Explore the vast range of titles available.

It is well established that sea turtles are vulnerable to atmospheric and oceanographic shifts associated with climate change. However, few studies have formally projected how their seasonal marine habitat may shift in response to warming ocean temperatures. Here we used a high-resolution global climate model and a large satellite tagging dataset to project changes in the future distribution of suitable thermal habitat for loggerheads along the northeastern continental shelf of the United States. Between 2009 and 2018, we deployed 196 satellite tags on loggerheads within the Middle Atlantic Bight (MAB) of the Northwest Atlantic continental shelf region, a seasonal foraging area. Tag location data combined with depth and remotely sensed sea surface temperature (SST) were used to characterize the species’ current thermal range in the MAB. The best-fitting model indicated that the habitat envelope for tagged loggerheads consisted of SST ranging from 11.0° to 29.7 °C and depths between 0 and 105.0 m. The calculated core bathythermal range consisted of SSTs between 15.0° and 28.0 °C and depths between 8.0 and 92.0 m, with the highest probability of presence occurred in regions with SST between 17.7° and 25.3 °C and at depths between 26.1 and 74.2 m. This model was then forced by a high-resolution global climate model under a doubling of atmospheric CO 2 to project loggerhead probability of presence over the next 80 years. Our results suggest that loggerhead thermal habitat and seasonal duration will likely increase in northern regions of the NW Atlantic shelf. This change in spatiotemporal range for sea turtles in a region of high anthropogenic use may prompt adjustments to the localized protected species conservation measures.

Leatherback sea turtles ( Dermochelys coriacea ) migrate along the east coast of the United States, traversing the South and Mid-Atlantic Bights (SAB and MAB) while traveling to and from well-known northern foraging areas off Southern New England (SNE) and Nova Scotia. However, there is limited information on leatherback movement behavior in these regions. To identify leatherback movement patterns, we fit hidden Markov models (HMMs) to satellite transmitter data from 52 leatherbacks tagged between 2017 and 2022 off the coasts of Massachusetts and North Carolina to estimate locations of area restricted searching (ARS) and transient behaviors. Depth-temperature profiles were then paired to locations associated with ARS behavior to understand the vertical use of the water column. We observed leatherbacks displaying ARS behavior in SNE as expected, but also in the MAB and SAB. The HMM results indicated that leatherbacks were primarily foraging in SNE between Nantucket and Long Island Sound and depth-temperature plots from ARS behavior on Nantucket Shoals implied turtles foraging throughout the entire water column. In the MAB, ARS behavior was concentrated between Cape Hatteras, North Carolina, and the mouth of Delaware Bay during the summer. Turtles were closely associated with a well-defined thermocline, but still appeared to dive to deeper cooler waters, which may be a sign of thermoregulatory behavior. There was evidence of foraging in the SAB along the coast as well as along the continental shelf edge. The ARS behavior we documented within the MAB and SAB is the first published empirical evidence that both areas may be important foraging grounds. Our results lay a path for future research to understand how leatherbacks use these areas and the potential anthropogenic threats encountered while moving through these regions.

Conservation planning for protected species often relies on estimates of life‐history parameters. A commonly used parameter is the instantaneous maximum population growth rate (rmax) that can be used to limit removals and design recovery targets. Estimation of rmax can be challenging because of limited availability of species‐ and population‐specific data and life‐history information. We applied a method proposed by Neil and Lebreton, originally developed for birds, to loggerhead turtles. The method uses age‐at‐first‐reproduction and adult survival to estimate rmax. We used a variety of datasets and matrix population models to confirm an allometric assumption required by the method, and to generate estimates of age‐at‐first‐reproduction and adult survival. A meta‐analysis was applied to parameters from reported growth curves, which were then combined with the size distribution of neophyte nesters to derive estimates of age‐at‐first‐reproduction. Adult survival rates were obtained from an existing matrix population model. Monte Carlo simulation was then used to combine the estimates of the allometric coefficients, age‐at‐first‐reproduction, and adult survival to obtain a probability distribution of approximate rmax values. Estimated annual maximum population growth rates averaged 0.024, with a mode of 0.017 and a 95% highest density interval of 0.006–0.047. These estimates were similar to values reported by others using different methods and captured the variability in positive, annual change estimates across nesting beach sites for the northwest Atlantic loggerhead population. The use of life‐history parameters has a long history in wildlife and fisheries management and conservation planning. Our estimates of rmax, while having some biases and uncertainty, encompassed values presently used in recovery planning for loggerhead turtles and offer additional information for the management of endangered and threatened species. A fundamental life‐history parameter in conservation planning is the maximum population growth rate, which can be used to limit removals or design recovery targets. However, estimating the maximum population growth rate can be challenging because of limited availability of species‐ and population‐specific data and life‐history information. We applied a data‐limited approach, developed and applied to birds by Niel and Lebreton (Conservation Biology, 19(3): 826–835, 2005), to the northwest Atlantic (NWA) population of loggerhead turtles (Caretta caretta). We estimated a distribution for the maximum population growth rate that encompassed values presently used in the NWA region for recovery planning of loggerhead turtles, using only estimates of age‐at‐first‐reproduction and adult survival.

Background Extreme weather events, including hurricanes, have considerable biological, ecological, and anthropogenic impacts. Hurricane Irene caused substantial economic damage when it hit the Mid-Atlantic Bight (MAB) off of the eastern United States in August of 2011. The MAB is highly stratified during the summer when a strong thermocline separates warm, surface water from deep, cold water, and this oceanographic phenomenon makes modeling hurricane strength difficult. Loggerhead sea turtles ( Caretta caretta ) forage in the MAB primarily during the stratified season and their dive behavior to the bottom allows them to experience the oceanographic conditions of the entire water column. Methods In this study, we analyzed the movements and dive behavior of juvenile and adult-sized loggerhead sea turtles ( n  = 18) that were foraging in the MAB as Hurricane Irene moved through the region. The satellite tags deployed on these turtles transmitted location data and dive behavior as well as sea surface temperature (SST) and temperature-depth profiles during this time. Results Behavioral and environmental shifts were observed during and after the hurricane compared to conditions before the storm. During the hurricane, most of the turtles ( n  = 15) moved north of their pre-storm foraging grounds. Following the storm, some turtles left their established foraging sites ( n =  8) moving south by 7.3–135.0 km, and for the others that remained ( n  = 10), 12% of the observed dives were longer (0.54–1.11 h) than dives observed before the storm. The in situ data collected by the turtle-borne tags captured the cooling of the SST (Mean difference = 4.47°C) and the deepening of the thermocline relative to the pre-storm conditions. Conclusions Some of the loggerhead behavior observed relative to a passing hurricane differed from the regular pattern of seasonal movement expected for turtles that forage in the MAB. These data documented the shifts in sea turtle behavior and distribution during an ecosystem-level perturbation and the recorded in situ data demonstrated that loggerheads observe environmental changes to the entire water column, including during extreme weather events.

The current level of annual incidental bycatch of gray seals Halichoerus grypus in the New England sink gillnet fishery is the highest for all marine mammal species in the USA. One way to evaluate the risk of bycatch is to examine the risk of encounter between an animal and fishing gear based on the animal’s habitat use in relation to fishing activity. Here we used satellite telemetry deployed on 30 gray seal pups in 2019 and 2020 to measure the risk of encounter with large-mesh sink gillnet fishing effort throughout the Gulf of Maine and southern New England. We estimated relative encounter risk within 30 min grid cells in each calendar quarter based on the overlap of seal presence and fishing effort, and then validated the expected risk based on bycatch events reported by independent observers on board fishing vessels. The relative risk of seals encountering gillnet fishing gear was highest off southeastern Massachusetts in spring. Patterns in the estimated encounter risk fit our expectation that relatively high levels of habitat use and fishing effort correspond to increased encounter risk. The approach taken here can be used to identify times and areas of high encounter risk to justify altered fishing practices for purposes of avoidance, or to target observer monitoring intended to characterize and quantify bycatch. Mitigation strategies will need to be continuously monitored and updated to incorporate new information as conflicts with fisheries and gray seals are likely to continue.

Successful management strategies are important for conservation and allow accurate surveying and monitoring of populations for presence, abundance, and trend. This becomes challenging for cryptic, low-density species, and for animals that have complicated life histories where not every stage of the life cycle can be surveyed effectively. We used information from animal-borne data loggers to characterize the dive-surfacing behavior of cryptic loggerhead turtles (Caretta caretta) in the northwest Atlantic from 2009–2018. Our data covered a large geographic area off the east coast of North America, and allowed us to present estimates for and variation in 3 metrics that can be used to assess availability bias affecting visual surveys: average dive duration, average surface duration, and the proportion of time at the surface. We used a stochastic partial differential equation approach to construct spatiotemporal regression models for the availability bias metrics. Model predictions showed pronounced individual, spatial, and spatiotemporal (seasonal) variation among the 245 turtles. Overall, we estimated an average dive duration of 14.5 ± 1.36minutes (SE), an average surface duration of 15.1 ± 2.77minutes, and an average proportion of time at the surface of 0.50 (95% CI = 0.41–0.59). We made predictions of the 3 availability bias metrics on a 20-km × 20-km grid and further used predictions to explore seasonal variations. Our results contribute new insights into loggerhead turtle behavior and provide information that enables survey counts to be translated into more accurate abundance estimates.

Identifying the overlap of commercial fishing grounds and seabird habitat can suggest areas of high bycatch risk and inform management and mitigation measures. We used Bayesian state space modeling to describe the movements of 10 satellite‐tagged Great Shearwaters and a bivariate kernel density technique to investigate spatial overlap with commercial fishing effort to predict areas of high bycatch in the Gulf of Maine. We then used contemporaneous fishery observer data to test the validity of our predictions, highlighting an area constituting 1% of the Gulf of Maine as having the highest bycatch risk that accounted for 50% of observed takes. Fishery observer data also provided insights into characteristics of the seabird‐fishery interactions. Our results indicate that a relatively small number of satellite‐tagged seabirds, when combined with fishery‐dependent data, can lead to identifying high‐bycatch areas, particular fishing practices that might increase risk, and fishing communities that could be targeted for education/mitigation.

Regional homogeneity (ReHo) is a measure of local functional brain connectivity that has been reported to be altered in a wide range of neuropsychiatric disorders. Computed from brain resting-state functional MRI time series, ReHo is also sensitive to fluctuations in cerebral blood flow (CBF) that in turn may be influenced by cerebrovascular health. We accessed cerebrovascular health with Framingham cardiovascular risk score (FCVRS). We hypothesize that ReHo signal may be influenced by regional CBF; and that these associations can be summarized as FCVRS→CBF→ReHo. We used three independent samples to test this hypothesis. A test-retest sample of N = 30 healthy volunteers was used for test-retest evaluation of CBF effects on ReHo. Amish Connectome Project (ACP) sample (N = 204, healthy individuals) was used to evaluate association between FCVRS and ReHo and testing if the association diminishes given CBF. The UKBB sample (N = 6,285, healthy participants) was used to replicate the effects of FCVRS on ReHo. We observed strong CBF→ReHo links (p<2.5 × 10−3) using a three-point longitudinal sample. In ACP sample, marginal and partial correlations analyses demonstrated that both CBF and FCVRS were significantly correlated with the whole-brain average (p<10−6) and regional ReHo values, with the strongest correlations observed in frontal, parietal, and temporal areas. Yet, the association between ReHo and FCVRS became insignificant once the effect of CBF was accounted for. In contrast, CBF→ReHo remained significantly linked after adjusting for FCVRS and demographic covariates (p<10−6). Analysis in N = 6,285 replicated the FCVRS→ReHo effect (p = 2.7 × 10−27). In summary, ReHo alterations in health and neuropsychiatric illnesses may be partially driven by region-specific variability in CBF, which is, in turn, influenced by cardiovascular factors.

•Non-psychiatric adults were clustered by brain similarity to severe mental illness.•Clusters with more similarity had higher volume of white matter hyperintensities.•Cluster with most similarity had poorer cardiometabolic health and processing speed.•Cluster with most similarity had higher levels of inflammation and alcohol use. Severe mental illnesses (SMIs) are often associated with compromised brain health, physical comorbidities, and cognitive deficits, but it is incompletely understood whether these comorbidities are intrinsic to SMI pathophysiology or secondary to having SMIs. We tested the hypothesis that cerebral, cardiometabolic, and cognitive impairments commonly observed in SMIs can be observed in non-psychiatric individuals with SMI-like brain patterns of deviation as seen on magnetic resonance imaging. 22,883 participants free of common neuropsychiatric conditions from the UK Biobank (age = 63.4 ± 7.5 years, range = 45–82 years, 50.9% female) were split into discovery and replication samples. The regional vulnerability index (RVI) was used to quantify each participant's respective brain similarity to meta-analytical patterns of schizophrenia spectrum disorder, bipolar disorder, and major depressive disorder in gray matter thickness, subcortical gray matter volume, and white matter integrity. Cluster analysis revealed five clusters with distinct RVI profiles. Compared with a cluster with no RVI elevation, a cluster with RVI elevation across all SMIs and brain structures showed significantly higher volume of white matter hyperintensities (Cohen's d = 0.59, pFDR < 10−16), poorer cardiovascular (Cohen's d = 0.30, pFDR < 10−16) and metabolic (Cohen's d = 0.12, pFDR = 1.3 × 10−4) health, and slower speed of information processing (|Cohen's d| = 0.11-0.17, pFDR = 1.6 × 10−3-4.6 × 10−8). This cluster also had significantly higher level of C-reactive protein and alcohol use (Cohen's d = 0.11 and 0.28, pFDR = 4.1 × 10−3 and 1.1 × 10−11). Three other clusters with respective RVI elevation in gray matter thickness, subcortical gray matter volume, and white matter integrity showed intermediate level of white matter hyperintensities, cardiometabolic health, and alcohol use. Our results suggest that cerebral, physical, and cognitive impairments in SMIs may be partly intrinsic via shared pathophysiological pathways with SMI-related brain anatomical changes.