Temporal trends (2000–2011) and influences on fishery-independent catch rates for loggerhead sea turtles (Caretta caretta) at an important coastal foraging region in the southeastern United States

Seasonal trawling was conducted randomly in coastal (depths of 4.6–17 m) waters from St. Augustine, Florida, (29.9°N) to Winyah Bay, South Carolina (33.1°N), during 2000–03, 2008–09, and 2011 to assess annual trends in the relative abundance of sea turtles. A total of 1262 loggerhead sea turtles (Caretta caretta) were captured in 23% (951) of 4207 sampling events. Capture rates (overall and among prevalent 5-cm size classes) were analyzed through the use of a generalized linear model with log link function for the 4097 events that had complete observations for all 25 model parameters. Final models explained 6.6% (70.1–75.0 cm minimum straight-line carapace length [SCLmin]) to 14.9% (75.1–80.0 cm SCLmin) of deviance in the data set. Sampling year, geographic subregion, and distance from shore were retained as significant terms in all final models, and these terms collectively accounted for 6.2% of overall model deviance (range: 4.5–11.7% of variance among 5-cm size classes). We retained 18 parameters only in a subset of final models: 4 as exclusively significant terms, 5 as a mixture of significant or nonsignificant terms, and 9 as exclusively nonsignificant terms. Four parameters also were dropped completely from all final models. The generalized linear model proved appropriate for monitoring trends for this data set that was laden with zero values for catches and was compiled for a globally protected species. Because we could not account for much model deviance, metrics other than those examined in our study may better explain catch variability and, once elucidated, their inclusion in the generalized linear model should improve model fits.

Effects of El Niño–Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean off Java

The effects of El Niño–Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean (EIO) off Java were evaluated through the use of remotely sensed environmental data (sea-surface-height anomaly [SSHA], sea-surface temperature [SST], and chlorophyll a concentration), and Bigeye Tuna catch data. Analyses were conducted for the period of 1997–2000, which included the 1997–98 El Niño and 1999–2000 La Niña events. The empirical orthogonal function (EOF) was applied to examine oceanographic parameters quantitatively. The relationship of those parameters to variations in catch distribution of Bigeye Tuna was explored with a generalized additive model (GAM). The mean hook rate was 0.67 during El Niño and 0.44 during La Niña, and catches were high where SSHA ranged from –21 to 5 cm, SST ranged from 24°C to 27.5°C, and chlorophyll-a concentrations ranged from 0.04 to 0.16 mg m–3. The EOF analysis confirmed that the 1997–98 El Niño affected oceanographic conditions in the EIO off Java. The GAM results indicated that SST was better than the other environmental factors (SSHA and chlorophyll-a concentration) as an oceanographic predictor of Bigeye Tuna catches in the region. According to the GAM predictions, the highest probabilities (70–80%) for Bigeye Tuna catch in 1997–2000 occurred during oceanographic conditions during the 1997–98 El Niño event.

Gene function prediction from synthetic lethality networks via ranking on demand.

MOTIVATION: Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks. RESULTS: In this article, we present a novel technique called kernelROD for gene function prediction from synthetic lethal interaction networks based on kernel machines. We apply our novel algorithm to Gene Ontology functional annotation prediction in yeast. Our experiments show that our method leads to improved gene function prediction compared with state-of-the-art competitors and that combining genetic and congruence networks leads to a further improvement in prediction accuracy.

Gear modifications for fishing octopus, Octopus vulgaris, on live-bottom and adjacent flat bottom habitats in coastal waters off North Carolina

The Common Octopus, Octopus vulgaris, is an r-selected mollusk found off the coast of North Carolina that interests commercial fishermen because of its market value and the cost-effectiveness of unbaited pots that can catch it. This study sought to: 1) determine those gear and environmental factors that influenced catch rates of octopi, and 2) evaluate the feasibility of small-scale commercial operations for this species. Pots were fished from August 2010 through September 2011 set in strings over hard and sandy bottom in waters from 18 to 30 m deep in Onslow Bay, N.C. Three pot types were fished in each string; octopus pots with- and without lids, and conch pots. Proportional catch was modeled as a function of gear design and environmental factors (location, soak time, bottom type, and sea surface water temperature) using binomially distributed generalized linear models (GLM’s); parsimony of each GLM was assessed with Akaike Information Criteria (AIC). A total of 229 octopi were caught throughout the study. Pots with lids, pots without lids, and conch pots caught an average of 0.15, 0.17, and 0.11 octopi, respectively, with high variability in catch rates for each pot type. The GLM that best fit the data described proportional catch as a function of sea surface temperature, soak time, and station; greatest proportional catches occurred over short soak times, warmest temperatures, and less well known reef areas. Due to operating expenses (fuel, crew time, and maintenance), low catch rates of octopi, and high gear loss, a directed fishery for this species is not economically feasible at the catch rates found in this study. The model fitting to determine factors most influential on catch rates should help fishermen determine seasons and gear soak times that are likely to maximize catch rates. Potting for octopi may be commercially practical as a supplemental activity when targeting demersal fish species that are found in similar habitats and depth ranges in coastal waters off North Carolina.