Flexible use of a dynamic energy landscape buffers a marine predator against extreme climate variability

Animal migrations track predictable seasonal patterns of resource availability and suitable thermal habitat. As climate change alters this 'energy landscape', some migratory species may struggle to adapt. We examined how climate variability influences movements, thermal habitat selection and energy intake by juvenile Pacific bluefin tuna (Thunnus orientalis) during seasonal foraging migrations in the California Current. We tracked 242 tuna across 15 years (2002-2016) with high-resolution archival tags, estimating their daily energy intake via abdominal warming associated with digestion (the 'heat increment of feeding'). The poleward extent of foraging migrations was flexible in response to climate variability, allowing tuna to track poleward displacements of thermal habitat where their standard metabolic rates were minimized. During a marine heatwave that saw temperature anomalies of up to +2.5 degrees C in the California Current, spatially explicit energy intake by tuna was approximately 15% lower than average. However, by shifting their mean seasonal migration approximately 900 km poleward, tuna remained in waters within their optimal temperature range and increased their energy intake. Our findings illustrate how tradeoffs between physiology and prey availability structure migration in a highly mobile vertebrate, and suggest that flexible migration strategies can buffer animals against energetic costs associated with climate variability and change

Provision of ECPR during COVID-19: evidence, equity, and ethical dilemmas

The use of extracorporeal cardiopulmonary resuscitation (ECPR) to restore circulation during cardiac arrest is a time-critical, resource-intensive intervention of unproven efficacy. The current COVID-19 pandemic has brought additional complexity and significant barriers to the ongoing provision and implementation of ECPR services. The logistics of patient selection, expedient cannulation, healthcare worker safety, and post-resuscitation care must be weighed against the ethical considerations of providing an intervention of contentious benefit at a time when critical care resources are being overwhelmed by pandemic demand

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest

Performance Evaluation of Cetacean Species Distribution Models Developed Using Generalized Additive Models and Boosted Regression Trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest

Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modeling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks lsurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997-2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (\u3c 100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models

Biventricular pacemaker therapy improves exercise capacity in patients with non‐obstructive hypertrophic cardiomyopathy via augmented diastolic filling on exercise

Aims Treatment options for patients with non‐obstructive hypertrophic cardiomyopathy (HCM) are limited. We sought to determine whether biventricular (BiV) pacing improves exercise capacity in HCM patients, and whether this is via augmented diastolic filling. Methods and results Thirty‐one patients with symptomatic non‐obstructive HCM were enrolled. Following device implantation, patients underwent detailed assessment of exercise diastolic filling using radionuclide ventriculography in BiV and sham pacing modes. Patients then entered an 8‐month crossover study of BiV and sham pacing in random order, to assess the effect on exercise capacity [peak oxygen consumption (VO2)]. Patients were grouped on pre‐specified analysis according to whether left ventricular end‐diastolic volume increased (+LVEDV) or was unchanged/decreased (–LVEDV) with exercise at baseline. Twenty‐nine patients (20 male, mean age 55 years) completed the study. There were 14 +LVEDV patients and 15 –LVEDV patients. Baseline peak VO2 was lower in –LVEDV patients vs. +LVEDV patients (16.2 ± 0.9 vs. 19.9 ± 1.1 mL/kg/min, P = 0.04). BiV pacing significantly increased exercise ΔLVEDV (P = 0.004) and Δstroke volume (P = 0.008) in –LVEDV patients, but not in +LVEDV patients. Left ventricular ejection fraction and end‐systolic elastance did not increase with BiV pacing in either group. This translated into significantly greater improvements in exercise capacity (peak VO2 + 1.4 mL/kg/min, P = 0.03) and quality of life scores (P = 0.02) in –LVEDV patients during the crossover study. There was no effect on left ventricular mechanical dyssynchrony in either group. Conclusion Symptomatic patients with non‐obstructive HCM may benefit from BiV pacing via augmentation of diastolic filling on exercise rather than contractile improvement. This may be due to relief of diastolic ventricular interaction. Clinical Trial Registration: ClinicalTrials.gov NCT00504647

Characteristics associated with quality of life among people with drug-resistant epilepsy

Quality of Life (QoL) is the preferred outcome in non-pharmacological trials, but there is little UK population evidence of QoL in epilepsy. In advance of evaluating an epilepsy self-management course we aimed to describe, among UK participants, what clinical and psycho-social characteristics are associated with QoL. We recruited 404 adults attending specialist clinics, with at least two seizures in the prior year and measured their self-reported seizure frequency, co-morbidity, psychological distress, social characteristics, including self-mastery and stigma, and epilepsy-specific QoL (QOLIE-31-P). Mean age was 42 years, 54% were female, and 75% white. Median time since diagnosis was 18 years, and 69% experienced ≥10 seizures in the prior year. Nearly half (46%) reported additional medical or psychiatric conditions, 54% reported current anxiety and 28% reported current depression symptoms at borderline or case level, with 63% reporting felt stigma. While a maximum QOLIE-31-P score is 100, participants’ mean score was 66, with a wide range (25–99). In order of large to small magnitude: depression, low self-mastery, anxiety, felt stigma, a history of medical and psychiatric comorbidity, low self-reported medication adherence, and greater seizure frequency were associated with low QOLIE-31-P scores. Despite specialist care, UK people with epilepsy and persistent seizures experience low QoL. If QoL is the main outcome in epilepsy trials, developing and evaluating ways to reduce psychological and social disadvantage are likely to be of primary importance. Educational courses may not change QoL, but be one component supporting self-management for people with long-term conditions, like epilepsy

Is ranitidine therapy sufficient for healing peptic ulcers associated with non-steroidal anti-inflammatory drug use?

Long-term use of non-steroidal anti-inflammatory drugs (NSAIDs) increases the risk of serious gastroduodenal events. To minimise these risks, patients often require concomitant acid-suppressive therapy. We conducted a literature review of clinical trials examining use of ranitidine 150 mg twice daily to heal gastroduodenal ulcers (GU) in NSAID recipients. Seven studies were identified. After 8 weeks’ treatment with ranitidine, GU healing rates ranged from 50% to 74% and rates of duodenal ulcer (DU) healing ranged from 81% to 84%. Ranitidine was more effective when NSAIDs were discontinued (healing rates reaching 95% and 100%, respectively). The ulcer healing rate with sucralfate was similar to that of ranitidine. However, proton pump inhibitor (PPI) therapy was associated with significantly greater rates of both GU and DU healing than ranitidine; 8-week GU rates were 92% and 88% with esomeprazole 40 mg and 20 mg, respectively (vs. 74% with ranitidine, p < 0.01). For omeprazole, 8-week healing rates were 87% with omeprazole 40 mg and 84% with omeprazole 20 mg (vs. 64% for ranitidine, p < 0.001), and for lansoprazole the corresponding values were 73–74% and 66–69% for the 30 mg and 15 mg doses, respectively (vs. 50–53% for ranitidine, p < 0.05). In the PPI study reporting DU healing the values were 92% for omeprazole 20 mg (vs. 81% for ranitidine, p < 0.05) and 88% for omeprazole 40 mg (p = 0.17 vs. ranitidine). NSAID-associated GU are more likely to heal when patients receive concomitant treatment with a PPI rather than ranitidine

Entanglement is a costly life-history stage in large whales

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecology and Evolution 7 (2017): 92–106, doi:10.1002/ece3.2615.Individuals store energy to balance deficits in natural cycles; however, unnatural events can also lead to unbalanced energy budgets. Entanglement in fishing gear is one example of an unnatural but relatively common circumstance that imposes energetic demands of a similar order of magnitude and duration of life-history events such as migration and pregnancy in large whales. We present two complementary bioenergetic approaches to estimate the energy associated with entanglement in North Atlantic right whales, and compare these estimates to the natural energetic life history of individual whales. Differences in measured blubber thicknesses and estimated blubber volumes between normal and entangled, emaciated whales indicate between 7.4 × 1010 J and 1.2 × 1011 J of energy are consumed during the course to death of a lethal entanglement. Increased thrust power requirements to overcome drag forces suggest that when entangled, whales require 3.95 × 109 to 4.08 × 1010 J more energy to swim. Individuals who died from their entanglements performed significantly more work (energy expenditure × time) than those that survived; entanglement duration is therefore critical in determining whales’ survival. Significant sublethal energetic impacts also occur, especially in reproductive females. Drag from fishing gear contributes up to 8% of the 4-year female reproductive energy budget, delaying time of energetic equilibrium (to restore energy lost by a particular entanglement) for reproduction by months to years. In certain populations, chronic entanglement in fishing gear can be viewed as a costly unnatural life-history stage, rather than a rare or short-term incident.Cooperative Institute for the North Atlantic Region (CINAR) Grant Number: NA14OAR4320158; Herrington-Fitch Family Foundation; M.S. Worthington Foundation; North Pond Foundation; Natural Sciences and Engineering Research Council of Canada; MIT Martin Family for Sustainability Fellowshi