Selectivity ratio: A useful tool for comparing size selectivity of multiple survey gears

Kotwicki, Stan; Lauth, Robert Russell; Williams, Kresimir; Goodman, Scott

doi:10.1016/j.fishres.2017.02.012

Cited by 31 publications

(38 citation statements)

References 53 publications

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“…Low values of

\bar{q}

are often observed for certain sizes of animals for which survey gear has poor selectivity. For example, bottom trawls have been found to have low values of

\bar{q}

for small sizes of demersal fauna in the Arctic, while beam trawls have low

\bar{q}

for larger animals as indicated by selectivity ratio (Kotwicki, Lauth, Williams, & Goodman, ). Estimates of

\bar{q}

> 1 imply that the survey catches considerably more fish than would be predicted to be in the path of the tow.…”

Section: Discussionmentioning

confidence: 99%

“…Somerton et al, 2007), numerous studies have done so. Values of q have been shown to vary widely among different survey gear types in the Arctic, while beam trawls have low q for larger animals as indicated by selectivity ratio (Kotwicki, Lauth, Williams, & Goodman, 2017). Estimates of q > 1 imply that the survey catches considerably more fish than would be predicted to be in the path of the tow.…”

Section: Prevalence Of Random Variation In Qmentioning

confidence: 99%

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The effect of random and density‐dependent variation in sampling efficiency on variance of abundance estimates from fishery surveys

Kotwicki

Ono

2019

Fish and Fisheries

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Abundance indices (AIs) provide information on population abundance and trends over time, while AI variance (AIV) provides information on reliability or quality of the AI. AIV is an important output from surveys and is commonly used in formal assessments of survey quality, in survey comparison studies, and in stock assessments. However, uncertainty in AIV estimates is poorly understood and studies on the precision and bias in survey AIV estimates are lacking. Typically, AIV estimates are “design based” and are derived from sampling theory under some aspect of randomized samples. Inference on population density in these cases can be confounded by unaccounted process errors such as those due to variable sampling efficiency (q). Here, we simulated fish distribution and surveys to assess the effect of q and variance in q on design‐based estimates of AIV. Simulation results show that the bias and precision of AIV depend on the mean q and variance in q. We conclude that to fully evaluate the reliability of AI, both observation error and variability in q must be accounted for when estimating AIV. A decrease in mean q and an increase in the variance in q results in increased bias and decreased precision in survey AIV estimates. These effects are likely small in surveys with mean q ≥ 1. However, for surveys where q ≤ 0.5, these effects can be large. Regardless of the survey type, AIV estimates can be improved with knowledge of q and variance in q.

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“…Low values of

\bar{q}

are often observed for certain sizes of animals for which survey gear has poor selectivity. For example, bottom trawls have been found to have low values of

\bar{q}

for small sizes of demersal fauna in the Arctic, while beam trawls have low

\bar{q}

for larger animals as indicated by selectivity ratio (Kotwicki, Lauth, Williams, & Goodman, ). Estimates of

\bar{q}

> 1 imply that the survey catches considerably more fish than would be predicted to be in the path of the tow.…”

Section: Discussionmentioning

confidence: 99%

Section: Prevalence Of Random Variation In Qmentioning

confidence: 99%

The effect of random and density‐dependent variation in sampling efficiency on variance of abundance estimates from fishery surveys

Kotwicki

Ono

2019

Fish and Fisheries

Self Cite

View full text Add to dashboard Cite

show abstract

“…() and Kotwicki et al. (). We tried other models, including fourth‐order polynomials and nonparametric smooth‐curve generalized additive models, but we found that the added complexity was not supported by our data.…”

Section: Methodsmentioning

confidence: 99%

“…For comparison with the bulk catch ratio estimates, we converted the fitted catch comparison rates to the size‐specific catch ratio (CR s ) as in Kotwicki et al. ():

{CR}_{s} = \frac{1}{r_{s}} - 1 .

…”

Section: Methodsmentioning

confidence: 99%

“…Quantitative estimation of size-selectivity curves for fishing gear has a long history but has recently evolved rapidly from traditional parametric fits of logistic or probit curves to incorporate nonparametric fitting and bootstrapping for CIs (Millar 1993) and the use of more flexible parametric polynomial or nonparametric smooth selectivity curves (Holst and Revill 2009;Krag et al 2014;Herrmann et al 2017;Kotwicki et al 2017).…”

Section: Effect Of a Mammal Excluder Devicementioning

confidence: 99%

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Effect of a Mammal Excluder Device on Trawl Catches of Salmon and Other Pelagic Animals

et al. 2019

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Concern about bycatch of marine mammals by fishery research gear has led to the use of mammal excluder devices (MEDs) in some surveys. We measured the effect of a rigid‐grate MED on trawl fishing effectiveness for juvenile Pacific salmon Oncorhynchus spp. and other upper‐pelagic species in the northern California Current. Catches of 11 taxa were compared among sets of a pelagic rope trawl without an MED and with an MED in two orientations: angled upward or angled downward. We estimated differences in overall catch ratios and size selectivity for each MED orientation compared to the unmodified net. The MED orientation had a species‐specific influence on both catch ratio and size selectivity, with median ratios ranging from 0.0 to 1.8 for the upward orientation and from 0.4 to 2.7 for the downward orientation. Video observations helped to explain performance differences and may help to improve MED design. Use of an MED in trawl surveys may substantially increase or decrease the catch of various taxa, contributing further uncertainty to the analysis of survey time series for either abundance or catch composition.

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Assessing the Relative Selectivity of Multiple Sampling Gears for Managed Reef Fishes in the Eastern Gulf of Mexico

et al. 2020

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Fishery-independent data are important for the effective management of reef fishes in the eastern Gulf of Mexico. Proper characterization of selectivity, or the effectiveness of each type of sampling gear in capturing a species or a size-class, is essential to ensure that assessment models treat various sources of data appropriately. In this study, we analyzed fishery-independent data that was collected using stereo-baited remote underwater video (S-BRUV) arrays, chevron traps (TRAP), and two types of hooked gear (repetitive timed-drop [RTD] and vertical longline [VLL]) to assess gear-related differences of species composition and size selectivity of managed reef fishes in the eastern Gulf of Mexico. Significant differences were detected in the assemblage structures of reef fishes in relation to region, gear type, and depth. Overall, eight managed species were identified as contributing to the top 70% of the assemblage structure for each gear type. Stereo-baited remote underwater video had the highest abundances for most of the species and the highest number of species captured, while VLL had the lowest. Two economically and ecologically important species (Red Grouper Epinephelus morio and Red Snapper Lutjanus campechanus) were common to all four gear types, and the size selectivity of these two species generally overlapped. However, significant differences among gear types were detected. Unimodal selection curves for hooked gears indicated that size of Red Snapper and Red Grouper increased as hook size increased. These data provide insight on species and size selectivity of multiple gears, which will contribute to future survey design and aid in the management of reef-fish populations.

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Selectivity ratio: A useful tool for comparing size selectivity of multiple survey gears

Cited by 31 publications

References 53 publications

The effect of random and density‐dependent variation in sampling efficiency on variance of abundance estimates from fishery surveys

The effect of random and density‐dependent variation in sampling efficiency on variance of abundance estimates from fishery surveys

Effect of a Mammal Excluder Device on Trawl Catches of Salmon and Other Pelagic Animals

Assessing the Relative Selectivity of Multiple Sampling Gears for Managed Reef Fishes in the Eastern Gulf of Mexico

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