Analysis of variance and standardization of longline hook rates of bigeye (Thunnus obesus) and yellowfin (Thunnus albacares) tunas in the Eastern Pacific Ocean during 1975-1987

ENGLISH: Longline hook rates of bigeye and yellowfin tunas in the eastern Pacific Ocean were standardized by maximum depth of fishing, area, and season, using generalized linear models (GLM's). The annual trends of the standardized hook rates differ from the unstandardized, and are more likely to represent the changes in abundance of tunas in the age groups most vulnerable to longliners in the fishing grounds. For both species all of the interactions in the GLM's involving years, depths of fishing, areas, and seasons were significant. This means that the annual trends in hook rates depend on which depths, areas, and seasons are being considered. The overall average hook rates for each were estimated by weighting each 5-degree quadrangle equally and each season by the number of months in it. Since the annual trends in hook rates for each fishing depth category are roughly the same for bigeye, total average annual hook rate estimates are possible with the GLM. For yellowfin, the situation is less clear because of a preponderance of empty cells in the model. The full models explained 55% of the variation in bigeye hook rate and 33% of that of yellowfin. SPANISH: Se estandardizaron las tasas de captura con palangre de atunes patudo y aleta amarilla en el Océano Pacífico oriental por la profunidad máxima de pesca, área, y temporada, usando modelos lineales generalizados (MLG). Las tendencias anuales de las tasas de captura estandardizadas son diferentes a las de las tasas no estandardizadas, y es más que representen los cambios en la abundancia de los atunes en los grupos de edad más vulnerables a los palangreros en las áreas de pesca. Para ambas especies fueron significativas todas las interacciones en los MLG con año, profundidad de pesca, área, y temporada. Esto significa que las tendencias anuales de las tasas de captura dependen de cuál profundidad, área, y temporado se está considerando. Para la estimación de la tasa de captura general media para cada especie se ponderó cada cuadrángulo de 5 grados igualmente y cada temporada por el número de meses que contiene. Ya que las tendencias anuales en las tasas de captura para cada categoría de profundidad de pesca son aproximadamente iguales para el patudo, son posibles estimaciones de la tasa de captura anual media total con el MLG. En el caso del aleta amarilla, la situación es más confusa, debido a una preponderancia de celdas vacías en el modelo. Los modelos completos explican el 55% de la variación de la tasa de captura de patudo y 33% de la del aleta amarilla. (PDF contains 19 pages.)

An Evaluation of the area stratification used for sampling tunas in the eastern Pacific Ocean and implications for estimating total annual catches

The Inter-American Tropical Tuna Commission (IATTC) staff has been sampling the size distributions of tunas in the eastern Pacific Ocean (EPO) since 1954, and the species composition of the catches since 2000. The IATTC staff use the data from the species composition samples, in conjunction with observer and/or logbook data, and unloading data from the canneries to estimate the total annual catches of yellowfin (Thunnus albacares), skipjack (Katsuwonus pelamis), and bigeye (Thunnus obesus) tunas. These sample data are collected based on a stratified sampling design. I propose an update of the stratification of the EPO into more homogenous areas in order to reduce the variance in the estimates of the total annual catches and incorporate the geographical shifts resulting from the expansion of the floating-object fishery during the 1990s. The sampling model used by the IATTC is a stratified two-stage (cluster) random sampling design with first stage units varying (unequal) in size. The strata are month, area, and set type. Wells, the first cluster stage, are selected to be sampled only if all of the fish were caught in the same month, same area, and same set type. Fish, the second cluster stage, are sampled for lengths, and independently, for species composition of the catch. The EPO is divided into 13 sampling areas, which were defined in 1968, based on the catch distributions of yellowfin and skipjack tunas. This area stratification does not reflect the multi-species, multi-set-type fishery of today. In order to define more homogenous areas, I used agglomerative cluster analysis to look for groupings of the size data and the catch and effort data for 2000–2006. I plotted the results from both datasets against the IATTC Sampling Areas, and then created new areas. I also used the results of the cluster analysis to update the substitution scheme for strata with catch, but no sample. I then calculated the total annual catch (and variance) by species by stratifying the data into new Proposed Sampling Areas and compared the results to those reported by the IATTC. Results showed that re-stratifying the areas produced smaller variances of the catch estimates for some species in some years, but the results were not significant.

A-SCALA: an age-structured statistical catch-at-length analysis for assessing tuna stocks in the eastern tropical Pacific Ocean

English: We describe an age-structured statistical catch-at-length analysis (A-SCALA) based on the MULTIFAN-CL model of Fournier et al. (1998). The analysis is applied independently to both the yellowfin and the bigeye tuna populations of the eastern Pacific Ocean (EPO). We model the populations from 1975 to 1999, based on quarterly time steps. Only a single stock for each species is assumed for each analysis, but multiple fisheries that are spatially separate are modeled to allow for spatial differences in catchability and selectivity. The analysis allows for error in the effort-fishing mortality relationship, temporal trends in catchability, temporal variation in recruitment, relationships between the environment and recruitment and between the environment and catchability, and differences in selectivity and catchability among fisheries. The model is fit to total catch data and proportional catch-at-length data conditioned on effort. The A-SCALA method is a statistical approach, and therefore recognizes that the data collected from the fishery do not perfectly represent the population. Also, there is uncertainty in our knowledge about the dynamics of the system and uncertainty about how the observed data relate to the real population. The use of likelihood functions allow us to model the uncertainty in the data collected from the population, and the inclusion of estimable process error allows us to model the uncertainties in the dynamics of the system. The statistical approach allows for the calculation of confidence intervals and the testing of hypotheses. We use a Bayesian version of the maximum likelihood framework that includes distributional constraints on temporal variation in recruitment, the effort-fishing mortality relationship, and catchability. Curvature penalties for selectivity parameters and penalties on extreme fishing mortality rates are also included in the objective function. The mode of the joint posterior distribution is used as an estimate of the model parameters. Confidence intervals are calculated using the normal approximation method. It should be noted that the estimation method includes constraints and priors and therefore the confidence intervals are different from traditionally calculated confidence intervals. Management reference points are calculated, and forward projections are carried out to provide advice for making management decisions for the yellowfin and bigeye populations. Spanish: Describimos un análisis estadístico de captura a talla estructurado por edad, A-SCALA (del inglés age-structured statistical catch-at-length analysis), basado en el modelo MULTIFAN- CL de Fournier et al. (1998). Se aplica el análisis independientemente a las poblaciones de atunes aleta amarilla y patudo del Océano Pacífico oriental (OPO). Modelamos las poblaciones de 1975 a 1999, en pasos trimestrales. Se supone solamente una sola población para cada especie para cada análisis, pero se modelan pesquerías múltiples espacialmente separadas para tomar en cuenta diferencias espaciales en la capturabilidad y selectividad. El análisis toma en cuenta error en la relación esfuerzo-mortalidad por pesca, tendencias temporales en la capturabilidad, variación temporal en el reclutamiento, relaciones entre el medio ambiente y el reclutamiento y entre el medio ambiente y la capturabilidad, y diferencias en selectividad y capturabilidad entre pesquerías. Se ajusta el modelo a datos de captura total y a datos de captura a talla proporcional condicionados sobre esfuerzo. El método A-SCALA es un enfoque estadístico, y reconoce por lo tanto que los datos obtenidos de la pesca no representan la población perfectamente. Además, hay incertidumbre en nuestros conocimientos de la dinámica del sistema e incertidumbre sobre la relación entre los datos observados y la población real. El uso de funciones de verosimilitud nos permite modelar la incertidumbre en los datos obtenidos de la población, y la inclusión de un error de proceso estimable nos permite modelar las incertidumbres en la dinámica del sistema. El enfoque estadístico permite calcular intervalos de confianza y comprobar hipótesis. Usamos una versión bayesiana del marco de verosimilitud máxima que incluye constreñimientos distribucionales sobre la variación temporal en el reclutamiento, la relación esfuerzo-mortalidad por pesca, y la capturabilidad. Se incluyen también en la función objetivo penalidades por curvatura para los parámetros de selectividad y penalidades por tasas extremas de mortalidad por pesca. Se usa la moda de la distribución posterior conjunta como estimación de los parámetros del modelo. Se calculan los intervalos de confianza usando el método de aproximación normal. Cabe destacar que el método de estimación incluye constreñimientos y distribuciones previas y por lo tanto los intervalos de confianza son diferentes de los intervalos de confianza calculados de forma tradicional. Se calculan puntos de referencia para el ordenamiento, y se realizan proyecciones a futuro para asesorar la toma de decisiones para el ordenamiento de las poblaciones de aleta amarilla y patudo.

Growth of Skipjack tuna (Katsuwonus pelamis) in the eastern Pacific Ocean as estimated from tagging data

English: Data obtained from tagging experiments initiated during 1953-1958 and 1969-1981 for skipjack tuna from the coastal eastern Pacific Ocean (EPO) are reanalyzed, using the Schnute generalized growth model. The objective is to provide information that can be used to generate a growth transition matrix for use in a length-structured population dynamics model. The analysis includes statistical approaches to include individual variability in growth as a function of length at release and time at liberty, measurement error, and transcription error. The tagging data are divided into northern and southern regions, and the results suggest that growth rates differ between the two regions. The Schnute model provides a significantly better fit to the data than the von Bertalanffy model, a sub-model of the Schnute model, for the northern region, but not for the southern region. Individual variation in growth is best described as a function of time at liberty and as a function of growth increment for the northern and southern regions, respectively. Measurement error is a significant part of the total variation, but the results suggest that there is no bias caused by the measurement error. Additional information, particularly for small and large fish, is needed to produce an adequate growth transition matrix that can be used in a length-structured population dynamics model for skipjack tuna in the EPO. Spanish: Los datos obtenidos de los experimentos de marcado iniciados durante los períodos de 1953- 1958 y de 1969-1981 para el atún barrilete en las costas del Océano Pacífico Oriental (OPO) fueron analizados nuevamente, utilizando el modelo de crecimiento generalizado de Schnute. El objetivo es brindar información que sea útil para producir una matriz sobre la tran-sición de crecimiento que pueda utilizarse en un modelo de dinámica poblacional estructurado por talla. El análisis usa enfoques estadísticos para poder incluir la variabilidad individual del crecimiento como función de la talla de liberación y tiempo en libertad, el error de medición, y el error de transcripción. Los datos de marcado son divididos en regiones norte y sur, y los resultados sugieren que las tasas de crecimiento en las dos regiones son diferentes. En la región norte, pero no en la región sur, el modelo de Schnute se ajusta significativamente mejor a los datos que el modelo von Bertalanffy, un sub-modelo del modelo de Schnute. La mejor descripción de la variación individual en el crecimiento es como una función del tiempo en libertad y como una función del incremento de crecimiento para las regiones norte y sur, respectivamente. El error de medición es una parte significativa de la variación total, pero los resultados sugieren que no existe un sesgo causado por el error de medición. Se necesita información adicional, particularmente para peces pequeños y grandes, para poder producir una matriz de transición de crecimiento adecuada que pueda utilizarse en el modelo de dinámica poblacional estructurado por tallas para el atún barrilete en el OPO.

A modeling approach to identify optimal habitat and suitable fishing grounds for neon flying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean

We developed a habitat suitability index (HSI) model to understand and identify the optimal habitat and potential fishing grounds for neon f lying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean. Remote sensing data, including sea surface temperature, sea surface salinity, sea surface height, and chlorophyll-a concentrations, as well as fishery data from Chinese mainland squid f leets in the main fishing ground (150–165°E longitude) from August to October, from 1999 to 2004, were used. The HSI model was validated by using fishery data from 2005. The arithmetic mean modeling with three of the environmental variables—sea surface temperature, sea surface height anomaly, and chlorophyll- a concentrations—was defined as the most parsimonious HSI model. In 2005, monthly HSI values >0.6 coincided with productive fishing grounds and high fishing effort from August to October. This result implies that the model can reliably predict potential f ishing grounds for O. bartramii. Because spatially explicit fisheries and environmental data are becoming readily available, it is feasible to develop a dynamic, near real-time habitat model for improving the process of identifying potential fishing areas for and optimal habitats of neon flying squid.

Timing and duration of mating and brooding periods of Atka mackerel (Pleurogrammus monopterygius) in the North Pacific Ocean

The timing and duration of the reproductive cycle of Atka mackerel (Pleurogrammus monopterygius) was validated by using observations from time-lapse video and data from archival tags, and the start, peak, and end of spawning and hatching were determined from an incubation model with aged egg samples and empirical incubation times ranging from 44 days at a water temperature of 9.85°C to 100 days at 3.89°C. From June to July, males ceased diel vertical movements, aggregated in nesting colonies, and established territories. Spawning began in late July, ended in mid-October, and peaked in early September. The male egg-brooding period that followed continued from late November to mid-January and duration was highly dependent on embryonic development as affected by ambient water temperature. Males exhibited brooding behavior for protracted periods at water depths from 23 to 117 m where average daily water temperatures ranged from 4.0° to 6.2°C. Knowledge about the timing of the reproductive cycle provides a framework for conserving Atka mackerel populations and investigating the physical and biological processes influencing recruitment.

Interdecadal change in growth of sablefish (Anoplopoma fimbria) in the northeast Pacific Ocean

Errors in growth estimates can affect drastically the spawner-perrecruit threshold used to recommend quotas for commercial fish catches. Growth parameters for sablefish (Anoplopoma fimbria) in Alaska have not been updated for stock assessment purposes for more than 20 years, although aging of sablefish has continued. In this study, length-stratified data (1981–93 data from the annual longline survey conducted cooperatively by the Fisheries Agency of Japan and the Alaska Fisheries Science Center of the National Marine Fisheries Service) were updated and corrected for discovered sampling bias. In addition, more recent, randomly collected samples (1996–2004 data from the annual longline survey conducted by the Alaska Fisheries Science Center) were analyzed and new length-at-age and weight-at-age parameters were estimated. Results were similar between this analysis with length-at-age data from 1981 to 2004 and analysis with updated longline survey data through 2010; therefore, we used our initial results from analysis done with data through 2004. We found that, because of a stratified sampling scheme, growth estimates of sablefish were overestimated with the older data (1981–93), and growth parameters used in the Alaskan sablefish assessment model were, thus, too large. In addition, a comparison of the bias-corrected 1981–93 data and the 1996–2004 data showed that, in more recent years, sablefish grew larger and growth differed among regions. The updated growth information improves the fit of the data to the sablefish stock assessment model with biologically reasonable results. These findings indicate that when the updated growth data (1996–2004) are used in the existing sablefish assessment model, estimates of fishing mortality increase slightly and estimates of female spawning biomass decrease slightly. This study provides evidence of the importance of periodically revisiting biological parameter estimates, especially as data accumulate, because the addition of more recent data often will be more biologically realistic. In addition, it exemplifies the importance of correcting biases from sampling that may contribute to erroneous parameter estimates.

Local solutions to challenges of West Indian Ocean fisheries development

The West Indian Ocean is rich in biodiversity and marine resources. This paper gives an overview of fisheries development and resource management in the region. There are many shared issues that must be addressed within countries and at the regional level. These are illustrated by examples from three countries. In Mozambique the issues of lack of information about artisanal fisheries, excessive harvesting of juveniles and conflicts between artisanal and commercial sectors are highlighted. Elements in addressing this include targeted research and decision-making support tools. The challenges faced in Somalia stem primarily from the political instability that contributed to an absence of sound fisheries policy. An example of a highly participatory process to develop the policy provides a model for other countries. In Tanzania, the issue of dynamite fishing was addressed by local communities initiating a program to promote wise use of the resources. There is a clear opportunity for better collaboration and greater integration of fisheries research and management on a regional basis. There is also much to be learnt by the sharing of experiences between countries. This has been initiated by some recently launched regional cooperation projects, but there are still many challenges facing this region.

Effects of El Niño–Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean off Java

The effects of El Niño–Southern Oscillation events on catches of Bigeye Tuna (Thunnus obesus) in the eastern Indian Ocean (EIO) off Java were evaluated through the use of remotely sensed environmental data (sea-surface-height anomaly [SSHA], sea-surface temperature [SST], and chlorophyll a concentration), and Bigeye Tuna catch data. Analyses were conducted for the period of 1997–2000, which included the 1997–98 El Niño and 1999–2000 La Niña events. The empirical orthogonal function (EOF) was applied to examine oceanographic parameters quantitatively. The relationship of those parameters to variations in catch distribution of Bigeye Tuna was explored with a generalized additive model (GAM). The mean hook rate was 0.67 during El Niño and 0.44 during La Niña, and catches were high where SSHA ranged from –21 to 5 cm, SST ranged from 24°C to 27.5°C, and chlorophyll-a concentrations ranged from 0.04 to 0.16 mg m–3. The EOF analysis confirmed that the 1997–98 El Niño affected oceanographic conditions in the EIO off Java. The GAM results indicated that SST was better than the other environmental factors (SSHA and chlorophyll-a concentration) as an oceanographic predictor of Bigeye Tuna catches in the region. According to the GAM predictions, the highest probabilities (70–80%) for Bigeye Tuna catch in 1997–2000 occurred during oceanographic conditions during the 1997–98 El Niño event.

Integrated conceptual ecosystem model development for the Florida Keys/Dry Tortugas coastal marine ecosystem: MARine Estuarine goal Setting (MARES) for South Florida

The overall goal of the MARine and Estuarine goal Setting (MARES) project for South Florida is “to reach a science-based consensus about the defining characteristics and fundamental regulating processes of a South Florida coastal marine ecosystem that is both sustainable and capable of providing the diverse ecosystem services upon which our society depends.” Through participation in a systematic process of reaching such a consensus, science can contribute more directly and effectively to the critical decisions being made by both policy makers and by natural resource and environmental management agencies. The document that follows briefly describes the MARES project and this systematic process. It then describes in considerable detail the resulting output from the first two steps in the process, the development of conceptual diagrams and an Integrated Conceptual Ecosystem Model (ICEM) for the first subregion to be addressed by MARES, the Florida Keys/Dry Tortugas (FK/DT). What follows with regard to the FK/DT is the input received from more than 60 scientists, agency resource managers, and representatives of environmental organizations beginning with a workshop held December 9-10, 2009 at Florida International University in Miami, Florida.

Integrated conceptual ecosystem model development for the southeast Florida coastal marine ecosystem: MARine Estuarine goal Setting (MARES) for South Florida

The overall goal of the MARES (MARine and Estuarine goal Setting) project for South Florida is “to reach a science-based consensus about the defining characteristics and fundamental regulating processes of a South Florida coastal marine ecosystem that is both sustainable and capable of providing the diverse ecosystem services upon which our society depends.” Through participation in a systematic process of reaching such a consensus, science can contribute more directly and effectively to the critical decisions being made both by policy makers and by natural resource and environmental management agencies. The document that follows briefly describes MARES overall and this systematic process. It then describes in considerable detail the resulting output from the first step in the process, the development of an Integrated Conceptual Ecosystem Model (ICEM) for the third subregion to be addressed by MARES, the Southeast Florida Coast (SEFC). What follows with regard to the SEFC relies upon the input received from more than 60 scientists, agency resource managers, and representatives of environmental organizations during workshops held throughout 2009–2012 in South Florida.

Integrated conceptual ecosystem model development for the southwest Florida shelf coastal marine ecosystem: MARine Estuarine goal Setting (MARES) for South Florida

The overall goal of the MARine and Estuarine goal Setting (MARES) project for South Florida is “to reach a science-based consensus about the defining characteristics and fundamental regulating processes of a South Florida coastal marine ecosystem that is both sustainable and capable of providing the diverse ecosystem services upon which our society depends.” Through participation in a systematic process of reaching such a consensus, science can contribute more directly and effectively to the critical decisions being made by both policy makers and by natural resource and environmental management agencies. The document that follows briefly describes the MARES project and this systematic process. It then describes in considerable detail the resulting output from the first two steps in the process, the development of conceptual diagrams and an Integrated Conceptual Ecosystem Model (ICEM) for the second subregion to be addressed by MARES, the Southwest Florida Shelf (SWFS). What follows with regard to the SWFS is the input received from more than 60 scientists, agency resource managers, and representatives of environmental organizations beginning with a workshop held August 19-20, 2010 at Florida Gulf Coast University in Fort Myers, Florida.

National Centers for Coastal Ocean Science (NCCOS) research highlights in the Chesapeake Bay

The Chesapeake Bay is the largest estuary in the United States. It is a unique and valuable national treasure because of its ecological, recreational, economic and cultural benefits. The problems facing the Bay are well known and extensively documented, and are largely related to human uses of the watershed and resources within the Bay. Over the past several decades as the origins of the Chesapeake’s problems became clear, citizens groups and Federal, State, and local governments have entered into agreements and worked together to restore the Bay’s productivity and ecological health. In May 2010, President Barack Obama signed Executive Order number 13508 that tasked a team of Federal agencies to develop a way forward in the protection and restoration of the Chesapeake watershed. Success of both State and Federal efforts will depend on having relevant, sound information regarding the ecology and function of the system as the basis of management and decision making. In response to the executive order, the National Oceanic and Atmospheric Administration’s National Centers for Coastal Ocean Science (NCCOS) has compiled an overview of its research in Chesapeake Bay watershed. NCCOS has a long history of Chesapeake Bay research, investigating the causes and consequences of changes throughout the watershed’s ecosystems. This document presents a cross section of research results that have advanced the understanding of the structure and function of the Chesapeake and enabled the accurate and timely prediction of events with the potential to impact both human communities and ecosystems. There are three main focus areas: changes in land use patterns in the watershed and the related impacts on contaminant and pathogen distribution and concentrations; nutrient inputs and algal bloom events; and habitat use and life history patterns of species in the watershed. Land use changes in the Chesapeake Bay watershed have dramatically changed how the system functions. A comparison of several subsystems within the Bay drainages has shown that water quality is directly related to land use and how the land use affects ecosystem health of the rivers and streams that enter the Chesapeake Bay. Across the Chesapeake as a whole, the rivers that drain developed areas, such as the Potomac and James rivers, tend to have much more highly contaminated sediments than does the mainstem of the Bay itself. In addition to what might be considered traditional contaminants, such as hydrocarbons, new contaminants are appearing in measurable amounts. At fourteen sites studied in the Bay, thirteen different pharmaceuticals were detected. The impact of pharmaceuticals on organisms and the people who eat them is still unknown. The effects of water borne infections on people and marine life are known, however, and the exposure to certain bacteria is a significant health risk. A model is now available that predicts the likelihood of occurrence of a strain of bacteria known as Vibrio vulnificus throughout Bay waters.

Using wildlife activity and antibiotic resistance analysis to model bacterial water quality in coastal ponds

Models that help predict fecal coliform bacteria (FCB) levels in environmental waters can be important tools for resource managers. In this study, we used animal activity along with antibiotic resistance analysis (ARA), land cover, and other variables to build models that predict bacteria levels in coastal ponds that discharge into an estuary. Photographic wildlife monitoring was used to estimate terrestrial and aquatic wildlife activity prior to sampling. Increased duck activity was an important predictor of increased FCB in coastal ponds. Terrestrial animals like deer and raccoon, although abundant, were not significant in our model. Various land cover types, rainfall, tide, solar irradiation, air temperature, and season parameters, in combination with duck activity, were significant predictors of increased FCB. It appears that tidal ponds allow for settling of bacteria under most conditions. We propose that these models can be used to test different development styles and wildlife management techniques to reduce bacterial loading into downstream shellfish harvesting and contact recreation areas.

Magnitude and distribution of sea turtle bycatch in the sea scallop (Placopecten magellanicus) dredge fishery in two areas of the northwestern Atlantic Ocean, 2001–2002

In May 2001, the National Marine Fisheries Service (NMFS) opened two areas in the northwestern Atlantic Ocean that had been previously closed to the U.S. sea scallop (Placopecten magellanicus) dredge fishery. Upon reopening these areas, termed the “Hudson Canyon Controlled Access Area” and the “Virginia Beach Controlled Access Area,” NMFS observers found that marine turtles were being caught incidentally in scallop dredges. This study uses the generalized linear model and the generalized additive model fitting techniques to identify environmental factors and gear characteristics that influence bycatch rates, and to predict total bycatch in these two areas during May-December 2001 and 2002 by incorporating environmental factors into the models. Significant factors affecting sea turtle bycatch were season, time-of-day, sea surface temperature, and depth zone. In estimating total bycatch, rates were stratified according to a combination of all these factors except time-of-day which was not available in fishing logbooks. Highest bycatch rates occurred during the summer season, in temperatures greater than 19°C, and in water depths from 49 to 57 m. Total estimated bycatch of sea turtles during May–December in 2001 and 2002 in both areas combined was 169 animals (CV=55.3), of which 164 (97%) animals were caught in the Hudson Canyon area. From these findings, it may be possible to predict hot spots for sea turtle bycatch in future years in the controlled access areas.