Climate strongly influences the distribution and diversity of animals and plants, but its affect on microbial communities is poorly understood. By using resource competition theory, fundamental physical principles and the fossil record we review how climate selects marine eukaryotic phytoplankton taxa. We suggest that climate determines the equator-to-pole and continent-to-land thermal gradients that provide energy for the wind-driven turbulent mixing in the upper ocean. This mixing, in turn, controls the nutrient fluxes that determine cell size and taxa-level distributions. Understanding this chain of linked processes will allow informed predictions to be made about how phytoplankton communities will change in the future.

Members of the class Flavobacteria in the phylum Bacteroidetes are among the most abundant picoplankton in coastal and polar oceans. Their diversity is high in marine waters. However, quantitative information about distribution patterns of flavobacterial clades is scarce. We analyzed the diversity and clade-specific abundances of individual Flavobacteria in different oceanic provinces in the North Atlantic Ocean. Samples were taken along the 301W meridian between the East Greenland current and the North Atlantic subtropical gyre. Comparative sequence analysis of 16S ribosomal RNA (rRNA) gene libraries revealed high diversity and significant spatial variability within the class Flavobacteria. Published and newly designed oligonucleotide probes were used to enumerate eleven flavobacterial clades by catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH). We found that different provinces harbor distinct flavobacterial communities. Clade DE2 accounted for a substantial fraction of total Flavobacteria only in the Polar Biome (BPLR), whereas the VISION clades VIS1 and VIS4 significantly increased in the Arctic (ARCT) province. Members of the genus Polaribacter were the most abundant clade in all the water masses analyzed, with highest absolute numbers in BPLR and ARCT. We improved the CARD-FISH protocol to quantify the rare clades VIS2, VIS3, VIS5 and VIS6, which were present in abundances below 0.5%. They all showed pronounced regional distribution patterns. Microscopic analysis proved a specific enrichment of Flavobacteria in the phycosphere of nanophytoplankton of BPLR and ARCT. Our results suggest that different marine flavobacterial clades have distinct niches and different life strategies.

Eukaryotic genome size varies over five orders of magnitude; however, the distribution is strongly skewed toward small values. Genome size is highly correlated to a number of phenotypic traits, suggesting that the relative lack of large genomes in eukaryotes is due to selective removal. Using phylogenetic contrasts, we show that the rate of genome size evolution is proportional to genome size, with the fastest rates occurring in the largest genomes. This trend is evident across the 20 major eukaryotic clades analyzed, indicating that over long time scales, proportional change is the dominant and universal mode of genome-size evolution in eukaryotes. Our results reveal that the evolution of eukaryotic genome size can be described by a simple proportional model of evolution. This model explains the skewed distribution of eukaryotic genome sizes without invoking strong selection against large genomes.

For terrestrial and marine benthic ecologists, landscape ecology provides a framework to address issues of complexity, patchiness, and scale—providing theory and context for ecosystem based management in a changing climate. Marine pelagic ecosystems are likewise changing in response to warming, changing chemistry, and resource exploitation. However, unlike spatial landscapes that migrate slowly with time, pelagic seascapes are embedded in a turbulent, advective ocean. Adaptations from landscape ecology to marine pelagic ecosystem management must consider the nature and scale of biophysical interactions associated with organisms ranging from microbes to whales, a hierarchical organization shaped by physical processes, and our limited capacity to observe and monitor these phenomena across global oceans. High frequency, multiscale, and synoptic characterization of the 4-D variability of seascapes are now available through improved classification methods, a maturing array of satellite remote sensing products, advances in autonomous sampling of multiple levels of biological complexity, and emergence of observational networks. Merging of oceanographic and ecological paradigms will be necessary to observe, manage, and conserve species embedded in a dynamic seascape mosaic, where the boundaries, extent, and location of features change with time.

A seven-year oceanographic time series in NW Mediterranean surface waters was combined with pyrosequencing of ribosomal RNA (16S rRNA) and ribosomal RNA gene copies (16S rDNA) to examine the environmental controls on SAR11 ecotype dynamics and potential activity. SAR11 diversity exhibited pronounced seasonal cycles remarkably similar to total bacterial diversity. The timing of diversity maxima was similar across narrow and broad phylogenetic clades and strongly associated with deep winter mixing. Diversity minima were associated with periods of stratification that were low in nutrients and phytoplankton biomass and characterised by intense phosphate limitation (turnover time<5 h). We propose a conceptual framework in which physical mixing of the water column periodically resets SAR11 communities to a high diversity state and the seasonal evolution of phosphate limitation competitively excludes deeper-dwelling ecotypes to promote low diversity states dominated (>80%) by SAR11 Ia. A partial least squares (PLS) regression model was developed that could reliably predict sequence abundances of SAR11 ecotypes (Q2=0.70) from measured environmental variables, of which mixed layer depth was quantitatively the most important. Comparison of clade-level SAR11 rRNA:rDNA signals with leucine incorporation enabled us to partially validate the use of these ratios as an in-situ activity measure. However, temporal trends in the activity of SAR11 ecotypes and their relationship to environmental variables were unclear. The strong and predictable temporal patterns observed in SAR11 sequence abundance was not linked to metabolic activity of different ecotypes at the phylogenetic and temporal resolution of our study.

The spatial and temporal dynamics of ocean biomes and their provincial subdivisions are affected by the dynamics of Earth's climate system, but the effect of climate change on the distribution and variability of ocean biomes and provinces is largely unknown. A time‐series analysis from multiple satellite platforms shows that the lowest productivity provinces have been growing over the last decade and that the growth rates of these provinces increase as they get larger, and decrease as they get smaller. The most oligotrophic provinces of the ocean grow by reducing the size of the slightly less oligotrophic provinces. As a consequence, while the ocean's most extreme deserts are increasing at an accelerating rate, some oligotrophic areas are simultaneously shrinking. The aggregate area of the oligotrophic provinces oscillated in phase with the Pacific Decadal Oscillation Index from 1998–2007.

[1] Biogeographic provinces are categories used for comparing and contrasting biogeochemical processes and biodiversity between ocean regions. Provinces provide a framework for reasonable extrapolation of point or transect data to broader areas. However, their use is limited due to the non-automatic, subjective nature of province classification. Furthermore, it is unknown how province boundaries respond to seasonal and climate forcing. These issues make province related hypotheses difficult to test with static provinces. To solve this problem, we use objective classification on global remote sensing data to automatically produce time and space resolved ocean provinces. Seasonal patterns in province geography reflect well-known ocean processes. Our predictions of province boundaries are verified by in-situ ship track data and province distributions in the equatorial Pacific correlate well with ENSO indexes. This objective classification system captures spatial and temporal province dynamics and provides objective categories for cross-province biogeochemical hypotheses to be rigorously tested. Citation: Oliver, M. J., and A.

Understanding and predicting the responses of wide‐ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat‐based models that can sufficiently capture their environmental preferences. Marine ecosystems are inherently dynamic, and animal–environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of environmental data layers are therefore key considerations in modelling the environmental determinants of habitat selection. The utility of environmental data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade‐offs between near real‐time, high resolution and composite (i.e. synoptic, cloud‐free) data fields. Using movement simulations with built‐in environmental preferences in combination with both modelled and remotely‐sensed (ROMS, MODIS‐Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3–111 km, daily–climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological data fields can introduce bias in presence‐availability designs based upon animal movement datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely‐sensed data fields. We found that model accuracy decreased substantially above 70% data loss. In cloudy regions, weekly or monthly environmental data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate‐mediated ecosystem changes.