Toward autonomous measurements of photosynthetic electron transport rates: An evaluation of active fluorescence-based measurements of photochemistry

This study presents a methods evaluation and intercalibration of active fluorescence-based measurements of the quantum yield ( inline image) and absorption coefficient ( inline image) of photosystem II (PSII) photochemistry. Measurements of inline image, inline image, and irradiance (E) can be scaled to derive photosynthetic electron transport rates ( inline image), the process that fuels phytoplankton carbon fixation and growth. Bio-optical estimates of inline image and inline image were evaluated using 10 phytoplankton cultures across different pigment groups with varying bio-optical absorption characteristics on six different fast-repetition rate fluorometers that span two different manufacturers and four different models. Culture measurements of inline image and the effective absorption cross section of PSII photochemistry ( inline image, a constituent of inline image) showed a high degree of correspondence across instruments, although some instrument-specific biases are identified. A range of approaches have been used in the literature to estimate inline image and are evaluated here. With the exception of ex situ inline image estimates from paired inline image and PSII reaction center concentration ( inline image) measurements, the accuracy and precision of in situ inline image methodologies are largely determined by the variance of method-specific coefficients. The accuracy and precision of these coefficients are evaluated, compared to literature data, and discussed within a framework of autonomous inline image measurements. This study supports the application of an instrument-specific calibration coefficient ( inline image) that scales minimum fluorescence in the dark ( inline image) to inline image as both the most accurate in situ measurement of inline image, and the methodology best suited for highly resolved autonomous inline image measurements.

Can the subtropical North Atlantic permanent thermocline be observed from space?

The analysis of remotely sensed altimeter data and in situ measurements shows that ERS 2 radar can monitor the ocean permanent thermocline from space. The remotely sensed sea level anomaly data account for similar to 2/3 of the temperature variance or vertical displacement of isotherms at a depth of similar to 550 m in the Subtropical North Atlantic Ocean near 32.5 degree N. This depth corresponds closely to the region of maximum temperature gradient in the permanent thermocline where near semi-annual internal vertical displacements reach 200 to 300 m. The gradient of the altimeter sea level anomaly data correlates well with measured ocean currents to a depth of 750 m. It is shown that observations from space can account for similar to 3/4 of the variance of ocean currents measured in situ in the permanent thermocline over a 2-y period. The magnification of the permanent thermocline displacement with respect to the displacement of the sea surface was determined as - x650 and gives a measure of the ratio of barotropic to baroclinic decay scale of geostrophic current with depth. The overall results are used to interpret an eight year altimeter data tie series in the Subtropical North Atlantic at 32.5 degree N which shows a dominant wave or eddy period near 200 days, rather than semi-annual and increases in energy propagating westward in 1995 (west of 25 degree W). The effects of rapid North Atlantic Oscillation climate change on ocean circulation are discussed. The altimeter data for the Atlantic were Fourier analysed. It is shown how the annual and semi-annual components relate to the seasonal maximum cholorophyll-a SeaWiFS signal in tropical and equatorial regions due to the lifting of the thermocline caused by seasonally varying ocean currents forced by wind stress.

Rapid biogeographical plankton shifts in the North Atlantic Ocean

Large-scale biogeographical changes in the biodiversity of a key zooplankton group (calanoid copepods) were detected in the north-eastern part of the North Atlantic Ocean and its adjacent seas over the period 1960–1999. These findings provided key empirical evidence for climate change impacts on marine ecosystems at the regional to oceanic scale. Since 1999, global temperatures have continued to rise in the region. Here, we extend the analysis to the period 1958–2005 using all calanoid copepod species assemblages (nine species assemblages based on an analysis including a total of 108 calanoid species or taxa) and show that this phenomenon has been reinforced in all regions. Our study reveals that the biodiversity of calanoid copepods are responding quickly to sea surface temperature (SST) rise by moving geographically northward at a rapid rate up to about 23.16 km yr−1. Our analysis suggests that nearly half of the increase in sea temperature in the northeast Atlantic and adjacent seas is related to global temperature rises (46.35% of the total variance of temperature) while changes in both natural modes of atmospheric and oceanic circulation explain 26.45% of the total variance of temperature. Although some SST isotherms have moved northwards by an average rate of up to 21.75 km yr−1 (e.g. the North Sea), their movement cannot fully quantify all species assemblage shifts. Furthermore, the observed rates of biogeographical movements are far greater than those observed in the terrestrial realm. Here, we discuss the processes that may explain such a discrepancy and suggest that the differences are mainly explained by the fluid nature of the pelagic domain, the life cycle of the zooplankton and the lesser anthropogenic influence (e.g. exploitation, habitat fragmentation) on these organisms. We also hypothesize that despite changes in the path and intensity of the oceanic currents that may modify quickly and greatly pelagic zooplankton species, these organisms may reflect better the current impact of climate warming on ecosystems as terrestrial organisms are likely to significantly lag the current impact of climate change.

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.