Decrease in water clarity of the southern and central North Sea during the 20th century

Light in the marine environment is a key environmental variable coupling physics to marine biogeochemistry and ecology. Weak light penetration reduces light available for photosynthesis, changing energy fluxes through the marine food web. Based on published and unpublished data, this study shows that the central and southern North Sea has become significantly less clear over the second half of the 20th century. In particular, in the different regions and seasons investigated, the average Secchi depth pre-1950 decreased between 25% and 75% compared to the average Secchi depth post-1950. Consequently, in summer pre-1950, most (74%) of the sea floor in the permanently mixed area off East Anglia was within the photic zone. For the last 25+ years, changes in water clarity were more likely driven by an increase in the concentration of suspended sediments, rather than phytoplankton. We suggest that a combination of causes have contributed to this increase in suspended sediments such as changes in sea-bed communities and in weather patterns, decreased sink of sediments in estuaries, and increased coastal erosion. A predicted future increase in storminess (Beniston et al., 2007; Kovats et al., 2014) could enhance the concentration of suspended sediments in the water column and consequently lead to a further decrease in clarity, with potential impacts on phytoplankton production, CO2 fluxes, and fishery production

Pruning of genetic programming trees using permutation tests

We present a novel approach based on statistical permutation tests for pruning redundant subtrees from genetic programming (GP) trees that allows us to explore the extent of effective redundancy . We observe that over a range of regression problems, median tree sizes are reduced by around 20% largely independent of test function, and that while some large subtrees are removed, the median pruned subtree comprises just three nodes; most take the form of an exact algebraic simplification. Our statistically-based pruning technique has allowed us to explore the hypothesis that a given subtree can be replaced with a constant if this substitution results in no statistical change to the behavior of the parent tree – what we term approximate simplification. In the eventuality, we infer that more than 95% of the accepted pruning proposals are the result of algebraic simplifications, which provides some practical insight into the scope of removing redundancies in GP trees

Symptoms predicting remission after divalproex augmentation with olanzapine in partially nonresponsive patients experiencing mixed bipolar I episode: a post-hoc analysis of a randomized controlled study

<p>Abstract</p> <p>Background</p> <p>Rating scale items in a 6-week clinical trial of olanzapine versus placebo augmentation in patients with mixed bipolar disorder partially nonresponsive to ≥14 days of divalproex monotherapy were analyzed to characterize symptom patterns that could predict remission. At baseline, the two treatment groups were similar.</p> <p>Findings</p> <p>Factor analysis with Varimax rotation was performed <it>post hoc </it>on baseline items of the 21-Item Hamilton Depression Rating Scale (HDRS-21) and Young Mania Rating Scale (YMRS). Backwards-elimination logistic regression ascertained factors predictive of protocol-defined endpoint remission (HDRS-21 score ≤ 8 and YMRS score ≤ 12) with subsequent determination of optimally predictive factor score cutoffs.</p> <p>Factors for Psychomotor activity (YMRS items for elevated mood, increased motor activity, and increased speech and HDRS-21 agitation item) and Guilt/Suicidality (HDRS-21 items for guilt and suicidality) significantly predicted endpoint remission in the divalproex+olanzapine group. No factor predicted remission in the divalproex+placebo group. Patients in the divalproex+olanzapine group with high pre-augmentation psychomotor activity (scores ≥10) were more likely to remit compared to those with lower psychomotor activity (odds ratio [OR] = 3.09, 95% confidence interval [CI] = 1.22-7.79), and patients with marginally high Guilt/Suicidality (scores ≥2) were less likely to remit than those with lower scores (OR = 0.37, 95% CI = 0.13-1.03). Remission rates for divalproex+placebo vs. divalproex+olanzapine patients with high psychomotor activity scores were 22% vs. 45% (p = 0.08) and 33% vs. 48% (p = 0.29) for patients with low Guilt/Suicidality scores.</p> <p>Conclusions</p> <p>Patients who were partially nonresponsive to divalproex treatment with remaining high vs. low psychomotor activity levels or minimal vs. greater guilt/suicidality symptoms were more likely to remit with olanzapine augmentation.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov; <url>http://clinicaltrials.gov/ct2/show/NCT00402324?term=NCT00402324&rank=1</url>, Identifier: NCT00402324</p

Mangroves enhance the biomass of coral reef fish communities in the Caribbean

Mangrove forests are one of the world's most threatened tropical ecosystems with global loss exceeding 35% (ref. 1). Juvenile coral reef fish often inhabit mangroves, but the importance of these nurseries to reef fish population dynamics has not been quantified. Indeed, mangroves might be expected to have negligible influence on reef fish communities: juvenile fish can inhabit alternative habitats and fish populations may be regulated by other limiting factors such as larval supply or fishing. Here we show that mangroves are unexpectedly important, serving as an intermediate nursery habitat that may increase the survivorship of young fish. Mangroves in the Caribbean strongly influence the community structure of fish on neighbouring coral reefs. In addition, the biomass of several commercially important species is more than doubled when adult habitat is connected to mangroves. The largest herbivorous fish in the Atlantic, Scarus guacamaia, has a functional dependency on mangroves and has suffered local extinction after mangrove removal. Current rates of mangrove deforestation are likely to have severe deleterious consequences for the ecosystem function, fisheries productivity and resilience of reefs. Conservation efforts should protect connected corridors of mangroves, seagrass beds and coral reefs

Migrations and habitat use of the smooth hammerhead shark (Sphyrna zygaena) in the Atlantic Ocean

The smooth hammerhead shark, Sphyrna zygaena, is a cosmopolitan semipelagic shark captured as bycatch in pelagic oceanic fisheries, especially pelagic longlines targeting swordfish and/or tunas. From 2012 to 2016, eight smooth hammerheads were tagged with Pop-up Satellite Archival Tags in the inter-tropical region of the Northeast Atlantic Ocean, with successful transmissions received from seven tags (total of 319 tracking days). Results confirmed the smooth hammerhead is a highly mobile species, as the longest migration ever documented for this species (> 6600 km) was recorded. An absence of a diel vertical movement behavior was noted, with the sharks spending most of their time at surface waters (0-50 m) above 23 degrees C. The operating depth of the pelagic long-line gear was measured with Minilog Temperature and Depth Recorders, and the overlap with the species vertical distribution was calculated. The overlap is taking place mainly during the night and is higher for juveniles (similar to 40% of overlap time). The novel information presented can now be used to contribute to the provision of sustainable management tools and serve as input for Ecological Risk Assessments for smooth hammerheads caught in Atlantic pelagic longline fisheries.Oceanario de Lisboa through Project "SHARK-TAG: Migrations and habitat use of the smooth hammerhead shark in the Atlantic Ocean"; Investigador-FCT from the Portuguese Foundation for Science and Technology (FCT, Fundacao para a Ciencia e Tecnologia) [Ref: IF/00253/2014]; EU European Social Fund; Programa Operacional Potencial Human

Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set

There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands

A review of techniques for spatial modeling in geographical, conservation and landscape genetics

Most evolutionary processes occur in a spatial context and several spatial analysis techniques have been employed in an exploratory context. However, the existence of autocorrelation can also perturb significance tests when data is analyzed using standard correlation and regression techniques on modeling genetic data as a function of explanatory variables. In this case, more complex models incorporating the effects of autocorrelation must be used. Here we review those models and compared their relative performances in a simple simulation, in which spatial patterns in allele frequencies were generated by a balance between random variation within populations and spatially-structured gene flow. Notwithstanding the somewhat idiosyncratic behavior of the techniques evaluated, it is clear that spatial autocorrelation affects Type I errors and that standard linear regression does not provide minimum variance estimators. Due to its flexibility, we stress that principal coordinate of neighbor matrices (PCNM) and related eigenvector mapping techniques seem to be the best approaches to spatial regression. In general, we hope that our review of commonly used spatial regression techniques in biology and ecology may aid population geneticists towards providing better explanations for population structures dealing with more complex regression problems throughout geographic space