Fisheries as a Predictive Science


Fisheries have caused significant changes in ocean ecology. The inability of various models to manage these changes can be related to the impossibility of writing all-inclusive mathematical equations of Nature. Forecasts of fish abundance should be related to the real-time variations in ocean properties and, in particular, to changes in the bottom-up structure of food chains. Using this diagnostic approach, fisheries science may come to emulate the more successful biological sciences of medicine and agriculture.

The Problem

While the loss of fish in the oceans can be linked to economic and social pressures, it is paramount that a predictive science be developed that will foster the long-term preservation of fish stocks and the natural ecology of the oceans. Scientific management of fisheries has been dominated by theories of population dynamics. The best known book on this subject, written by Beverton and Holt (1957), has been widely used for many decades. Numerous modifications and extrapolations to the original text exist. Unfortunately, the general concept that equations can be written which will account for future fish abundance is flawed by the fact that ecological systems have many ways of varying over time. Predictive changes in fish abundance can not be tied down to a few variables. Consequently, population dynamics has generally failed as a science. Management strategies linked to this approach have not prevented the depletion of fish stock, sometimes almost to the point of extinction (e.g. Myers and Worm, 2003; Frank et al, 2005).

A more recent attempt to model oceanic marine populations using a trophodynamic approach has also not met with any marked predictive success (e.g. Trites, et al, 1999). In such models, attempts have been made to tie the abundance of fish more closely to the food chains of the ocean. Here too the impossibility of writing equations for the complete diversity of Nature, together with variability in assumed constants (e.g. transfer efficiencies, see Cyr and Pace, 1993), have not improved prediction. Much can be learned from such models in terms of concepts of energy flow throughout a system, but the results are hypothetical and not useful for annual predictions of values for management. As an example, Shannon et al (2009), in reviewing the use of trophic models, aptly state: “model simulations presented here are purely illustrative of extreme situations of collapse or closure of small pelagic fisheries and should not be considered in the quantitative predictive sense”.


Parallels in Biology

Fisheries ecology and an understanding of stock abundance is only one branch of biology practiced by scientists. Medicine and agriculture are two other branches that are widely practiced in our society. These fields of science have certain properties that are different from those of fisheries ecology, but the general rules governing biological systems are the same regardless of the exact subject being studied. Biological systems are infinitely variable in terms of genetics, food resources, physical impacts, disease organisms, predation and exploitation; no single mathematical equation can make life easy for the scientist who wishes to understand and manage biological systems. This is a lesson that has been learned better in medicine and agriculture than in fisheries management.

Physicians practice medicine. That common word “practice” means a lot. Every doctor is taught to take a diagnostic approach to a patient, using the most up-to-date measurements that will tell him what is going on in the complicated system of the human body. He does not use a preconceived mathematical model to integrate his diagnosis but relies on knowledge obtained from his experience, the use of the newest instruments, and his academic background. Such an approach allows the physician to entertain many possibilities in forming his initial conclusions. A similar approach is used in agriculture, where the possibility of increasing production is based on a diagnostic approach to animals, plants, climate, and disease. These two branches of biology have met with considerable success in the world, unlike our understanding and management of ocean fisheries.


The diagnostic approach

While the survival of fish is recognized as being dependent on a large number of factors including predation, disease, climate, etc., there is no doubt that all these effects are much greater on undernourished fish than on fish that are well fed under natural conditions. For example in an experiment on lake fertilization, the larger salmon smolts produced in a fertilized lake (Lebrasseur et al, 1979) yielded seven times the number of adult returns compared with an unfertilized adjacent lake where the fish were exposed to practically the same survival hazards over a four year period.

From the failure of fisheries models to the success of the diagnostic approach in other branches of biology, it should be clear that only by the collection and analyses of contemporary ocean data can we approach successful forecasts of fishery abundance and help to preserve fish populations through an ecological understanding of the oceans (e.g. such as in the GLOBEC approach). Further, the preservation of fish and precise forecasts must be based on the collection of real-time data using many of the oceanwide analytical systems that have recently become available (e.g. sophisticated satellite data, gliders, continuous plankton recorders, etc).

The use of these data should be applied to the structure of fish communities from the bottom up, by focusing on how energy is transferred in the ocean. This real-time trophodynamic approach should replace older models based on population dynamic or trophodynamic equations that have not achieved great success as a predictive science useful to management. Bottom-up control of fish abundance has been available in the literature for sometime (e.g. Ryther, 1969). More recently (e.g. Iverson, 1990; Chassot, 2007) a strong correlation has been shown between primary productivity and fish abundance. However, because the primary producers of the sea can vary by over nine orders of magnitude in volumetric size (ca. 1 to 1000 microns in diameter), it is apparent that the relationship can not be used with great precision. An extensive discussion of how this relationship might be used is given by Barange et al (2011), but no coverage is given to the species of the primary producers. Agriculturists would not accept that a field of thistles is of the same nutritional value as a field of clover having the same primary production value. Likewise, high primary production from eutrophication and natural upwelling systems is not the same in the ecology of fish. Major pelagic fisheries of the world are supported by the growth of diatoms (e.g. Parsons, 1979). Thus a more accurate use of the Iverson (1990) relationship requires that the type of primary producer be known.

If it were possible to diagnose this phytoplankton size difference over large areas of ocean, then the food chain of highly productive fisheries should be predictable. The value of diatom assemblages in supporting the major fisheries of the ocean has been discussed by Parsons (1979) and Parsons and Lalli (2002); by contrast, the same authors also emphasized the opposite low production of flagellate communities, which generally lead to dominance by corals and/or gelatinous zooplankton (Cnidarians, ctenophores and salps). Saito et al (2012)A and Saito et al (2012)B have recently confirmed this difference in a comparison of the eastern (Gulf of Alaska) and western gyres of the North Pacific. These two bodies of water at the same latitude show very different pelagic ecologies. The western gyre is characterized by diatom growth, large zooplankton and significant fisheries whereas the eastern gyre is characterized by small flagellate production and abundant growth of the jellyfish Aglantha except when environmental conditions bring iron to these HNLC ( high nutrient/low chlorophyll) waters. Such an event did occur on a massive scale in 2008 resulting in a large diatom bloom at the time of the seaward migration of juvenile sockeye salmon. This was hypothesized to have caused the subsequent Fraser River run of an unprecedented 35 million adult salmon in 2010 (Parsons and Whitney, 2012).

The importance of timing in food supplies and the survival of young fish has been brought into focus by the use of satellite data in connection with the spring bloom (Platt et al, 2003). If the feeding of larval and juvenile fish can be closely tracked in time, then the two important issues of growth and survival can observed in real time and used to predict the future population. This trophic phasing (a.k.a. “coupling”; “match/mismatch”) approach needs to be further developed.

Some methodology showing differences in primary producers in real time over large areas of ocean has been developed. Sathyendranath et al (2004) have described a method for measuring diatom pigments from satellite data. Barnes et al (2011) have described a method for determining the size structure of phytoplankton communities using environmental data. By using such real-time methodologies, it should be possible to accurately forecast variations in fish abundance over wide areas.



Barnes, C., Irigoien X.,De Oliveira H.A.A., Maxwell D. & Jennings S. (2011) Predicting marine phytoplankton community size structure from empirical relationships with remotely sensed variables. Journal of Plankton Research 33:13-24.

Barange, M. Allen I.Allison E. et al (2011) Predicting the impacts and socio-economic change on global marine ecosystems and fisheries. World Fisheries – A Social-Ecological Analysis. Publ. Wiley-Blackwell. Chapt.3, 32-59.

Beverton, R.J.H & Holt S.J. (1957) On the Dynamics of exploited Fish Populations. U.K. Ministry of Agriculture, Fisheries and Food Investment, Ser.2, 19, 533pp.

Chassot, E., Melin, F., LePape, O. Gascuel, D. (2007) Bottom-up control regulates fisheries production at the scale of eco-regions in European seas. Marine Ecology Progress Series 343: 45-55.

Cyr,H and Pace M.L. (1993) Magnitude and patterns of herbivory in aquatic and terrestrial ecosystems. Nature 361: 148-150.

Frank K.T., Petrie B.,Choi J.S. & Leggett W.C. (2005) Trophic cascades in a formerly cod-dominated ecosystem. Science 308: 1621–1623.

Iverson, R.L. (1990) Control of marine fish production. Limnology and Oceanography 35:1593-1604.

LeBrasseur, R.J., C.D. McAllister and T.R. Parsons. 1979. Additions of nutrients to a lake leads to greatly increased salmon catch. Environ. Cons. 6: 187-190.

Myers, R.A. & Worm B.(2003) Rapid worldwide depletion of predatory fish communities. Nature 423: 280-283

Parsons, T.R. (1979) Some ecological, experimental and evolutionary aspects of the upwelling ecosystem. South African Journal of Science 75: 536-540.

Parsons, T.R. & Lalli C.M. (2002) Jellyfish population explosions. Revisiting a hypothesis of possible causes. La Mer 40: 111-121.

Parsons, T.R. and Whitney, F. (2012) Did volcanic ash from Mt. Kasatoshi in 2008 contribute to a phenomenal increase in Fraser River sockeye salmon ( Oncohynchus nerka) in 2010 ? Fish. Oceanogr. 21: 374-377.

Platt, T., Fuentes-Yaco, C. and Frank, K.T. (2003) Spring algal bloom and larval fish survival. Nature 423: 398-399.

Ryther, J.H. (1969) Photosynthesis and fish production in the sea. The production of organic matter and its conversion to higher forms of life vary throughout the world ocean. Science 166: 72-76.

Saito, R., Yamoguchi, A., Saitoh, S-I., Kuma, K. & Imai I. (2012)A Geographical variations in abundance of the hydromedusa Aglantha digitale in the northern N.Pacific and its adjacent seas. Bull.Fish.Sci. Hokkaido Univ. 62: 63-69.

Saito, R., Yamoguchi A., Saito S-I., Kuma K. & I.Imai (2012)B Abundance, biomass and body size of hydromedusa Aglantha digitale in western and eastern subarctic Pacific during the summers 2003-2006. Plankton and Benthos 7: 96-99

Sathyendranath, S., Watts L., Devred E., Platt T.,Caverhill C. and Maass H. (2004) Discrimination of diatoms from other phytoplankton using ocean-colour data. Marine Ecology, Progress Seri, 272: 59-68.

Shannon, L., Coll M., Neiro S., Cury P. & Roux J.P. (2009) Impacts of fishing and climate change explored using trophic models. Climate Change and Small Pelagic Fish. Publ. Cambridge U. Press. 158-190.

Trites, A.W., P.A. Livingston, P.A., Vasconcellos M.C., Mackinson S., A.M.Springer A.M. and Pauly D. (1999) Ecosystem considerations and the limitations of ecosystem models in fisheries management: Insights from the Bering Sea. Ecosystem Approaches for Fisheries Management. University of Alaska Sea Grant, AK-SG-99-01, Fairbanks, pp 609-635.