Pelagic habitat characterization of loggerhead sea turtles, Caretta caretta, in the North Pacific Ocean (1997–2006): Insights from satellite tag tracking and remotely sensed data

Kobayashi, Donald R.; Polovina, Jeffrey J.; Parker, Denise M.; Kamezaki, Naoki; Cheng, I-Jiunn; Uchida, Itaru; Dutton, Peter H.; Balazs, George H.

doi:10.1016/j.jembe.2007.12.019

Cited by 98 publications

(49 citation statements)

References 73 publications

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“…The range of SST with positive sea turtle captures was more protracted in the Pacific, with the frequency of sets with sea turtle bycatch highest when SST ranged between ∼17 and 19 • C for both loggerheads and leatherbacks. These ranges overlap entirely with the species' atcapture peak SST ranges previously reported in the North Pacific (Howell et al, 2008(Howell et al, , 2015Kobayashi et al, 2008;Abecassis et al, 2013). "Within hook type comparison" results represent the percent increase for squid or "other" category baits relative to fish bait within each hook type category (Circle, J, or Other).…”

Section: Spatial Distribution and Sstsupporting

confidence: 56%

Sea Turtle Bycatch Mitigation in U.S. Longline Fisheries

et al. 2017

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Capture of sea turtles in longline fisheries has been implicated in population declines of loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) turtles. Since 2004, United States (U.S.) longline vessels targeting swordfish and tunas in the Pacific and regions in the Atlantic Ocean have operated under extensive fisheries regulations to reduce the capture and mortality of endangered and threatened sea turtles. We analyzed 20 + years of longline observer data from both ocean basins during periods before and after the regulations to assess the effectiveness of the regulations. Using generalized additive mixed models (GAMMs), we investigated relationships between the probability of expected turtle interactions and operational components such as fishing location, hook type, bait type, sea surface temperature, and use of light sticks. GAMMs identified a two to three-fold lower probability of expected capture of loggerhead and leatherback turtle bycatch in the Atlantic and Pacific when circle hooks are used (vs. J hook). Use of fish bait (vs. squid) was also found to significantly reduce the capture probability of loggerheads in both ocean basins, and for leatherbacks in the Atlantic only. Capture probabilities are lowest when using a combination of circle hook and fish bait. Influences of light sticks, hook depth, geographic location, and sea surface temperature are discussed specific to species and regions. Results confirmed that in two U.S.-managed longline fisheries, rates of sea turtle bycatch significantly declined after the regulations. In the Atlantic (all regions), rates declined by 40 and 61% for leatherback and loggerhead turtles, respectively, after the regulations. Within the NED area alone, where additional restrictions include a large circle hook (18/0) and limited use of squid bait, rates declined by 64 and 55% for leatherback and loggerhead turtles, respectively. Gains were even more pronounced for the Pacific shallow set fishery, where mean bycatch rates declined by 84 and 95%, for leatherback and loggerhead turtles, respectively, for the post-regulation period. Similar management approaches could be used within regional fisheries management organizations to reduce capture of sea turtles and to promote sustainable fisheries on a global scale.

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Section: Spatial Distribution and Sstsupporting

confidence: 56%

Sea Turtle Bycatch Mitigation in U.S. Longline Fisheries

et al. 2017

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“…Many studies in the ocean have described the macroscale habitats of large marine predators (Kobayashi et al 2008, Weng et al 2008, Block et al 2011. In contrast, our study focused on mesoscale habitat selection in dynamic neritic waters and demonstrates the utility of using AUVs equipped with environmental sensors to measure habitat associations of coastal marine species.…”

Section: Discussionmentioning

confidence: 90%

“…This test compares the distribution of an environmental parameter in a habitat to the distribution of that parameter where an individual was detected (Johnson 1980, Kobayashi et al 2008. In this study, habitat utilization was the frequency distribution of each environmental variable in the ± 250 m where a sand tiger was detected by the AUV.…”

Section: Environmental Selectivity Analysesmentioning

confidence: 99%

“…Similar to Kobayashi et al (2008), the cumulative distribution function (CDF) for habitat utilization and availability were compared using a 2-sample KS test in R (R Core Team 2013). The KS test compares the distributions and estimates the maximum vertical difference (D max ) between the 2 CDFs (Conover 1971).…”

Section: Environmental Selectivity Analysesmentioning

confidence: 99%

“…Researchers interested in habitat selection on macroscales (100−1000 km) have used satellite telemetry capable of estimating locations (accuracy of 1−10 km) of marine organisms for a few months to a year (Kobayashi et al 2008, Weng et al 2008, Block et al 2011. These locations are then compared to habitat parameters, represented by remotely sensed sea surface conditions, or temperature and depth recorders within the tags themselves.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Habitat selection of a coastal shark species estimated from an autonomous underwater vehicle

Haulsee¹,

Breece²,

Miller³

et al. 2015

Mar. Ecol. Prog. Ser.

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Quantifying habitat selection in marine organisms is challenging because it is difficult to obtain species location information with multiple corresponding habitat measurements. In the ocean, habitat conditions vary on many spatiotemporal scales, which have important consequences for habitat selection. While macroscale biotic and abiotic features influence seasonal movements (spatial scales of 100−1000 km), selectivity of conditions on mesoscales (1−100 km) reflects an animal's response to the local environment. In this study, we examined habitat selectivity by pairing acoustic telemetry with environmental habitat parameters measured by an autonomous underwater vehicle (AUV), and demonstrate that migrating sand tiger sharks Carcharias taurus along the East Coast of the USA did not randomly use the coastal environment. Of the variables examined, we found evidence to suggest that sand tigers were selecting their habitat based on distance to shore, salinity, and colored dissolved organic matter (CDOM). Notably, temperature was not predictive of habitat use in our study. We posit that during their coastal migration, sand tigers select for specific mesoscale coastal habitats that may inform navigation or feeding behaviors. To our knowledge, this is the first empirical measure of mesoscale habitat selection by a coastal marine organism using an AUV. The applications of this method extend beyond the habitat selectivity of sand tigers, and will prove useful for future studies combining in situ observations of marine habitats and animal observations.

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Evaluating the use of marine protected areas by endangered species: A habitat selection approach

Roberts

Smith

Burkholder

et al. 2021

Ecol Sol and Evidence

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1. Optimizing the design of marine protected area (MPA) networks for the conservation of migratory marine species and their habitats involves a suite of important considerations, such as appropriate scale requirements and the distribution of anthropogenic impacts. Often, a fundamental component of the conservation planning process is delineating areas of high use or high biodiversity within a region of interest. 2. However, basing conservation strategies off merely the number of individuals in an ecosystem is outdated and potentially subject to arbitrary thresholds. To be effective at protecting marine megafauna, MPAs would ideally encompass habitats used by focal species. Through satellite-tracking studies, evidence of whether species actually use protected areas is emerging. 3. Here, we present a multispecies perspective on habitat selection within existing MPAs throughout the Floridian ecoregion, which encompasses coastal Florida and the Gulf of Mexico. Using an 11-year satellite-tracking dataset on 235 marine turtles, we used integrated step selection analysis to quantify the effects of sea turtle behavioural state (identified by a switching state-space model), protected area status, chlorophyll and bathymetry on habitat selection. 4. Our results show that sea turtles do select for existing protected areas, specifically multi-use zones, while controlling for the effects of depth and primary productivity. However, our analysis revealed that turtles showed no selection for the no-take zones within MPAs, during either transiting or foraging. 5. These findings contribute to the existing literature base of MPA use for highly mobile, imperilled species and could inform management of existing MPAs or changes to zoning, specifically multi-use to no-take. Our use of a robust spatial modelling framework to evaluate habitat selection relative to MPAs could be incorporated into conservation planning to build MPA networks designed to accommodate migratory species.

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Pelagic habitat characterization of loggerhead sea turtles, Caretta caretta, in the North Pacific Ocean (1997–2006): Insights from satellite tag tracking and remotely sensed data

Cited by 98 publications

References 73 publications

Sea Turtle Bycatch Mitigation in U.S. Longline Fisheries

Sea Turtle Bycatch Mitigation in U.S. Longline Fisheries

Habitat selection of a coastal shark species estimated from an autonomous underwater vehicle

Evaluating the use of marine protected areas by endangered species: A habitat selection approach

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