United States East Coast Research Articles

Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio

This research investigates the comparative predictive efficacy of two leading machine learning methodologies, specifically the XGBoost and Random Forest models, in estimating ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions along the east coast of North America. Using annual temperature datasets and relevant oceanographic parameters, the data is carefully processed, cleaned and sorted into training and test subsets via the RStudio Platform. The performance evaluation model is carried out using predetermined machine learning assessment criteria, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared. The results show the superiority of the XGBoost model compared to Random Forest in terms of prediction accuracy and minimizing prediction errors. The XGBoost model shows lower MSE values and higher R-squared values than the Random Forest model, indicating its better capacity in explaining data variations. XGBoost consistently provides more accurate predictions and shows higher sensitivity in identifying important factors influencing ocean temperature fluctuations than Random Forest. This research significantly improves understanding and prognostic capabilities regarding ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions. Empirical evidence underlines the efficacy of the XGBoost model in predicting ocean temperatures in the studied region. Continuous model evaluation and parameter refinement for both methodologies is critical to establishing standards for optimal prediction performance. The findings of this research have implications for the fields of oceanography and climate science, and offer potential pathways to comprehensively understand and mitigate the impacts of climate change on marine ecosystems.

Microplastic Volatile Organic Compounds Found within Chrysaora chesapeakei in the Patuxent River, Maryland

Microplastics are tangible particles of less than 0.2 inches in diameter that are ubiquitously distributed in the biosphere and accumulate in water bodies. During the east-coast hot summers (23–29 °C) of 2021 and 2022, June through September, we captured copious amounts of the jellyfish Chrysaora chesapeakei, a predominant species found in the Patuxent River of the Chesapeake Bay in Maryland on the United States East Coast. We determined that their gelatinous bodies trapped many microplastics through fluorescent microscopy studies using Rhodamine B staining and Raman Spectroscopy. The chemical nature of the microplastics was detected using gas chromatography–mass spectroscopy headspace (SPME-GC-MS) and solvent extraction (GC-MS) methods through a professional commercial materials evaluation laboratory. Numerous plastic-affiliated volatile organic compounds (VOCs) from diverse chemical origins and their functional groups (alkanes, alkenes, acids, aldehydes, ketones, ethers, esters, and alcohols) along with other non-microplastic volatile organic compounds were observed. Our findings corroborate data in the available scientific literature, distinguishing our finding’s suitability.

Design of sustainable supply chains for managing plastic waste: The case of low density polyethylene

Plastic waste management presents a significant challenge that requires the integration of supply chain and sustainability principles. This study proposes a comprehensive supply chain model for plastic waste and management, specifically low-density polyethylene (LDPE), focusing on economic and environmental considerations. A model based on Mixed Integer Linear Programming (MILP) is developed to address the type of plastic waste management technology, their location through the United States East Coast and the effect of transitioning to an electrified transportation. The model determines plastic waste pyrolysis as the preferred technology to maximize profitability, and mechanical recycling combined with hydrogenolysis and hydrocracking to minimize the global warming potential (GWP). The mix of technologies for minimizing the GWP is highly correlated with the type of transportation, being desired to remove hydrogenolysis when electric trucks are introduced. This electrification of the transportation reduces the emissions by 23 %, but with an increase in the costs by 4 %.

A direct insertion technique to assimilate sea surface height into a storm surge model

Natural hazards like hurricanes, cyclones, and typhoons frequently cause major economic and livelihood loss in coastal areas around the world. However, accurately predicting sea surface height and flood inundation extent at the operational level remains a challenge. This work tests the hypothesis that assimilating satellite-like sea surface observations within a storm surge model can improve the estimation of storm surge dynamics for operational forecasting. A synthetic experiment is proposed based on Hurricane Irene, which moved along the United States East Coast in 2011. With a focus on the Chesapeake Bay area, a framework is developed to simulate coastal sea surface height using a storm surge model and merging synthetic satellite observations. To represent the “truth” of the system, a hindcast simulation was run for the Hurricane Irene event. Different satellite revisit times are considered to assess the improvement over the numerical model open-loop (i.e., no data assimilation) simulation. Results indicate that the assimilation of synthetic sea surface height observations has the potential to reduce errors and improve the accuracy in the hydrodynamic model simulations by up to 25% (in terms of correlation coefficient), especially when the satellite has short revisit times (i.e., daily, half-daily and flood tides). Such improvements are consistent regardless of the bias sign in the satellite observations and the location, which is fundamental in an operational setting where errors in satellite observations are often unknown.

Multivariate Analysis of the Community Composition of Tidal Freshwater Forests on the Altamaha River, Georgia

Situated in the transitional zone between non-tidal forests upstream and tidal freshwater marshes downstream, tidal freshwater forests (TFF) occupy a unique and increasingly precarious habitat due to the threat of saltwater intrusion and sea level rise. Salinization causes tree mortality and forest-to-marsh transition, which reduces biodiversity and carbon sequestration. The Altamaha River is the longest undammed river on the United States East Coast and has extensive TFF, but there have been only limited field studies examining TFF along the entire gradient of salinity and flooding. We surveyed thirty-eight forest plots on the Altamaha River along a gradient of tidal influence, and measured tree species composition, diameter, and height. Hierarchical clustering and indicator species analysis were used to identify TFF communities. The relationship of these communities to elevation and river distance was assessed using non-metric multidimensional scaling (NMDS). We identified six significantly different forest communities: Oak/Hornbeam, Water Tupelo, Bald Cypress/Tupelo, Pine, Swamp Tupelo, and Bald Cypress. Both elevation and river distance were significantly correlated with plot species composition (p = 0.001). Plots at the downstream extent of our study area had lower stem density, basal area, and species diversity than those further upstream, suggesting saltwater intrusion. This study demonstrates the importance of and need for thorough and robust analyses of tidal freshwater forest composition to improve prediction of TFF response to sea level rise.

A critical evaluation of adult blacktip shark, Carcharhinus limbatus, distribution off the United States East Coast

A critical evaluation of adult blacktip shark, Carcharhinus limbatus, distribution off the United States East Coast

Extreme Events and Probability Analysis Along the United States East Coast Based on High Spatial‐Coverage Reconstructed Storm Surges

AbstractAnalyzing the features of extreme events and estimating their probabilities robustly require high spatial coverage, high temporal resolution, and sufficiently long storm surge (SS) records. However, in situ observations cannot always meet these demands due to spatiotemporal sparseness. Here, we proposed a novel regional all‐site modeling framework based on a machine learning method, the extreme gradient boosting tree. This framework can reconstruct long SS records simply and quickly and can estimate storm surges simultaneously at both gauged and ungauged locations. Compared to in situ observations, the distribution patterns of SS variations during extreme events can be recognized easily from the reconstructed hourly SS data set. Since its available record is longer than 60 years (1959–2020), the estimation uncertainties of extreme event probabilities are significantly decreased. Noticeably high extreme SS return levels were found along the coast of the northern Gulf of Mexico, which should be given great attention.

Self-Injurious Thoughts & Behaviors of Firefighters: A Quantitative Descriptive Study

Introduction:Little is known about Self-Injurious Thoughts & Behaviors and Non-Suicidal Self-Injury in firefighters in two East Coast United States metropolitan fire departments based on fire service tenure.Method:• Study comprised of two parts, a survey and a questionnaire, both conducted online. Using the Computer Adaptive Test–Suicide Scale The only computer-based adaptive mental and behavioral health assessment clinically validated worldwide.Validated against face-to-face structured clinician-led assessment.Participants received a unique identifier and hyperlink allowing them access and confidentiality.Study was completed on participants' personal electronic devices, on their own time, at their own pace.A single-factor or One-Way ANOVA tested for a significant relationship between the variables and the four tenure groups simultaneously. Time of administration averaged 86 seconds, with a median of eleven questions.Results:The C-SSRS identified six participants triggering suicide alerts. One in the early-career category and five in the late-career category. The CAT-SS identified one participant as high-risk and 33 participants for suicidality. One participant in the early-career category.Conclusion:Early-career and late-career firefighters have more self-injurious thoughts and behaviors and mid-career firefighters have the least.

Typologies of multiple vulnerabilities and climate gentrification across the East Coast of the United States

Climate change is expected to alter community dynamics and characteristics in complex ways, with the most severe implications for already vulnerable populations. The theory of climate gentrification provides a lens for investigating the ways in which climate change interacts with community demographics, housing, and socioeconomic characteristics in coastal communities. Here we apply unsupervised machine learning to 51 counties along the United States East Coast to identify four distinct clusters of multifaceted vulnerability related to social, housing, and environmental variables. Two of the four clusters, interpreted as superior investment and disinvestment, may be indicative of different pathways of climate gentrification. We also demonstrate the applicability of our methodology at finer spatial scales using a case study within North Carolina. As coastal climate impacts increase, this work demonstrates the need for adaptation planning that proactively considers compounding vulnerabilities to avoid disproportionate impacts on the most vulnerable.

Dynamic downscaling of wind speed over the North Atlantic Ocean using CMIP6 projections: Implications for offshore wind power density

Offshore wind energy is an important agent to fight climate change. However, it is simultaneously very sensitive to climate change. This study analyzes the future changes in wind speed of 10 m above sea surface (V10) in the North Atlantic Ocean and how these variations may affect offshore wind energy resources for three potential subregions (the United States (US) East Coast, western Iberian Peninsula, and the Caribbean Sea). Dynamic downscaling of three different future scenarios of the CESM2 global climate model (CMIP6 project) was performed using the WRF-ARW atmospheric model. V10 is expected to decrease in the winter and spring seasons but increase in summer and autumn, mainly in tropical regions up to 30 °N. Annually, it shows the maximum increase in the tropical region. For the Iberian Peninsula subregion, significant increases in summer are expected for wind power density (WPD) along the 21st century, but there is uncertainty for the other seasons. A WPD decrease in winter and increases in summer and autumn are expected along the 21st century for the US subregion. No significant changes were observed at annual scale. Finally, for the Caribbean Sea, a decrease is projected in the Yucatan Basin and considerable increases are foreseen for the Colombia and Venezuela basins.

Experiences of social work students in the early stages of the COVID-19 pandemic: a mixed-methods study

ABSTRACT The COVID-19 pandemic has triggered a growing body of research on the impacts of pandemics on students globally. This holds true for the social work discipline: pandemic-related research on the overall social work education and practice has been on the ascendancy. That said, there is still the need for research studies that specifically focus on the impacts of pandemics and crises on social work students. This exploratory, IRB-approved mixed-methods study collected quantitative and qualitative data from 66 undergraduate and graduate social work students at a large public state university on the United States east coast. Participants responded to survey questions about their experiences and stress responses and areas of support during the early part of the ongoing pandemic. Utilizing the researchers’ own public university in the U.S. east coast as a case study, this article highlights examples and explores the level of institutional, faculty, and staff support from both the undergraduate and graduate levels. This paper contributes to the social work field by examining student stress responses through quantitative and qualitative data from students’ self-reported responses. It also highlights certain protective factors—including support systems and coping mechanisms—for students during the early stages of the pandemic.

A Hybrid Dynamical Approach for Seasonal Prediction of Sea‐Level Anomalies: A Pilot Study for Charleston, South Carolina

AbstractUsing Earth system models for seasonal sea‐level prediction remains challenging due to model biases and initialization shocks. Here we present a hybrid dynamical approach for seasonal sea‐level prediction to alleviate some of these issues. The approach is based on convolving atmospheric forcings with sea‐level sensitivities to these forcings. The sensitivities are pre‐computed by the adjoint model of the Estimating Circulation and Climate of the Ocean (ECCO) system. The forcings are a concatenation of ECCO forcings before prediction initialization and a 10‐member predicted atmospheric forcing ensemble from the Community Climate System Model version 4 (CCSM4) after initialization, with offline forcing bias corrections applied using the observationally‐constrained ECCO seasonal forcing climatology. As a pilot study, we conducted 12‐month hindcasts from 1995 to 2016 in Charleston (United States East Coast). Our approach avoids drifts in CCSM4 sea‐level predictions and beats seasonal climatology and damped persistence as predictors up to a 6‐month lead time. The prediction skill comes from two factors: (a) ECCO forcings prior to prediction initialization influence sea level after initialization through delayed oceanic adjustments (e.g., coastally‐trapped waves, open‐ocean Rossby waves, and advection of steric anomalies) leading to skillful predictions beyond 2 months after initialization, and (b) the 10‐member CCSM4 ensemble forcing predictions have relatively good skill at 1–2 months lead times. Our method is computationally efficient for operational sea‐level prediction at specific locations and can attribute sea‐level prediction skill and uncertainty to specific forcings or forcing from particular regions, thereby providing useful information to seasonal prediction centers for improving their prediction systems.

Genomic Degeneration and Reduction in the Fish Pathogen Mycobacterium shottsii.

ABSTRACTMycobacterium shottsii is a dysgonic, nonpigmented mycobacterium originally isolated from diseased striped bass (Morone saxatilis) in the Chesapeake Bay, USA. Genomic analysis reveals that M. shottsii is a Mycobacterium ulcerans/Mycobacterium marinum clade (MuMC) member, but unlike the superficially similar M. pseudoshottsii, also isolated from striped bass, it is not an M. ulcerans ecovar, instead belonging to a transitional group of strains basal to proposed “Aronson” and “M” lineages. Although phylogenetically distinct from the human pathogen M. ulcerans, the M. shottsii genome shows parallel but nonhomologous genomic degeneration, including massive accumulation of pseudogenes accompanied by proliferation of unique insertion sequences (ISMysh01, ISMysh03), large-scale deletions, and genomic reorganization relative to typical M. marinum strains. Coupled with its observed ecological characteristics and loss of chromogenicity, the genomic structure of M. shottsii is suggestive of evolution toward a state of obligate pathogenicity, as observed for other Mycobacterium spp., including M. ulcerans, M. tuberculosis, and M. leprae.IMPORTANCE Morone saxatilis (striped bass) is an ecologically and economically important finfish species on the United States east coast. Mycobacterium shottsii and Mycobacterium pseudoshottsii were originally described in the early 2000s as novel species from outbreaks of visceral and dermal mycobacteriosis in this species. Biochemical and genetic characterization place these species within the Mycobacterium ulcerans/M. marinum clade (MuMC), and M. pseudoshottsii has been proposed as an ecovar of M. ulcerans. Here, we describe the complete genome of M. shottsii, demonstrating that it is clearly not an M. ulcerans ecovar; however, it has undergone parallel genomic modification suggestive of a transition to obligate pathogenicity. As in M. ulcerans, the M. shottsii genome demonstrates widespread pseudogene formation driven by proliferation of insertion sequences, as well as genomic reorganization. This work clarifies the phylogenetic position of M. shottsii relative to other MuMC members and provides insight into processes shaping its genomic structure.

Common Environmental Pollutants Negatively Affect Development and Regeneration in the Sea Anemone Nematostella vectensis Holobiont

The anthozoan sea anemoneNematostella vectensisbelongs to the phylum of cnidarians which also includes jellyfish and corals. Nematostella are native to United States East Coast marsh lands, where they constantly adapt to changes in salinity, temperature, oxygen concentration and pH. Its natural ability to continually acclimate to changing environments coupled with its genetic tractability renderNematostellaa powerful model organism in which to study the effects of common pollutants on the natural development of these animals. Potassium nitrate, commonly used in fertilizers, and Phthalates, a component of plastics are frequent environmental stressors found in coastal and marsh waters. Here we present data showing how early exposure to these pollutants lead to dramatic defects in development of the embryos and eventual mortality possibly due to defects in feeding ability. Additionally, we examined the microbiome of the animals and identified shifts in the microbial community that correlated with the type of water that was used to grow the animals, and with their exposure to pollutants.

Biomass Burning Over the United States East Coast and Western North Atlantic Ocean: Implications for Clouds and Air Quality.

Biomass burning (BB) aerosol events were characterized over the U.S. East Coast and Bermuda over the western North Atlantic Ocean (WNAO) between 2005 and 2018 using a combination of ground-based observations, satellite data, and model outputs. Days with BB influence in an atmospheric column (BB days) were identified using criteria biased toward larger fire events based on anomalously high AERONET aerosol optical depth (AOD) and MERRA-2 black carbon (BC) column density. BB days are present year-round with more in June-August (JJA) over the northern part of the East Coast, in contrast to more frequent events in March-May (MAM) over the southeast U.S. and Bermuda. BB source regions in MAM are southern Mexico and by the Yucatan, Central America, and the southeast U.S. JJA source regions are western parts of North America. Less than half of the BB days coincide with anomalously high PM2.5 levels in the surface layer, according to data from 14 IMPROVE sites over the East Coast. Profiles of aerosol extinction suggest that BB particles can be found in the boundary layer and into the upper troposphere with the potential to interact with clouds. Higher cloud drop number concentration and lower drop effective radius are observed during BB days. In addition, lower liquid water path is found during these days, especially when BB particles are present in the boundary layer. While patterns are suggestive of cloud-BB aerosol interactions over the East Coast and the WNAO, additional studies are needed for confirmation.

Underwater Sound Propagation Modeling in a Complex Shallow Water Environment

Three-dimensional (3D) effects can profoundly influence underwater sound propagation in shallow-water environments, hence, affecting the underwater soundscape. Various geological features and coastal oceanographic processes can cause horizontal reflection, refraction, and diffraction of underwater sound. In this work, the ability of a parabolic equation (PE) model to simulate sound propagation in the extremely complicated shallow water environment of Long Island Sound (United States east coast) is investigated. First, the 2D and 3D versions of the PE model are compared with state-of-the-art normal mode and beam tracing models for two idealized cases representing the local environment in the Sound: (i) a 2D 50-m flat bottom and (ii) a 3D shallow water wedge. After that, the PE model is utilized to model sound propagation in three realistic local scenarios in the Sound. Frequencies of 500 and 1500 Hz are considered in all the simulations. In general, transmission loss (TL) results provided by the PE, normal mode and beam tracing models tend to agree with each other. Differences found emerge with (1) increasing the bathymetry complexity, (2) expanding the propagation range, and (3) approaching the limits of model applicability. The TL results from 3D PE simulations indicate that sound propagating along sand bars can experience significant 3D effects. Indeed, for the complex shallow bathymetry found in some areas of Long Island Sound, it is challenging for the models to track the interference effects in the sound pattern. Results emphasize that when choosing an underwater sound propagation model for practical applications in a complex shallow-water environment, a compromise will be made between the numerical model accuracy, computational time, and validity.

Bacterial and fungal growth in sputum cultures from 165 COVID-19 pneumonia patients requiring intubation: evidence for antimicrobial resistance development and analysis of risk factors

BackgroundCoronavirus SARS-CoV-2 causes COVID-19 illness which can progress to severe pneumonia. Empiric antibacterials are often employed though frequency of bacterial coinfection superinfection is debated and concerns raised about selection of bacterial antimicrobial resistance. We evaluated sputum bacterial and fungal growth from 165 intubated COVID-19 pneumonia patients. Objectives were to determine frequency of culture positivity, risk factors for and outcomes of positive cultures, and timing of antimicrobial resistance development.MethodsRetrospective reviews were conducted of COVID-19 pneumonia patients requiring intubation admitted to a 1058-bed four community hospital system on the east coast United States, March 1 to May 1, 2020. Length of stay (LOS) was expressed as mean (standard deviation); 95% confidence interval (95% CI) was computed for overall mortality rate using the exact binomial method, and overall mortality was compared across each level of a potential risk factor using a Chi-Square Test of Independence. All tests were two-sided, and significance level was set to 0.05.ResultsAverage patient age was 68.7 years and LOS 19.9 days. Eighty-three patients (50.3% of total) originated from home, 10 from group homes (6.1% of total), and 72 from nursing facilities (43.6% of total). Mortality was 62.4%, highest for nursing home residents (80.6%). Findings from 253 sputum cultures overall did not suggest acute bacterial or fungal infection in 73 (45%) of 165 individuals sampled within 24 h of intubation. Cultures ≥ 1 week following intubation did grow potential pathogens in 72 (64.9%) of 111 cases with 70.8% consistent with late pneumonia and 29.2% suggesting colonization. Twelve (10.8% of total) of these late post-intubation cultures revealed worsened antimicrobial resistance predominantly in Pseudomonas, Enterobacter, or Staphylococcus aureus.ConclusionsIn severe COVID-19 pneumonia, a radiographic ground glass interstitial pattern and lack of purulent sputum prior to/around the time of intubation correlated with no culture growth or recovery of normal oral flora ± yeast. Discontinuation of empiric antibacterials should be considered in these patients aided by other clinical findings, history of prior antimicrobials, laboratory testing, and overall clinical course. Continuing longterm hospitalisation and antibiotics are associated with sputum cultures reflective of hospital-acquired microbes and increasing antimicrobial resistance.Trial registration: Not applicable as this was a retrospective chart review study without interventional arm.

Regime Behavior in the Upper Stratosphere as a Precursor of Stratosphere-Troposphere Coupling in the Northern Winter

AbstractA new regime index is constructed to capture the seasonal development of the stratospheric polar vortex in the northern winter, based on the standard deviation of Ertel’s potential vorticity in the upper stratosphere in November-December. The narrow-jet flow regime is characterized by a polar vortex that is more confined to high latitudes in the early winter upper stratosphere. This upper-level vortex configuration is more susceptible to the disturbances of upward propagating planetary-scale Rossby waves; the stratospheric polar vortex thus weakens earlier and is vertically shallower. The wide-jet flow regime is characterized by a broader-than-average polar vortex that extends further into the subtropics in the early-winter upper stratosphere. The polar night jet then gradually strengthens, moves poleward and penetrates deep into the lowermost stratosphere, with a sharply-defined polar vortex edge accompanied due to more frequent Rossby wave breaking.Composite differences analyses show that the wide and narrow jet regimes, defined in the uppermost stratosphere in November-December, lead to different circulation anomalies of the lower-stratosphere and the troposphere in January-February, offering the potential for improved predictability of sub-seasonal to seasonal forecasts up to two months ahead. The lower tropospheric responses in January-February are zonally asymmetric. The narrow-jet regime projects most strongly over the North Atlantic, with an equatorward-shifted, and/or broader mid-latitude westerly jet. The wide-jet-regime response is characterized by a weakened mid-latitude westerly jet over the North Pacific. The two flow regimes also differ in their impacts on the storm track over Western Europe and the east coast of America, which may have implications for extreme weather events in those regions.

The Path to an Ecosystem Approach for Forage Fish Management: A Case Study of Atlantic Menhaden

Atlantic menhaden (Brevoortia tyrannus) support the largest fishery by volume on the United States East Coast, while also playing an important role as a forage species. Managers’ and stakeholders’ increasing concerns about the impact of Atlantic menhaden harvest on ecosystem processes led to an evolution in the assessment and management of this species from a purely single-species approach to an ecosystem approach. The first coastwide stock assessment of Atlantic menhaden for management used a single-species virtual population analysis (VPA). Subsequent assessments used a forward projecting statistical catch-at-age framework that incorporated estimates of predation mortality from a multispecies VPA while analytical efforts continued toward the development of ecosystem models and explicit ecological reference points (ERPs) for Atlantic menhaden. As an interim step while ecosystem models were being developed, a series of ad hoc measures to preserve Atlantic menhaden biomass for predators were used by managers. In August 2020, the Atlantic States Marine Fisheries Commission formally adopted an ecological modeling framework as a tool to set reference points and harvest limits for the Atlantic menhaden that considers their role as a forage fish. This is the first example of a quantitative ecosystem approach to setting reference points on the United States Atlantic Coast and it represents a significant advance for forage fish management. This case study reviews the history of Atlantic menhaden stock assessments and management, outlines the progress on the current implementation of ERPs for this species, and highlights future research and management needs to improve and expand ecosystem-based fisheries management.

Application of a parametric survival model to understand capture-related mortality and predation of yellowfin tuna (Thunnus albacares) released in a recreational fishery

Distinguishing the cause and magnitude of capture-related mortality (CRM) in fishes is important for effective management. To better understand CRM in yellowfin tuna (Thunnus albacares) released in the recreational troll fishery off the United States east coast, 48 fish (76–127 cm curved fork length, CFL) were monitored for up to 86 days postrelease with survivorship pop-up satellite archival tags. Recovered data indicated 40 fish were alive at the time of tag detachment and eight died, including six from predation, from 0 to 30 days postrelease. Survival model variants were constructed to independently estimate the rates of immediate capture and handling (CH), postrelease (PR), total CRM (CH + PR), and natural mortality (M) for small (≤103 cm CFL) and large (>103 cm CFL) fish under different hypotheses and causes of mortality. CH was low (0%–8%) for both size classes but predation was an important component of PR, particularly in the small size class. Total CRM was 51% (95% CI: 26%, 81%) for small and 8% (95% CI: 2%, 30%) for large fish.