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.

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.

BUGS in Bayesian stock assessments

1999 ◽  
Vol 56 (6) ◽  
pp. 1078-1087 ◽  
Author(s):  
Renate Meyer ◽  
Russell B Millar
Keyword(s):  
State Space ◽  
Stock Assessment ◽  
State Space Model ◽  
Production Model ◽  
Posterior Density ◽  
Convergence Diagnostics ◽  
Surplus Production Model ◽  
Nonlinear State Space Models ◽  
Age Structured ◽  
Joint Posterior Density

This paper illustrates the ease with which Bayesian nonlinear state-space models can now be used for practical fisheries stock assessment. Sampling from the joint posterior density is accomplished using Gibbs sampling via BUGS, a freely available software package. By taking advantage of the model representation as a directed acyclic graph, BUGS automates the hitherto tedious calculation of the full conditional posterior distributions. Moreover, the output from BUGS can be read directly into the software CODA for convergence diagnostics and statistical summary. We illustrate the BUGS implementation of a nonlinear nonnormal state-space model using a Schaefer surplus production model as a basic example. This approach extends to other assessment methodologies, including delay difference and age-structured models.

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.

Long-term ecological trends of flow-dependent ecosystems in a major regulated river basin

2015 ◽  
Vol 66 (11) ◽  
pp. 957 ◽  
Author(s):  
Matthew J. Colloff ◽  
Peter Caley ◽  
Neil Saintilan ◽  
Carmel A. Pollino ◽  
Neville D. Crossman
Keyword(s):  
Time Series ◽  
State Space ◽  
Meta Analysis ◽  
State Space Model ◽  
Regulated River ◽  
Observation Error ◽  
Murray Darling Basin ◽  
Exponential Rates ◽  
Log Linear ◽  
Condition Assessments

The case for restoring water to the environment in the Murray–Darling Basin, Australia, is based mainly on condition assessments, although time series provide valuable information on trends. We assessed trends of 301 ecological time series (mean 23 years, range 1905–2013) in two categories: (1) ‘population’ (abundance, biomass, extent) and (2) ‘non-population’ (condition, occurrence, composition). We analysed trends using log-linear regression, accounting for observation error only, and a state–space model that accounts for observation error and environmental ‘noise’. Of the log-linear series (n=239), 50 (22%) showed statistically significant decline, but 180 (78%) showed no trend. For state–space series (n=197) one increased, but others were stable. Distribution of median exponential rates of increase (r) indicated a small but statistically significant declining trend, though 35–39% of the series were positive. Our analysis only partly supports, though does not refute, prevailing assumptions of recent ecological decline in the Murray–Darling Basin. The pattern is of fluctuating stability, with declines during droughts and recovery after flood. The overall trend from our meta-analysis is consistent with a pattern of historical decline to a hybrid ecosystem followed by slow, recent decline for some components and stability for others, with considerable variation in trends of specific ecological components: in short, there are ecological ‘winners’ and ‘losers’.

scholarly journals State-Space Model of Quasi-Z-Source Inverter-PV Systems for Transient Dynamics Studies and Network Stability Assessment

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4150
Author(s):  
Lluís Monjo ◽  
Luis Sainz ◽  
Juan José Mesas ◽  
Joaquín Pedra
Keyword(s):  
Power Systems ◽  
State Space ◽  
State Space Model ◽  
Pv System ◽  
Pv Systems ◽  
Renewable Power ◽  
Ac Networks ◽  
Transient Interactions ◽  
The Stability ◽  
Averaged Model

Photovoltaic (PV) power systems are increasingly being used as renewable power generation sources. Quasi-Z-source inverters (qZSI) are a recent, high-potential technology that can be used to integrate PV power systems into AC networks. Simultaneously, concerns regarding the stability of PV power systems are increasing. Converters reduce the damping of grid-connected converter systems, leading to instability. Several studies have analyzed the stability and dynamics of qZSI, although the characterization of qZSI-PV system dynamics in order to study transient interactions and stability has not yet been properly completed. This paper contributes a small-signal, state-space-averaged model of qZSI-PV systems in order to study these issues. The model is also applied to investigate the stability of PV power systems by analyzing the influence of system parameters. Moreover, solutions to mitigate the instabilities are proposed and the stability is verified using PSCAD time domain simulations.

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