988 resultados para Rejection-sampling Algorithm


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Adaptive cluster sampling (ACS) has been the subject of many publications about sampling aggregated populations. Choosing the criterion value that invokes ACS remains problematic. We address this problem using data from a June 1999 ACS survey for rockfish, specifically for Pacific ocean perch (Sebastes alutus), and for shortraker (S. borealis) and rougheye (S. aleutianus) rockfish combined. Our hypotheses were that ACS would outperform simple random sampling (SRS) for S. alutus and would be more applicable for S. alutus than for S. borealis and S. aleutianus combined because populations of S. alutus are thought to be more aggregated. Three alternatives for choosing a criterion value were investigated. We chose the strategy that yielded the lowest criterion value and simulated the higher criterion values with the data after the survey. Systematic random sampling was conducted across the whole area to determine the lowest criterion value, and then a new systematic random sample was taken with adaptive sampling around each tow that exceeded the fixed criterion value. ACS yielded gains in precision (SE) over SRS. Bootstrapping showed that the distribution of an ACS estimator is approximately normal, whereas the SRS sampling distribution is skewed and bimodal. Simulation showed that a higher criterion value results in substantially less adaptive sampling with little tradeoff in precision. When time-efficiency was examined, ACS quickly added more samples, but sampling edge units caused this efficiency to be lessened, and the gain in efficiency did not measurably affect our conclusions. ACS for S. alutus should be incorporated with a fixed criterion value equal to the top quartile of previously collected survey data. The second hypothesis was confirmed because ACS did not prove to be more effective for S. borealis-S. aleutianus. Overall, our ACS results were not as optimistic as those previously published in the literature, and indicate the need for further study of this sampling method.

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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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Light traps and channel nets are fixed-position devices that involve active and passive sampling, respectively, in the collection of settlement-stage larvae of coral-reef fishes. We compared the abundance, taxonomic composition, and size of such larvae caught by each device deployed simultaneously near two sites that differed substantially in current velocity. Light traps were more selective taxonomically, and the two sampling devices differed significantly in the abundance but not size of taxa caught. Most importantly, light traps and channel nets differed greatly in their catch efficiency between sites: light traps were ineffective in collecting larvae at the relatively high-current site, and channel nets were less efficient in collecting larvae at the low-current site. Use of only one of these sampling methods would clearly result in biased and inaccurate estimates of the spatial variation in larval abundance among locations that differ in current velocity. When selecting a larval sampling device, one must consider not only how well a particular taxon may be represented, but also the environmental conditions under which the device will be deployed.

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Experiments were conducted to study the significance of difference between samples taken from the surface and interior of a frozen shrimps block, as well as to determine the size of sample necessary to represent the whole block, with respect to bacterial count determination. The results showed that the surface samples and interior samples did not differ significantly at 5% level of significance and that the minimum quantity representative of the block was 21-26 gms in the case of a block weighing about 1300 gms. The procedure adopted for taking the bacterial count was the normal standard plate count method.