Spatial ecology of blue shark and shortfin mako in southern Peru: local abundance, habitat preferences and implications for conservation

Adams, Grant; Flores, Daniel; Flores, Oscar Galindo; Aarestrup, Kim; Svendsen, Jon Christian

doi:10.3354/esr00744

Cited by 17 publications

(8 citation statements)

References 60 publications

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“…Considering the predictions of each index used in this work (NEI, NPGO, and ONI), it can be seen that the best fitting effect of blue shark catch was in the cold and neutral regime of SST anomalies (Figure 11a,b,c). The effect of SST anomalies that were shown with the MEI index reported by Adams et al (2016) in Peruvian waters, also reflected the preference of blue sharks in temperate waters or in La Niña events. Although some authors have modeled to demonstrate the influence of different factors on the distribution and abundance of blue sharks, their relationship with climate indices in the North Pacific Ocean has not yet been compared and explored to better understand their population dynamics.…”

Section: Discussionsupporting

confidence: 52%

“…Among the most significant predictor variables (top predictor) used in various GAMs, developed by other authors to describe their possible influence on blue shark catch rates were the spatial–temporal factors (latitude, longitude, month, quarter, and year), environmental (SST, Chl), and fishing effort (hooks) stand out (Adams et al, 2016; Bigelow et al, 1999; Carvalho et al, 2011; Vögler et al, 2012; Walsh & Kleiber, 2001). Predictor variables such as SST, NPGO, Year, Lat, DistCoast‐Lat, Quarter‐Lat, and the fishing effort (Hook) were the most important and present in all the modeling analyses in our study, for each maturity group of blue sharks.…”

Section: Discussionmentioning

confidence: 99%

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Marine‐climate interactions with the blue shark (Prionace glauca) catches in the western coast of Baja California Peninsula, Mexico

Castillo‐Géniz¹,

Torre²,

González-Ania³

et al. 2022

Fisheries Oceanography

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Fishery and size data by sex of 28,110 blue sharks (Prionace glauca) from 2162 longline sets documented by observers on board 204 fishery trips from the industrial fleet based in Ensenada, Mexico during 2006–2016, were used to conduct a spatial–temporal analysis of the catch‐per‐unit‐effort (CPUE) and its relationship with climate indices along the west coast of the Baja California Peninsula. Catch length analysis by maturity groups indicated catches were composed mainly by juvenile females (58–199 cm TL) and males (60–179 cm TL). Relationships of seasonal CPUE with sea surface temperature (SST) and Chlorophyll‐a (Chl) were analyzed determining aggregations were in areas characterized by oceanographic physical processes. From the exploratory analysis of annual correlations of climate indices with CPUE, the local climate SanDiAs Index explained most variation in CPUE. A generalized additive model (GAM) with 13 predictor variables was applied to gain insight on their relationship with the total CPUE by size and sex groups. The model explained 50.5% of the total blue shark CPUE and 65.5% for juvenile females. The GAM results revealed blue shark CPUE is influenced by five relevant factors: SST, NPGO, year, latitude with distance to coast and quarter interactions, and hooks set. There is a trend to increase or decrease of CPUE when compared with the delay of NEI and SanDiAs indices in more than 1 year. Local and regional climate indices can be successful tools for forecasting blue shark catches in the Northwestern Mexican Pacific.

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Section: Discussionsupporting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Marine‐climate interactions with the blue shark (Prionace glauca) catches in the western coast of Baja California Peninsula, Mexico

Castillo‐Géniz¹,

Torre²,

González-Ania³

et al. 2022

Fisheries Oceanography

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“…While aspects of blue shark habitat preferences have been explored in the Atlantic (Adams, Flores, Flores, Aarestrup, & Svendsen, 2016;Campana et al, 2011;Carvalho et al, 2015;Howey, Wetherbee, Tolentino, & Shivji, 2017;Queiroz, Humphries, Noble, Santos, & Sims, 2012;Queiroz et al, 2005;Vandeperre, Aires-da-Silva, Fontes, et al, 2014a), the influence of the physical environment on regional blue shark distributions in the North Pacific has not yet been characterized outside of exclusively pelagic environments far from coastal influence. Here we use blue shark satellite tracking data and habitat modelling to address this knowledge gap through (a) identifying seasonal home range and key habitat areas in more coastal waters in the Northeast Pacific; (b) exploring the physical drivers of habitat selection across seasons; and (c) examining how the influence of these drivers varies among sexes and size classes across seasons.…”

Section: Introductionmentioning

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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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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“…Ocean temperature often relates to a species distribution exhibited by marine organisms due to physiological constraints (Manderson 2016), and water color properties are often considered proxies for in situ properties such as turbidity, salinity, and chlorophyll a concentrations, an indicator of primary productivity (Siegel et al 2005, Geiger et al 2013. Even though remotely sensed temperature and color are measured on the surface of the ocean, a growing number of studies have documented their value in predicting marine species distributions (Adams et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Spatial ecology of Carcharias taurus in the northwestern Mid-Atlantic coastal ocean

Haulsee¹,

Breece²,

Brown³

et al. 2018

Mar. Ecol. Prog. Ser.

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The sand tiger shark Carcharias taurus is a highly migratory coastal species with declining populations worldwide. This species exhibits many behaviors that make coastal sharks difficult to manage, including aggregatory behavior, sexual segregation, and large-scale migrations through shallow coastal waters with many opportunities for human interactions. Sand tigers from the Western North Atlantic subpopulation are known to seasonally inhabit Delaware Bay and surrounding coastal waters. This region has been recommended as a habitat area of particular concern for the Western North Atlantic sand tiger population, and increased understanding of their movements and habitat requirements will facilitate management efforts. We developed models to predict sand tiger occupancy using spatially dynamic environmental predictors. Our models predicted sand tiger (juveniles, adult males, adult females, and all sharks combined) occurrences in 2 study regions, the Delaware Bay and the western Mid-Atlantic coastal ocean. Sea surface temperature, day of year, water depth, and remote sensing reflectance at 555 nm were the most important environmental predictors of occurrence, and correctly predicted 80−89% of sand tiger acoustic telemetry records in the 2 study regions. Our models predicted differences in the timing and location of occurrences among juveniles and adults, as well as areas where these life history stages overlap in the Mid-Atlantic coastal ocean. Our hope is that a daily forecast of sand tiger occurrence from our modeling efforts could be useful for conservation and management efforts in this important region, as well as for studying the spatial and behavioral ecology of this important top predator.

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Spatial ecology of blue shark and shortfin mako in southern Peru: local abundance, habitat preferences and implications for conservation

Cited by 17 publications

References 60 publications

Marine‐climate interactions with the blue shark (Prionace glauca) catches in the western coast of Baja California Peninsula, Mexico

Marine‐climate interactions with the blue shark (Prionace glauca) catches in the western coast of Baja California Peninsula, Mexico

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

Spatial ecology of Carcharias taurus in the northwestern Mid-Atlantic coastal ocean

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