Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T03:46:32.767Z Has data issue: false hasContentIssue false

Using min/max autocorrelation factors of survey-based indicators to follow the evolution of fish stocks in time

Published online by Cambridge University Press:  17 June 2009

Mathieu Woillez
Affiliation:
IFREMER, Département EMH, BP 21105, 44311 Nantes Cedex 03, France
Jacques Rivoirard
Affiliation:
Mines-ParisTech, Centre de Géosciences/Géostatistique, 35 rue St Honoré, 77300 Fontainebleau, France
Pierre Petitgas
Affiliation:
IFREMER, Département EMH, BP 21105, 44311 Nantes Cedex 03, France
Get access

Abstract

Fisheries research monitoring surveys provide an ensemble of measurements on fish stocks and their environment. Because the interannual variability in such survey-based indicators is high and because diagnostics on fish stocks cannot be based on noise, our concern is to make use of what is continuous in time to obtain a reliable diagnostic. In this paper, we show how min/max autocorrelation factors (MAFs) can be useful for assessing the status of a fish stock. Indeed, MAFs will allow us to (i) summarize the multivariate indicator signals into orthogonal factors that are continuous in time, (ii) select those indicators that carry the major signal in time, and (iii) forecast stock status by modelling the time continuity of the MAFs. These different potential uses of MAFs in an indicator-based approach to assessment were illustrated with North Sea cod, for which a suite of biological and spatial indicators are available over a 21-year survey series.

Type
Research Article
Copyright
© EDP Sciences, IFREMER, IRD, 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bouleau M., 2005, Combinaison géostatistique de l'acoustique et des captures dans les campagnes scientifiques de pêche par chalutage. Thèse dr Géostatistique, Ecole Nationale Supérieure des Mines, Paris.
Conradsen K., Ersboll B.K., Thyrsted T., 1985, A comparison of min/max autocorrelation factor analysis and ordinary factor analysis. Nordic Symposium in Applied Statistics, Lyngby, pp. 47-56.
Cotter, J., Mesnil, B., Witthames, P., Uriarte, A., Parker-Humphreys, M., 2009, Notes on nine biological indicators estimable from trawl surveys with an illustrative assessment for North Sea cod. Aquat. Living Resour. 22, 135153. CrossRef
Desbarats A.J., 2001, Geostatistical modelling of regionalized grain-size distributions using min/max autocorrelation factors. In: Monestiez P., Allard D., Froidevaux R. (Eds.) Geostatistics for Environmental Applications III, Kluwer Academic Publisher, pp. 441–452.
Desbarats, A.J., Dimitrakopoulos, R., 2000, Geostatistical simulation of regionalized pore-size distributions using min/max autocorrelation factors. Math. Geol. 32, 919942. CrossRef
Erzini, K., 2005, Trends in NE Atlantic landings (southern Portugal): identifying the relative importance of fisheries and environmental variables. Fish. Oceanogr. 14, 195209. CrossRef
Erzini, K., Inejih, C.A.O., Stobberup, K.A., 2005, An application of two techniques for the analysis of short, multivariate non-stationary time-series of Mauritanian trawl survey data. ICES J. Mar. Sci. 62, 353359. CrossRef
Hedger, R., McKenzie, E., Heath, M., Wright, P., Scott, B., Gallego, A., Andrews, J. 2004, Analysis of the spatial distribution of mature cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) abundance in the North Sea (1980-1999) using generalised additive models. Fish. Res. 70, 17–25.
ICES, 2003, Report of the ICES Advisory Committee on Fishery Management, ICES Coop. Res. Rep. 261.
Jennings, S., 2005, Indicators to support an ecosystem approach to fisheries. Fish Fish. 6, 212232. CrossRef
Löfgren, K.-G., Ranneby, B., Sjöstedt, S., 1993, Forecasting the business cycle without using minimum autocorrelation factors. J. Forecasting 12, 481498. CrossRef
Pearce, K.F., Frid, C.L.J., 1999, Coincident changes in four components of the North Sea ecosystem. J. Mar. Biol. Assoc. UK 79, 183185. CrossRef
R development Core Team, 2005, R: A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing. URL http://cran.r-project.org/
Rindorf, A., Lewy, P., 2006, Warm windy winters drive cod north and homing keeps them there. J. Appl. Ecol. 43, 445453. CrossRef
Shapiro D.E., Switzer P., 1989, Extracting time trends from multiple monitoring sites. Department of Statistics, Stanford University. Tech. Rep. 132.
Solow, A.R., 1994, Detecting change in the composition of a multispecies community. Biometrics 50, 556565. CrossRef
Switzer P., Green A.A., 1984, Min/max autocorrelation factors for multivariate spatial imaging. Department of Statistics, Stanford University, Tech. Rep. 6.
Woillez M., 2007, Contributions géostatistiques à la biologie halieutique. Thèse dr. Géostatistique, Ecole Nationale Supérieure des Mines, Paris.
Woillez, M., Poulard, J-C., Rivoirard, J., Petitgas, P., Bez, N., 2007a, Indices for capturing spatial patterns and their evolution in time, with application to European hake (Merluccius merluccius) in the Bay of Biscay. ICES J. Mar. Sci. 64, 537550. CrossRef
Woillez M., Rivoirard J., Petitgas P., 2007b, Selecting and combining survey-based indices of fish stocks using their correlation in time to make diagnostics of their status. ICES CM 2007/O:07.
Woillez, M., Rivoirard, J., Petitgas, P., 2009, Notes on survey-based spatial indicators for monitoring fish populations. Aquat. Living Resour. 22, 155164. CrossRef
Zuur, A.F., Pierce, G.J., 2004, Common trends in Northeast Atlantic squid time series. J. Sea Res. 52, 5772. CrossRef
Zuur, A.F., Fryer, R.J., Jolliffe, I.T., Dekker, R., Beukema, J.J., 2003a, Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics 14, 665685. CrossRef
Zuur, A.F., Tuck, I.D., Bailey, N., 2003b, Dynamic factor analysis to estimate common trends in fisheries time series. Can. J. Fish. Aquat. Sci. 60, 542552. CrossRef
Supplementary material: File

OLM - alr 22(2) 2009 p.193 - Using min/max autocorrelation factors of ...

Supplemental Material (Electronic-only) - DOI: 10.1051/alr/2009020.olm01

Download OLM - alr 22(2) 2009 p.193 - Using min/max autocorrelation factors of ...(File)
File 21.5 KB