Global distributions of diazotrophs nitrogen fixation rates - Depth integrated values computed from a collection of source datasets - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. The present data set presents depth integrated values of diazotrophs nitrogen fixation rates, computed from a collection of source data sets.

Global distributions of diazotrophs abundance, biomass and nitrogen fixation rates - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. This is a gridded data product about diazotrophic organisms . There are 6 variables. Each variable is gridded on a dimension of 360 (longitude) * 180 (latitude) * 33 (depth) * 12 (month). The first group of 3 variables are: (1) number of biomass observations, (2) biomass, and (3) special nifH-gene-based biomass. The second group of 3 variables is same as the first group except that it only grids non-zero data. We have constructed a database on diazotrophic organisms in the global pelagic upper ocean by compiling more than 11,000 direct field measurements including 3 sub-databases: (1) nitrogen fixation rates, (2) cyanobacterial diazotroph abundances from cell counts and (3) cyanobacterial diazotroph abundances from qPCR assays targeting nifH genes. Biomass conversion factors are estimated based on cell sizes to convert abundance data to diazotrophic biomass. Data are assigned to 3 groups including Trichodesmium, unicellular diazotrophic cyanobacteria (group A, B and C when applicable) and heterocystous cyanobacteria (Richelia and Calothrix). Total nitrogen fixation rates and diazotrophic biomass are calculated by summing the values from all the groups. Some of nitrogen fixation rates are whole seawater measurements and are used as total nitrogen fixation rates. Both volumetric and depth-integrated values were reported. Depth-integrated values are also calculated for those vertical profiles with values at 3 or more depths.

Impact of salinity and growth phase on alkenone distributions in coastal haptophytes

Batch cultures of Isochrysis galbana (strain CCMP 1323) and Chrysotila lamellosa (strain CCMP 1307) were grown at salinity ca. 10 to ca. 35 and the alkenone distributions determined for different growth phases. UK'37 values decreased slightly with salinity for C. lamellosa but were largely unaffected for I. galbana except during the decline phase. The values decreased with incubation time in both species. The proportion of C37:4, used as proxy for salinity, increased in both species at 0.16-0.20% per salinity unit, except during the stationary phase for I. galbana. C37:4 was much more abundant in C. lamellosa (30-44%) than in I. galbana (4-12%). Although our results suggest that salinity has a direct effect on alkenone distributions, growth phase and species composition will also have a marked impact, complicating the use of alkenone distributions as a proxy for salinity in the marine environment.

Estimation of Sample Size and Power For Quantile Regression

Quantile regression (QR) was first introduced by Roger Koenker and Gilbert Bassett in 1978. It is robust to outliers which affect least squares estimator on a large scale in linear regression. Instead of modeling mean of the response, QR provides an alternative way to model the relationship between quantiles of the response and covariates. Therefore, QR can be widely used to solve problems in econometrics, environmental sciences and health sciences. Sample size is an important factor in the planning stage of experimental design and observational studies. In ordinary linear regression, sample size may be determined based on either precision analysis or power analysis with closed form formulas. There are also methods that calculate sample size based on precision analysis for QR like C.Jennen-Steinmetz and S.Wellek (2005). A method to estimate sample size for QR based on power analysis was proposed by Shao and Wang (2009). In this paper, a new method is proposed to calculate sample size based on power analysis under hypothesis test of covariate effects. Even though error distribution assumption is not necessary for QR analysis itself, researchers have to make assumptions of error distribution and covariate structure in the planning stage of a study to obtain a reasonable estimate of sample size. In this project, both parametric and nonparametric methods are provided to estimate error distribution. Since the method proposed can be implemented in R, user is able to choose either parametric distribution or nonparametric kernel density estimation for error distribution. User also needs to specify the covariate structure and effect size to carry out sample size and power calculation. The performance of the method proposed is further evaluated using numerical simulation. The results suggest that the sample sizes obtained from our method provide empirical powers that are closed to the nominal power level, for example, 80%.

Effects of a harmful algal bloom on the community ecology, movements and spatial distributions of fishes in a microtidal estuary

Harmful algal blooms can adversely affect fish communities, though their impacts are highly context-dependent and typically differ between fish species. Various approaches, comprising univariate and multivariate analyses and multimetric Fish Community Indices (FCI), were employed to characterise the perceived impacts of a Karlodinium veneficum bloom on the fish communities and ecological condition of the Swan Canning Estuary, Western Australia. The combined evidence suggests that a large proportion of the more mobile fish species in the offshore waters of the bloom-affected area relocated to other regions during the bloom. This was indicated by marked declines in mean species richness, catch rates and FCI scores in the bloom region but concomitant increases in these characteristics in more distal regions, and by pronounced and atypical shifts in the pattern of inter-regional similarities in fish community composition during the bloom. The lack of any significant changes among the nearshore fish communities revealed that bloom impacts were less severe there than in deeper, offshore waters. Nearshore habitats, which generally are in better ecological condition than adjacent offshore waters in this system, may provide refuges for fish during algal blooms and other perturbations, mirroring similar observations of fish avoidance responses to such stressors in estuaries worldwide.

Effects of a harmful algal bloom on the community ecology, movements and spatial distributions of fishes in a microtidal estuary

Harmful algal blooms can adversely affect fish communities, though their impacts are highly context-dependent and typically differ between fish species. Various approaches, comprising univariate and multivariate analyses and multimetric Fish Community Indices (FCI), were employed to characterise the perceived impacts of a Karlodinium veneficum bloom on the fish communities and ecological condition of the Swan Canning Estuary, Western Australia. The combined evidence suggests that a large proportion of the more mobile fish species in the offshore waters of the bloom-affected area relocated to other regions during the bloom. This was indicated by marked declines in mean species richness, catch rates and FCI scores in the bloom region but concomitant increases in these characteristics in more distal regions, and by pronounced and atypical shifts in the pattern of inter-regional similarities in fish community composition during the bloom. The lack of any significant changes among the nearshore fish communities revealed that bloom impacts were less severe there than in deeper, offshore waters. Nearshore habitats, which generally are in better ecological condition than adjacent offshore waters in this system, may provide refuges for fish during algal blooms and other perturbations, mirroring similar observations of fish avoidance responses to such stressors in estuaries worldwide.