Population-Dynamics And Production Of Euphausiids. III Meganyctiphanes-Norvegica And Nyctiphanes-Couchi In The North-Atlantic Ocean And The North-Sea

Seasonal changes in abundance, size and aspects of the population structure of Meganyctiphanes norvegica (M. Sars) and Nyctiphanes couchi (Bell) are described from samples taken with the “Continuous Plankton Recorder” at 10 m depth over a 2 yr period (1966 and 1967) in the North Atlantic Ocean and the North Sea. M. norvegica lived for a maximum of just over 2 yr, and adults of both year-classes spawned during a limited breeding season in the spring or summer. N. couchi spawned over a prolonged breeding season, giving rise to a complex of cohorts with overlapping size ranges. It was concluded that 3 or 4 cohorts were spawned in each year and that the maximum life span was probably greater than 1 yr, although maturity may be attained in less than a year. Estimated annual production at 10 m depth for M. norvegica ranged from 0.80 to 18.74 mg m-3yr-1 and for N. couchi from 0.67 to 8.23 mg m-3yr-1. P:B ratios ranged from 1.3:1 to 6.3:1 for M. norvegica and 4.0:1 to 5.5:1 for N. couchi.

Population-Dynamics And Production Of Euphausiids .2. Thysanoessa-Inermis And Thysanoessa-Raschi In The North-Sea And American Coastal Waters

Results from the Continuous Plankton Recorder (CPR) survey for 1966 and 1967 are used to describe seasonal changes in abundance, size and aspects of the population structure of Thysanoessa inermis (Krøyer) and T. raschi (M. Sars) at a depth of 10 m in the North Sea and in American coastal waters from the Grand Banks to the Gulf of Maine. Production and dry weight were estimated from these data. Two year-groups were usually present in the breeding population, the proportion surviving into a second year being higher in American waters than in the North Sea. Annual production for each species was within the range 0.69 to 4.66 mg m-3 and the ratio between production and biomass (P:B) was between 1.3 and 4.2; values outside these ranges were obtained only for American coastal waters in 1967, when the frequency of sampling was low.

Space-borne study of seasonal, multi-year, and decadal phytoplankton dynamics in the Bay of Biscay

Seasonal and inter-annual variations in phytoplankton community abundance in the Bay of Biscay are studied. Preliminarily processed by the National Aeronautics and Space Administration (NASA) to yield normalized water-leaving radiance and the top-of-the-atmosphere solar radiance, Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Coastal Zone Color Scanner (CZCS) data are further supplied to our dedicated retrieval algorithms to infer the sought for parameters. By applying the National Oceanic and Atmospheric Administration's (NOAA's) Advanced Very High Resolution Radiometer (AVHRR) data, the surface reflection coefficient in the only band in the visible spectrum is derived and employed for analysis. Decadal bridged time series of variations of diatom-dominated phytoplankton and green dinoflagellate Lepidodinium chlorophorum within the shelf zone and the coccolithophore Emiliania huxleyi in the pelagic area of the Bay are documented and analysed in terms of impacts of some biogeochemical and geophysical forcing factors.

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.