the particle­type detection set­up (Fig. 1a) or in the wave­type set­up (Fig. 1b). From the com­bination of these measurements, they extracted the degree of entanglement of the light shared between the four boxes. Using a method previously developed4 for a single photon travelling simultaneously along four possible paths, they identified quantitative criteria, involving combinations of particle­type and wave­type detection results, that allowed them to distinguish among entanglement between all four boxes, or three, or just two of them. In the presence of noise and other imperfections, they observed a gradual transition from four­party entanglement to no entanglement.

Although entanglement among more than four parties has been observed (the current record is for a system of 14 ions5, and entan­glement has been inferred among 100 atoms6),

Choi and colleagues’ system2 is special because the entanglement can be efficiently mapped on demand from a material system onto a light field. Atomic ensembles such as those used by the authors have already reached light­storage times of milliseconds at the single­photon level7,8. If those storage times can be extended to seconds, and some other technical per­formance parameters improved, such sources will have a variety of potential applications in secure quantum communication over long dis­tances1. The ensembles could then be used to build quantum networks over which quantum information can be distributed.

The astute reader may wonder how it is that quantum correlations can be observed with a single photon given that any correla­tion requires more than one system. The controversy about this issue can be resolved9

by viewing the four boxes as the systems that exhibit correlations (in photon number), rather than considering a single photon with qualms about its parent box. ■

Vladan Vuletic is in the Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. e­mail: vuletic@mit.edu

1. Duan, l.-M., lukin, M. D., cirac, J. i. & Zoller, P. Nature 414, 413–418 (2001).

2. choi, K. s., Goban, a., Papp, s. B., van Enk, s. J. & Kimble, h. J. Nature 468, 412–416 (2010).

3. Vuletic, V. Nature Phys. 2, 801–802 (2006).4. Papp, s. B. et al. Science 324, 764–768 (2009).5. Monz, t. et al. Preprint at http://arxiv.org/

abs/1009.6126 (2010).6. Gross, c. et al. Nature 464, 1165–1169 (2010).7. Zhao, r. et al. Nature Phys. 5, 100–104 (2009).8. Zhao, B. et al. Nature Phys. 5, 95–99 (2009).9. van Enk, s. J. Phys. Rev. A 72, 064306 (2005).

f I s h e r I e s

Measuring biodiversity in marine ecosystemsThe use of catch data to determine indicators of biodiversity such as ‘mean trophic level’ does not adequately measure ecosystem changes induced by fishing. Improved ways to assess those changes are required. See Letter p.431

J o s e p h e . p o w e r s

Accurate indicators of biodiversity are essential for managing exploited marine ecosystems. The currently most

widely adopted indicator is the ‘mean trophic level’ of catches, the position of a specific species in the food chain (trophic level) aver­aged over all the species in the catch. Declines in catch mean trophic levels have been inter­preted as showing shifts in ecosystem diversity from high­trophic­level predators to lower­trophic­level species. But are indicators based on catch data accurately depicting what is happening to an ecosystem? This question has now been addressed by Branch and co­workers on page 431 of this issue1.

Catch databases from marine fisheries are a reflection of economic, biological, ecological and technological factors. As a result, some species are unduly emphasized in the catches, distorting their true occurrence in the ecosys­tem. Additionally, catch databases, or more cor­rectly ‘reported catches’, might not reflect the full extent of exploitation. Discarded bycatch, recreational fisheries and rare species are dif­ficult to monitor and are therefore often not fully represented in the data. Finally, the data­bases themselves are often organized around political jurisdictions and do not necessarily encompass the entire ecosystem. Nevertheless,

catch databases are easily accessible and have relatively comprehensive species composition. So, despite the drawbacks, they remain attrac­tive for formulating diversity indicators such as indices of mean trophic level.

Branch et al.1 examined how useful these databases really are. They did this by com­paring the mean trophic level of catches with the mean trophic level of ecosystems (mean trophic level weighted by the estimated true abundance of species in the ecosystem), using two avenues of research.

First, they collated 25 existing and well­documented marine­ecosystem models, rep­resenting regions in the Northern and Southern Hemispheres, over a wide range of latitudes. For each model, components encompassing the existing fisheries of the region had already been incorporated. Time series of catch and abundance were projected under four fish­ing scenarios: ‘fishing down’, in which higher trophic levels were fished to depletion followed by the advent of fishing on lower trophic levels; ‘fishing through’, in which there was an expan­sion of fishing from some higher trophic spe­cies to other higher and lower trophic species; fishing ‘based on availability’, in which those species that were most abundant and acces­sible were exploited first, followed by expan­sion to less available and abundant species; and ‘increase to overfishing’, in which exploitation

rates of all species gradually increased until they were overfished. The simulation pro­jections were used to compute catch­ and abundance­weighted trophic indices and compare their time series.

Branch and colleagues’ second method was to compare catch­ and abundance­weighted mean trophic levels for individual eco systems. They used relative abundance from trawl surveys from 29 ecosystems, representing regions in the Northern and Southern Hemi­spheres, five continents and various latitudes, to calculate ecosystem (abundance­weighted) trophic indices. Additionally, estimates of absolute abundance from a database of 242 single­species stock assessments were also used to compute abundance­weighted trophic indices.

The results showed an inconsistent relation­ship between catch­ and abundance­weighted trophic indices. In other words, catch­weighted trophic indices are not generally indicative of the changes in trophic level of the ecosystem. For example, simulated trophic indices from the ecosystem models, as depicted in the top two rows of the authors’ Figure 1 (page 431), showed that in some cases the decline in eco­system mean trophic level (blue lines) was more rapid than that of the catch mean trophic level (red lines), particularly when ‘fishing down’ occurred. In other cases, the change in mean trophic level of either the catch or the ecosystem was hardly noticeable, yet many species were depleted.

When the individual ecosystems were exam­ined, almost half of the comparisons between catch mean trophic level and ecosystem mean trophic level from trawl or stock­assessment data were found to be negatively correlated. In particular, the relationship between catch and ecosystem trophic level tends to break down when fishing is not distributed across all por­tions of the ecosystem. On the face of it, then, the way forward is to use abundance­weighted rather than catch­weighted indices.

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However, abundance databases have their limitations, too. Although stock­assessment estimates of abundance are considered to provide the best available data2,3, the suite of species for which such assessments are done are limited, being driven by economic and management considerations rather than eco­logical factors. Trawl survey data provide rela­tive abundance estimates that are skewed by differential susceptibilities of the species and sizes to the sampling gear. Additionally, surveys are not normally designed to sample top preda­tors. The results of Branch et al. highlight the need to expand research to estimate abundance through stock assessments of a broader range

of species and more extensive trawl surveys.But is there still some utility in using catch­

weighted mean trophic levels? Perhaps so. Branch and colleagues’ results1 suggest condi­tions in which they might be useful (for exam­ple, to indicate major shifts in exploitation patterns). Additionally, catch­weighted trophic level might be used as a ‘policy­triggering’ tool rather than as a monitoring index — that is, a major change in catch mean trophic level would trigger more detailed research and/or more precautionary management strategies. Indeed, it can be argued that this is exactly how catch­weighted mean trophic levels have been used previously, in that they have provoked

consideration of broad ecosystem policy issues. However, further simulation research is needed to evaluate which management actions are most effective for specific ecosys­tems. Branch et al. have provided the basis for doing that. ■

Joseph E. Powers is in the Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, Louisiana 70803, USA. e­mail: jepowers@lsu.edu

1. Branch, t. a. et al. Nature 468, 431–435 (2010).2. Polacheck, t. Mar. Policy 30, 470–482 (2006).3. Worm, B. et al. Science 325, 578–585 (2009).

r e p r o D U C t I V e A G e I N G

Of worms and womenIn roundworms, age-related decline in egg quality is regulated by specific humoral signalling pathways. If similar mechanisms operate in mammals, these findings may suggest ways to delay reproductive ageing in women.

K e V I N f l U r K e Y & D A V I D e . h A r r I s o N

Female mammals are not alone in expe­riencing an age­related increase in birth defects and decline in fertility;

the roundworm Caenorhabditis elegans faces similar reproductive challenges in mid­adult­hood. Writing in Cell, Luo et al.1 report that, in C. elegans, the age­related decline in oocyte (egg) quality and increase in chromosomal abnormalities are regulated by evolutionarily conserved signal­transduction pathways. If this senescence mechanism is also conserved, age­related decline in the quality of mam­malian oocytes may not be, as is commonly thought, simply due to the old age of these cells or the diminishing size of the ovarian follicle pool; it may also be influenced by molecular signalling cascades.

Previous work in C. elegans showed2 that a mutation that reduces the function of daf­2 — a gene involved in an insulin/IGF­I­like signalling pathway — delays reproductive senescence. Moreover, in an earlier study3, Luo and colleagues showed that reproductive lifespan is extended by mutations that decrease the activity of the TGF­β Sma/Mab signalling pathway, which regulates cell growth, body size and the development of male traits.

Confirming and extending these findings, Luo et al.1 now show that decreasing activity in both of these pathways increases reproductive lifespan by delaying age­specific reductions in germline cell numbers, oocyte fertilizability and embryo hatching, as well as by diminish­ing the age­related increase in chromosomal abnormalities. Using C. elegans stocks with pathway­specific and tissue­specific mutations

in components of the insulin/IGF­I or TGF­β Sma/Mab pathways, the authors show that these signalling cascades act at distal sites to affect germline function. In fact, they propose a model to describe such neuroendocrine reg­ulation of reproductive senescence (Fig. 1).

These ideas could be of clinical relevance owing to similarities in oocyte development between C. elegans and humans. In both species, oocyte development is temporarily halted at the prophase I stage of meiotic cell division, when chromosomal abnormalities most frequently occur. What’s more, chromosomal abnormali­ties — including aneuploidies that result from chromosome non­disjunction — are the main defect in human embryos from ageing moth­ers4, and rates of chromosome non­disjunction also increase with age in C. elegans.

Luo et al.1 observe other aspects of dimin­ished oocyte quality with reproductive ageing in C. elegans that are similar to those previ­ously reported in older women. For instance, the authors’ transcriptional analyses of worms with mutations in TGF­β signalling indicate that numerous molecular mechanisms that have a bearing on age­related diminished oocyte quality are influenced by this pathway, and that many of these mechanisms are shared between C. elegans and humans.

Two main species differences, however, temper the understandable enthusiasm that Luo and colleagues express for the possibil­ity of translational application of their work to humans. First, the reproductive system of female mammals is more complex than that of the roundworm, with ageing involving both neuroendocrine and oocyte defects5. Second, the progressive shrinkage that occurs in the

Gonad(germ line and oocytes)

Intestineand muscle

Subcutaneoustissue

Neurons

Secondarysignals

TGF-β Sma/Mabligands

Insulin/IGF-I-likeligands

Secondarysignals

Reproductive ageing

Figure 1 | Neuroendocrine regulation of reproductive ageing1. Neurons secrete ligands that act on cells of the intestine and muscle tissue (the TGF­β Sma/Mab ligands) or the subcutaneous tissue (insulin/IGF­I ligands) to generate as­yet­unidentified secondary signals. The secondary signals affect germline senescence by altering these cells’ morphology, diminishing their proliferation, reducing oocyte quality and increasing chromosomal abnormalities. Consequently, embryonic viability declines and infertility increases.

pool of ovarian follicles during mammalian ageing has no parallel in roundworms.

Indeed, mammals stop producing oocytes even before birth, whereas roundworms continue to produce them throughout their reproductive lives. Theories of mammalian reproductive ageing posit that the age­related decline in oocyte number is the primary factor driving decline in fertility, with the associated deterioration in oocyte quality and increased

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