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)

A stochastic model of temporal variations in monthly temperature, precipitation, snowfall, and resulting snowpack

We report a Monte Carlo representation of the long-term inter-annual variability of monthly snowfall on a detailed (1 km) grid of points throughout the southwest. An extension of the local climate model of the southwestern United States (Stamm and Craig 1992) provides spatially based estimates of mean and variance of monthly temperature and precipitation. The mean is the expected value from a canonical regression using independent variables that represent controls on climate in this area, including orography. Variance is computed as the standard error of the prediction and provides site-specific measures of (1) natural sources of variation and (2) errors due to limitations of the data and poor distribution of climate stations. Simulation of monthly temperature and precipitation over a sequence of years is achieved by drawing from a bivariate normal distribution. The conditional expectation of precipitation. given temperature in each month, is the basis of a numerical integration of the normal probability distribution of log precipitation below a threshold temperature (3°C) to determine snowfall as a percent of total precipitation. Snowfall predictions are tested at stations for which long-term records are available. At Donner Memorial State Park (elevation 1811 meters) a 34-year simulation - matching the length of instrumental record - is within 15 percent of observed for mean annual snowfall. We also compute resulting snowpack using a variation of the model of Martinec et al. (1983). This allows additional tests by examining spatial patterns of predicted snowfall and snowpack and their hydrologic implications.

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.