Editor’s note: This essay by Trevor Branch of the University of Washington’s School of Aquatic and Fisheries Sciences is in response to earlier Cool Green Science posts by marine scientists Daniel Pauly and Ray Hilborn on the state of the world’s fisheries. We continue to solicit other voices in this debate and will post them here on Cool Green Science as we receive them; none of these posts should be taken as reflecting the position of The Nature Conservancy on any issue.

How do you measure the health of an ecosystem? Ideally, you need a measurement that increases when things are getting better and decreases when things are getting worse.

For some time, the most widely adopted indicator of marine ecosystem health has been mean trophic level (MTL) in fish catches. (Trophic level is the average position of a living creature in a food web. Microscopic plankton and seaweed, which are at the base of marine food webs, are ranked trophic level 1; herbivores and filter feeds like oysters are at trophic level 2; and predators like cod are at trophic level 3 or higher.) So increases in MTL in catches have been assumed to indicate improving health in marine ecosystems.

Catch MTL has been reported to decline when “fishing down the food web” occurs — i.e., when fisheries first target top predators, depleting them, and then sequentially deplete groups further and further down the food web (Pauly et al. 1998). But in a recent paper that I coauthored in Nature entitled “The trophic fingerprint of marine fisheries,” we tested whether MTL is in fact the right tool to track changes in marine ecosystems (Branch et al. 2010).

To our surprise, it turns out that catch, survey and assessment MTLs have not declined in recent years but have increased. More seriously, though, we found that changes in catch MTL do not track changes in ecosystem health.

Our paper has prompted some vigorous public responses from Daniel Pauly; he argues (including in a recent Cool Green Science post) that we do not account for spatial increases in catches — in other words, the expansion of fishing over wider areas — which he says causes catch MTL to increase in the current data.

Catches are the most widely available fisheries data. But there are many factors that confound attempts to link changes in catches to changes in ecosystem abundance — including market forces, regulations, technological innovations and spatial expansion.

We first repeated Pauly et al.’s analysis on newer catch and trophic level data, finding that catches at virtually all levels of marine food webs are now higher. The new data show that catch MTL is increasing, and is higher now than in the 1960s. But this increase is not because of spatial expansion; it is because of new trophic level estimates and fluctuations in the catches of South American anchoveta and sardine.

More importantly than these catch-based results, however, our new research used all the available tools and overcame the above issues that have traditionally rendered catch data problematic. In particular, we compiled and analyzed large numbers of scientific surveys and stock assessments, which directly track ecosystem changes. Surveys and assessments come from the same area over time and are therefore unaffected by spatial expansion and other factors. We found that MTLs from surveys and assessments are also increasing in recent years.

One more tool that we used was compilations of ecosystem models. An ecosystem model reconstructs an entire food web inside the computer to answer how real life ecosystems would react to fishing. We used these ecosystem models, which are also unaffected by spatial expansion, to test whether trends in catch MTL would be expected to match trends in ecosystem MTL. If catch MTL is a useful indicator, it would increase when ecosystem MTL increases, and decrease when ecosystem MTL decreases.

But our ecosystem models demonstrated that catch MTL often changes in the opposite direction to ecosystem MTL. Our real-life data from surveys and assessments also showed frequent divergent trends between catch MTL to ecosystem MTL.

For example, in the Gulf of Thailand, catch MTL increased continuously (seemingly suggesting increasing ecosystem health), yet over the same time period, survey MTL declined and most species were reduced to one-tenth of their original numbers (reflecting actual ecosystem degradation). Thus in the Gulf of Thailand, catch MTL predicts the exact opposite trend in ecosystem health (getting better) to what is actually happening in the ecosystem (getting worse).

Daniel Pauly also argues that our compilations are a “grossly biased sample, drawn from the well-managed fisheries of a few countries or regions at the world’s end, like Alaska or New Zealand.” While our compilations exceed the scope of all previous efforts, we are fully aware that our spatial coverage is not complete, because we could not access data from many regions. This lack of coverage was attributable to the unavailability of surveys or (more commonly) because surveys are not conducted in many regions.

The bias that results from the lack of coverage is not towards the fisheries of Alaska or New Zealand, but towards those of the North Atlantic, an area with a well-documented history of heavy exploitation, fishing down and mixed management success (Pauly et al. 1998, Christensen et al. 2003, Essington et al. 2006, Worm et al. 2009, Swartz et al. 2010). Notwithstanding this, the overall analysis of survey MTL does not decline, reinforcing our model results that MTL can remain stable for long periods of time even as stocks collapse and ecosystems are increasingly modified by fishing.

The take-home message from our paper is that we need to shift our focus away from examining only what we take out of marine ecosystems (i.e. catches). Instead, we should focus our scarce resources on expanding our use of tools like surveys and assessments that measure the numbers and trends of species in the world’s oceans, especially in developing countries. Only through such direct tools can we accurately track changes in ecosystem health.


Branch, T. A., R. Watson, E. A. Fulton, S. Jennings, C. R. McGilliard, G. T. Pablico, D. Ricard, and S. R. Tracey. 2010. The trophic fingerprint of marine fisheries. Nature 468:431-435.

Christensen, V., S. Guénette, J. J. Heymans, C. J. Walters, R. Watson, D. Zeller, and D. Pauly. 2003. Hundred-year decline of North Atlantic predatory fishes. Fish and Fisheries 4:1-24.

Essington, T. E., A. H. Beaudreau, and J. Wiedenmann. 2006. Fishing through marine food webs. Proceedings of the National Academy of Sciences U.S.A. 103:3171-3175.

Pauly, D., V. Christensen, J. Dalsgaard, R. Froese, and F. Torres Jr. 1998. Fishing down marine food webs. Science 279:860-863.

Swartz, W., E. Sala, S. Tracey, R. Watson, and D. Pauly. 2010. The spatial expansion and ecological footprint of fisheries (1950 to present). PLoS ONE 5(12): e15143.

Worm, B., R. Hilborn, J. K. Baum, T. A. Branch, J. S. Collie, C. Costello, M. J. Fogarty, E. A. Fulton, J. A. Hutchings, S. Jennings, O. P. Jensen, H. K. Lotze, P. M. Mace, T. R. McClanahan, C. Minto, S. R. Palumbi, A. M. Parma, D. Ricard, A. A. Rosenberg, R. Watson, and D. Zeller. 2009. Rebuilding global fisheries. Science 325:578-585.

(Image credit: NKPhillips/Flickr through a Creative Commons license.)

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  1. To your conclusion my own fundamental conceptual research settled on aggregating a caloric value within a “pulse: distribution.
    Density time and volume. Here in the northeast USA our marine estuarine our teleost vector the majority of water column calories during specific times that of migration, mating, die offs. emergence. A hunting strategy is just to energy costly. That postulate has yet to be quantified however after decades of observation this is the rationale.

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