The need for spatio-temporal modeling to determine catch-per-unit effort based indices of abundance and associated composition data for inclusion in stock assessment models

2020 ◽  
Vol 229 ◽  
pp. 105594 ◽  
Author(s):  
Mark N. Maunder ◽  
James T. Thorson ◽  
Haikun Xu ◽  
Ricardo Oliveros-Ramos ◽  
Simon D. Hoyle ◽  
...  

<em>Abstract.</em>—The New Zealand eel fishery comprises two species, the shortfin eel <em>Anguilla australis </em>and the New Zealand longfin eel <em>A. dieffenbachii</em>. A third species, the speckled longfin eel <em>A. reinhardtii</em>, is present in small numbers in some areas. Major fisheries in New Zealand are managed under the Quota Management System. Individual transferable quotas are set as a proportion of an annual total allowable commercial catch. The Quota Management System was introduced into the South Island eel fishery on 1 October 2000 and the North Island fishery on 1 October 2004. Freshwater eels have particular significance for customary Maori. Management policies allow for customary take and the granting of commercial access rights on introduction into the Quota Management System. Eel catches have remained relatively constant since the early 1970s. The average annual catch from 1989–1990 to 2001–2002 (fishing year) was 1,313 mt. Catch per unit effort remained constant from 1983 to 1989 and reduced from 1990 to 1999. Statistically significant declines in catch per unit effort for New Zealand longfin eel were found in some areas over the latter period. For management, an annual stock-assessment process provides an update on stock status.


2009 ◽  
Vol 60 (12) ◽  
pp. 1273 ◽  
Author(s):  
Siquan Tian ◽  
Yong Chen ◽  
Xinjun Chen ◽  
Liuxiong Xu ◽  
Xiaojie Dai

Spatial scale is an important factor that needs to be considered in data collection and analysis in ecological studies. Studies focusing on the quantitative evaluation of impacts of spatial scales are, however, limited in fisheries. Using the Chinese squid-jigging fishery in the north-western Pacific Ocean as an example, we evaluated impacts of spatial scale used in grouping fisheries and environmental data on the standardisation of fisheries catch per unit effort (CPUE). We developed 18 scenarios of different spatial scales with a combination of three latitudinal levels (0.5°, 1° and 2°) and six longitudinal levels (0.5°, 1°, 2°, 3°, 4° and 5°) to aggregate the data. We then applied generalised additive models to analyse the 18 scenarios of data for the CPUE standardisation, and quantified differences among the scenarios. This study shows that longitudinal and latitudinal spatial scale and size of the spatial area for data aggregation can greatly influence the standardisation of CPUE. We recommend that similar studies be undertaken whenever possible to evaluate the roles of spatial scales and to identify the optimal spatial scale for data aggregations in the standardisation of CPUE and fisheries stock assessment.


2019 ◽  
Vol 213 ◽  
pp. 75-93 ◽  
Author(s):  
Arnaud Grüss ◽  
John F. Walter ◽  
Elizabeth A. Babcock ◽  
Francesca C. Forrestal ◽  
James T. Thorson ◽  
...  

2003 ◽  
Vol 54 (4) ◽  
pp. 409 ◽  
Author(s):  
C. Phillip Goodyear

Atlantic blue and white marlin are currently overfished, primarily as a result of bycatch in pelagic longlines directed at other species. One possible management measure to reduce fishing mortality on these species would be to restrict fishing effort in times and places with exceptionally high marlin catch per unit effort (CPUE). The International Commission for the Conservation of Atlantic Tunas maintains a database of catch and catch-effort statistics of participating nations. These data were analysed to determine whether the distribution of CPUE is sufficiently heterogeneous in time and space that such measures might provide meaningful management alternatives. The resulting distributions of catch rates were also contrasted with monthly average sea surface temperatures to examine the possible association between temperature and CPUE. The results show spatio-temporal heterogeneity in catch rates that may be partly explained by seasonal changes in sea surface temperatures. The time–area concentrations of high CPUE differ between the species. This observed heterogeneity might be exploited to develop alternatives for reducing fishing mortality for future management of the fisheries, but additional research is needed to refine the spatial scale of the analysis and to more fully understand the factors contributing to the observed distribution.


2018 ◽  
Vol 75 (4) ◽  
pp. 1318-1328 ◽  
Author(s):  
Katharina Friederike Schulte ◽  
Andreas Dänhardt ◽  
Marc Hufnagl ◽  
Volker Siegel ◽  
Werner Wosniok ◽  
...  

Abstract Brown shrimps (Crangon crangon) occur in high densities in the southern North Sea and support a large, but so far unmanaged fishery with &gt;500 vessels. Cohort-based stock assessment is not possible, and catch per unit effort from scientific surveys and commercial landings are the only variables collected. Landings per unit effort are currently used to approximate the state of stock and to trigger catch restrictions, but, although decisive in interpreting unit catches or landings, factors affecting catch rates are rarely understood. Using data from two long-term (1997–2010) scientific surveys conducted in autumn and winter, respectively, in the southern North Sea and from a vertically resolving stow net deployed at two stations in the German Wadden Sea (2005–2007), we investigate the effects of season, reproductive state, size, tidal state, daylight, and water depth on catch rates of brown shrimp. Log-linear random intercept models revealed an influence of all factors examined on the catch rate. Depth had a clear effect on the composition of size and reproductive state, supporting the hypothesis that brown shrimp utilize selective tidal stream transport to migrate to depths preferred during certain periods within their life cycle. The vertical distribution of brown shrimp across the water column revealed that on average two thirds to three quarters of the brown shrimps were located above reach of the standard shrimp catching gear. Our findings indicate that multiple factors and interactions affect catch rates of brown shrimp and, thus, need to be accounted for when interpreting unit catches or landings for management purposes. We suggest that brown shrimps are not primarily demersal, and that stock size estimates solely relying on beam trawl data may underestimate the true density of shrimps per area.


1983 ◽  
Vol 40 (9) ◽  
pp. 1496-1506 ◽  
Author(s):  
Derek A. Roff

The models of Schnute and Deriso are compared with a simple autoregressive model (SA model). Comparisons are made using both stepwise and omnibus prediction. In the latter case predictions are made for at least 3 or 10 yr ahead. The SA model is shown to be consistently better at predicting both catch and catch-per-unit effort than either of the other two models. Furthermore, the percentage error is frequently sufficiently small to make the predictions useful for stock assessment. It is shown that in practice the Schnute and the Deriso models are variants of the SA model. It is suggested that catch and effort data cannot be used to establish equilibrium catches. The reasons for this are that unique equilibria probably do not exist and the catch/effort data are generally too unreliable to detect anything except major fluctuations in population size. It is suggested that catch-and-effort data can be used by adopting strategies designed to track the population fluctuations.


2018 ◽  
Vol 75 (3) ◽  
pp. 452-463 ◽  
Author(s):  
Hiroshi Okamura ◽  
Shoko H. Morita ◽  
Tetsuichiro Funamoto ◽  
Momoko Ichinokawa ◽  
Shinto Eguchi

Standardized catch per unit effort (CPUE) is a fundamental component of fishery stock assessment. In multispecies fisheries, catchability can differ depending on which species is being targeted, and so the yearly trend extracted from the standardized CPUE is likely to be biased. We have, therefore, developed a method for predicting the unobserved variable related to targeted species from among multispecies composition data using a mixture regression model for the transformed residuals. In contrast with traditional methods, the proposed method predicts the target variable in CPUE standardization without removing a subset of the data. Keeping the entire data set avoids information loss, and so CPUE standardization with the predicted target variable should yield an unbiased estimate of the yearly trend. Simple simulation tests demonstrate that our method outperforms traditional methods. We illustrate the use of our method by applying it to CPUE data on arabesque greenling (Pleurogrammus azonus) caught in multispecies trawl fisheries in Hokkaido, Japan.


2006 ◽  
Vol 63 (8) ◽  
pp. 1373-1385 ◽  
Author(s):  
Mark N. Maunder ◽  
John R. Sibert ◽  
Alain Fonteneau ◽  
John Hampton ◽  
Pierre Kleiber ◽  
...  

AbstractDespite being one of the most common pieces of information used in assessing the status of fish stocks, relative abundance indices based on catch per unit effort (cpue) data are notoriously problematic. Raw cpue is seldom proportional to abundance over a whole exploitation history and an entire geographic range, because numerous factors affect catch rates. One of the most commonly applied fisheries analyses is standardization of cpue data to remove the effect of factors that bias cpue as an index of abundance. Even if cpue is standardized appropriately, the resulting index of relative abundance, in isolation, provides limited information for management advice or about the effect of fishing. In addition, cpue data generally cannot provide information needed to assess and manage communities or ecosystems. We discuss some of the problems associated with the use of cpue data and some methods to assess and provide management advice about fish populations that can help overcome these problems, including integrated stock assessment models, management strategy evaluation, and adaptive management. We also discuss the inappropriateness of using cpue data to evaluate the status of communities. We use tuna stocks in the Pacific Ocean as examples.


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