Fisheries yield and primary productivity in large marine ecosystems

Abstract

Primary productivity (PP) and fisheries yield were analysed in 14 large marine ecosystems (LMEs), which encompassed temperate boreal shelves and the Eastern Boundary Currents (EBCs), from 1998 to 2002. PP was estimated by means of a depth-integrated neural network model based on Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data and aimed at providing conservative PP estimates. Landings records were extracted from the global spatial database provided by the Sea Around Us Project (SAUP). Correspondence analysis performed on yield data outlined the role played by different trophic levels (TLs) in LME catches. PP temporal variability was significantly and positively correlated to average trophic level of catches (TL c ) so that higher yields in less variable ecosystems were characterised by a lower TL c . From a functional perspective, high PP temporal variability was associated preferentially with demersal fishes and lower yields, while pelagic-dominated catches were harvested in conditions with lower PP variability. Primary production required (PPR) to sustain fisheries in each LME showed that the highest yield occurred in combination with moderate fishing pressure especially when TL c was intermediate to low. High fishing pressures were associated with intermediate total yields and high TL c , a condition which seemed to occur in high-latitude boreal LMEs. The %PPR and TL c were used to assess fisheries impact on ecosystems. PP model choice affects the assessment of exploitation levels, in that a more conservative estimation of PP could contribute to a more precautionary approach to fisheries management where high levels of exploitation are more easily attained. KEY WORDS: Primary productivity · Fisheries yield · Large marine ecosystems · PPR · Overexploitation Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 410: [233][234][235][236][237][238][239][240][241][242][243][244] 2010 Pace et al. , Cury et al. 2001. Several studies pointed out that both systems act locally (e.g. Ware & Thomson 2005, Mueter et al. 2006, Mackinson et al. 2008.The shift from local to regional and global spatial scales, added to the potential effects of climate change and regime shifts, highlights the complexity that underlies marine food webs. Cyclic and punctual events, both environmental and anthropogenic, influence the structure and functioning of marine ecosystems, hindering the identification of the relative role of each driving force. Landings statistics are the main source of data for these kinds of studies, representing the only spatial and temporal large-scale data sets available for the detection of different sources of variation. Recently, Perry & Schweigert (2008) suggested that these highly aggregated data and large (often global) spatial domains may have played a key role in the identification of the general relationship between PP and fisheries yields. The primary criticism about large-scale fisheries studies is their reliance on poor quality of data: landings m...

Reconciling fisheries catch and ocean productivity

“…The blending of empiricism and mechanistic principles applied in this study is intended to help bridge the gap between predominantly data-driven analysis of catch indicators (14,15,19) and the development and application of more mechanistic global fisheries models rooted in theoretical constraints but of often challenging complexity (13,(62)(63)(64)(65). Parameters and emergent patterns from the simple trophodynamic models herein can be directly compared against simulated patterns from these latter models.…”

“…These challenges have led many to forgo explicit trophodynamic approaches to understand catch constraints in favor of exploring correlations with diverse oceanographic indicators, such as average chlorophyll and variability, the presence or strength of fronts, ecosystem size, and dispersal properties (14)(15)(16)(17)(18). Others have included subsets of trophodynamic factors within large multifactor regressions (19,20).…”

Current state and trends in Canadian Arctic marine ecosystems: I. Primary production

“…The contribution of diatoms (a group of large-sized and nutritious (or palatable) phytoplankton) to primary production generally increases with total production (Chisholm 1992), favouring the efficient and relatively direct transfer (i.e., involving few feeding steps) of photosynthetically fixed organic matter to top predators and, if applicable, commercial fish stocks (Conti and Scardi 2010;Cushing 1989). In the most productive upwelling regions, the direct consumption of large-sized diatoms by large zooplankton and fish leads to high ecological efficiency and high fisheries yields.…”

“…A recent review indicated that fisheries yield and the average trophic level (TL) of catches are related to the extent of intra-annual temporal variability in PP across Large Marine Ecosystems (Conti and Scardi 2010). The TL of the catch ranges from~2.7 for small pelagic fish with a mixed diet of phytoplankton and zooplankton, to~3.8 for demersal fish feeding primarily on smaller fish and large invertebrates.…”

“…1 of Darnis et al 2012). Different indices of marine food web productivity, ranging from zooplankton biomass to fish landings, are strongly correlated to estimates of primary production at regional (Ware and Thomson 2005) and global (Chassot et al 2010;Conti and Scardi 2010;Irigoien et al 2004;Nixon and Thomas 2001) scales. There is no reason a priori to expect otherwise in the Arctic Ocean.…”