11 resultados para in-depth analysis

em Aquatic Commons


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The National Marine Fisheries Service is required by law to conduct social impact assessments of communities impacted by fishery management plans. To facilitate this process, we developed a technique for grouping communities based on common sociocultural attributes. Multivariate data reduction techniques (e.g. principal component analyses, cluster analyses) were used to classify Northeast U.S. fishing communities based on census and fisheries data. The comparisons indicate that the clusters represent real groupings that can be verified with the profiles. We then selected communities representative of different values on these multivariate dimensions for in-depth analysis. The derived clusters are then compared based on more detailed data from fishing community profiles. Ground-truthing (e.g. visiting the communities and collecting primary information) a sample of communities from three clusters (two overlapping geographically) indicates that the more remote techniques are sufficient for typing the communities for further in-depth analyses. The in-depth analyses provide additional important information which we contend is representative of all communities within the cluster.

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The paper highlights social status and socio-economic problems of fishermen living along the Ganga and the Yamuna at Allahabad. Since the problems of fishermen varied with respect to category (with owned boat and with hired boat) and location, significance of this variation was tested by x²-test. The paper further emphasises on an intensive study of fishermen community to have in depth analysis of their problems and to suggest remedial measures for their upliftment.

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Environmental variability affects the distributions of most marine fish species. In this analysis, assemblages of rockfish (Sebastes spp.) species were defined on the basis of similarities in their distributions along environmental gradients. Data from 14 bottom trawl surveys of the Gulf of Alaska and Aleutian Islands (n=6767) were used. Five distinct assemblages of rockfish were defined by geographical position, depth, and temperature. The 180-m and 275-m depth contours were major divisions between assemblages inhabiting the shelf, shelf break, and lower continental slope. Another noticeable division was between species centered in southeastern Alaska and those found in the northern Gulf of Alaska and Aleutian Islands. The use of environmental variables to define the species composition of assemblages is different from the use of traditional methods based on clustering and nonparametric statistics and as such, environmentally based analyses should result in predictable assemblages of species that are useful for ecosystem-based management.

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A study of the height-depth relation in the Indian backwater oyster Crassostrea madrasensis (Preston) was carried out. The plot of height against depth showed an exponential trend and a relationship of the form H = ADB. Plot of height against depth also showed larger deviations in height for oysters with greater depth. Analysis showed that variations in height do not result in corresponding variations in depth, particularly in oysters with increased height.

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Biodiversity and distribution of benthic meiofauna in the sediments of the Southern Caspian Sea (Mazandaran) was studied in order to introducing and determining of their relationship with the environmental factors. From 12 stations (ranging in depths 5, 10, 20 and 50 meters), sediment samples were gathered in 6 months (2012). Environmental factors of water near the bottom including temperature, salinity, dissolved oxygen and pH were measured during sampling with CTD and grain size and total organic matter percentage and calcium carbonate were measured in laboratory. In different months, the average water temperature (9.52-23.93), dissolved oxygen (7.71-10.53 mg/L), salinity (10.57±0/07 and 10.75±0/04 ppt), pH (7.44±0/29 and 7.41±0/22), EC (17.97±0/12 and 18.30±0/04μs/cm2), TDS (8.92±0/04 and 9.14±0/02 mg/L), total organic matter (5.83±1/43 and 6.25±0/97%) and calcium carbonate (2.36±0/36 and 1.68±0/19%) were measured respectively. Structure of the sediment samples mostly consisted of fine sand; very fine sand, silt and clay. From the 4 group animals (Foraminifera, Crustacea, Worms and Mollusca), there were identified 40species belong to 29 genera of 25 families. The cosmopolitan foraminifer, Ammonia beccarii caspica, was common in all sampling stations. Result showed that depth was important factor on distribution of meiofauna. Most density of foraminifera and crustacean was observed in depth of 20m and for mollusca and worms observed in 5m. Shannon diversity index decreased with depth that showed in shallow water diversity was higher than deep water. Mean of maximum and minimum Shannon index was obsorvers in depth of 5m and 50 m that was measured in order 0.93 and 0.43. Account of Shannon index showed that this area is under pressure. Account of peioleo index showed distribution in this area was not steady.

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This report presents the results of the 2007 baseflow condition surveys of the four major streams flowing through Santa Lucia Preserve- Las Garzas, Portrero, San Jose, and San Clemente Creeks. This report has been prepared for the Santa Lucia Conservancy and is primarily intended for the staff of Monterey County and California Department of Fish and Game, in accordance with the baseflow monitoring and reporting requirements outlined in County Conditions 14 and 15. The scope of this report is limited to the presentation and evaluation of existing baseflow conditions as required by Conditions 14 and 15, and is not intended as a comprehensive analysis. However, data presented here are an important part of the long term data set that will be used for future in depth watershed analyses. (Document contains 13 pages & 14 figs)

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Catch rates for both Nile perch (Lates niloticus) and dagaa (Rastrineobola argentea) from Kenyan waters of Lake Victoria have steadily increased through the 1980s, even though the fishing effort also increased during the same period. However, analysis of catch and effort data within and outside the Nyanza Gulf suggests an increase in catch rates due to a shift in effort from the inshore Gulf region to higher catch rates in the offshore region, rather than an increase in abundance. Analysis of catch rates by gear type both in and outside the Nyanza Gulf show that 1991 catch rates are lower than 1989 levels by 60-80% in some instances. Since the fishing power of these gears has increased during this period, it is likely that fish abundance declined more than catch rates. A dynamic population model is used to stimulate Nile perch dynamics. It indicates that a decline in catches should be anticipated.

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The primary objective of this study was to predict the distribution of mesophotic hard corals in the Au‘au Channel in the Main Hawaiian Islands (MHI). Mesophotic hard corals are light-dependent corals adapted to the low light conditions at approximately 30 to 150 m in depth. Several physical factors potentially influence their spatial distribution, including aragonite saturation, alkalinity, pH, currents, water temperature, hard substrate availability and the availability of light at depth. Mesophotic corals and mesophotic coral ecosystems (MCEs) have increasingly been the subject of scientific study because they are being threatened by a growing number of anthropogenic stressors. They are the focus of this spatial modeling effort because the Hawaiian Islands Humpback Whale National Marine Sanctuary (HIHWNMS) is exploring the expansion of its scope—beyond the protection of the North Pacific Humpback Whale (Megaptera novaeangliae)—to include the conservation and management of these ecosystem components. The present study helps to address this need by examining the distribution of mesophotic corals in the Au‘au Channel region. This area is located between the islands of Maui, Lanai, Molokai and Kahoolawe, and includes parts of the Kealaikahiki, Alalākeiki and Kalohi Channels. It is unique, not only in terms of its geology, but also in terms of its physical oceanography and local weather patterns. Several physical conditions make it an ideal place for mesophotic hard corals, including consistently good water quality and clarity because it is flushed by tidal currents semi-diurnally; it has low amounts of rainfall and sediment run-off from the nearby land; and it is largely protected from seasonally strong wind and wave energy. Combined, these oceanographic and weather conditions create patches of comparatively warm, calm, clear waters that remain relatively stable through time. Freely available Maximum Entropy modeling software (MaxEnt 3.3.3e) was used to create four separate maps of predicted habitat suitability for: (1) all mesophotic hard corals combined, (2) Leptoseris, (3) Montipora and (4) Porites genera. MaxEnt works by analyzing the distribution of environmental variables where species are present, so it can find other areas that meet all of the same environmental constraints. Several steps (Figure 0.1) were required to produce and validate four ensemble predictive models (i.e., models with 10 replicates each). Approximately 2,000 georeferenced records containing information about mesophotic coral occurrence and 34 environmental predictors describing the seafloor’s depth, vertical structure, available light, surface temperature, currents and distance from shoreline at three spatial scales were used to train MaxEnt. Fifty percent of the 1,989 records were randomly chosen and set aside to assess each model replicate’s performance using Receiver Operating Characteristic (ROC), Area Under the Curve (AUC) values. An additional 1,646 records were also randomly chosen and set aside to independently assess the predictive accuracy of the four ensemble models. Suitability thresholds for these models (denoting where corals were predicted to be present/absent) were chosen by finding where the maximum number of correctly predicted presence and absence records intersected on each ROC curve. Permutation importance and jackknife analysis were used to quantify the contribution of each environmental variable to the four ensemble models.

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The mucus surface layer of corals plays a number of integral roles in their overall health and fitness. This mucopolysaccharide coating serves as vehicle to capture food, a protective barrier against physical invasions and trauma, and serves as a medium to host a community of microorganisms distinct from the surrounding seawater. In healthy corals the associated microbial communities are known to provide antibiotics that contribute to the coral’s innate immunity and function metabolic activities such as biogeochemical cycling. Culture-dependent (Ducklow and Mitchell, 1979; Ritchie, 2006) and culture-independent methods (Rohwer, et al., 2001; Rohwer et al., 2002; Sekar et al., 2006; Hansson et al., 2009; Kellogg et al., 2009) have shown that coral mucus-associated microbial communities can change with changes in the environment and health condition of the coral. These changes may suggest that changes in the microbial associates not only reflect health status but also may assist corals in acclimating to changing environmental conditions. With the increasing availability of molecular biology tools, culture-independent methods are being used more frequently for evaluating the health of the animal host. Although culture-independent methods are able to provide more in-depth insights into the constituents of the coral surface mucus layer’s microbial community, their reliability and reproducibility rely on the initial sample collection maintaining sample integrity. In general, a sample of mucus is collected from a coral colony, either by sterile syringe or swab method (Woodley, et al., 2008), and immediately placed in a cryovial. In the case of a syringe sample, the mucus is decanted into the cryovial and the sealed tube is immediately flash-frozen in a liquid nitrogen vapor shipper (a.k.a., dry shipper). Swabs with mucus are placed in a cryovial, and the end of the swab is broken off before sealing and placing the vial in the dry shipper. The samples are then sent to a laboratory for analysis. After the initial collection and preservation of the sample, the duration of the sample voyage to a recipient laboratory is often another critical part of the sampling process, as unanticipated delays may exceed the length of time a dry shipper can remain cold, or mishandling of the shipper can cause it to exhaust prematurely. In remote areas, service by international shipping companies may be non-existent, which requires the use of an alternative preservation medium. Other methods for preserving environmental samples for microbial DNA analysis include drying on various matrices (DNA cards, swabs), or placing samples in liquid preservatives (e.g., chloroform/phenol/isoamyl alcohol, TRIzol reagent, ethanol). These methodologies eliminate the need for cold storage, however, they add expense and permitting requirements for hazardous liquid components, and the retrieval of intact microbial DNA often can be inconsistent (Dawson, et al., 1998; Rissanen et al., 2010). A method to preserve coral mucus samples without cold storage or use of hazardous solvents, while maintaining microbial DNA integrity, would be an invaluable tool for coral biologists, especially those in remote areas. Saline-saturated dimethylsulfoxide-ethylenediaminetetraacetic acid (20% DMSO-0.25M EDTA, pH 8.0), or SSDE, is a solution that has been reported to be a means of storing tissue of marine invertebrates at ambient temperatures without significant loss of nucleic acid integrity (Dawson et al., 1998, Concepcion et al., 2007). While this methodology would be a facile and inexpensive way to transport coral tissue samples, it is unclear whether the coral microbiota DNA would be adversely affected by this storage medium either by degradation of the DNA, or a bias in the DNA recovered during the extraction process created by variations in extraction efficiencies among the various community members. Tests to determine the efficacy of SSDE as an ambient temperature storage medium for coral mucus samples are presented here.

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To bring out the relative efficiency of various types of fishing gears, in the analysis of catch data, a combination of Tukey's test, consequent transformation and graphical analysis for outlier elimination has been introduced, which can be advantageously used for applying ANOVA techniques, Application of these procedures to actual sets of data showed that nonadditivity in the data was caused by either the presence of outliers, or the absence of a suitable transformation or both. As a corollary, the concurrent model: X sub(ij) = µ + α sub(i) + β sub(j) + λ α sub(i) β sub(j) + E sub(ij) adequately fits the data.