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Results for “Journal of Applied Statistics”

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At least 109 records · Page 6Linked to original sources
USGS2013

Seismicity around Parkfield correlates with static shear stress changes following the 2003 M w 6.5 San Simeon earthquake

Earthquakes trigger other earthquakes, but the physical mechanism of the triggering is currently debated. Most studies of earthquake triggering rely on earthquakes listed in catalogs, which are known to be incomplete around the origin times of large earthquakes and therefore missing potentially triggered events. Here we apply a waveform matched-filter technique to systematically detect earthquakes along the Parkfield section of the San Andreas Fault from 46 days before to 31 days after the nearby 2003 M w 6.5 San Simeon earthquake. After removing all possible false detections, we identify ~8 times more earthquakes than in the Northern California Seismic Network catalog. The newly identified events along the creeping section of the San Andreas Fault show a statistically significant decrease following the San Simeon main shock, which correlates well with the negative static stress changes (i.e., stress shadow) cast by the main shock. In comparison, the seismicity rate around Parkfield increased moderately where the static stress changes are positive. The seismicity rate changes correlate well with the static shear stress changes induced by the San Simeon main shock, suggesting a low friction in the seismogenic zone along the Parkfield section of the San Andreas Fault.

California↗
USGS2017

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.

International Journal of Applied Earth Observation↗
USGS1991

Rock chemistry and fluid inclusion studies as exploration tools for ore deposits in the Sila batholith, southern Italy

The Sila batholith is the focus of an extensive petrogenetic research program, which includes an assessment of its potential to host granite-related ore deposits. Univariate and multivariate statistical techniques were applied to major- and minor-element rock geochemical data. The analysis indicates that the highest potential for mineralization occurs in corundum-normative, peraluminous, unfoliated, relatively late-stage plutons. The plutons are enriched in Rb, Nb, Ta and U, but depleted in Fe, Mg and Sr. The K/Rb, Ba/Rb, Rb/Sr and Rb 3 /Ba·Sr·K indices and high R -factor scores of Si-K-Rb are typical of mineralized granitic rocks. A reconnaissance fluid inclusion study indicates that the sub-solidus rock was infiltrated by solutions of widely different temperatures (50–416°C) and variable salinities (0 to ∼26 wt.% NaCl equivalent). The higher-temperature solutions probably represent granite or magmatic-related Hercynian fluids, whereas the lower-temperature fluids may be either Hercynian or Alpine in age. Fluids with characteristics typical of mineralized “porphyry” systems have not been recognized.

Journal of Geochemical Exploration↗
USGS2015

Statistical analysis of soil geochemical data to identify pathfinders associated with mineral deposits: An example from the Coles Hill uranium deposit, Virginia, USA

Soil geochemical anomalies can be used to identify pathfinders in exploration for ore deposits. In this study, compositional data analysis is used with multivariate statistical methods to analyse soil geochemical data collected from the Coles Hill uranium deposit, Virginia, USA, to identify pathfinders associated with this deposit. Elemental compositions and relationships were compared between the collected Coles Hill soil and reference soil samples extracted from a regional subset of a national-scale geochemical survey. Results show that pathfinders for the Coles Hill deposit include light rare earth elements (La and Ce), which, when normalised by their Al content, are correlated with U/Al, and elevated Th/Al values, which are not correlated with U/Al, supporting decoupling of U from Th during soil generation. These results can be used in genetic and weathering models of the Coles Hill deposit, and can also be applied to future prospecting for similar U deposits in the eastern United States, and in regions with similar geological/climatic conditions.

Virginia↗
USGS2023

Mapping landslide susceptibility over large regions with limited data

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

California, Kentucky, North Carolina, West Virgini↗
USGS2025

Assessing decadal-scale coastal change likelihood to define the accuracy and application of scientific information

Defining the accuracy and uncertainties of scientific data products is critical to the usability and trustworthiness of scientific information for environmental management and conservation purposes, such as coastal resource prioritization, design, adaptation, and mitigation. The U.S. Geological Survey has a new decadal-scale coastal change assessment product that synthesizes nearly two dozen coastal datasets. A supervised machine-learning framework is used to combine existing datasets that describe the landscape and the hazards that affect it to determine the coastal change likelihood (CCL) in the coming decade at a resolution of 10 m per pixel for the NE United States from Maine to Virginia. Here, results from a series of statistical tests conducted on source data, the supervised classification, and the CCL outcomes as compared with historical land-cover change are presented. The overall accuracy of the aggregated land-cover dataset that serves as the foundation to which other source datasets are appended is 94%. The supervised learning classification that determines the final CCL output has an overall accuracy of 92%. The CCL predictions of high expected coastal change were consistent with 95% of the coastal and low-elevation landscape change in the last 20 years, as recorded by the Coastal Change Analysis Program land-cover change atlas. Results suggest that CCL provides accurate estimates of coastal landscape change in the next decade that are consistent with recent observed change. Additionally, best practices for applying CCL for planning purposes are outlined, and citing limitations, knowledge gaps, and opportunities for improved accuracy and further investigation are considered.

Journal of Coastal Research↗
USGS2023

Obtaining and applying public data for training students in technical statistical writing: Case studies with data from U.S. Geological Survey and general ecological literature

Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area expertise beyond those of a general education or lack connections to real-world applications. Many governments, including the United States, now require the release of data from projects they directly complete or fund though grants and contracts. We show how statistical educators may find datasets and incorporate them into courses. Specifically, we use two examples from the U.S. Geological Survey (USGS) and one example from the ecology literature. We demonstrate the use of these datasets in an upper-level analysis of variance (ANOVA) class. In addition to describing how we found the datasets, we describe how to include them into course work and the course’s student assessments. We have used these datasets over multiple semesters and included student feedback from these courses. Although our examples focus on an ANOVA class, the general methods for finding data shared here could be used for statistical classes ranging from high school to graduate education. Supplementary materials for this article are available online.

Journal of Statistics and Data Science Education↗
USGS2025

Spatially explicit power analyses to inform occupancy‐based multi‐species wildlife monitoring programmes

1. Current and accurate information on wildlife populations is integral to successful biodiversity management and conservation globally. Nevertheless, many monitoring programs fail in their attempts to accurately monitor populations of interest due to interlinked issues including insufficient sample sizes, inappropriate duration, lack of reproducibility, and lack of clearly stated objectives. These common pitfalls could be avoided through the elicitation of explicit monitoring objectives and the a priori use of simulations to inform minimum sampling design requirements to meet said objectives. 2. Here, we provide a blueprint for using spatially explicit power analyses to inform the design and implementation of multi-species monitoring programs on landscape-scales. As a demonstration, we used spatially explicit simulations to devise a suitable sampling regime to meet clearly specified monitoring objectives in New York State: to use annual occupancy-based monitoring to be able to detect 25% and 50% changes in abundance of populations over five- and ten- year periods for all species of management interest in New York State, USA. We focused our simulation efforts on three challenging focal species (black bear, Ursus americanus, bobcat, Lynx rufus, and American marten, Martes americana) that differ notably in their morphology, life histories, space use, detection probability, habitat suitability, and population sizes/trajectories, and thus provide extremes in the challenges presented when it comes to sampling appropriately to detect changes in abundance. 3. Our simulations demonstrate variable context dependent trade-offs in sampling designs (i.e. number of sites [J] and number of sampling occasions [K]), and identify necessary minimum detection probabilities that must be attained to achieve statistical power to detect changes of varying magnitudes in populations of varying sizes in the three focal species. The simulations also highlight that monitoring population increases is likely beyond the reach of occupancy-based monitoring programs for wide-ranging or locally abundant species. 4. Synthesis and applications : We combine the results from the single-species simulations to produce a multi-species sampling design that meets the specified objectives for all three species. While the case study is centered on developing a multi-species sampling regime for New York State, it provides a reproducible step-by-step framework using established methods for wildlife managers and other practitioners to inform their own context- and objective- specific multi-species occupancy-based monitoring programs.

New York↗
USGS2001

Survey protocol for assessment of endangered freshwater mussels the Allegheny River, Pennsylvania

The United States Endangered Species Act (ESA) requires a biological assessment of any activity that is authorized, funded, or carried out by a federal agency and likely to affect a federally listed endangered species or its critical habitat. We developed a standardized survey protocol for biological assessments of the effects of bridge replacements on 2 federally listed endangered freshwater mussels, Epioblasma torulosa rangiana and Pleurobema clava, found in the Allegheny River, Pennsylvania. The protocol combines qualitative sampling to determine species present with quantitative sampling to estimate density. Data on species present satisfy the minimum requirement of a biological assessment, whereas estimates of density are needed to assess the number of individuals that would die as a result of bridge replacement. Some excavation of substrate is necessary for unbiased population estimates because of species and sex-specific differences in detection at the substrate surface. We reduced the amount of excavation and cost of the survey by using a statistical sampling technique called double sampling, which uses counts from excavating a subset of quadrats to calibrate counts from searching the substrate surface of all quadrats. We applied the survey protocol to the Allegheny River at West Hickory where E. t. rangiana was the 3 rd and P. clava was the 4 th most abundant mussel at the site. Only 31% of P. clava and 52% of E. t. rangiana (80% of females, 45% of males) were detected at the substrate surface. We estimated that 9173 (95% CI: 6309–13,336) E. t. rangiana and 7010 (95% CI: 4462–11,013) P. clava lived within 50 m of the existing bridge and would be affected immediately by bridge construction. (Population estimates did not include mussels too small to be retained on a 6.35-mm-mesh sieve.) Application of the protocol is not limited to biological assessment under the ESA, but is appropriate where site-specific status of freshwater mussel populations is required.

Pennsylvania↗
USGS2012

Multivariate statistical approach to estimate mixing proportions for unknown end members

A multivariate statistical method is presented, which includes principal components analysis (PCA) and an end-member mixing model to estimate unknown end-member hydrochemical compositions and the relative mixing proportions of those end members in mixed waters. PCA, together with the Hotelling T 2 statistic and a conceptual model of groundwater flow and mixing, was used in selecting samples that best approximate end members, which then were used as initial values in optimization of the end-member mixing model. This method was tested on controlled datasets (i.e., true values of estimates were known a priori) and found effective in estimating these end members and mixing proportions. The controlled datasets included synthetically generated hydrochemical data, synthetically generated mixing proportions, and laboratory analyses of sample mixtures, which were used in an evaluation of the effectiveness of this method for potential use in actual hydrological settings. For three different scenarios tested, correlation coefficients ( R 2 ) for linear regression between the estimated and known values ranged from 0.968 to 0.993 for mixing proportions and from 0.839 to 0.998 for end-member compositions. The method also was applied to field data from a study of end-member mixing in groundwater as a field example and partial method validation.

Journal of Hydrology↗
USGS2024

Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization

The low-carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics-informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics-informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector-specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges .

Journal of Industrial Ecology↗
USGS2011

Density estimation in a wolverine population using spatial capture-recapture models

Classical closed-population capture-recapture models do not accommodate the spatial information inherent in encounter history data obtained from camera-trapping studies. As a result, individual heterogeneity in encounter probability is induced, and it is not possible to estimate density objectively because trap arrays do not have a well-defined sample area. We applied newly-developed, capture-recapture models that accommodate the spatial attribute inherent in capture-recapture data to a population of wolverines (Gulo gulo) in Southeast Alaska in 2008. We used camera-trapping data collected from 37 cameras in a 2,140-km 2 area of forested and open habitats largely enclosed by ocean and glacial icefields. We detected 21 unique individuals 115 times. Wolverines exhibited a strong positive trap response, with an increased tendency to revisit previously visited traps. Under the trap-response model, we estimated wolverine density at 9.7 individuals/1,000-km 2 (95% Bayesian CI: 5.9-15.0). Our model provides a formal statistical framework for estimating density from wolverine camera-trapping studies that accounts for a behavioral response due to baited traps. Further, our model-based estimator does not have strict requirements about the spatial configuration of traps or length of trapping sessions, providing considerable operational flexibility in the development of field studies.

Alaska↗
USGS2001

A geostatistical approach to predicting sulfur content in the Pittsburgh coal bed

The US Geological Survey (USGS) is completing a national assessment of coal resources in the five top coal-producing regions in the US. Point-located data provide measurements on coal thickness and sulfur content. The sample data and their geologic interpretation represent the most regionally complete and up-to-date assessment of what is known about top-producing US coal beds. The sample data are analyzed using a combination of geologic and Geographic Information System (GIS) models to estimate tonnages and qualities of the coal beds. Traditionally, GIS practitioners use contouring to represent geographical patterns of "similar" data values. The tonnage and grade of coal resources are then assessed by using the contour lines as references for interpolation. An assessment taken to this point is only indicative of resource quantity and quality. Data users may benefit from a statistical approach that would allow them to better understand the uncertainty and limitations of the sample data. To develop a quantitative approach, geostatistics were applied to the data on coal sulfur content from samples taken in the Pittsburgh coal bed (located in the eastern US, in the southwestern part of the state of Pennsylvania, and in adjoining areas in the states of Ohio and West Virginia). Geostatistical methods that account for regional and local trends were applied to blocks 2.7 mi (4.3 km) on a side. The data and geostatistics support conclusions concerning the average sulfur content and its degree of reliability at regional- and economic-block scale over the large, contiguous part of the Pittsburgh outcrop, but not to a mine scale. To validate the method, a comparison was made with the sulfur contents in sample data taken from 53 coal mines located in the study area. The comparison showed a high degree of similarity between the sulfur content in the mine samples and the sulfur content represented by the geostatistically derived contours. Published by Elsevier Science B.V.

International Journal of Coal Geology↗
USGS2018

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

Cuenca Los Ojos, San Bernardino National Wildlife ↗
USGS2017

Migratory connectivity of american woodcock using band return data

American woodcock ( Scolopax minor ) are managed as a Central and an Eastern population in the United States and Canada based on band return data showing little crossover between populations or management regions. The observed proportion of crossover between management regions, however, depends on the criteria used to subset the band return data. We analyzed the amount of crossover between management regions using only band return records that represent complete migrations between the breeding and wintering grounds by using only band return records in which the capture took place during the breeding season and the band recovery took place during the wintering season or vice versa ( n = 224). Additionally, we applied spatial statistics and a clustering algorithm to investigate woodcock migratory connectivity using this subset of migratory woodcock band return records. Using raw counts, 17.9% of records showed crossover between management regions, a higher proportion than the <5% crossover reported in studies that did not use only migratory band returns. Our results showed woodcock from the breeding grounds in the Central Region largely migrate to destinations within the Central Region, whereas woodcock from the breeding grounds in the Eastern Region migrate to destinations across the entire wintering range and mix with individuals from the Central Region. Using the division coefficient, we estimated that 54% of woodcock from the breeding grounds of the Eastern Region migrate to the Central Region wintering grounds. Our result that many woodcock from separate regions of the breeding grounds mix on the wintering grounds has implications for the 2-region basis for woodcock management. Elucidating finer scale movement patterns among regions provides a basis for reassessing the need for separate management regions to ensure optimal conservation and management of the species.

Journal of Wildlife Management↗
USGS2022

From drought to deluge: Spatiotemporal variation in migration routing, survival, travel time and floodplain use of an endangered migratory fish

We developed a novel statistical model to relate the daily survival and migration dynamics of an endangered anadromous fish to river flow and water temperature during both extreme drought and severe flooding in an intensively managed river system. Our Bayesian temporally stratified multistate mark recapture model integrates over unobserved travel times and route transitions to efficiently estimate covariate relationships and includes an adjustment for telemetry tag battery failure. We applied the model to acoustic-tagged juvenile Sacramento river winter-run Chinook salmon (Oncorhynchus tshawytscha) and found that survival decreased with decreasing river flows and increased water temperatures. We found that fish were likely to enter at a large floodplain during flood conditions and that survival in floodplain was comparable to the mainstem Sacramento river. Our study demonstrates the response of an endangered anadromous fish population to extreme spatial and temporal variability in habitat accessibility and quality. The general model framework we introduce here can be applied to telemetry of migratory fish through systems with multiple routes to efficiently estimate spatiotemporal variation in survival, travel time, and routing.

California↗
USGS2018

Comparison of HSPF and PRMS model simulated flows using different temporal and spatial scales in the Black Hills, South Dakota

The hydrological simulation program Fortran ( HSPF ) [ Hydrological Simulation Program Fortran version 12.2 (Computer software). USEPA, Washington, DC] and the precipitation runoff modeling system ( PRMS ) [ Precipitation Runoff Modeling System version 4.0 (Computer software). USGS, Reston, VA] models are semidistributed, deterministic hydrological tools for simulating the impacts of precipitation, land use, and climate on basin hydrology and streamflow. Both models have been applied independently to many watersheds across the United States. This paper reports the statistical results assessing various temporal (daily, monthly, and annual) and spatial (small versus large watershed) scale biases in HSPF and PRMS simulations using two watersheds in the Black Hills, South Dakota. The Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient ( r "> r r ), and coefficient of determination ( R 2 "> R 2 R2 ) statistics for the daily, monthly, and annual flows were used to evaluate the models’ performance. Results from the HSPF models showed that the HSPF consistently simulated the annual flows for both large and small basins better than the monthly and daily flows, and the simulated flows for the small watershed better than flows for the large watershed. In comparison, the PRMS model results show that the PRMS simulated the monthly flows for both the large and small watersheds better than the daily and annual flows, and the range of statistical error in the PRMS models was greater than that in the HSPF models. Moreover, it can be concluded that the statistical error in the HSPF and the PRMS daily, monthly, and annual flow estimates for watersheds in the Black Hills was influenced by both temporal and spatial scale variability.

South Dakota↗
USGS2016

Mercury accumulation and the mercury-PCB-sex interaction in summer flounder

Patterns in the relative differences in contaminant concentrations between the sexes of mature fish may reveal important behavioral and physiological differences between the sexes. We determined whole-fish total mercury (Hg) concentrations in 23 female summer flounder (Paralichthys dentatus) and 27 male summer flounder from New Jersey coastal waters. To estimate the change in Hg concentration due to release of eggs at spawning, Hg concentration in the somatic tissue and ovaries of 5 of the 23 female summer flounder were also determined. To ascertain whether most of the Hg in the summer flounder was methylmercury (MeHg), whole-fish MeHg concentrations were determined in all 50 summer flounder. Whole-fish Hg concentrations averaged 113 ng/g for females and 111 ng/g for males. Thus, females were 2% higher in Hg concentration than males, on average, but the difference was not statistically significant. Based on Hg determinations in the somatic tissue and ovaries, we predicted that Hg concentration of females would increase by 3.6%, on average, immediately after spawning due to release of eggs. On average, 92% of the Hg in the summer flounder was MeHg. To determine whether the effect of sex on Hg concentration was significantly different from the effect of sex on polychlorinated biphenyl (PCB) concentration, we paired our Hg determinations with PCB determinations from a previous study, and applied regression analysis. Sex significantly interacted with contaminant type (Hg or PCBs), as males were 43% higher in PCB concentration than females, whereas females were 2% higher in Hg concentration than males. Males eliminating Hg from their bodies at a faster rate than females was a likely explanation for this discrepancy between the two contaminant types. Overall, the Hg and PCB concentrations in the summer flounder were relatively low, and therefore our findings also had implications for continued operation of the summer flounder fishery.

Journal of Marine Science: Research & Development↗