Trophic structure on the West Antarctic Peninsula shelf: Detritivory and benthic inertia revealed by delta(13)C and delta(15)N analysis

Summer bloom-derived phytodetritus settles rapidly to the seafloor on the West Antarctic Peninsula (WAP) continental shelf, where it appears to degrade relatively slowly, forming a sediment ""food bank"" for benthic detritivores. We used stable carbon and nitrogen isotopes to examine sources and sinks of particulate organic material (POM) reaching the WAP shelf benthos (550-625 m depths), and to explore trophic linkages among the most abundant benthic megafauna. We measured delta(13)C and delta(15)N values in major megafaunal taxa (n = 26) and potential food sources, including suspended and sinking POM, ice algae, sediment organic carbon, phytodetritus, and macrofaunal polychaetes. The range in delta(13)C values (> 14 parts per thousand) of suspended POM was considerably broader than in sedimentary POC, where little temporal variability in stable isotope signatures was observed. While benthic megafauna also exhibited a broad range of VC values, organic carbon entering the benthic food web appeared to be derived primarily from phytoplankton production, with little input from ice algae. One group of organisms, primarily deposit-feeders, appeared to rely on fresh phytodetritus recovered from the sediments, and sediment organic material that had been reworked by sediment microbes. A second group of animals, including many mobile invertebrate and fish predators, appeared to utilize epibenthic or pelagic food resources such as zooplankton. One surface-deposit-feeding holothurian (Protelpidia murrayi) exhibited seasonal variability in stable isotope values of body tissue, while other surface- and subsurface-deposit-feeders showed no evidence of seasonal variability in food source or trophic position. Detritus from phytoplankton blooms appears to be the primary source of organic material for the detritivorous benthos; however, seasonal variability in the supply of this material is not mirrored in the sediments, and only to a minor degree in the benthic fauna. This pattern suggests substantial inertia in benthic-pelagic coupling, whereby the sediment ecosystem integrates long-term variability in production processes in the water column above. Published by Elsevier Ltd.

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.