Marine snow studies in the Northeast Atlantic Ocean: distribution, composition and role as a food source for migrating plankton

During a 25 d Lagrangian study in May and June 1990 in the Northeast Atlantic Ocean, marine snow aggregates were collected using a novel water bottle, and the composition was determined microscopically. The aggregates contained a characteristic signature of a matrix of bacteria, cyanobacteria and autotrophic picoplankton with inter alia inclusions of the tintiniid Dictyocysta elegans and large pennate diatoms. The concentration of bacteria and cyanobacteria was much greater on the aggregates than when free-living by factors of 100 to 6000 and 3000 to 2 500 000, respectively, depending on depth. Various species of crustacean plankton and micronekton were collected, and the faecal pellets produced after capture were examined. These often contained the marine snow signature, indicating that these organisms had been consuming marine snow. In some cases, marine snow material appeared to dominate the diet. This implies a food-chain short cut wherby material, normally too small to be consumed by the mesozooplankton, and considered to constitute the diet of the microplankton can become part of the diet of organisms higher in the food-chain. The micronekton was dominated by the amphipod Themisto compressa, whose pellets also contained the marine snow signature. Shipboard incubation experiments with this species indicated that (1) it does consume marine snow, and (2) its gut-passage time is sufficiently long for material it has eaten in the upper water to be defecated at its day-time depth of several hundred meters. Plankton and micronekton were collected with nets to examine their vertical distribution and diel migration and to put into context the significance of the flux of material in the guts of migrants. “Gut flux” for the T. compressa population was calculated to be up to 2% of the flux measured simultaneously by drifting sediment traps and <5% when all migrants are considered. The in situ abundance and distribution of marine snow aggregates (>0.6 mm) was examined photographically. A sharp concentration peak was usually encountered in the depth range 40 to 80 m which was not associated with peaks of in situ fluorescence or attenuation but was just below or at the base of the upper mixed layer. The feeding behaviour of zooplankton and nekton may influence these concentration gradients to a considerable extent, and hence affect the flux due to passive settling of marine snow aggregates.

Questioning the role of phenology shifts and trophic mismatching in a planktonic food web

In a warming climate, differential shifts in the seasonal timing of predators and prey have been suggested to lead to trophic ‘‘mismatches’’ that decouple primary, secondary and tertiary production. We tested this hypothesis using a 25-year time-series of weekly sampling at the Plymouth L4 site, comparing 57 plankton taxa spanning 4 trophic levels. During warm years, there was a weak tendency for earlier timings of spring taxa and later timings of autumn taxa. While this is in line with many previous findings, numerous exceptions existed and only a few taxa (e.g. Gyrodinium spp., Pseudocalanus elongatus, and Acartia clausi) showed consistent, strong evidence for temperature-related timing shifts, revealed by all 4 of the timing indices that we used. Also, the calculated offsets in timing i.e. ‘‘mismatches’’) between predator and prey were no greater in extreme warm or cold years than during more average years. Further, the magnitude of these offsets had no effect on the ‘‘success’’ of the predator, in terms of their annual mean abundance or egg production rates. Instead numerous other factors override, including: inter-annual variability in food quantity, high food baseline levels, turnover rates and prolonged seasonal availability, allowing extended periods of production. Furthermore many taxa, notably meroplankton, increased well before the spring bloom. While theoretically a chronic mismatch, this likely reflects trade-offs for example in predation avoidance. Various gelatinous taxa (Phaeocystis, Noctiluca, ctenophores, appendicularians, medusae) may have reduced these predation constraints, with variable, explosive population outbursts likely responding to improved conditions. The match–mismatch hypothesis may apply for highly seasonal, pulsed systems or specialist feeders, but we suggest that the concept is being over-extended to other marine systems where multiple factors compensate.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.