Beluga, Delphinapterus leucas, Group Sizes in Cook Inlet, Alaska, Based on Observer Counts and Aerial Video

Belugas, Delphinapterus leucas, groups were videotaped concurrent to observer counts during annual NMFS aerial surveys of Cook Inlet, Alaska, from 1994 to 2000. The videotapes provided permanent records of whale groups that could be examined and compared to group size estimates ade by aerial observers.Examination of the video recordings resulted in 275 counts of 79 whale groups. The McLaren formula was used to account for whales missed while they were underwater (average correction factor 2.03; SD=0.64). A correction for whales missed due to video resolution was developed by using a second, paired video camera that magnified images relative to the standard video. This analysis showed that some whales were missed either because their image size fell below the resolution of hte standard video recording or because two whales surfaced so close to each other that their images appeared to be one large whale. The correction method that resulted depended on knowing the average whale image size in the videotapes. Image sizes were measured for 2,775 whales from 275 different passes over whale groups. Corrected group sizes were calcualted as the product of the original count from video, the correction factor for whales missed underwater, and the correction factor for whales missed due to video resolution (averaged 1.17; SD=0.06). A regression formula was developed to estimate group sizes from aerial observer counts; independent variables were the aerial counts and an interaction term relative to encounter rate (whales per second during the counting of a group), which were regressed against the respective group sizes as calculated from the videotapes. Significant effects of encounter rate, either positive or negative, were found for several observers. This formula was used to estimate group size when video was not available. The estimated group sizes were used in the annual abundance estimates.

Age determination and growth of the night shark (Carcharhinus signatus) off the northeastern Brazilian coast

Age and growth of the night shark (Carcharhinus signatus) from areas off northeastern Brazil were determined from 317 unstained vertebral sections of 182 males (113–215 cm total length [TL]), 132 females (111.5–234.9 cm) and three individuals of unknown sex (169–242 cm). Although marginal increment (MI) analysis suggests that band formation occurs in the third and fourth trimesters in juveniles, it was inconclusive for adults. Thus, it was assumed that one band is formed annually. Births that occur over a protracted period may be the most important source of bias in MI analysis. An estimated average percent error of 2.4% was found in readings for individuals between two and seventeen years. The von Bertalanffy growth function (VBGF) showed no significant differences between sexes, and the model derived from back-calculated mean length at age best represented growth for the species (L∞=270 cm, K=0.11/yr, t0=–2.71 yr) when compared to the observed mean lengths at age and the Fabens’ method. Length-frequency analysis on 1055 specimens (93–260 cm) was used to verify age determination. Back-calculated size at birth was 66.8 cm and maturity was reached at 180–190 cm (age 8) for males and 200–205 cm (age ten) for females. Age composition, estimated from an age-length key, indicated that juveniles predominate in commercial catches, representing 74.3% of the catch. A growth rate of 25.4 cm/yr was estimated from birth to the first band (i.e. juveniles grow 38% of their birth length during the first year), and a growth rate of 8.55 cm/yr was estimated for eight- to ten-year-old adults.

Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network

Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area.

Length correction for larval and early-juvenile Atlantic menhaden (Brevoortia tyrannus) after preservation in alcohol

Body length measurement is an important part of growth, condition, and mortality analyses of larval and juvenile fish. If the measurements are not accurate (i.e., do not reflect real fish length), results of subsequent analyses may be affected considerably (McGurk, 1985; Fey, 1999; Porter et al., 2001). The primary cause of error in fish length measurement is shrinkage related to collection and preservation (Theilacker, 1980; Hay, 1981; Butler, 1992; Fey, 1999). The magnitude of shrinkage depends on many factors, namely the duration and speed of the collection tow, abundance of other planktonic organisms in the sample (Theilacker, 1980; Hay, 1981; Jennings, 1991), the type and strength of the preservative (Hay, 1982), and the species of fish (Jennings, 1991; Fey, 1999). Further, fish size affects shrinkage (Fowler and Smith, 1983; Fey, 1999, 2001), indicating that live length should be modeled as a function of preserved length (Pepin et al., 1998; Fey, 1999).

The measurement error in marine survey catches: the bottom trawl case

We have formulated a model for analyzing the measurement error in marine survey abundance estimates by using data from parallel surveys (trawl haul or acoustic measurement). The measurement error is defined as the component of the variability that cannot be explained by covariates such as temperature, depth, bottom type, etc. The method presented is general, but we concentrate on bottom trawl catches of cod (Gadus morhua). Catches of cod from 10 parallel trawling experiments in the Barents Sea with a total of 130 paired hauls were used to estimate the measurement error in trawl hauls. Based on the experimental data, the measurement error is fairly constant in size on the logarithmic scale and is independent of location, time, and fish density. Compared with the total variability of the winter and autumn surveys in the Barents Sea, the measurement error is small (approximately 2–5%, on the log scale, in terms of variance of catch per towed distance). Thus, the cod catch rate is a fairly precise measure of fish density at a given site at a given time.

Measurements of resistance and reactance in fish with the use of bioelectrical impedance analysis: sources of error

New technologies can be riddled with unforeseen sources of error, jeopardizing the validity and application of their advancement. Bioelectrical impedance analysis (BIA) is a new technology in fisheries research that is capable of estimating proximate composition, condition, and energy content in fish quickly, cheaply, and (after calibration) without the need to sacrifice fish. Before BIA can be widely accepted in fisheries science, it is necessary to identify sources of error and determine a means to minimize potential errors with this analysis. We conducted controlled laboratory experiments to identify sources of errors within BIA measurements. We concluded that electrode needle location, procedure deviations, user experience, time after death, and temperature can affect resistance and reactance measurements. Sensitivity analyses showed that errors in predictive estimates of composition can be large (>50%) when these errors are experienced. Adherence to a strict protocol can help avoid these sources of error and provide BIA estimates that are both accurate and precise in a field or laboratory setting.

Establishing species–habitat associations for 4 eteline snappers with the use of a baited stereo-video camera system

With the use of a baited stereo-video camera system, this study semiquantitatively defined the habitat associations of 4 species of Lutjanidae: Opakapaka (Pristipomoides filamentosus), Kalekale (P. sieboldii), Onaga (Etelis coruscans), and Ehu (E. carbunculus). Fish abundance and length data from 6 locations in the main Hawaiian Islands were evaluated for species-specific and size-specific differences between regions and habitat types. Multibeam bathymetry and backscatter were used to classify habitats into 4 types on the basis of substrate (hard or soft) and slope (high or low). Depth was a major influence on bottomfish distributions. Opakapaka occurred at depths shallower than the depths at which other species were observed, and this species showed an ontogenetic shift to deeper water with increasing size. Opakapaka and Ehu had an overall preference for hard substrate with low slope (hard-low), and Onaga was found over both hard-low and hard-high habitats. No significant habitat preferences were recorded for Kalekale. Opakapaka, Kalekale, and Onaga exhibited size-related shifts with habitat type. A move into hard-high environments with increasing size was evident for Opakapaka and Kalekale. Onaga was seen predominantly in hard-low habitats at smaller sizes and in either hard-low or hard-high at larger sizes. These ontogenetic habitat shifts could be driven by reproductive triggers because they roughly coincided with the length at sexual maturity of each species. However, further studies are required to determine causality. No ontogenetic shifts were seen for Ehu, but only a limited number of juveniles were observed. Regional variations in abundance and length were also found and could be related to fishing pressure or large-scale habitat features.