Lowered cameras reveal hidden behaviors of Antarctic krill

Antarctic krill (Euphausia superba, hereafter ‘krill’) exemplify the methodological challenges of studying small, mobile, aggregating pelagic organisms.1 Krill are a central species in the Southern Ocean food web, provide important biogeochemical functions and support a valuable commercial fishery.2 Most of what we know about krill has been derived from acoustic surveys and net samples, the former being essential for estimating krill biomass and catch limits. However, understanding krill behavior, particularly in the poorly-studied autumn–winter seasons, is key for management and conservation. Here, we used seasonal video observations collected with a profiling camera system of krill along the Western Antarctic Peninsula to reveal krill vertical distribution, aggregation density and individual behaviors that have remained hidden from traditional sampling methods.

Inherent High Correlation of Individual Motility Enhances Population Dispersal in a Heterotrophic, Planktonic Protist

Quantitative linkages between individual organism movements and the resulting population distributions are fundamental to understanding a wide range of ecological processes, including rates of reproduction, consumption, and mortality, as well as the spread of diseases and invasions. Typically, quantitative data are collected on either movement behaviors or population distributions, rarely both. This study combines empirical observations and model simulations to gain a mechanistic understanding and predictive ability of the linkages between both individual movement behaviors and population distributions of a single-celled planktonic herbivore. In the laboratory, microscopic 3D movements and macroscopic population distributions were simultaneously quantified in a 1L tank, using automated video- and image-analysis routines. The vertical velocity component of cell movements was extracted from the empirical data and used to motivate a series of correlated random walk models that predicted population distributions. Validation of the model predictions with empirical data was essential to distinguish amongst a number of theoretically plausible model formulations. All model predictions captured the essence of the population redistribution (mean upward drift) but only models assuming long correlation times (minute), captured the variance in population distribution. Models assuming correlation times of 8 minutes predicted the least deviation from the empirical observations. Autocorrelation analysis of the empirical data failed to identify a de-correlation time in the up to 30-second-long swimming trajectories. These minute-scale estimates are considerably greater than previous estimates of second-scale correlation times. Considerable cell-to-cell variation and behavioral heterogeneity were critical to these results. Strongly correlated random walkers were predicted to have significantly greater dispersal distances and more rapid encounters with remote targets (e.g. resource patches, predators) than weakly correlated random walkers. The tendency to disperse rapidly in the absence of aggregative stimuli has important ramifications for the ecology and biogeography of planktonic organisms that perform this kind of random walk

Distributions of phytoplankton carbohydrate, protein and lipid in the world oceans from satellite ocean colour

Energy value of phytoplankton regulates the growth of higher trophic species, affecting the tropic balance and sustainability of marine food webs. Therefore, developing our capability to estimate and monitor, on a global scale, the concentrations of macromolecules that determine phytoplankton energy value, would be invaluable. Reported here are the first estimates of carbohydrate, protein, lipid, and overall energy value of phytoplankton in the world oceans, using ocean-colour data from satellites. The estimates are based on a novel bio-optical method that utilises satellite-derived bio-optical fingerprints of living phytoplankton combined with allometric relationships between phytoplankton cells and cellular macromolecular contents. The annually-averaged phytoplankton energy value, per cubic meter of sub-surface ocean, varied from less than 0.1 kJ in subtropical gyres, to 0.5–1.0 kJ in parts of the equatorial, northern and southern latitudes, and rising to more than 10 kJ in certain coastal and optically complex waters. The annually-averaged global stocks of carbohydrate, protein and lipid were 0.044, 0.17 and 0.108 gigatonnes, respectively, with monthly stocks highest in September and lowest in June, over 1997-2013. The fractional contributions of phytoplankton size classes e.g., picoplankton, nanoplankton and microplankton to surface concentrations and global stocks of macromolecules varied considerably across marine biomes classified as Longhurst provinces. Among these provinces, the highest annually-averaged surface concentrations of carbohydrate, protein, and lipid were in North-East Atlantic Coastal Shelves, whereas, the lowest concentration of carbohydrate or lipid were in North Atlantic Tropical Gyral, and that of protein was in North Pacific Subtropical Gyre West. The regional accuracy of the estimates and their sensitivity to satellite inputs are quantified from the bio-optical model, which show promise for possible operational monitoring of phytoplankton energy value from satellite ocean colour. Adequate in situ measurements of macromolecules and improved retrievals of inherent optical properties from high-resolution satellite images, would be required to validate these estimates at local sites, and to further improve their accuracy in the world oceans

Unusual <i>Hemiaulus</i> bloom influences ocean productivity in Northeastern US Shelf waters

Because of its temperate location, high dynamic range of environmental conditions, and extensive human activity, the long-term ecological research site in the coastal Northeastern US Shelf (NES) of the northwestern Atlantic Ocean offers an ideal opportunity to understand how productivity shifts in response to changes in planktonic community composition. Ocean production and trophic transfer rates, including net community production (NCP), net primary production (NPP), gross oxygen production (GOP), and microzooplankton grazing rates, are key metrics for understanding marine ecosystem dynamics and associated impacts on biogeochemical cycles. Although small phytoplankton usually dominate phytoplankton community composition and Chl a concentration in the NES waters during the summer, in August 2019, a bloom of the large diatom genus Hemiaulus, with N2-fixing symbionts, was observed in the mid-shelf region. NCP was 2.5 to 9 times higher when Hemiaulus dominated phytoplankton carbon compared to NCP throughout the same geographic area during the summers of 2020–2022. The Hemiaulus bloom in summer 2019 also coincided with higher trophic transfer efficiency from phytoplankton to microzooplankton and higher GOP and NPP than in the summers 2020–2022. This study suggests that the dominance of an atypical phytoplankton community that alters the typical size distribution of primary producers can significantly influence productivity and trophic transfer, highlighting the dynamic nature of the coastal ocean. Notably, summer 2018 NCP levels were also high, although the size distribution of Chl a was typical and an atypical phytoplankton community was not observed. A better understanding of the dynamics of the NES in terms of biological productivity is of primary importance, especially in the context of changing environmental conditions due to climate processes.</p

Anatomy of the recurrent coastal sediment plume in Lake Michigan and its impacts on light climate, nutrients, and plankton

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94624/1/jgrc9664.pd

Mechanistic origins of variability in phytoplankton dynamics. Part II: analysis of mesocosm blooms under climate change scenarios

Driving factors of phytoplankton spring blooms have been discussed since long, but rarely analyzed quantitatively. Here, we use a mechanistic size-based ecosystem model to reconstruct observations made during the Kiel mesocosm experiments (2005–2006). The model accurately hindcasts highly variable bloom developments including community shifts in cell size. Under low light, phytoplankton dynamics was mostly controlled by selective mesozooplankton grazing. Selective grazing also explains initial dominance of large diatoms under high light conditions. All blooms were mainly terminated by aggregation and sedimentation. Allometries in nutrient uptake capabilities led to a delayed, post-bloom dominance of small species. In general, biomass and trait dynamics revealed many mutual dependencies, while growth factors decoupled from the respective selective forces. A size shift induced by one factor often changed the growth dependency on other factors. Within climate change scenarios, these indirect effects produced large sensitivities of ecosystem fluxes to the size distribution of winter phytoplankton. These sensitivities exceeded those found for changes in vertical mixing, whereas temperature changes only had minimal impacts

Abundance of a chlorophyll a precursor and the oxidation product hydroxychlorophyll a during seasonal phytoplankton community progression in the Western English Channel

This study presents the first in-situ measurements of the chlorophyll a oxidation product, hydroxychlorophyll a as well as the chlorophyll a precursor, chlorophyll aP276 conducted over an annual cycle. Chlorophyll a oxidation products, such as hydroxychlorophyll a may be associated with the decline of algal populations and can act as an initial step in the degradation of chlorophyll a into products which can be found in the geochemical record, important for studying past climate change events. Here, hydroxychlorophyll a and chlorophyll aP276 were measured at the long-term monitoring station L4, Western Channel Observatory (UK, www.westernchannelobservatory.org) over an annual cycle (2012). Weekly measurements of phytoplankton species composition and abundance enabled detailed analysis of possible sources of hydroxychlorophyll a. Dinoflagellates, 2 diatom species, the prymnesiophyte Phaeocystis spp. and the coccolithophorid Emiliania huxleyi were all associated with hydroxychlorophyll a occurrence. However, during alternate peaks in abundance of the diatoms, no association with hydroxychlorophyll a occurred, indicating that the oxidation of chlorophyll a was dependant not only on species but also on additional factors such as the mode of mortality, growth limiting factor (i.e. nutrient concentration) or phenotypic plasticity. Surface sediment samples contained 10 times more hydroxychlorophyll a (relative to chlorophyll a) than pelagic particulate samples, indicating that more chlorophyll a oxidation occurred during sedimentation or at the sediment-water interface, than in the pelagic environment. In addition, chlorophyll aP276 correlated with chl-a concentration, thus supporting its assignment as a chl-a precursor

Effects of elevated CO2 on phytoplankton community biomass and species composition during a spring Phaeocystis spp. bloom in the western English Channel

A 21-year time series of phytoplankton community structure was analysed in relation to Phaeocystis spp. to elucidate its contribution to the annual carbon budget at station L4 in the western English Channel (WEC). Between 1993–2014 Phaeocystis spp. contributed ∼4.6% of the annual phytoplankton carbon and during the March − May spring bloom, the mean Phaeocystis spp. biomass constituted 17% with a maximal contribution of 47% in 2001. Upper maximal weekly values above the time series mean ranged from 63 to 82% of the total phytoplankton carbon (∼42–137 mg carbon (C) m −3 ) with significant inter-annual variability in Phaeocystis spp. Maximal biomass usually occurred by the end of April, although in some cases as early as mid-April (2007) and as late as late May (2013). The effects of elevated pCO 2 on the Phaeocystis spp. spring bloom were investigated during a fifteen-day semi-continuous microcosm experiment. The phytoplankton community biomass was estimated at ∼160 mg C m −3 and was dominated by nanophytoplankton (40%, excluding Phaeocystis spp.), Phaeocystis spp. (30%) and cryptophytes (12%). The smaller fraction of the community biomass comprised picophytoplankton (9%), coccolithophores (3%), Synechococcus (3%), dinoflagellates (1.5%), ciliates (1%) and diatoms (0.5%). Over the experimental period, total biomass increased significantly by 90% to ∼305 mg C m −3 in the high CO 2 treatment while the ambient pCO 2 control showed no net gains. Phaeocystis spp. exhibited the greatest response to the high CO 2 treatment, increasing by 330%, from ∼50 mg C m −3 to over 200 mg C m −3 and contributing ∼70% of the total biomass. Taken together, the results of our microcosm experiment and analysis of the time series suggest that a future high CO 2 scenario may favour dominance of Phaeocystis spp. during the spring bloom. This has significant implications for the formation of hypoxic zones and the alteration of food web structure including inhibitory feeding effects and lowered fecundity in many copepod species

Active Brownian Particles. From Individual to Collective Stochastic Dynamics

We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically powered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active particles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte