Pathways between primary production and fisheries yields of large marine ecosystems

Abstract

The shift in marine resource management from a compartmentalized approach of dealing with resources on a species basis to an approach based on management of spatially defined ecosystems requires an accurate accounting of energy flow. The flow of energy from primary production through the food web will ultimately limit upper trophic-level fishery yields. In this work, we examine the relationship between yield and several metrics including net primary production, chlorophyll concentration, particle-export ratio, and the ratio of secondary to primary production. We also evaluate the relationship between yield and two additional rate measures that describe the export of energy from the pelagic food web, particle export flux and mesozooplankton productivity. We found primary production is a poor predictor of global fishery yields for a sample of 52 large marine ecosystems. However, chlorophyll concentration, particle-export ratio, and the ratio of secondary to primary production were positively associated with yields. The latter two measures provide greater mechanistic insight into factors controlling fishery production than chlorophyll concentration alone. Particle export flux and mesozooplankton productivity were also significantly related to yield on a global basis. Collectively, our analyses suggest that factors related to the export of energy from pelagic food webs are critical to defining patterns of fishery yields. Such trophic patterns are associated with temperature and latitude and hence greater yields are associated with colder, high latitude ecosystems.

Figure 1. RMSD error and bias for two primary production algorithms.

(a) RMSD for VGPM and Eppley-VGPM estimates of NPP based on the observational database of NPP from the fourth PPAR comparison binned by SST. (b) Bias associated with VGPM and Eppley-VGPM estimates of NPP binned by SST.

Figure 2. Contours of pe-ratio and z-ratio.

(a) Contours of pe-ratio and (b) z-ratio as functions of SST and NPP/Z_eu and NPP, respectively.

Figure 3. Fishery yield versus ecosystem temperature and absolute value of latitude.

Scattergrams of: (a) total monthly fishery yield and mean annual sea surface temperature; (b) same data as panel (a) transformed to approximate bivariate normality; (c) total monthly fishery yield and absolute value of latitude; and (d) same data as panel (c) transformed to approximate bivariate normality. N = 52, the total number of LMEs in the analysis, for all plots. r_S = Spearman rank-order correlation and r_P = Pearson product-moment correlation.

Figure 4. Fishery yield versus chlorophyll concentration and net primary production.

Scattergrams of: (a) total monthly fishery yield and mean annual chlorophyll concentration; (b) same data as panel (a) transformed to approximate bivariate normality; (c) total monthly fishery yield and NPP computed using the Eppley-VGPM algorithm; and (d) same data as panel (c) transformed to approximate bivariate normality. N = 52, the total number of LMEs in the analysis, for all plots. r_S = Spearman rank-order correlation and r_P = Pearson product-moment correlation.

Figure 5. Fishery yield versus particle export ratio and particle export flux.

Scattergrams of: (a) total monthly fishery yield and pe-ratio; (b) same data as panel (a) transformed to approximate bivariate normality; (c) total monthly fishery yield and pe-flux computed using the Eppley-VGPM algorithm; and (d) same data as panel (c) transformed to approximate bivariate normality. N = 52, the total number of LMEs in the analysis, for all plots. r_S = Spearman rank-order correlation and r_P = Pearson product-moment correlation.

Figure 6. Fishery yield versus z-ratio and mesozooplankton productivity.

Scattergrams of: (a) total monthly fishery yield and z-ratio; (b) same data as panel (a) transformed to approximate bivariate normality; (c) total monthly fishery yield and mesozooplankton productivity computed using the Eppley-VGPM algorithm; and (d) same data as panel (c) transformed to approximate bivariate normality. N = 52, the total number of LMEs in the analysis, for all plots. r_S = Spearman rank-order correlation and r_P = Pearson product-moment correlation.