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Quantifying trends and thresholds in responses of ecological indicators to the combined effects of fishing and environmental pressure

Scott Large (1), Gavin Fay (1), Kevin D. Friedland (2), Jason S. Link (1) (1) NOAA-Fisheries, Woods Hole, Massachusetts, USA; NOAA, National Marine Fisheries Service, Narragansett, Rhode Island, USA. Presenter contact details: [email protected], Phone +1 508 495 2046

Summary Both fishing and environmental forces can influence the structure and function of marine . Increased demand for living marine resources (LMR) has resulted in global declines in targeted species’ , shifts in biotic processes, and reduced fisheries status; impacts that are often exacerbated in a changing environment. Traditional management techniques generally use single or multi-species biological reference points to inform management action. These tactics, however, often fail to fully account for environmental variability and ecological interactions. -based (EBFM) concurrently addresses human, ecological, and environmental factors that influence LMR and evaluate these considerations systematically. Analogous to traditional decision criteria (e.g., BMSY), ecological indicators are quantifiable attributes that represent ecosystem status. For EBFM, decision criteria for management action can be quantified as thresholds in ecological indicator response to fishing and environmental pressures. Using generalized additive models and the second partial-derivative test, we empirically determined critical points (i.e., bivariate thresholds) at which a small change in fishing or environmental pressure resulted in a significant or abrupt change in ecosystem status. For the Northeast Shelf Large , we found these critical points in several ecological indicators; suggesting that decision criteria should account for both anthropogenic and environmental pressures concurrently.

Introduction To implement EBFM, the status of an ecosystem must be assessed and decision criteria should be established to achieve pre-defined management goals. Ecological indicators have been suggested as a means to evaluate ecosystem status and inform reference levels for management. To translate ecological indicators into decision criteria we must identify ecological thresholds. Ecological thresholds have been theoretically and empirically evaluated in response to fishing pressure (Link, 2005, Samhouri et al., 2010, Fay et al., 2013, Large et al., 2013); however, more work is necessary to establish decision criteria in response to the combined influence of multiple pressures.

Here, we propose a novel approach that uses principles of additive modeling and calculus to estimate critical points (i.e., bivariate thresholds) between ecological indicators calculated from fisheries- independent survey data, commercial fisheries landings, and large-scale climate processes for the (NES LME). Bivariate thresholds, or critical points, are tipping points where a small increase in either pressure results in a large or abrupt change in ecosystem status.

Materials and Methods Ecological indicators used in this study were compiled from long-term fisheries independent bottom trawl survey designed to characterize the NES LME. We selected indicators that represent key ecosystem processes and that have been vetted as useful in assessing ecosystem status for the region. We also selected a broad range of environmental pressure variables that influence circulation patterns, , vertical mixing, and available nutrients within the NES LME. Fishing pressure was quantified as the total live weight of commercial species landings in the NES LME.

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We identified common trends between the time series using dynamic factor analysis (DFA). As a dimension reduction technique, DFA is similar in concept to principal component analysis; however, DFA restricts axes by the temporal structure of the data (Zuur et al., 2003). We fit pair-wise bivariate general additive models (GAM) for each ecological indicator- DFA trend combination and included fishing pressure as a covariate. Including DFA trends in the GAM allowed us to account for multiple environmental variables in a single model. Since we were primarily interested in identifying bivariate thresholds, we only selected models with significant nonlinear components for further analysis.

The shape of a bivariate relationship between an indicator and two pressure variables was captured as a surface (Figure 1). The slope of this surface was defined by two partial derivatives in each direction ƒx(x) and ƒx(y); the change in x in the x direction and the change in x in the y direction. When partial derivatives pass beyond zero, they indicate regions in direction x and y where an ecological indicator has a negative or positive slope in response to a pressure variable. Critical points are defined as regions where the sign of the slope changes in both x and y directions, fx(a,b) = (0) = fy(a,b). The second partial derivative test was used to classify critical points as local maxima, minima, or as a saddle point.

Results and Discussion Of the 21 possible bivariate GAM models, eight included significant nonlinear components for both Figure 1 Surface fit of bivariate GAM (x = Landings, y fishing and the environment. While we only present a = DFA Trend 3, z = Pelagic/Demersal ratio). Shaded single example here, these models suggest that in polygons are significant critical points that fall within many instances ecological indicators are sensitive to 95% CI of zero for partial derivatives in both x and y both fishing and environmental pressures. We used directions and indicate no significant difference from bootstrap replicated confidence intervals to identify zero. Using the second partial derivative test we critical points for each significant bivariate pair. In define each polygon as a local maximum (black) or saddle point (gray). Figure 1, we found a significant local maximum and a region of saddle points in both directions. When fishing pressure was greater than 400,000 t, ecosystem status quickly decreased in value (Figure 1). Ecological indicator value is highly dependent upon environmental pressure, and this value alone might not be sufficient to describe ecosystem status, rather, it is also necessary to incorporate environmental pressure in determining decision criteria.

References Fay, G., Large, S. I., Link, J. S., and Gamble, R. J. 2013. Testing systemic fishing responses with ecosystem indicators. Ecological Modelling, 265: 45-55. Large, S. I., Fay, G., Friedland, K. D., and Link, J. S. 2013. Defining trends and thresholds in responses of ecological indicators to fishing and environmental pressures. ICES Journal of Marine Science, 70: 755- 767. Link, J. S. 2005. Translating ecosystem indicators into decision criteria. ICES Journal of Marine Science, 62: 569- 576. Samhouri, J. F., Levin, P. S., and Ainsworth, C. H. 2010. Identifying thresholds for ecosystem-based management. PLoS One, 5: e8907. Zuur, A., Tuck, I., and Bailey, N. 2003. Dynamic factor analysis to estimate common trends in fisheries time series. Canadian Journal of Fisheries and Aquatic Sciences, 60: 542-552.