Motorcycle Motion During Braking for its Stabilization Control System Design

2013 ◽  
Author(s):  
Shintaroh Murakami ◽  
Hidekazu Nishimura
Keyword(s):  
Control System ◽  
Modal Analysis ◽  
State Space ◽  
State Space Model ◽  
State Space Models ◽  
Control System Design ◽  
Space Model ◽  
Stabilization Control ◽  
Mode Separation ◽  
Nonlinear State

In this paper, modal motion of a motorcycle during braking is analyzed to clarify influence of a stabilization control system designed to the modes. A thirteen degree-of-freedom nonlinear state-space model including rider’s motion is linearized around an equilibrium point of quasi-steady state straight running with constant deceleration, and the modal analysis is carried out using the linearized state-space models. Conducting mode separation and performing simulations utilizing the linearized state-space models, the behavior of the modes including capsize, weave, and wobble modes are analyzed. The characteristic of each mode is clarified from relationships among the impulsive responses of simulations and the eigenvectors obtained from eigenanalysis. Furthermore, the influence of a motorcycle stabilization control system to each mode is analyzed from simulation results.

scholarly journals On Sequential Learning for Parameter Estimation in Particle Algorithms for State-Space Models

2016 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Chunlin Ji
Keyword(s):  
State Space ◽  
Blind Deconvolution ◽  
State Space Model ◽  
The State ◽  
State Space Models ◽  
Sequential Learning ◽  
Model Parameters ◽  
Learning Method ◽  
Space Model ◽  
Deconvolution Problem

Particle methods, also known as Sequential Monte Carlo, have been ubiquitous for Bayesian inference for state-space models, particulary when dealing with nonlinear non-Gaussian scenarios. However, in many practical situations, the state-space model contains unknown model parameters that need to be estimated simultaneously with the state. In this paper, We discuss a sequential analysis for combined parameter and state estimation. An online learning method is proposed to approach the distribution of the model parameter by tuning a flexible proposal mixture distribution to minimize their Kullback-Leibler divergence. We derive the sequential learning method by using a truncated Dirichlet processes normal mixture and present a general algorithm under a framework of the auxiliary particle filtering. The proposed algorithm is verified in a blind deconvolution problem, which is a typical state-space model with unknown model parameters. Furthermore, in a more challenging application that we call meta-modulation, which is a more complex blind deconvolution problem with sophisticated system evolution equations, the proposed method performs satisfactorily and achieves an exciting result for high efficiency communication.

scholarly journals Incorporating demographic information into spawner-recruit analyses alters biological reference point estimates for a western Alaska population

2021 ◽  
Author(s):  
Benjamin A. Staton ◽  
Matthew J. Catalano ◽  
Steven J. Fleischman ◽  
Jan Ohlberger
Keyword(s):  
Time Trends ◽  
Reproductive Output ◽  
Oncorhynchus Tshawytscha ◽  
Pacific Salmon ◽  
Reference Points ◽  
Trade Offs ◽  
Per Capita ◽  
Age Structured ◽  
Over Time ◽  
Harvest Policies

Changes over time in age, sex, and length-at-age of returning Pacific salmon have been widely observed, suggesting concurrent declines in per capita reproductive output. Thus, assessment models assuming stationary reproductive output may inaccurately estimate biological reference points that inform harvest policies. We extended age-structured state-space spawner-recruit models to accommodate demographic time trends and fishery selectivity to investigate temporal changes in reference points using Kuskokwim River Chinook salmon (<i>Oncorhynchus tshawytscha</i>). We illustrate that observed demographic changes have likely reduced per capita reproductive output in an additive manner, for example, models including changes in both length-at-age or age composition showed larger declines than models incorporating only one time trend. Translated into biological reference points using a yield-per-recruit algorithm, we found escapement needed for maximum sustained catch has likely increased over time, but the magnitude further depended on size-selective harvest (i.e., larger changes for reference points based on larger mesh gillnets). Compared to traditional salmon assessments, our approach that acknowledges demographic time trends allows more complete use of available data and facilitates evaluating trade-offs among gear-specific harvest policies.

An age-structured state-space stock–recruit model for Pacific salmon (Oncorhynchusspp.)

2013 ◽  
Vol 70 (3) ◽  
pp. 401-414 ◽  
Author(s):  
Steven J. Fleischman ◽  
Matthew J. Catalano ◽  
Robert A. Clark ◽  
David R. Bernard
Keyword(s):  
State Space ◽  
Oncorhynchus Tshawytscha ◽  
Pacific Salmon ◽  
State Space Model ◽  
Auxiliary Information ◽  
Observation Error ◽  
Age Composition ◽  
Probability Profile ◽  
Explicit Consideration ◽  
Age Structured

We describe an age-structured state-space model for stock–recruit analysis of Pacific salmon data. The model allows for incorporation of process variation in stock productivity, recruitment, and maturation schedules, as well as observation error in run abundance, harvest, and age composition. Explicit consideration of age structure allows for realistic depiction of system dynamics and sample design, more complete use of recent data, and forecasts that consider sibling relationships. A Bayesian framework is adopted, implemented with Markov chain Monte Carlo methods, which provides an enhanced ability to incorporate auxiliary information, convenient and rigorous consideration of measurement error and missing data, and a more complete assessment of uncertainty. We fit the model to annual upstream weir counts, commercial and recreational harvest estimates, and age composition data from Chinook salmon (Oncorhynchus tshawytscha) in Karluk River, Alaska. For the case study, the model is configured with a Ricker stock–recruit relationship, autoregressive lag-1 productivity, and Dirichlet age-at-maturity. Details of alternate configurations are also described. We introduce the optimal yield probability profile as an objective tool for informing the selection of escapement goals based on yield considerations and describe alternative versions useful for addressing other management questions.

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