Effect of sampling interval on estimates of larval supply

Estimates of larval supply can provide information on year-class strength that is useful for fisheries management. However, larval supply is difficult to monitor because long-term, high-frequency sampling is needed. The purpose of this study was to subsample an 11-year record of daily larval supply of blue crab (Callinectes sapidus) to determine the effect of sampling interval on variability in estimates of supply. The coefficient of variation in estimates of supply varied by 0.39 among years at a 2-day sampling interval and 0.84 at a 7-day sampling interval. For 8 of the 11 years, there was a significant correlation between mean daily larval supply and lagged fishery catch per trip (coefficient of correlation [r]=0.88). When these 8 years were subsampled, a 2-day sampling interval yielded a significant correlation with fishery data only 64.5% of the time and a 3-day sampling interval never yielded a significant correlation. Therefore, high-frequency sampling (daily or every other day) may be needed to characterize interannual variability in larval supply.

Staging ovaries of Haddock (Melanogrammus aeglefinus): implications for maturity indices and field sampling practices

We build on recent efforts to standardize maturation staging methods through the development of a field-proof macroscopic ovarian maturity index for Haddock (Melanogrammus aeglefinus) for studies on diel spawning periodicity. A comparison of field and histological observations helped us to improve the field index and methods, and provided useful insight into the reproductive biology of Haddock and other boreal determinate fecundity species. We found reasonable agreement between field and histological methods, except for the regressing and regenerating stages (however, differentiation of these 2 stages is the least important distinction for determination of maturity or reproductive dynamics). The staging of developing ovaries was problematic for both methods partly because of asynchronous oocyte hydration during the early stage of oocyte maturation. Although staging on the basis of histology in a laboratory is generally more accurate than macroscopic staging methods in the field, we found that field observations can uncover errors in laboratory staging that result from bias in sampling unrepresentative portions of ovaries. For 2 specimens, immature ovaries observed during histological examination were incorrectly assigned as regenerating during macroscopic staging. This type of error can lead to miscalculation of length at maturity and of spawning stock biomass, metrics that are used to characterize the state of a fish population. The revised field index includes 3 new macroscopic stages that represent final oocyte maturation in a batch of oocytes and were found to be reliable for staging spawning readiness in the field. The index was found to be suitable for studies of diel spawning periodicity and conforms to recent standardization guidelines.

Efficient sampling of atomic configurational spaces.

We describe a method to explore the configurational phase space of chemical systems. It is based on the nested sampling algorithm recently proposed by Skilling (AIP Conf. Proc. 2004, 395; J. Bayesian Anal. 2006, 1, 833) and allows us to explore the entire potential energy surface (PES) efficiently in an unbiased way. The algorithm has two parameters which directly control the trade-off between the resolution with which the space is explored and the computational cost. We demonstrate the use of nested sampling on Lennard-Jones (LJ) clusters. Nested sampling provides a straightforward approximation for the partition function; thus, evaluating expectation values of arbitrary smooth operators at arbitrary temperatures becomes a simple postprocessing step. Access to absolute free energies allows us to determine the temperature-density phase diagram for LJ cluster stability. Even for relatively small clusters, the efficiency gain over parallel tempering in calculating the heat capacity is an order of magnitude or more. Furthermore, by analyzing the topology of the resulting samples, we are able to visualize the PES in a new and illuminating way. We identify a discretely valued order parameter with basins and suprabasins of the PES, allowing a straightforward and unambiguous definition of macroscopic states of an atomistic system and the evaluation of the associated free energies.

Sampling design tool for ArcGIS: Instruction manual.[for ESRI ArcGIS 10.0 Service Pack 3 or higher]

The Biogeography Branch’s Sampling Design Tool for ArcGIS provides a means to effectively develop sampling strategies in a geographic information system (GIS) environment. The tool was produced as part of an iterative process of sampling design development, whereby existing data informs new design decisions. The objective of this process, and hence a product of this tool, is an optimal sampling design which can be used to achieve accurate, highprecision estimates of population metrics at a minimum of cost. Although NOAA’s Biogeography Branch focuses on marine habitats and some examples reflects this, the tool can be used to sample any type of population defined in space, be it coral reefs or corn fields.

Statistical analyses to support guidelines for marine avian sampling: Final report. [Digital Supplements A-G attached]

Interest in development of offshore renewable energy facilities has led to a need for high-quality, statistically robust information on marine wildlife distributions. A practical approach is described to estimate the amount of sampling effort required to have sufficient statistical power to identify species specific “hotspots” and “coldspots” of marine bird abundance and occurrence in an offshore environment divided into discrete spatial units (e.g., lease blocks), where “hotspots” and “coldspots” are defined relative to a reference (e.g., regional) mean abundance and/or occurrence probability for each species of interest. For example, a location with average abundance or occurrence that is three times larger the mean (3x effect size) could be defined as a “hotspot,” and a location that is three times smaller than the mean (1/3x effect size) as a “coldspot.” The choice of the effect size used to define hot and coldspots will generally depend on a combination of ecological and regulatory considerations. A method is also developed for testing the statistical significance of possible hotspots and coldspots. Both methods are illustrated with historical seabird survey data from the USGS Avian Compendium Database.

Biogeographic characterization of fish communities and associated benthic habitats within the Flower Garden Banks National Marine Sanctuary: sampling design and implementation of scuba surveys on the coral caps

The Flower Garden Banks National Marine Sanctuary (FGBNMS) is located in the northwestern Gulf of Mexico approximately 180 km south of Galveston, Texas. The sanctuary’s distance from shore combined with its depth (the coral caps reach to within approximately 17 m of the surface) result in limited exposure of this coral reef ecosystem to natural and human-induced impacts compared to other coral reefs of the western Atlantic. In spite of this, the sanctuary still confronts serious impacts including hurricanes events, recent outbreaks of coral disease, an increase in the frequency of coral bleaching and the massive Diadema antillarum die-off during the mid-1980s. Anthropogenic impacts include large vessel anchoring, commercial and recreational fishing, recreational scuba diving, and oil and gas related activities. The FGBNMS was designated in 1992 to help protect against some of these impacts. Basic monitoring and research efforts have been conducted on the banks since the 1970s. Early on, these efforts focused primarily on describing the benthic communities (corals, sponges) and providing qualitative characterizations of the fish community. Subsequently, more quantitative work has been conducted; however, it has been limited in spatial scope. To complement these efforts, the current study addresses the following two goals put forth by sanctuary management: 1) to develop a sampling design for monitoring benthic fish communities across the coral caps; and 2) to obtain a spatial and quantitative characterization of those communities and their associated habitats.

Manual for the Sampling Design Tool for ArcGIS

The Biogeography Branch’s Sampling Design Tool for ArcGIS provides a means to effectively develop sampling strategies in a geographic information system (GIS) environment. The tool was produced as part of an iterative process of sampling design development, whereby existing data informs new design decisions. The objective of this process, and hence a product of this tool, is an optimal sampling design which can be used to achieve accurate, high-precision estimates of population metrics at a minimum of cost. Although NOAA’s Biogeography Branch focuses on marine habitats and some examples reflects this, the tool can be used to sample any type of population defined in space, be it coral reefs or corn fields.

Applications in adaptive cluster sampling of Gulf of Alaska rockfish

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.

Assessment of sampling methods to estimate horseshoe crab (Limulus polyphemus L.) egg density in Delaware Bay

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.