The Distribution of Juvenile Fishes in Maryland Coastal Bays in Relation to Environmental Factors: a Multivariate Analysis

Fish assemblage structure of Maryland's coastal lagoon complex was analyzed for spatial and seasonal patterns for the period 1991-2000. Data was made available by Maryland Department of Natural Resources from their MD Coastal Bays Finfish Survey. Dominant species from separate trawl and wiw surveys included blue crab Callinectes sapidus (erroneously included here as a "fish" due to its dominance and commercial importance), bay anchovy Anchoa mitchilli, spot Leiostomous xanthurus, silver perch Bairdiella ehrysoura, and Atlantic menhaden Brevwrtia tyrannus. Ninety-four fish species were identified in the two surveys, a diversity substantially higher than other survey records for Middle Atlantic Bight estuarine and lagoon systems (richness=26 to 78 species). Total species richness for the trawl survey was highest in Chincoteague and lowest in Assawoman and Sinepuxent. On the other hand, mean richness per tow (-area) and related Shannon Weiner Diversity Index were significantly higher in the northern two bays (Assawoman and Isle of Wight Bays) than in the two southern bays (Chincoteague or Sinepuxent Bays). For the seine survey, effort-adjusted diversity indices were significantly lower for Chincoteague Bay than for the other three bays. Higher relative abundances were observed in the northern bays than in the southern bays. The trawl survey exhibited the lowest catch-per-site in Sinepuxent Bay and the highest in Assawoman Bay. The seine survey had the lowest catch-per-site in Chincoteague Bay while the other three embayments were of similar magnitude. There was clear seasonality in assemblage structure with peak abundance and diversity in the summer compared to other seasons. Blue crabs in particular showed a c. 2-fold decline in relative abundance from early summer to fall, which is likely attributable to harvest removals (i.e., an exploitation rate of c. 50%). Seagrass coverage, although increasing over the course of the 10 year survey, did not have obvious effects on species diversity and abundance across or within the embayments, although it did have positive associations with two important species: bay anchovy and summer flounder Pavalich thys dentatus. Atlantic menhaden were most dominant in Assawoman Bay, which could be related to higher primary production typically observed in this Bay in comparison to the other three. (PDF contains 99 pages)

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed

Multivariate climate reconstruction for the last 14,000 years in southernmost South America

Comparison between past changes in pollen assemblages and stable isotope ratios (deuterium and carbon) analyzed in the same peat core from Tierra del Fuego at latitude 55°S permitted identification of the relative contribution of precipitation versus temperature responsible for the respective change. Major steps in the sequence of paleoenvironmental changes, such as at 12700, 9000, 5000, and 4000 years ago are apparently related only to increase in precipitation, reflecting the latitudinal location and intensity of the westerly storm tracks. On the other hand, high paleoenvironmental variability, which is characteristic for the late-glacial and the latest Holocene, is related to temperature variability, which affects the relative moisture content. Comparison with other paleoenvironmental records suggests that the late-glacial temperature variability is probably related to variability in the extent of Antarctic sea-ice, which in turn appears to be related to the intensity of Atlantic deep-water circulation. Temperature variability during the latest Holocene, on the other hand, is probably related to the dynamics of the El Niño/Southern Oscillation.

A multivariate statistical study with a factor analysis of recent planktonic foraminiferal distribution in the Coromandel Coast of India

A study of planktonic foraminiferal assemblages from 19 stations in the neritic and oceanic regions off the Coromandel Coast, Bay of Bengal has been made using a multivariate statistical method termed as factor analysis. On the basis of abundance, 17 foraminiferal species, species were clustered into 5 groups with row normalisation and varimax rotation for Q-mode factor analysis. The 19 stations were also grouped into 5 groups with only 2 groups statistically significant using column normalisation and varimax rotation for R-mode analysis. This assemblage grouping method is suitable because groups of species/stations can explain the maximum amount of variation in them in relation to prevailing environmental conditions in the area of study.

APPROXIMATE GAUSS-MARKOV REALISATION OF MULTIVARIATE STOCHASTIC PROCESSES.

Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks to determine a finite-dimensional linear time-invariant system which, when driven by white noise, will produce an output whose spectral density is approximately PHI ( omega ), and an approximate spectral factor of PHI ( omega ). The author employs the Anderson-Faurre theory in his analysis.

MULTIVARIATE COMPARISON OF THE FORELIMB BETWEEN STUMP-TAILED AND RHESUS MACAQUES

Rhesus macaques and stump-tailed macaques are sympatric in western Yunnan (China), coexisting or occupying habitats that show little difference. This paper tests hypotheses based on theoretical expectation from the differing biomechanical demands of terrestrial and arboreal quadrupedalism in stump-tailed macaques and rhesus macaques, respectively. Individuals of these two macaque taxa were markedly separated by the first two principal components and discriminant analyses based on 18 variables of the upper limb. The rhesus macaques appear to be more adapted for arboreal quadruped habits because of elongation of the clavicle and forearm, a larger humeral head and greater midshaft sagittal diameters of the radius and ulna.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.