Distribution and biomass of gelatinous zooplankton in relation to an oxygen minimum zone and a shallow seamount in the Eastern Tropical North Atlantic Ocean

Highlights: • Gelatinous zooplankton (GZ) is common in Eastern Tropical North Atlantic (ETNA). • Weak negative correlation between oxygen and key GZ groups with higher densities at lower oxygen conditions. • High GZ biomass also found at lowest oxygen concentration depths. • Strong positive correlation between temperature and key GZ group abundance. • GZ important component of oceanic ecosystems including low oxygenated waters. Physical and topographic characteristics can structure pelagic habitats and affect the plankton community composition. For example, oxygen minimum zones (OMZs) are expected to lead to a habitat compression for species with a high oxygen demand, while upwelling of nutrient-rich deep water at seamounts can locally increase productivity, especially in oligotrophic oceanic waters. Here we investigate the response of the gelatinous zooplankton (GZ) assemblage and biomass to differing oxygen conditions and to a seamount in the Eastern Tropical North Atlantic (ETNA) around the Cape Verde archipelago. A total of 16 GZ taxa (>1100 specimens) were found in the upper 1000 m with distinct species-specific differences, such as the absence of deep-living species Atolla wyvillei and Periphylla periphylla above the shallow seamount summit. Statistical analyses considering the most prominent groups, present at all stations, namely Beroe spp., hydromedusae (including Zygocanna vagans, Halicreas minimum, Colobonema sericeum, Solmissus spp.) and total GZ, showed a strong positive correlation of abundance with temperature for all groups, whereas oxygen had a weak negative correlation only with abundances of Beroe spp. and hydromedusae. To account for size differences between species, we established length-weight regressions and investigated total GZ biomass changes in relation to physical (OMZ) and topographic characteristics. The highest GZ biomass was observed at depths of lowest oxygen concentrations and deepest depth strata at the southeastern flank of the seamount and at two stations south of the Cape Verde archipelago. Our data suggest that, irrespective of their patchy distribution, GZ organisms are ubiquitous food web members of the ETNA, and their habitat includes waters of low oxygen content

Ten lessons on the resilience of the EU common fisheries policy towards climate change and fuel efficiency - A call for adaptive, flexible and well-informed fisheries management

To effectively future-proof the management of the European Union fishing fleets we have explored a suite of case studies encompassing the northeast and tropical Atlantic, the Mediterranean, Baltic and Black Seas. This study shows that European Union (EU) fisheries are likely resilient to climate-driven short-term stresses, but may be negatively impacted by long-term trends in climate change. However, fisheries' long-term stock resilience can be improved (and therefore be more resilient to increasing changes in climate) by adopting robust and adaptive fisheries management, provided such measures are based on sound scientific advice which includes uncertainty. Such management requires regular updates of biological reference points. Such updates will delineate safe biological limits for exploitation, providing both high long-term yields with reduced risk of stock collapse when affected by short-term stresses, and enhanced compliance with advice to avoid higher than intended fishing mortality. However, high resilience of the exploited ecosystem does not necessarily lead to the resilience of the economy of EU fisheries from suffering shocks associated with reduced yields, neither to a reduced carbon footprint if fuel use increases from lower stock abundances. Fuel consumption is impacted by stock development, but also by changes in vessel and gear technologies, as well as fishing techniques. In this respect, energy-efficient fishing technologies already exist within the EU, though implementing them would require improving the uptake of innovations and demonstrating to stakeholders the potential for both reduced fuel costs and increased catch rates. A transition towards reducing fuel consumption and costs would need to be supported by the setup of EU regulatory instruments. Overall, to effectively manage EU fisheries within a changing climate, flexible, adaptive, well-informed and well-enforced management is needed, with incentives provided for innovations and ocean literacy to cope with the changing conditions, while also reducing the dependency of the capture fishing industry on fossil fuels. To support such management, we provide 10 lessons to characterize 'win-win' fishing strategies for the European Union, which develop leverages in which fishing effort deployed corresponds to Maximum Sustainable Yield targets and Common Fisheries Policy minimal effects objectives. In these strategies, higher catch is obtained in the long run, less fuel is spent to attain the catch, and the fisheries have a higher resistance and resilience to shock and long-term factors to face climate-induced stresses

Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension

OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo

Estimation of Individual Growth Trajectories When Repeated Measures Are Missing

Individuals in a population vary in their growth due to hidden and observed factors such as age, genetics, environment, disease, and carryover effects from past environments. Because size affects fitness, growth trajectories scale up to affect population dynamics. However, it can be difficult to estimate growth in data from wild populations with missing observations and observation error. Previous work has shown that linear mixed models (LMMs) underestimate hidden individual heterogeneity when more than 25% of repeated measures are missing. Here we demonstrate a flexible and robust way to model growth trajectories. We show that state-space models (SSMs), fit using R package growmod, are far less biased than LMMs when fit to simulated data sets with missing repeated measures and observation error. This method is much faster than Markov chain Monte Carlo methods, allowing more models to be tested in a shorter time. For the scenarios we simulated, SSMs gave estimates with little bias when up to 87.5% of repeated measures were missing. We use this method to quantify growth of Soay sheep, using data from a long-term mark-recapture study, and demonstrate that growth decreased with age, population density, weather conditions, and when individuals are reproductive. The method improves our ability to quantify how growth varies among individuals in response to their attributes and the environments they experience, with particular relevance for wild populations

Data on red-eyed tree frogs: size (total length) in mm vs. time in days.

<p>Points represent tank means; red lines are estimated Michaelis-Menten growth curves; blue dotted lines are estimated asymptotic sizes.</p

Number of individuals needed to detect positive growth autocorrelation.

<p>The line represents the minimum number of individuals in a cohort needed to statistically detect that <i>ρ</i>² is greater than 0 at least 80 percent of the time, based on the 95% confidence intervals of 1000 simulations with  = 16 and <i>n_t</i> = 16 (see <i>Simulating growth data</i> section for details). When <i>ρ</i>²<0.36, more than 512 individuals are needed, beyond the simulated range.</p

Simulated growth of individuals: growth parameters are the same across panels except for the assumptions: (left) uncorrelated variation among individuals, (center) autocorrelated variation, (right) positively size-dependent growth.

<p>A - Patterns of growth rate Growth rates of five individuals are represented in each graph by five separate lines. B - Patterns of size variation Average cohort variance in 2000 cohorts of 50 indviduals: mean (solid line) and 2.5% and 97.5% quantiles (grey ribbons).</p

Estimated scaled variance per tank over time in different treatment combinations.

<p>Color scale represents the fraction of individual measurements dropped because they exceeded the estimated asymptotic size for the treatment combination (and hence could not be used in our linearizing transformation).</p