The importance of influence diagnostics: examples from Snake River chinook salmon spawner-recruit models

Hinrichsen, Richard A.

doi:10.1139/f01-014

Cited by 6 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data used for estimating recruits includes spawning ground surveys, estimates of age distribution, Columbia River main stem and tributary harvest rates, and estimates of mortality during upstream passage through the FCRPS dams (Beamesderfer et al 1997;Deriso et al 2001). Several of the major assumptions used for the run reconstructions, the quality of the data used, and the influence of outlier observations have been debated and analyzed in the scientific literature (Schaller et al 1999;Zabel and Williams 2000;Hinrichsen 2001). Trends in number of recruits per spawner (R/S) for the upstream and downstream populations were sharply downward during the early 1970s and again during the 1980s (Figure 2a).…”

Section: Methodsmentioning

confidence: 99%

“…However, the key assumption of similar productivity between upstream (Snake River) and downstream (lower Columbia River) populations is a disadvantage in this model because it is doubtful that lower Columbia River populations serve as a suitable control for Snake River populations (Zabel and Williams 2000;Schaller et al 2000). Another disadvantage is that certain observations exert strong influences on the estimated passage mortality (Hinrichsen 2001).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Inferences on the Latent Mortality of Snake River Spring–Summer‐Run Chinook Salmon Using Spawner–Recruit Models

Hinrichsen¹,

Fisher

2009

Trans Am Fish Soc

View full text Add to dashboard Cite

The rapid decline of the Snake River spring-summer-run Chinook salmon Oncorhynchus tshawytscha evolutionarily significant unit (ESU) in the 1990s led a group of scientists to develop the Plan for Analyzing and Testing Hypotheses (PATH). Under this plan, researchers used spawner-recruit (SR) data to estimate the survival of out-migrating smolts through eight dams of the Federal Columbia River Power System (FCRPS). Direct measurements of survival during out-migration, known as passage survival, were not available, so the PATH scientists estimated survival for index populations using trends from Ricker-type SR models. This modeling framework had the advantage of estimating both the direct and indirect (or latent) effects (FCRPS-related mortalities that do not occur until the smolts have passed the FCRPS dams) on the life cycle survival of the populations. We evaluated the SR model used by the PATH scientists by examining how changes in model structure affected important inferences. We calculated condition indexes as measures of the sensitivity of the model results to perturbations in the SR data and model structure, finding that the results were highly sensitive to certain assumptions. In particular, we found that changing the Ricker a term from a population-specific parameter to a parameter common to all of the populations in the ESU changed total passage survival from 9% to 56% and latent mortality from one-half the total passage mortality to a value that is not significantly different from zero. Therefore, the condition indexes revealed high potential sensitivities of the SR model results to perturbations in data and model structure. Although information criteria indicated that the population-specific model had a poorer fit than lower-parameter models, it was impossible to resolve the question as to whether there was latent mortality.

show abstract

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Inferences on the Latent Mortality of Snake River Spring–Summer‐Run Chinook Salmon Using Spawner–Recruit Models

Hinrichsen¹,

Fisher

2009

Trans Am Fish Soc

View full text Add to dashboard Cite

show abstract

“…Influence diagnostics (e.g., Cook and Weisberg, 1982) can be used to measure the sensitivity of an LRP. Hinrichsen (2001) illustrated the importance of examining such diagnostics in SR analyses.…”

Section: Introductionmentioning

confidence: 99%

Local Influence Diagnostics for Quasi‐Likelihood and Lognormal Estimates of a Biological Reference Point from Some Fish Stock and Recruitment Models

Cadigan¹

2006

Biometrics

View full text Add to dashboard Cite

We present local influence diagnostics to measure the sensitivity of a biological limit reference point (LRP) estimated from fitting a model to stock and recruitment data. LRPs are low levels of stock size that the management of commercial fisheries should avoid with high probability. The LRP we examine is the stock size at which recruitment is 50% of the maximum (S(50%)). We derive analytic equations to describe the effects on S(50%) of changing the weight that observations are given in estimation. We derive equations for the Ricker, Beverton-Holt, and hockey-stick stock-recruit models, and four estimation methods including the error sums of squares method on log responses and three quasi-likelihood methods. We conclude from case studies that the hockey-stick model produces the most robust estimates.

show abstract

“…a change in sampling protocols). The data subject to atypical errors may become outliers, which can have large impacts on fisheries stock assessment modelling (Chen et al, 2000;Hinrichsen, 2001).…”

Section: Introductionmentioning

confidence: 99%

Quality of fisheries data and uncertainty in stock assessment

Chen¹

2003

Sci. Mar.

View full text Add to dashboard Cite

SUMMARY: The quality of fisheries data has great impacts on the quality of stock assessment, and thus fisheries management. In this paper, using a case study I evaluate the impacts of two types of error, biased error and atypical error, that can negatively affect the quality of fisheries data in stock assessment. These errors are commonly associated with fisheries data, and assumptions on their sources and statistical properties can have great impacts on the outcome of stock assessment. Although the sources and statistical properties of these errors differed, both of them could result in errors in stock assessment if estimation methods are not appropriate. Different statistical approaches used in fitting models differ in their robustness with respect to errors of different statistical properties in data. This study showed the importance of evaluating the quality of input data and the possibility of developing an approach that is robust to errors in data. Considering the likelihood of fisheries data being affected by errors of different statistical properties, I suggest that the robustness of a stock assessment be evaluated with respect to data quality.

show abstract

The importance of influence diagnostics: examples from Snake River chinook salmon spawner-recruit models

Cited by 6 publications

References 6 publications

Inferences on the Latent Mortality of Snake River Spring–Summer‐Run Chinook Salmon Using Spawner–Recruit Models

Inferences on the Latent Mortality of Snake River Spring–Summer‐Run Chinook Salmon Using Spawner–Recruit Models

Local Influence Diagnostics for Quasi‐Likelihood and Lognormal Estimates of a Biological Reference Point from Some Fish Stock and Recruitment Models

Quality of fisheries data and uncertainty in stock assessment

Contact Info

Product

Resources

About