Simple application of using residuals from catch-curve regressions to assess year-class strength in fish

Maceina, Michael J.

doi:10.1016/s0165-7836(97)00051-9

Cited by 96 publications

(126 citation statements)

References 4 publications

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“…Recruitment variability was estimated from the age structure of the sampled bream using catch curve regression (the natural log of the number of fish classified to each year class regressed against age) following the methods of Maceina (1997) and Staunton-Smith et al (2004). We assumed that deviation from the catch curve regression was a reflection of variable recruitment (Staunton- Smith et al 2004), with large positive and negative studentised residuals representing strong and weak year classes, respectively (Halliday et al 2008).…”

Section: Methodsmentioning

confidence: 99%

Highly variable recruitment in an estuarine fish is determined by salinity stratification and freshwater flow: implications of a changing climate

Jenkins¹,

Conron²,

Morison³

2010

Mar. Ecol. Prog. Ser.

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Estuarine-dependent fish live in a complex and dynamic environment that is primarily influenced by the salinity structure resulting from the mixing of fresh and marine waters. In response to this variable environment, the recruitment of estuarine-dependent fish can also be highly variable. Recruitment variability in black bream Acanthopagrus butcheri in Gippsland Lakes, southeastern Australia, was determined both from annual recruitment monitoring and the age structure of the population. Recruitment was found to be episodic, with the population dominated by a few year classes. For the 2 methods of estimating recruitment and associated time periods, we found a consistent linear relationship between water column stratification (difference between surface and bottom salinity) and recruitment. There was also a significant, non-linear (dome-shaped) relationship between freshwater flow and recruitment. This relationship suggested that the highest recruitment occurred at intermediate flows greater than ~3000 Ml d -1 in the main tributary rivers. The combination of stratification and freshwater flow in a multiple regression model improved the variability in recruitment explained to 71 and 79%, respectively, for the 2 methods. Analysis of age structure of bream from other Victorian estuaries showed that, as in Gippsland Lakes, recruitment was highly variable and episodic; however, the timing of strong and weak year classes varied between estuaries. This suggests that freshwater flow and salinity structure, and consequent recruitment variability, are unique to each estuary based on characteristics of catchment, channel topography, and entrance opening and closing. Climate change predictions for the Gippsland Lakes region include less rainfall and higher evaporation, potentially leading to higher salinities and lower stratification in Gippsland Lakes. The bream population is therefore likely to be negatively affected, and these effects may be exacerbated by any human activities in the catchment that further reduce freshwater flows or increase marine incursion.

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Section: Methodsmentioning

confidence: 99%

Highly variable recruitment in an estuarine fish is determined by salinity stratification and freshwater flow: implications of a changing climate

Jenkins¹,

Conron²,

Morison³

2010

Mar. Ecol. Prog. Ser.

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“…False predictions have been applied elsewhere to successfully answer questions of biological processes. For example, residual analysis of linear models is used as a fisheries management tool to assess year class variability for some species of game fish (Maceina 1997;Sammons et al 2002). In the case of the models presented in this paper, residuals were categorical misclassifications of observed versus predicted category.…”

Section: Discussionmentioning

confidence: 99%

Predicting biological impairment from habitat assessments

Doll¹

2011

Environ Monit Assess

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The goal of biological monitoring programs is to determine impairment classification and identify local stressors. Biological monitoring performs well at detecting impairment but when used alone falls short of determining the cause of the impairment. Following detection a more thorough survey is often conducted using extensive biological, chemical, and physical analysis coupled with exhaustive statistical treatments. These methods can be prohibitive for small programs that are limited by time and budget. The objective of this study was to develop a simple and useful model to predict the probability of biological impairment based on routinely collected habitat assessments. Biological communities were assessed with the Index of Biotic Integrity (IBI), and habitat was assessed with the Qualitative Habitat Evaluation Index. Two models were constructed from a validation dataset. The first predicted a binary outcome of impaired (IBI < 35) or non-impaired (IBI ≥ 35) and the second predicted a categorical gradient of impairment. Categories include very poor, poor, fair, good, and excellent. The models were then validated with an independently collected dataset. Both models successfully predicted biological integrity of the validation dataset with an accuracy of 0.84 (binary) and 0.75 (categorical). Based on the binary outcome model, 22 sites were observed to be impaired while the model predicted them to not be impaired. The categorical model misclassified 47 samples while only seven of those were misclassified by two or more categories. The impairment source was subsequently identified by known stressors. The models developed here can be easily applied to other datasets from the Eastern Corn Belt Plain to aid in stressor identification by predicting the probability of observing an impaired fish community based on habitat. Predicted probabilities from the models can also be used to support conclusions that have already been determined.

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“…Our quantification of year-class strength followed the residual method proposed by Maceina (1997). We indexed year-class strength by computing the residuals from the catch-curve regression analysis.…”

Section: Methodsmentioning

confidence: 99%

White bass population demographics in a northwestern South Dakota reservoir

Phelps

Ward

Willis

2011

Journal of Freshwater Ecology

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The white bass (Morone chrysops) is an important sport fish species in the upper Midwest. As such, understanding white bass dynamics is critical for managing its populations. Thus, we evaluated population dynamics of white bass in a northwestern South Dakota reservoir and attempted to determine whether bass recruitment patterns were more related to reservoir inflows or spring weather patterns. We found that white bass growth increased from 243 mm for age-2 fish, to 322 mm for age-6 fish, and then to 346 mm at age 9. The total annual mortality rate was 29.7%. In terms of year-class strength, white bass recruitment was erratic. The recruitment pattern was more related to spring weather patterns than reservoir inflow. In comparison, white bass population dynamics in this South Dakota irrigation reservoir were more similar to those of white bass populations in Midwestern glacial lakes than those found in southern US reservoirs.

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Simple application of using residuals from catch-curve regressions to assess year-class strength in fish

Cited by 96 publications

References 4 publications

Highly variable recruitment in an estuarine fish is determined by salinity stratification and freshwater flow: implications of a changing climate

Highly variable recruitment in an estuarine fish is determined by salinity stratification and freshwater flow: implications of a changing climate

Predicting biological impairment from habitat assessments

White bass population demographics in a northwestern South Dakota reservoir

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