867 resultados para fisheries management


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Demersal groundfish densities were estimated by conducting a visual strip-transect survey via manned submersible on the continental shelf off Cape Flattery, Washington. The purpose of this study was to evaluate the statistical sampling power of the submersible survey as a tool to discriminate density differences between trawlable and untrawlable habitats. A geophysical map of the study area was prepared with side-scan sonar imagery, multibeam bathymetry data, and known locations of historical NMFS trawl survey events. Submersible transects were completed at randomly selected dive sites located in each habitat type. Significant differences in density between habitats were observed for lingcod (Ophiodon elongatus), yelloweye rockfish (Sebastes ruberrimus), and tiger rockfish (S. nigrocinctus) individually, and for “all rockfish” and “all flatfish” in the aggregate. Flatfish were more than ten times as abundant in the trawlable habitat samples than in the untrawlable samples, whereas rockfish as a group were over three times as abundant in the untrawlable habitat samples. Guidelines for sample sizes and implications for the estimation of the continental shelf trawl-survey habitat-bias are considered. We demonstrate an approach that can be used to establish sample size guidelines for future work by illustrating the interplay between statistical sampling power and 1) habitat specific-density differences, 2) variance of density differences, and 3) the proportion of untrawlable area in a habitat.

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The U.S. Marine Mammal Protection Act requires that the abundance of marine mammals in U.S. waters be assessed. Because this requirement had not been met for a large portion of the North Atlantic Ocean (U.S. waters south of Maryland), a ship-based, line-transect survey was conducted with a 68 m research ship between Maryland (38.00°N) and central Florida (28.00°N) from the 10-m isobath to the boundary of the U.S. Exclusive Economic Zone. The study area (573,000 km2) was surveyed between 8 July and 17 August 1998. Minimum abundance estimates were based on 4163 km of effort and 217 sightings of at least 13 cetacean species and other taxonomic categories. The most commonly sighted species (number of groups) were bottlenose dolphins, Tursiops truncatus (38); sperm whales, Physeter macrocephalus (29); Atlantic spotted dolphins, Stenella frontalis (28); and Risso’s dolphins, Grampus griseus (22). The most abundant species (abundance; coeffi cient of variation) were Atlantic spotted dolphins (14,438; 0.63); bottlenose dolphins (13,085; 0.40); pantropical spotted dolphins, S. attenuate (12,747; 0.56); striped dolphins, S. coeruleoalba (10,225; 0.91); and Risso’s dolphins (9533; 0.50). The abundance estimate for the Clymene dolphin, S. clymene (6086; 0.93), is the first for the U.S. Atlantic Ocean. Sperm whales were the most abundant large whale (1181; 0.51). Abundances for other species or taxonomic categories ranged from 20 to 5109. There were an estimated 77,139 (0.23) cetaceans in the study area. Bottlenose dolphins and Atlantic spotted dolphins were encountered primarily in continental shelf (<200 m) and continental slope waters (200−2000 m). All other species were generally sighted in oceanic waters (>200 m). The distribution of some species varied north to south. Striped dolphins, Clymene dolphins, and sperm whales were sighted primarily in the northern part of the study area; whereas pantropical spotted dolphins were sighted primarily in the southern portion.

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The growth of red sea urchins (Strongylocentrotus franciscanus) was modeled by using tag-recapture data from northern California. Red sea urchins (n=211) ranging in test diameter from 7 to 131 mm were examined for changes in size over one year. We used the function Jt+1 = Jt + f(Jt) to model growth, in which Jt is the jaw size (mm) at tagging, and Jt+1 is the jaw size one year later. The function f(Jt), represents one of six deterministic models: logistic dose response, Gaussian, Tanaka, Ricker, Richards, and von Bertalanffy with 3, 3, 3, 2, 3, and 2 minimization parameters, respectively. We found that three measures of goodness of fi t ranked the models similarly, in the order given. The results from these six models indicate that red sea urchins are slow growing animals (mean of 7.2 ±1.3 years to enter the fishery). We show that poor model selection or data from a limited range of urchin sizes (or both) produces erroneous growth parameter estimates and years-to-fishery estimates. Individual variation in growth dominated spatial variation at shallow and deep sites (F=0.246, n=199, P=0.62). We summarize the six models using a composite growth curve of jaw size, J, as a function of time, t: J = A(B – e–Ct) + Dt, in which each model is distinguished by the constants A, B, C, and D. We suggest that this composite model has the flexibility of the other six models and could be broadly applied. Given the robustness of our results regarding the number of years to enter the fishery, this information could be incorporated into future fishery management plans for red sea urchins in northern California.

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Recreational fisheries in the waters off the northeast U.S. target a variety of pelagic and demersal fish species, and catch and effort data sampled from recreational fisheries are a critical component of the information used in resource evaluation and management. Standardized indices of stock abundance developed from recreational fishery catch rates are routinely used in stock assessments. The statistical properties of both simulated and empirical recreational fishery catch-rate data such as those collected by the National Marine Fisheries Service (NMFS) Marine Recreational Fishery Statistics Survey (MRFSS) are examined, and the potential effects of different assumptions about the error structure of the catch-rate frequency distributions in computing indices of stock abundance are evaluated. Recreational fishery catch distributions sampled by the MRFSS are highly contagious and overdispersed in relation to the normal distribution and are generally best characterized by the Poisson or negative binomial distributions. The modeling of both the simulated and empirical MRFSS catch rates indicates that one may draw erroneous conclusions about stock trends by assuming the wrong error distribution in procedures used to developed standardized indices of stock abundance. The results demonstrate the importance of considering not only the overall model fit and significance of classification effects, but also the possible effects of model misspecification, when determining the most appropriate model construction.

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Spatial variation in demographic parameters of the red throat emperor (Lethrinus miniatus) was examined among 12 coral reefs in three geographic regions (Townsville, Mackay, and Storm Cay) spanning over 3° of latitude of the Great Barrier Reef, Australia. Estimates of demographic parameters were based on age estimates from counts of annuli in whole otoliths because there was no significant difference in age estimates between whole and sectioned otoliths. There were significant regional differences in age structures, rates of somatic and otolith growth, and total mortality. The Townsville region was characterized by the greatest proportion of older fish, the smallest maximum size, and the lowest rates of otolith growth and total mortality. In contrast the Mackay region was characterized by the highest proportion of younger fish, the largest maximum size, and the highest rates of otolith growth and total mortality. Demographic parameters for the Storm Cay region were intermediate between the other two regions. Historic differences in fishing pressure and regional differences in productivity are two alternative hypotheses given to explain the regional patterns in demographic parameters. All demographic parameters were similar among the four reefs within each region. Thus, subpopulations with relatively homogeneous demographic parameters occurred on scales of reef clusters. Previous studies, by contrast, have found substantial between-reef variation in demographic parameters within regions. Thus spatial variation in demographic parameters for L. miniatus may differ from what is assumed typical for a coral-reef fish metapopulation.

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Each spring horseshoe crabs (Limulus polyphemus L.) emerge from Delaware Bay to spawn and deposit their eggs on the foreshore of sandy beaches (Shuster and Botton, 1985; Smith et al., 2002a). From mid-May to early June, migratory shorebirds stopover in Delaware Bay and forage heavily on horseshoe crab eggs that have been transported up onto the beach (Botton et al., 1994; Burger et al., 1997; Tsipoura and Burger, 1999). Thus, estimating the quantity of horseshoe crab eggs in Delaware Bay beaches can be useful for monitoring spawning activity and assessing the amount of forage available to migratory shorebirds.

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The spotted seatrout (Cynoscion nebulosus) is one of the most sought after recreational fish in Florida Bay, and it spends its entire life history within the bay (Rutherford et al.,1989b). The biology of adult spotted seatrout in Florida Bay is well known (Rutherford et al., 1982, 1989b) as is the distribution and abundance of juveniles within the bay. The habitats and diets of juveniles are well documented (Hettler, 1989; Chester and Thayer, 1990; Thayer et al., 1999; Florida Department of Environmental Protection1). Nevertheless, the spatial and temporal spawning habits of spotted seatrout and the distribution of larvae have only been partially described (Powell et al., 1989; Rutherford et al., 1989a).

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Bycatch studies have largely ignored population level effects on fish species of little commercial interest. Here we analyze bycatch of the lined seahorse (Hippocampus erectus) in the bait-shrimp trawl fishery in Hernando Beach, Florida, providing the first fisheries data for this species. Based on catch per unit of effort (CPUE), size, sex, and reproductive status of trawled H. erectus, 1) approximately 72,000 seahorses were caught annually by this fleet, from a population of unknown size, 2) trawling affected population cohorts differentially because of temporal and spatial variation in CPUE and population size, and 3) a greater proportion of females than males was removed in trawling. Our findings suggest that trawling may affect seahorse populations through direct mortality, social disruption, and habitat damage. However, the lack of specific abundance or catchability estimates for H. erectus means that the precise impact of trawling on this fish remains uncertain. This paper focuses attention on the need for research and monitoring of small fishes that are caught incidentally in nonselective gear.

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Estimates of instantaneous mortality rates (Z) and annual apparent survival probabilities (Φ) were generated from catch-curve analyses for oceanic-stage juvenile loggerheads (Caretta caretta) in the waters of the Azores. Two age distributions were analyzed: the “total sample” of 1600 loggerheads primarily captured by sighting and dipnetting from a variety of vessels in the Azores between 1984 and 1995 and the “tuna sample” of 733 loggerheads (a subset of the total sample) captured by sighting and dipnetting from vessels in the commercial tuna fleet in the Azores between 1990 and 1992. Because loggerhead sea turtles begin to emigrate from oceanic to neritic habitats at age 7, the best estimates of instantaneous mortality rate (0.094) and annual survival probability (0.911) not confounded with permanent emigration were generated for age classes 2 through 6. These estimates must be interpreted with caution because of the assumptions upon which catch-curve analyses are based. However, these are the first directly derived estimates of mortality and survival probabilities for oceanic-stage sea turtles. Estimation of survival probabilities was identified as “an immediate and critical requirement” in 2000 by the Turtle Expert Working Group of the U.S. National Marine Fisheries Service.

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The green sea urchin (Strongylocentrotus droebachiensis) is important to the economy of Maine. It is the state’s fourth largest fishery by value. The fishery has experienced a continuous decline in landings since 1992 because of decreasing stock abundance. Because determining the age of sea urchins is often difficult, a formal stock assessment demands the development of a size-structured population dynamic model. One of the most important components in a size-structured model is a growth-transition matrix. We developed an approach for estimating the growth-transition matrix using von Bertalanffy growth parameters estimated in previous studies of the green sea urchin off Maine. This approach explicitly considers size-specific variations associated with yearly growth increments for these urchins. The proposed growth-transition matrix can be updated readily with new information on growth, which is important because changes in stock abundance and the ecosystem will likely result in changes in sea urchin key life history parameters including growth. This growth-transition matrix can be readily incorporated into the size-structured stock assessment model that has been developed for assessing the green sea urchin stock off Maine.

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Culture of a non-native species, such as the Suminoe oyster (Crassostrea ariakensis), could offset the harvest of the declining native eastern oyster (Crassostrea virginica) fishery in Chesapeake Bay. Because of possible ecological impacts from introducing a fertile non-native species, introduction of sterile triploid oysters has been proposed. However, recent data show that a small percentage of triploid individuals progressively revert toward diploidy, introducing the possibility that Suminoe oysters might establish self-sustaining populations. To assess the risk of Suminoe oyster populations becoming established in Chesapeake Bay, a demographic population model was developed. Parameters modeled were salinity, stocking density, reversion rate, reproductive potential, natural and harvest-induced mortality, growth rates, and effects of various management strategies, including harvest strategies. The probability of a Suminoe oyster population becoming self-sustaining decreased in the model when oysters are grown at low salinity sites, certainty of harvest is high, mini-mum shell length-at-harvest is small, and stocking density is low. From the results of the model, we suggest adopting the proposed management strategies shown by the model to decrease the probability of a Suminoe oyster population becoming self-sustaining. Policy makers and fishery managers can use the model to predict potential outcomes of policy decisions, supporting the ability to make science-based policy decisions about the proposed introduction of triploid Suminoe oysters into the Chesapeake Bay.

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Although subsampling is a common method for describing the composition of large and diverse trawl catches, the accuracy of these techniques is often unknown. We determined the sampling errors generated from estimating the percentage of the total number of species recorded in catches, as well as the abundance of each species, at each increase in the proportion of the sorted catch. We completely partitioned twenty prawn trawl catches from tropical northern Australia into subsamples of about 10 kg each. All subsamples were then sorted, and species numbers recorded. Catch weights ranged from 71 to 445 kg, and the number of fish species in trawls ranged from 60 to 138, and invertebrate species from 18 to 63. Almost 70% of the species recorded in catches were “rare” in subsamples (less than one individual per 10 kg subsample or less than one in every 389 individuals). A matrix was used to show the increase in the total number of species that were recorded in each catch as the percentage of the sorted catch increased. Simulation modelling showed that sorting small subsamples (about 10% of catch weights) identified about 50% of the total number of species caught in a trawl. Larger subsamples (50% of catch weight on average) identified about 80% of the total species caught in a trawl. The accuracy of estimating the abundance of each species also increased with increasing subsample size. For the “rare” species, sampling error was around 80% after sorting 10% of catch weight and was just less than 50% after 40% of catch weight had been sorted. For the “abundant” species (five or more individuals per 10 kg subsample or five or more in every 389 individuals), sampling error was around 25% after sorting 10% of catch weight, but was reduced to around 10% after 40% of catch weight had been sorted.