In their Report “Strong top-down control in southern California kelp forest ecosystems” (26 May, p. [1230][1]), B. S. Halpern et al. conclude that these forests show strong top-down (consumer-driven) control and that bottom-up (resource-driven) control in such systems may often be overestimated. These conclusions run counter to most of the extensive literature ([1][2]–[4][3]) on the ecology and natural history of kelp forests in southern California. There are numerous examples of the importance of storms and low nutrients over large spatial and temporal scales, especially during El Niños ([3][4], [5][5]–[7][6]) but also from decadal climate shifts ([8][7]). Halpern et al. used a short-term data set that did not include El Niño-Southern Oscillation or decadal climate shifts. Moreover, they used satellite-derived offshore chlorophyll a concentration data as a measure of “resources” without establishing that these data were a good proxy for nutrients or primary production in nearshore kelp forests and despite evidence to the contrary [e.g., ([9][8], [10][9])]. The primary evidence for top-down effects was correlations interpreted by Halpern et al. as showing that spiny lobsters and Kellet’s whelks were “significantly important species, likely due to their strong impacts on key grazers of kelp (urchins) and algae (limpets and snails).” There is indirect evidence that lobsters may affect urchins ([11][10], [12][11]), but Kellet’s whelk is primarily a scavenger ([13][12]) whose abundance has been negatively correlated with kelp forests ([14][13]). Neither animal has been shown to have “strong” impacts on their prey species in California kelp forests. Halpern et al. could think of no mechanism by which the two other significant species “control” algae. The diets of these fish indicate no such mechanism; the correlations likely result from habitat preferences ([15][14]). The lack of significant correlation between kelp and urchins is counter to their hypothesis but was not discussed. The analytical results may be generally misleading due to weak trophic links [e.g., many of the grazers eat other algae in addition to kelp, and commonly eat drift, not attached plants ([3][4])]. Thus, neither bottom-up nor top-down effects were tested and the conclusions, therefore, are unsubstantiated. 1. 1.[↵][15] 1. W. J. North , Nova Hedwigia 32, 1 (1971). [OpenUrl][16] 2. 2. 1. P. K. Dayton , Annu. Rev. Ecol. Syst. 16, 215 (1985). [OpenUrl][17][CrossRef][18][Web of Science][19] 3. 3.[↵][20] 1. M. S. Foster, 2. D. R. Schiel , The Ecology of Giant Kelp Forests in California: A Community Profile (U.S. Fish and Wildlife Service, Washington, DC, 1985). 4. 4.[↵][21] 1. M. H. Graham, 2. J.A. Vásquez, 3. A. H. Buschmann , Oceanogr. Mar. Biol. Annu. 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Rosenthal , Fish. Bull. 69, 669 (1971). [OpenUrl][46] 14. 14.[↵][47] 1. M. D. Behrens, 2. K. D. Lafferty , Mar. Ecol. Prog. Ser. 279, 129 (2004). [OpenUrl][48][CrossRef][49][Web of Science][50] 15. 15.[↵][51] 1. S. J. Holbrook, 2. R. J. Schmitt, 3. R. F. Ambrose , Aust. J. Ecol. 15, 489 (1990). [OpenUrl][52][CrossRef][53] # #article-title-2 In their Report “Strong top-down control in southern California kelp forest ecosystems” (26 May, p. [1230][1]), B. S. Halpern et al. conclude that top-down (predatory) effects are strong and more important than bottom-up (nutrient) effects in setting kelp forest community structure. They reach this conclusion through a statistical technique that examines mathematical associations among variables. Like any statistical technique that tests for correlations, it is unable to assign causality or deal effectively with highly correlated explanatory variables (“multicolinearity”). By including several highly intercorrelated predictor variables in their statistical model, it is essentially impossible to estimate bottom-up effects ([1][54], [2][55]). For example, because nutrient concentrations are tightly correlated with water temperature in southern California ([3][56]), it is probably impossible to separate temperature from bottom-up effects. Furthermore, their exclusion of sites from the warmest and most nutrient-poor waters ([4][57]) limits the ability to detect bottom-up effects. Statistical associations between predator abundance and aspects of community structure lead Halpern and colleagues to conclude that predators drive community structure, but they offer few plausible mechanisms. A more likely causal link, bottom-up effects driving kelp forest community structure (including predator abundance) ([5][58]–[7][59]), would produce identical statistical results. For example, the predatory kelp rockfish was identified as exerting significant “top-down control,” but this fish is found almost exclusively with kelp because it is dependent on it, not vice versa ([7][59]). Finally, the purported top-down effects are weak, explaining at most 20% of the variation in community structure. Other variables (e.g., water temperature and geographic location) explained 2 to 27 times more of the variation in community structure for all trophic levels but kelps ([4][57]). Modern statistical tests give us unprecedented ability to discover patterns in complex data sets, but such patterns can only be interpreted when combined with a sound understanding of the natural history of the system. 1. 1.[↵][60] 1. B. G. Tabachnick, 2. L. S. Fidell , Using Multivariate Statistics (HarperCollins, New York, 1996). 2. 2.[↵][61] 1. M. H. Graham , Ecology 84, 2809 (2003). [OpenUrl][62][CrossRef][63][Web of Science][64] 3. 3.[↵][65] 1. P. K. Dayton, 2. M. J. Tegner, 3. P. B. Edwards, 4. K. L. Riser , Ecol. Monogr. 69, 219 (1999). [OpenUrl][66][CrossRef][67][Web of Science][68] 4. 4.[↵][69] 1. B. S. Halpern 2. et al. , ’s Supporting Online Material at [www.sciencemag.org/cgi/content/full/312/5777/1230/DC1][70]. 5. 5.[↵][71] 1. P. K. Dayton , Annu. Rev. Ecol. Syst. 16, 215 (1985). [OpenUrl][17][CrossRef][18][Web of Science][19] 6. 6. 1. M. H. Graham , Ecosystems 7, 341 (2004). [OpenUrl][72] 7. 7.[↵][73] 1. M. H. Carr , J. Exp. Mar. Biol. Ecol. 59, 126 (1989). [OpenUrl][74] # Response #article-title-3 Foster et al. and Steele et al. raise a number of concerns about our recent study. An important aspect of our analyses is that our dependent variables were species abundances, not the aggregate trophic values that are traditionally used. Our approach enhances the possibility of detecting either bottom-up or top-down patterns for individual or groups of species because assumptions about the nature (direct or indirect) of the relationships between species and trophic levels are unnecessary, and because it can detect compensatory dynamics within a trophic level that could eliminate aggregate top-down or bottom-up effects. Foster et al. ’s concern about the species highlighted by our analyses likely arises from their expectation that direct trophic links must exist for the results to be valid. We were not assessing whether a “trophic cascade” existed, but instead evaluating the direction of control within communities. Indeed, our results may not have emerged from traditional approaches, and they highlight the potential importance of indirect effects in controlling community structure. Both Letters express concern that we suggest cause and effect through correlations and not experiments. Despite reliance on correlational relationships, large-scale studies like ours have a long record of providing new insight through analysis over spatial and temporal scales beyond the reach and budget of experimental study. We focused on the hypothesized mechanisms that would be responsible for either bottom-up or top-down control—nutrient availability and predator abundance—and then determined the amount of variation explained by these two different groups of variables for algal and mid-trophic level abundances. The expected cause and effect are certainly implicit in our study, but will require significant resources before they can be tested experimentally. Steele et al. are correct in noting that multicolinearity can create problems ([1][75]). However, our principal objective was to construct the best predictive model for both top-down and bottom-up variables, a situation in which “multicolinearity can be effectively ignored” [([1][75]), p. 2811]. Nevertheless, we limited multicolinearity problems within each different predictor group by eliminating highly multicolinear variables, an approach ([1][75]) that acts to conservatively decrease significant results (top-down control, in our case). Furthermore, we reported “pure” top-down and bottom-up effects in table S2 and Fig. 3, which are the amounts of explained variation after eliminating the multicolinearities between the different predictor groups. Contrary to Steele et al. ’s expectations, top-down and spatial or temperature variables were colinear while bottom-up and temperature variables were not (see [table][76]), such that adding multicolinearity to our results would have suggested even stronger top-down effects. View this table: In addition, the results from our cited companion paper ([2][77]) show that the combination of wave disturbance and El Niño-Southern Oscillation (ENSO) explains only 6% of the variance, and in situ temperature explains less than 1% of the variance in kelp forest community structure based on 18 years of data spanning several strong and weak ENSO events. As we noted (see SOM), the use of satellite-derived productivity data in coastal waters has been extensively validated in our study region [(see also ([3][78])]. Importantly, the variation in primary production ([4][79]) is sufficient to detect potential bottom-up effects, despite missing the extreme nutrient limitation encountered at the southern limit of M. pyrifera . Other variables such as geographic location are, indeed, at least as important as the top-down variables we identified ([2][77]). However, explaining 20% of variation in community structure is a notable result ([5][80]), and these “other” variables are largely outside the human influence and so less useful for management purposes. The claim that our results run counter to the literature on kelp forest ecology is untrue [see, for example, ([6][81]–[9][82])], and we disagree with the suggestion that bottom-up effects offer a more parsimonious explanation of our results. Also, the referenced bottom-up associations are not tests of nutrient versus predator effects on entire kelp forest communities and counter examples exist, as with the monitoring of extreme eutrophication of kelp forests off San Diego that found no effect on kelp forest communities ([10][83]). Consequently, compensatory mechanisms among species are likely more important than a simple trophic cascade framework would suggest, with these effects driven by top-down forces. 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openurl?query=rft.jtitle%253DEcol.%2BLett.%26rft.volume%253D8%26rft.spage%253D1175%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1461-0248.2005.00820.x%26rft_id%253Dinfo%253Apmid%252F21352441%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [92]: /lookup/external-ref?access_num=10.1111/j.1461-0248.2005.00820.x&link_type=DOI [93]: /lookup/external-ref?access_num=21352441&link_type=MED&atom=%2Fsci%2F313%2F5794%2F1737.3.atom [94]: /lookup/external-ref?access_num=000232535300006&link_type=ISI [95]: #xref-ref-28-1 “View reference 6. in text” [96]: openurl?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.issn%253D0036-8075%26rft.aulast%253DEstes%26rft.auinit1%253DJ.%2BA.%26rft.volume%253D185%26rft.issue%253D4156%26rft.spage%253D1058%26rft.epage%253D1060%26rft.atitle%253DSea%2BOtters%253A%2BTheir%2BRole%2Bin%2BStructuring%2BNearshore%2BCommunities%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.185.4156.1058%26rft_id%253Dinfo%253Apmid%252F17738247%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [97]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIxODUvNDE1Ni8xMDU4IjtzOjQ6ImF0b20iO3M6MjU6Ii9zY2kvMzEzLzU3OTQvMTczNy4zLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [98]: openurl?query=rft.jtitle%253DEcology%26rft.volume%253D59%26rft.spage%253D822%26rft_id%253Dinfo%253Adoi%252F10.2307%252F1938786%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [99]: /lookup/external-ref?access_num=10.2307⁄1938786&link_type=DOI [100]: /lookup/external-ref?access_num=A1978GB33300024&link_type=ISI [101]: openurl?query=rft.jtitle%253DEcoscience%26rft.volume%253D2%26rft.spage%253D321%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [102]: /lookup/external-ref?access_num=A1995TK48700003&link_type=ISI [103]: #xref-ref-31-1 “View reference 9. in text” [104]: 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