Distribution and abundance of sardine (Sardina pilchardus) eggs in the English Channel from Continuous Plankton Recorder sampling

Continuous Plankton Recorder (CPR) samples from the English Channel and adjacent Celtic shelf, taken over the period 1958-1980, were analysed for sardine (Sardina pilchardus) eggs. Results showed the progression of sardine spawning along the English Channel from west to east from March to August and a return from east to west from September to November. This corresponds with the two seasonal peaks of sardine egg abundance in the western Channel: the main summer peak being in May/June, with a smaller autumn peak in October/November. Long-term changes in sardine egg abundance in CPR samples showed a decline in summer spawning from the late 1960s, but no clear trend in autumn-spawned egg abundance. Similar patterns were observed in the numbers of sardine eggs sampled by conventional plankton net tows at the time-series Station L5 off Plymouth. This supports the use of the longer time-series of sardine egg data at L5 as being representative of a wider area and emphasizes the importance in continuation of the L5 time-series.

Continuous Plankton Records - Geographical Variations In Numerical Abundance Biomass And Production Of Euphausiids In The North-Atlantic Ocean And The North-Sea

Geographical variations in the numbers, biomass and production of euphausiids and the contribution of common species to the total are described from samples taken during 1966 and 1967 in the North Atlantic Ocean and the North Sea by the Continuous Plankton Recorder at 10 m depth. Euphausiids were most abundant in the central and western North Atlantic Ocean and the Norwegian Sea. Thysanoessa longicaudata (Krøyer) was numerically dominant. Biomass was greatest in the Norwegian Sea and the north-eastern North Sea where Meganyctiphanes norvegica (M. Sars) accounted for 81 and 59%, respectively, of the total biomass. Production was highest off Nova Scotia and in Iberian coastal waters; the dominant species were T. raschi (M. Sars) in the former area and Nyctiphanes couchi (Bell) in the latter. The mean P:B ratios were correlated with temperature.

Ecological Relations Between the Herring and the Plankton off The North-East Coast of England

I. 430 plankton samples, which were taken by several herring drifters using the Continuous Plankton Recorder in the Shields fishing area during the summer seasons of 1931 to 1933, are analysed to show the main changes in the plankton during those seasons. 2. A comparison is made between the proportions of the different zooplankton organisms found in the plankton and the proportions of these recorded by Savage (1937) in the stomachs of herring obtained from drifters working in the same area and during the same time. The comparisons are made for 29 ten-day periods in the seasons 1931 to 1933, and in addition, for 6 ten-day periods relating to a single drifter which obtained both plankton and stomach samples at the same time in 1932. 3. The comparisons in 2 provide evidence that the herring feeds by selecting certain organisms by individual acts of capture and not by swimming open-mouthed to strain out the plankton indiscriminately: (a) Calanus and Temora in the stomachs either correspond fairly closely to the proportions in the plankton or they may be in very much higher proportions. The latter is always true regarding Anomalocera. (b) Acartia, Oithona, Cladocera and Lamellibranch larvae are always in larger proportions in the plankton than in the stomachs; this applies also to Centropages with two insignificant exceptions. (c) There is a close correspondence between the numbers of Limacina and Sagitta in the plankton and stomachs in the latter half of the 1931 season, but not during 1932 and 1933, when the numbers in the stomachs were insignificant ; during the former period there was a great scarcity of Calanus in the plankton.

High Level Environmental Screening Study for Offshore Wind Farm Developments – Marine Habitats and Species Project

High level environmental screening study for offshore wind farm developments – marine habitats and species This report provides an awareness of the environmental issues related to marine habitats and species for developers and regulators of offshore wind farms. The information is also relevant to other offshore renewable energy developments. The marine habitats and species considered are those associated with the seabed, seabirds, and sea mammals. The report concludes that the following key ecological issues should be considered in the environmental assessment of offshore wind farms developments: • likely changes in benthic communities within the affected area and resultant indirect impacts on fish, populations and their predators such as seabirds and sea mammals; • potential changes to the hydrography and wave climate over a wide area, and potential changes to coastal processes and the ecology of the region; • likely effects on spawning or nursery areas of commercially important fish and shellfish species; • likely effects on mating and social behaviour in sea mammals, including migration routes; • likely effects on feeding water birds, seal pupping sites and damage of sensitive or important intertidal sites where cables come onshore; • potential displacement of fish, seabird and sea mammals from preferred habitats; • potential effects on species and habitats of marine natural heritage importance; • potential cumulative effects on seabirds, due to displacement of flight paths, and any mortality from bird strike, especially in sensitive rare or scarce species; • possible effects of electromagnetic fields on feeding behaviour and migration, especially in sharks and rays, and • potential marine conservation and biodiversity benefits of offshore wind farm developments as artificial reefs and 'no-take' zones. The report provides an especially detailed assessment of likely sensitivity of seabed species and habitats in the proposed development areas. Although sensitive to some of the factors created by wind farm developments, they mainly have a high recovery potential. The way in which survey data can be linked to Marine Life Information Network (MarLIN) sensitivity assessments to produce maps of sensitivity to factors is demonstrated. Assessing change to marine habitats and species as a result of wind farm developments has to take account of the natural variability of marine habitats, which might be high especially in shallow sediment biotopes. There are several reasons for such changes but physical disturbance of habitats and short-term climatic variability are likely to be especially important. Wind farm structures themselves will attract marine species including those that are attached to the towers and scour protection, fish that associate with offshore structures, and sea birds (especially sea duck) that may find food and shelter there. Nature conservation designations especially relevant to areas where wind farm might be developed are described and the larger areas are mapped. There are few designated sites that extend offshore to where wind farms are likely to be developed. However, cable routes and landfalls may especially impinge on designated sites. The criteria that have been developed to assess the likely marine natural heritage importance of a location or of the habitats and species that occur there can be applied to survey information to assess whether or not there is anything of particular marine natural heritage importance in a development area. A decision tree is presented that can be used to apply ‘duty of care’ principles to any proposed development. The potential ‘gains’ for the local environment are explored. Wind farms will enhance the biodiversity of areas, could act as refugia for fish, and could be developed in a way that encourages enhancement of fish stocks including shellfish.

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.