A fishery-independent survey of juvenile shortfin mako (Isurus oxyrinchus) and blue (Prionace glauca) sharks in the Southern California Bight, 1994–2013

Runcie, Rosa M.; Holts, David B.; Wraith, James; Xu, Yi; Ramon, Darlene A.; Rasmussen, Rand; Kohin, Suzanne

doi:10.1016/j.fishres.2016.06.010

Cited by 15 publications

(12 citation statements)

References 39 publications

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“…Despite this bias in our tracking data, studies using fishery observer data from the drift gillnet fishery have shown that shark bycatch spans all size classes and occurs in the same regions as our tracking data (Urbisci et al, ). It is possible that our tagging sample size, or location of tagging particularly for the different size classes, resulted in our inability to capture the N‐S pattern as expected; however, at sea tagging is costly due to vessel time, and our tagging was semi‐random as most of tag deployments occurred in association with periodic survey efforts over a broad region (Runcie et al, ). While the tracking data reflect similar patterns to fisheries observer studies (Urbisci et al, ), we cannot be certain about the degree to which our tracking data are fully reflective of the entire blue shark population given the tagging location bias.…”

Section: Discussionmentioning

confidence: 99%

“…A total of 54 blue sharks were tagged between 2004 and 2012 using fin‐mounted Smart Position‐Only Tag (SPOT) satellite transmitters (Wildlife Computers Inc.). All sharks were caught using commercial longline gear either (a) off California during the NOAA Southwest Fisheries Science Center's annual juvenile shark survey (Runcie et al, ), (b) on commercial vessels operating in Mexican waters off the Baja Peninsula or (c) during surveys conducted by the Department of Fisheries and Oceans of Canada off British Columbia (Figure ). Sharks were pulled onto a cradle, their eyes were covered with a soft cloth, the hook removed, and the gills irrigated with seawater.…”

Section: Methodsmentioning

confidence: 99%

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Seasonal spatial segregation in blue sharks (Prionace glauca) by sex and size class in the Northeast Pacific Ocean

Maxwell

Scales

Bograd

et al. 2019

Diversity and Distributions

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Aim Animal tracking can provide unique insights into the ecology and conservation of marine species, such as the partitioning of habitat, including differences between life history stages or sexes, and can inform fisheries stock assessments, bycatch reduction and spatial management such as dynamic management. Location Northeast Pacific Ocean. Methods We used satellite tracking data from 47 blue sharks (Prionace glauca) from the Northeast Pacific to determine movements and home range along the west coast of North America, and sex–size class (immature females, mature males) specific habitat preferences using boosted regression trees. Using a suite of static and dynamic environmental variables, we determined distribution and habitat preferences across summer and fall for each sex–size class. Results We found that there was spatial segregation between sex–size classes particularly in the summer months with immature females found largely north of 33°N, and males south of 35°N. In fall, females travelled south, resulting in an overlap in distributions south of 37°N. Sea surface temperature (SST), latitude and longitude were top predictors. However, immature females and adult males demonstrated unique habitat preferences including SST, with immature females preferring cooler temperatures (SST < 15°C) than adult males in summer, and a broader band of SST than adult males in fall. All models performed well, explaining 50%–67% of deviance, and 23%–41% of deviance when predictions were cross‐validated. Main conclusions We provide first insights into coastal movements and habitat preferences of blue sharks in the Northeast Pacific. We found that immature females undergo a seasonal southward migration in this more coastal habitat, similar to patterns observed in the North Atlantic. We also found some overlap between adult males and immature females in fall months, suggesting the importance of more coastal habitat in managing this species, particularly in determining population structure for blue shark stock assessments, and reducing blue shark bycatch.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Seasonal spatial segregation in blue sharks (Prionace glauca) by sex and size class in the Northeast Pacific Ocean

Maxwell

Scales

Bograd

et al. 2019

Diversity and Distributions

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“…However, CPUE is not always proportional to relative abundance because many factors affect catch rates (e.g., fleet efficiency, gear effects, and environment), and, mainly for sexually dimorphic species like the Smooth Dogfish, the CPUE often differs between sexes due to intrinsic variability in the catchability coefficient as fish size changes (Hilborn and Walters 1992;Walters 2003;Maunder and Punt 2004;Maunder et al 2006). Nevertheless, CPUE has been commonly used as a proxy for fish population trends and abundance when derived from standardized fishery-independent surveys (Runcie et al 2016;Dell'Apa et al 2017), which was done in this study, as this can reduce the lack of linearity between CPUE and fish relative abundance (Maunder and Punt 2004).…”

Section: Discussionmentioning

confidence: 99%

A Hierarchical Bayesian Modeling Approach for the Habitat Distribution of Smooth Dogfish by Sex and Season in Inshore Coastal Waters of the U.S. Northwest Atlantic

et al. 2018

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The Smooth Dogfish Mustelus canis is an abundant, small coastal shark occurring along the U.S. Atlantic coast. Despite being targeted by a directed fishery and having recently undergone a stock assessment that found the population neither overfished nor experiencing overfishing, little is known about the spatial and temporal distribution of this species. Here, we used catch data from the spring and fall Northeast Area Monitoring and Assessment Program's fishery‐independent trawl surveys conducted between 2007 and 2016 and various environmental factors to perform hierarchical Bayesian modeling as a first attempt to spatially predict adult Smooth Dogfish CPUE in U.S. northwest Atlantic Ocean waters by sex and season. Relevant environmental variables differed between both sexes and seasons. Male and female CPUEs were similarly associated with lower salinity and shallower depth in the spring. During fall, male CPUE was associated with sea surface temperature and bottom rugosity, and female CPUE was associated with chlorophyll‐a concentration, bottom rugosity, and year. Habitat modeling results predicted that areas of high male and female CPUEs would overlap during spring but strongly diverge during fall, when greater predicted CPUEs for males were distributed considerably farther north. These results suggest sexual segregation among Smooth Dogfish during fall, with the springtime overlap in distribution coinciding with the pupping and mating season in this population. This difference in distribution during fall may allow for a male‐only directed fishery for Smooth Dogfish in the northern extent of the species’ range in waters near southern New England and Georges Bank.

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“…Data from 34 SLRT tags were used for model development. All tags were placed on Makos (between 152 and 259 cm TL; total length) in the Southern California Bight as part of the annual juvenile shark survey conducted by the NOAA Southwest Fisheries Science Center (SWFSC) [ 38 ]. Tags were deployed between 2004–2015 primarily during the summer (June-August).…”

Section: Methodsmentioning

confidence: 99%

An analytical approach to sparse telemetry data

et al. 2017

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Horizontal behavior of highly migratory marine species is difficult to decipher because animals are wide-ranging, spend minimal time at the ocean surface, and utilize remote habitats. Satellite telemetry enables researchers to track individual movements, but population level inferences are rare due to data limitations that result from difficulty of capture and sporadic tag reporting. We introduce a Bayesian modeling framework to address population level questions with satellite telemetry data when data are sparse. We also outline an approach for identifying informative variables for use within the model. We tested our modeling approach using a large telemetry dataset for Shortfin Makos (Isurus oxyrinchus), which allowed us to assess the effects of various degrees of data paucity. First, a permuted Random Forest analysis is implemented to determine which variables are most informative. Next, a generalized additive mixed model is used to help define the relationship of each remaining variable with the response variable. Using jags and rjags for the analysis of Bayesian hierarchical models using Markov Chain Monte Carlo simulation, we then developed a movement model to generate parameter estimates for each of the variables of interest. By randomly reducing the tagging dataset by 25, 50, 75, and 90 percent and recalculating the parameter estimates, we demonstrate that the proposed Bayesian approach can be applied in data-limited situations. We also demonstrate how two commonly used linear mixed models with maximum likelihood estimation (MLE) can be similarly applied. Additionally, we simulate data from known parameter values to test each model’s ability to recapture those values. Despite performing similarly, we advocate using the Bayesian over the MLE approach due to the ability for later studies to easily utilize results of past study to inform working models, and the ability to use prior knowledge via informed priors in systems where such information is available.

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A fishery-independent survey of juvenile shortfin mako (Isurus oxyrinchus) and blue (Prionace glauca) sharks in the Southern California Bight, 1994–2013

Cited by 15 publications

References 39 publications

Seasonal spatial segregation in blue sharks (Prionace glauca) by sex and size class in the Northeast Pacific Ocean

Seasonal spatial segregation in blue sharks (Prionace glauca) by sex and size class in the Northeast Pacific Ocean

A Hierarchical Bayesian Modeling Approach for the Habitat Distribution of Smooth Dogfish by Sex and Season in Inshore Coastal Waters of the U.S. Northwest Atlantic

An analytical approach to sparse telemetry data

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