No file available
To read the file of this research,
you can request a copy directly from the authors.
CPUE as an index of relative abundance for nearshore reef fishes
Abstract
Using density measurements derived by SCUBA diving, we have verified that research angling catch per unit of effort (CPUE) is a useful measurement of the relative abundance of nearshore reef species when the appropriate habitat is targeted. We found a strictly proportional relationship between lingcod and copper rockfish CPUE and density using a ranged major axis regression. This relationship did not hold for quillback rockfish since this study did not target their preferred depth range; nor for kelp greenling. Researchers must be aware of such limitations when using CPUE as a measure of relative abundance.
ResearchGate has not been able to resolve any citations for this publication.
Misinterpretations of elevated catch-per-unit-effort (CPUE) in the northern cod (Gadus morhua) fishery contributed to overestimations of stock size, inflated quotas, and unsustainable fishing mortality in the 1980s and early 1990s. We hypothesize that concentration of the fish and fishery led to extreme hyperstability in the CPUE–abundance relationship. In the late 1980s, migrant cod began to concentrate within the Bonavista corridor, their most southerly cross-shelf migration route. By the spring of 1990, approximately 450 000 t was concentrated within 7000 km2at densities quadruple those of the 1980s. Densities remained high through 1992, while abundance declined fivefold. During this period, cod hyperaggregated (local densities increased with decreasing biomass) in the Bonavista corridor and CPUE increased. To the north, no hyperaggregation occurred, and densities and CPUE declined with biomass. In the Bonavista corridor from 1990 to 1993, CPUE was hyperstable with local cod density. Areas of high cod densities (>0.1 fish·m–2) shrunk as regional estimates of cod biomass declined. The spatial extent of the fishery contracted proportional to the shrinkage in area occupied by the fish. Hence, CPUE was related to abundance at the local scales of a fishing set (local acoustic density) but not to abundance at regional or stock scales.
As sport-fishing pressures increase in coastal marine waters, management agencies are expected to lose direct control over fishing effort, total harvest, and fishery-based information for stock assessment. Harvest control will be difficult because most sport fisheries remain open to unlimited public use without direct license or effort limitation. Without effort control, management tactics such as bag limits aimed at controlling total harvest typically fail because they regulate individual anglers. We develop a simple model of recreational fishery dynamics to show that current harvest-control methods should not be expected to control or reduce exploitation rates in open-access sport fisheries. The model predictions are (1) linear effort response to changes in fish abundance, (2) fish abundance limit below which effort is not attracted, and (3) rapidly increasing exploitation at low effort. Also, exploitation is expected to be insensitive to effort over a relatively wide range. Empirical data show that harvest restrictions tend to reduce participation by consumptive anglers, and we incorporate this effect into the effort-response model. We conclude that typical regulations such as bag limits and seasonal closures are not drastic enough to affect total exploitation. Although bag limits are usually ineffective for their intended purpose (direct harvest reduction), they probably act as an indirect means of effort control, but only temporarily. We suggest that managers of sport fisheries should consider direct license limitation or harvest permits where control of exploitation is needed.
We compiled 297 series of catch-per-unit-effort (CPUE) and independent abundance data (as estimated by research trawl surveys) and used observation error and random effects models to test the hypothesis that CPUE is proportional to true abundance. We used a power curve, for which we were interested in the shape parameter (beta). There was little difference among species, ages, or gear types in the distributions of the raw estimates of beta for each CPUE series. We examined three groups: cod, flatfish, and gadiformes, finding strong evidence that CPUE was most likely to remain high while abundance declines (i.e., hyperstability, where beta < 1). The range in the mean of the random effects distribution for beta was quite small, 0.64-0.75. Cod showed the least hyperstability, but still, 76% of the mass of the random effects distribution was below 1. Based on simulations, our estimates of beta are positively biased by approximately 10%; this should be considered in the application of our findings here. We also considered the precision of CPUE indices through a meta-analysis of observation error variances. The most precise indices were those from flatfish (median coefficient of variation of approximately approximate to0.42).
The influence of fish behaviour on the most common stock assessment methods is reviewed. Fish behaviour may be divided into four major groups : habitat selection, aggregation pattern, avoidance reactions, and learning. Examples of temperate and tropical species are presented. (Résumé d'auteur)
This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness. The authors have developed statistical techniques--such as formal fitting methods and less formal graphical techniques-- that can be applied to many problems across a range of disciplines, including pharmacokinetics, econometrics, biochemical assays, and fisheries research. While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random coefficients. The authors discuss the computation of estimates and give numerous examples using real data. The book also includes an extensive treatment of estimating variance functions in regression.
Despite recognized biases, catch per unit effort (CPUE) statistics remain widely used for the estimation of fish abundance. Previous workers have shown that CPUE can be a misleading index of abundance due to fish behavior, the nominal effort units used, and increases through time in efficiency of fishing (catchability). We examine the theoretical implications of a different factor, interactions among fishing vessels, for the relationship between abundance and CPUE. Our model simulates a fishery that occurs in several adjacent fishing grounds. The spatial distribution of catch and effort is based on a simplification of the Baranov catch equation, the relationship between fishing efficiency and local fishing effort (interference), and the assumptions of the ideal free distribution. Our results indicate that (i) even low levels of interference among fishing vessels can cause a breakdown in the correlation between CPUE and local abundance and (ii) the influence of interference on this relationship is dependent on the correlation of abundances among adjacent areas. Our model suggests an alternative index of abundance, based on the proportion of fishing effort on a ground, that would be appropriate for cases where interference occurs among fishing gear.
A number of regression situations in fish and fishery biology are examined, in which both of the variates are subject to error of measurement, or inherent variability, or both. For most of these situations a functional regression line is more suitable than the ordinary predictive regressions that have usually been employed, so that many estimates now in use are in some degree biased. Examples are (1) estimation of the exponent in the weight:length relationship, where almost all published values are somewhat too small; and (2) estimating the regression of logarithm of metabolic rate on log body weight of fish, where the best average figure proves to be 0.85 rather than 0.80. In the very common situation where the distribution of the variates is non-normal and open-ended, a functional regression is the most appropriate one even for purposes of prediction. Two ways of estimating the functional regression are (1) from arithmetic means of segments of the distribution, when computed symmetrically; and (2) from the geometric mean of one predictive regression and the reciprocal of the other. The GM regression gives a more accurate estimate when it is applicable; it is appropriate in all situations where the variability is mainly inherent in the material (little of it due to errors of measurement), or where the measurement variances are approximately proportional to the total variance of each variate; and it is the best estimate available for short series with moderate or large variability even when neither of these conditions applies. When error in X results solely from the measuring process the predictive regression of Y on X is also the functional regression if observations of X are not taken at random but rather have pre-established values, as is usual in experimental work. The uses of the various regressions are summarized in Table 8.
Many investigators have noted that estimates of coral reef fish populations by visual census are biased but its precision has never been quantitatively determined. It is still used, however, because this technique is usually assumed to be the best non-destructive method of population assessment. This study compares the results of visual censuses conducted on an isolated 1,500 m2 patch reef to the collection of all fishes made subsequently with rotenone on that reef. The visual censuses missed the presence or underestimated abundance of cryptic fish species. Diurnally active species were reasonably well censused, but the most common were often underestimated. Thus comparisons between fish communities based on visual census data should be restricted to the diurnally exposed species.
A visual census technique is described in which the results of three separate enumerations of fish at a site are combined to produce a best estimate of the fish fauna present. Its precision and accuracy are examined, and compared to those of censuses obtained by modifications of the technique. Visual censuses can display high repeatability, but they seldom (if ever) completely sample the fish present at a site. Accuracy varies with technique used. In our tests, the preferred method yielded 82% of species and 75% of individuals known to be present and potentially censurable at the time the observations were made. Visual censuses are of comparable accuracy to ichthyocide collections of unenclosed sites, but the two methods sample different components of the total fish fauna. It is important when using visual censuses to remember that their accuracy is not 100%.
The Model I linear regression theory is often used in the analysis of data under conditions when the Model II theory is clearly needed. Implications derived from the use of the two theories can differ greatly when there is not a high degree of correlation between the X and Y variables. The geometric mean Model II method is easy to use, and is probably needed in the analysis of most field data, since the X variable in field data is rarely under the control of the investigator.
Catch per unit effort (CPUE) is often assumed to be an index of stock abundance. Here we present an experiment and a general model for testing this assumption. We used the submersible Pisces IV to make visual estimates of reef-fish density in the Strait of Georgia, British Columbia. These density estimates were compared with CPUE estimates obtained by research angling at the same sites. Our model allows for no relationship between CPUE and density, strict proportionality, and departures from proportionality at either low or high densities or both. We performed the analysis using an ordinary least squares (OLS) model and an errors-in-variables (EV) model that includes error in both CPUE and density. For the dominant species (quillback rockfish, Sebastes maliger), the relationship was one of strict proportionality. However, CPUE was a poor abundance index when data were combined across species. In these cases the OLS and EV models generally resulted in different conclusions; the EV model explained a low CPUE at high density by allowing for error in the density measurement.
Hook and line survey of lingcod (Ophiodon elongatus) and rockfish (Sebastes sp.) stocks in southern Strait of Georgia (statistical areas 17
- D R Haggarty
- J R King
Haggarty, D.R., King, J.R., 2004. Hook and line survey of lingcod
(Ophiodon elongatus) and rockfish (Sebastes sp.) stocks in southern Strait of Georgia (statistical areas 17, 18 and 19), October 2003.
Can. Tech. Rep. Fish. Aquat. Sci. 2533, 38.
Hook and line survey of lingcod (Ophiodon elongatus) and rockfish (Sebastes spp
- D R Haggarty
- J R King
Haggarty, D.R., King, J.R., 2005. Hook and line survey of lingcod
(Ophiodon elongatus) and rockfish (Sebastes spp.) in northern Strait
of Georgia (statistical areas 13, 14, 15 and 16), June 14-July 9, 2004.
Can. Tech. Rep. Fish. Aquat. Sci. 2590, 57.
Lingcod egg mass and reef fish density SCUBA survey in the Strait of Georgia
- D R Haggarty
- J R King
- V R Hodes
Haggarty, D.R., King, J.R., Hodes, V.R., 2005. Lingcod egg mass and
reef fish density SCUBA survey in the Strait of Georgia, February
19-March 11, 2005. Can. Data Rep. Fish. Aquat. Sci. 1161, iv + 16.
- P Legendre
- L Legendre
Legendre, P., Legendre, L., 1998. Numerical Ecology. Developments in
Environmental Modelling, vol. 20., second English ed. Elsevier, Amsterdam.
research catch and effort data on nearshore reef-fishes in British Columbia Statistical Areas 12, 13 and 16
- L J Richards
- A J Cass
Richards, L.J., Cass, A.J., 1987. 1986 research catch and effort data on
nearshore reef-fishes in British Columbia Statistical Areas 12, 13 and
16. Can. Man. Rep. Fish. Aquat. Sci. 1903, 119.