Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-15T14:02:49.046Z Has data issue: false hasContentIssue false
  • English
  • Français

Recruitment variability in exploited aquatic populations

Published online by Cambridge University Press:  25 November 2006

Hiro-Sato Niwa
Hiro-Sato Niwa*
Affiliation:
Behavioral Ecology Section, National Research Institute of Fisheries Engineering, 7620-7 Hasaki, Kamisu, Ibaraki 314-0408, Japan
*
Hiro.S.Niwa@fra.affrc.go.jp
Get access

Abstract

While lognormal distributions have been proposed as useful descriptors of recruitment variability, the very nature of the recruitment distributions is still debated.To account quantitatively for recruitment distributions,I here propose a Weibull exponential model;it derives from a simple and natural hypothesis for uncorrelated recruitment processes of spawning, hatching, growth and survival through the early life stages to the point of vulnerability to the fishery.The quantification of Weibull exponentials is particularly important with regards to extrapolations to low recruits that have not yet been observed.To test the Weibull exponential null-hypothesis, I examine annual time-series of recruitment in major aquatic stocks.The Weibull exponential quite describes the bulk (95%) of the recruitment distributions of widely differing stocks,while the remaining 5% of the largest recruits are occurring with a much larger rate than predicted by the Weibull exponential.Further, I study the inter-event times between unusually high numbers in recruitment time-series data and find that intermittent pulses of strong recruitment follow non-Poisson statistics, which arises from year-to-year persistence of the magnitude of recruitment:large (or small) recruits are more likely to be followed by large (or small) recruits.This recruitment clustering effect is confirmed by the rescaled range analysis method.The empirical results imply that individual survivals on recruitment levels are independent of initial cohort sizes but year-to-year recruiting events exhibit long-term correlations.

Keywords

Weibull exponential distributionLognormalStochastic survivorshipEarly life historyNon-Poisson intermittencyRecruitment clustering

Information

Type
Research Article
Information
Aquatic Living Resources , Volume 19 , Issue 3 , July 2006, pp. 195 - 206
Copyright
© EDP Sciences, IFREMER, IRD, 2006

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.)

Article purchase

Temporarily unavailable

References

Altmann, E.G., Kantz, H., 2005, Recurrence time analysis, long-term correlations, and extreme events. Phys. Rev. E 71, 056106. CrossRef
Ariño, A., Pimm, S.L., 1995, On the nature of population extremes. Evol. Ecol. 9, 429-443. CrossRef
Barabási, A.-L., 2005, The origin of bursts and heavy tails in human dynamics. Nature 435, 207-211. CrossRef
Beddington J.R., Cooke J.G., 1983, The potential yield of fish stocks. FAO Fish. Tech. Pap. No. 242. FAO, Rome.
Bouchaud J.-P., Potters M., 2000, Theory of Financial Risks: From Statistical Physics to Risk Management, Cambridge University Press, Cambridge, UK.
Bunde, A., Eichner, J.F., Kantelhardt, J.W., Havlin, S., 2005, Long-term memory: a natural mechanism for the clustering of extreme events and anomalous residual times in climate records. Phys. Rev. Lett. 94, 048701. CrossRef
Caputi, N., 1988, Factors affecting the time series bias in stock-recruitment relationships and the interaction between time series and measurement error bias. Can. J. Fish. Aquat. Sci. 45, 178-184. CrossRef
Cramér, H., 1938, Sur un nouveau théorème-limite de la théorie des probabilités. Actualités Sci. Indust. 736, 5-23.
Daan, N., Fogarty, M.J., 2000, Recruitment dynamics of exploited marine populations: physical-biological interactions-foreword and symposium overview. ICES J. Mar. Sci. 57, 189-190. CrossRef
Deriso, R.B., 1980, Harvesting strategies and parameter estimation for as age-structured model. Can. J. Fish. Aquat. Sci. 37, 268-282. CrossRef
Dixon, P.A., Milicich, M.J., Sugihara, G., 1999, Episodic fluctuations in larval supply. Science 283, 1528-1530. CrossRef
Doherty, P.J., Williams, D.M., 1988, The replenishment of coral reef fish populations. Oceanogr. Mar. Biol. Ann. Rev. 26, 487-551.
Fogarty, M.J., 1993a, Recruitment in randomly varying environments. ICES J. Mar. Sci. 50, 247-260. CrossRef
Fogarty, M.J., 1993b, Recruitment distributions revisited. Can. J. Fish. Aquat. Sci. 50, 2723-2728. CrossRef
Fogarty, M.J., 2001, Recruitment dynamics of exploited marine populations: physical-biological interactions. part 2-introduction. ICES J. Mar. Sci. 58, 936. CrossRef
Fogarty, M.J., Sissenwine, M.P., Cohen, E.B., 1991, Recruitment variability and the dynamics of exploited marine populations. Trends Ecol. Evol. 6, 241-246. CrossRef
Frisch U., Sornette, D. 1997. Extreme deviations and applications. J. Phys. I France 7, 1155-1171.
Gracia, A. 1991, Spawning stock-recruitment relationships of white shrimp in the southwestern Gulf of Mexico. Trans. Am. Fish. Soc. 120, 519-527.
Halley J., Inchausti, P. 2002. Lognormality in ecological time series. Oikos 99, 518-530.
Hancock, D.A., 1973, The relationship between stock and recruitment in exploited invertebrates. Rapp. P.-V. Réun. Cons. Int. Explor. Mer 164, 113-131.
Hennemuth, R.C., Palmer, J.E., Brown, B.E., 1980, A statistical description of recruitment in eighteen selected fish stocks. J. Northw. Atl. Fish. Sci. 1, 101-111. CrossRef
Hurst, H.E., 1951, Long-term storage capacity of reservoirs. Trans. Am. Soc. Civil Engrs. 116, 770-808.
Iles, T.C., Beverton, R.J.H., 2000, The concentration hypothesis: the statistical evidence. ICES J. Mar. Sci. 57, 216-227. CrossRef
Inchausti P., Halley, J. 2001, Investigating long-term ecological variability using the Global Population Dynamics Database. Science 293, 655-657.
Inchausti, P., Halley, J., 2002, The long-term temporal variability and spectral color of animal populations. Evol. Ecol. Res. 4, 1033-1048.
Johansen, A., Sornette, D., 2001, Large stock market price drawdowns are outliers. J. Risk 4, 69-110. CrossRef
Katz, R.W., Brush, G.S., Parlange, M.B., 2005, Statistics of extremes: modeling ecological disturbances. Ecology 86, 1124-1134. CrossRef
Laherrère, J.H., Sornette, D., 1998, Stretched exponential distributions in nature and economy: “fat tails” with characteristic scales. Eur. Phys. J. B 2, 525-539. CrossRef
MacArthur, R.H., 1960, On the relative abundance of species. Am. Nat. 94, 25-36. CrossRef
MacCall A.D., 1999, Use of decision tables to develop a precautionary approach to problems in behavior, life history and recruitment variability. In: Restrepo, V. R. (Ed.), Proceedings of the Fifth National NMFS Stock Assessment Workshop, NOAA Tech. Memo. NMFS-F/SPO-40. U.S. Dep. Commer., pp. 53-64.
Mandelbrot B.B., 1983, The Fractal Geometry of Nature. W. H. Freedman and Company, New York.
Mertz, G., Myers, R.A., 1996, Influence of fecundity on recruitment variability of marine fish. Can. J. Fish. Aquat. Sci. 53, 1618-1625. CrossRef
Myers, R.A., Barrowman, N.J., Hutchings, J.A., Rosenberg, A.A., 1995, Population dynamics of exploited fish stocks at low population levels. Science 269, 1106-1108. CrossRef
Niwa, H.-S., 2006, Exploitation dynamics of fish stocks. Ecol. Informatics 1, 87-99. CrossRef
Penn, J.W., Caputi, N., 1986, Spawning stock-recruitment relationships and environmental influences on the tiger prawn (Penaeus esculentus) fishery in Exmouth Gulf, western Australia. Aust. J. Mar. Freshw. Res. 37, 491-505. CrossRef
Power, M., 1996, The testing and selection of recruitment distributions for North Atlantic fish stocks. Fish. Res. 25, 77-95. CrossRef
Preston, F.W., 1962, The canonical distribution of commonness and rarity. Ecology 43, 185-215, 410-432. CrossRef
Quinn T.J., Deriso R.B., 1999, Quantitative Fish Dynamics. Oxford University Press, New York.
Ricker, W.E., 1954, Stock and recruitment. J. Fish. Res. Board Can. 11, 559-623. CrossRef
Ricker, W.E., 1997, Cycles of abundance among Fraser River sockeye salmon (Oncorhynchus nerka). Can. J. Fish. Aquat. Sci. 54, 950-968. CrossRef
Ripley, B.J., Caswell, H., 2006, Recruitment variability and stochastic population growth of the soft-shell clam, Mya arenaria. Ecol. Model. 193, 517-530. CrossRef
Rothschild, B.J., 2000, “Fish stocks and recruitment”: the past thirty years. ICES J. Mar. Sci. 57, 191-201. CrossRef
Shelton, P.A., 1992, The shape of recruitment distributions. Can. J. Fish. Aquat. Sci. 49, 1754-1761. CrossRef
Shepherd, J.G., Cushing, D.H., 1990, Regulation in fish populations: myth or mirage? Phil. Trans. R. Soc. Lond. B 330, 151-164. CrossRef
Shojima, E., Otaki, H., 1982, Stock assessment of the Korai prawn, Penaeus orientalis. Bull. Seikai Reg. Fish. Res. Lab. Japan 58, 23-51.
Sugihara, G., 1980, Minimal community structure: an explanation of species abundance. Am. Nat. 116, 770-787. CrossRef
Winemiller, K.O., Rose, K.A., 1993, Why do most fish produce so many tiny offspring? Am. Nat. 142, 585-603. CrossRef
Yamasaki, K., Muchnik, L., Havlin, S., Bunde, A., Stanley, H.E., 2005, Scaling and memory in volatility return intervals in financial markets. Proc. Natl. Acad. Sci. USA 102, 9424-9428. CrossRef
Zheng, J., Murphy, M.C., Kruse, G.H., 1995, Updated length-based population model and stock-recruitment relationships for red king crab in Bristol Bay, Alaska. Alaska Fish. Res. Bull. 2, 114-124.