The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic.

We present a model that estimates the spectral phytoplankton absorption coefficient ( a p h ( λ ) ) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on a p h ( λ ) (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total a p h ( λ ) with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine a p h ( λ ) for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific a p h ( λ ) using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.

Mechanisms shaping size structure and functional diversity of phytoplankton communities in the ocean.

The factors regulating phytoplankton community composition play a crucial role in structuring aquatic food webs. However, consensus is still lacking about the mechanisms underlying the observed biogeographical differences in cell size composition of phytoplankton communities. Here we use a trait-based model to disentangle these mechanisms in two contrasting regions of the Atlantic Ocean. In our model, the phytoplankton community can self-assemble based on a trade-off emerging from relationships between cell size and (1) nutrient uptake, (2) zooplankton grazing, and (3) phytoplankton sinking. Grazing 'pushes' the community towards larger cell sizes, whereas nutrient uptake and sinking 'pull' the community towards smaller cell sizes. We find that the stable environmental conditions of the tropics strongly balance these forces leading to persistently small cell sizes and reduced size diversity. In contrast, the seasonality of the temperate region causes the community to regularly reorganize via shifts in species composition and to exhibit, on average, bigger cell sizes and higher size diversity than in the tropics. Our results raise the importance of environmental variability as a key structuring mechanism of plankton communities in the ocean and call for a reassessment of the current understanding of phytoplankton diversity patterns across latitudinal gradients.

Estimating field metabolic rates for Australian marsupials using phylogeny.

Field metabolic rate (FMR) is a useful measure for the energy expenditure in free-ranging animals. Field metabolic rates for species that have not been measured are usually predicted by allometric equations on the basis of their body mass (BM). Phylogenetically informed methods improve estimates of both allometric relationships and species-specific FMR values by considering the evolutionary history of species. Further improvement is possible by incorporating isolated measurements on BM and FMR, but most existing methods force the user to discard such incomplete data. In the present study the FMR of most Australian marsupial species was predicted for the first time using a phylogenetic method that was explicitly designed to handle incomplete data. This allows full use of the dataset containing 35 samples of FMR and 130 samples of BM. Cross-validation demonstrated that FMRs were estimated with high accuracy. The resulting prediction equation was FMR (kJday(-1))=5.27 BM (g)(0.69). Field metabolic rate and BM were highly phylogenetically correlated (r=0.96), i.e. FMR and BM co-evolved. Differences between species-specific and generic marsupial estimates of FMR revealed that herbivores have lower energy expenditure than carnivores. Specifically, herbivorous macropods have on average lower relative FMR (kJ/d) (3.75±0.53 BM(0.69); mean±SD) than carnivorous dasyurids (7.64±0.84 BM(0.69)). Phylogenetically informed estimates for most extant Australian marsupial species are now available.

A PHYLOGENETIC APPROACH TO THE ESTIMATION OF PHYTOPLANKTON TRAITS(1).

The quantitative characterization of the ecology of individual phytoplankton taxa is essential for model resolution of many aspects of aquatic ecosystems. Existing literature cannot directly parameterize all phytoplankton taxa of interest, as many traits and taxa have not been sampled. However, valuable clues on the value of traits are found in the evolutionary history of species and in common correlations between traits. These two resources were exploited with an existing, statistically consistent method built upon evolutionary concepts. From a new data set with >700 observations on freshwater phytoplankton traits and a qualitative phytoplankton phylogeny, estimates were derived for the size, growth rate, phosphate affinity, and susceptibility to predation of 277 phytoplankton types, from evolutionary ancestors to present-day species. These estimates account simultaneously for phylogenetic relationships between types, as imposed by the phylogeny, and approximate power-law relationships (e.g., allometric scaling laws) between traits, as reconstructed from the data set. Results suggest that most phytoplankton traits are to some extent conserved in evolution: cross-validation demonstrated that the use of phylogenetic information significantly improves trait value estimates. By providing trait value estimates as well as uncertainties, these results could benefit most quantitative studies involving phytoplankton.

PhyloPars: estimation of missing parameter values using phylogeny.

A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments on the species of interest are not feasible. The PhyloPars web server provides a statistically consistent method that combines an incomplete set of empirical observations with the species phylogeny to produce a complete set of parameter estimates for all species. It builds upon a state-of-the-art evolutionary model, extended with the ability to handle missing data. The resulting approach makes optimal use of all available information to produce estimates that can be an order of magnitude more accurate than ad-hoc alternatives. Uploading a phylogeny and incomplete feature matrix suffices to obtain estimates of all missing values, along with a measure of certainty. Real-time cross-validation provides further insight in the accuracy and bias expected for estimated values. The server allows for easy, efficient estimation of metabolic parameters, which can benefit a wide range of fields including systems biology and ecology. PhyloPars is available at: http://www.ibi.vu.nl/programs/phylopars/.

A text-mining analysis of the human phenome.

A number of large-scale efforts are underway to define the relationships between genes and proteins in various species. But, few attempts have been made to systematically classify all such relationships at the phenotype level. Also, it is unknown whether such a phenotype map would carry biologically meaningful information. We have used text mining to classify over 5000 human phenotypes contained in the Online Mendelian Inheritance in Man database. We find that similarity between phenotypes reflects biological modules of interacting functionally related genes. These similarities are positively correlated with a number of measures of gene function, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein-protein interaction. Phenotype grouping reflects the modular nature of human disease genetics. Thus, phenotype mapping may be used to predict candidate genes for diseases as well as functional relations between genes and proteins. Such predictions will further improve if a unified system of phenotype descriptors is developed. The phenotype similarity data are accessible through a web interface at http://www.cmbi.ru.nl/MimMiner/.