Age correction in dementia--matching to a healthy brain

In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences.     

Safety and tolerability of sitagliptin in patients with type 2 diabetes: a pooled analysis

Background

Sitagliptin, a highly selective dipeptidyl peptidase-4 inhibitor, is the first in a new class of oral antihyperglycemic agents (AHAs) for the treatment of patients with type 2 diabetes. Type 2 diabetes is a life-long disease requiring chronic treatment and management. Therefore, robust assessment of the long-term safety and tolerability of newer therapeutic agents is of importance. The purpose of this analysis was to assess the safety and tolerability of sitagliptin by pooling 12 large, double-blind, Phase IIb and III studies up to 2 years in duration. Methods: This analysis included 6139 patients with type 2 diabetes receiving either sitagliptin 100 mg/day (N = 3415) or a comparator agent (placebo or an active comparator) (N = 2724; non-exposed group). The 12 studies from which this pooled population was drawn represent the double-blind, randomized, Phase IIB and III studies that included patients treated with the clinical dose of sitagliptin (100 mg/day) for at least 18 weeks up to 2 years and that were available in a single safety database as of November 2007. These 12 studies assessed sitagliptin as monotherapy, initial combination therapy with metformin, or add-on combination therapy with other oral AHAs (metformin, pioglitazone, sulfonylurea, sulfonylurea + metformin, or metformin + rosiglitazone). Patients in the non-exposed group were taking placebo, pioglitazone, metformin, sulfonylurea, sulfonylurea + metformin, or metformin + rosiglitazone. This safety analysis used patient-level data from each study to evaluate clinical and laboratory adverse experiences.

Results

For clinical adverse experiences, the incidence rates of adverse experiences overall, serious adverse experiences, and discontinuations due to adverse experiences were similar in the sitagliptin and non-exposed groups. The incidence rates of specific adverse experiences were also generally similar in the two groups, with the exception of an increased incidence rate of hypoglycemia observed in the non-exposed group. The incidence rates of drug-related adverse experiences overall and discontinuations due to drug-related adverse experiences were higher in the non-exposed group, primarily due to the increased incidence rate of hypoglycemia in this group. For cardiac- and ischemia-related adverse experiences (including serious events), there were no meaningful between-group differences. No meaningful differences between groups in laboratory adverse experiences, either summary measures or specific adverse experiences, were observed.

Conclusion

In patients with type 2 diabetes, sitagliptin 100 mg/day was well tolerated in clinical trials up to 2 years in duration.     

Systematic reviews: a primer for plastic surgery research

Clinicians rely on review articles to keep current with the rapid accumulation of medical and surgical literature. Traditional expert reviews, however, often suffer from inherent personal biases and may not reflect a true synthesis of the existing literature on a particular subject. Systematic reviews are structured, scientific articles that address the shortcomings of traditional reviews by adhering to strict, reproducible methods and recommended guidelines. The methods are designed to eliminate possible sources of bias, ensure as complete a review of the existing literature as possible, and present the results in a way that is useful for its intended audience. Systematic reviews may at times include a quantitative synthesis of the available data in the form of a meta-analysis. Meta-analysis is a statistical tool for combining the numerical results of separate studies to obtain a summary outcome with increased precision due to the larger, combined number of patients. Meta-analyses may be particularly helpful when individual study results are conflicting and the existing literature is inconclusive. The validity of meta-analysis, however, is highly dependent on the quality of data available in the literature. In its strictest form, meta-analysis is used to combine data from only randomized controlled clinical trials. Because randomized controlled clinical trials are infrequently performed in plastic surgery research, this article will focus on systematic reviews to provide the readers with a useful guide in performing this field of study.     

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs

There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations—MASS, RAREMETAL, MetaSKAT, and seqMeta—in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.     

Graphical procedures for evaluating overall and subject-specific incremental values from new predictors with censored event time data

Quantitative procedures for evaluating added values from new markers over a conventional risk scoring system for predicting event rates at specific time points have been extensively studied. However, a single summary statistic, for example, the area under the receiver operating characteristic curve or its derivatives, may not provide a clear picture about the relationship between the conventional and the new risk scoring systems. When there are no censored event time observations in the data, two simple scatterplots with individual conventional and new scores for "cases" and "controls" provide valuable information regarding the overall and the subject-specific level incremental values from the new markers. Unfortunately, in the presence of censoring, it is not clear how to construct such plots. In this article, we propose a nonparametric estimation procedure for the distributions of the differences between two risk scores conditional on the conventional score. The resulting quantile curves of these differences over the subject-specific conventional score provide extra information about the overall added value from the new marker. They also help us to identify a subgroup of future subjects who need the new predictors, especially when there is no unified utility function available for cost-risk-benefit decision making. The procedure is illustrated with two data sets. The first is from a well-known Mayo Clinic primary biliary cirrhosis liver study. The second is from a recent breast cancer study on evaluating the added value from a gene score, which is relatively expensive to measure compared with the routinely used clinical biomarkers for predicting the patient's survival after surgery.     

A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci

Background

Characterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs.

Methods

We apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1.

Results

We demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes.

Conclusions

We conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations.     

Placebo Devices as Effective Control Methods in Acupuncture Clinical Trials: A Systematic Review

While the use of acupuncture has been recognised by the World Health Organisation, its efficacy for many of the common clinical conditions is still undergoing validation through randomised controlled trials (RCTs). A credible placebo control for such RCTs to enable meaningful evaluation of its efficacy is to be established. While several non-penetrating acupuncture placebo devices, namely the Streitberger, the Park and the Takakura Devices, have been developed and used in RCTs, their suitability as inert placebo controls needs to be rigorously determined. This article systematically reviews these devices as placebo interventions. Electronic searches were conducted on four English and two Chinese databases from their inceptions to July 2014; hand searches of relevant references were also conducted. RCTs, in English or Chinese language, comparing acupuncture with one of the aforementioned devices as the control intervention on human participants with any clinical condition and evaluating clinically related outcomes were included. Thirty-six studies were included for qualitative analysis while 14 were in the meta-analysis. The meta-analysis does not support the notion of either the Streitberger or the Park Device being inert control interventions while none of the studies involving the Takakura Device was included in the meta-analysis. Sixteen studies reported the occurrence of adverse events, with no significant difference between verum and placebo acupuncture. Author-reported blinding credibility showed that participant blinding was successful in most cases; however, when blinding index was calculated, only one study, which utilised the Park Device, seemed to have an ideal blinding scenario. Although the blinding index could not be calculated for the Takakura Device, it was the only device reported to enable practitioner blinding. There are limitations with each of the placebo devices and more rigorous studies are needed to further evaluate their effects and blinding credibility.     

The statistical analysis of multivariate serological frequency data

Data occurring in the form of frequencies are common in genetics—for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis (“raw” principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject.     

Meta-Analysis of Genome-wide Association Studies with Overlapping Subjects

Data from multiple genome-wide association studies are often analyzed together for the purposes of combining information from several studies of the same disease or comparing results across different disorders. We provide a valid and efficient approach to such meta-analysis, allowing for overlapping study subjects. The available data may contain individual participant records or only meta-analytic summary results. Simulation studies demonstrate that failure to account for overlapping subjects can greatly inflate type I error when combining results from multiple studies of the same disease and can drastically reduce power when comparing results across different disorders. In addition, the proposed approach can be substantially more powerful than the simple approach of splitting the overlapping subjects among studies, especially for comparing results across different disorders. The advantages of the new approach are illustrated with empirical data from two sets of genome-wide association studies.     

A simple meta-analytic approach for using a binary surrogate endpoint to predict the effect of intervention on true endpoint

A surrogate endpoint is an endpoint that is obtained sooner, at lower cost, or less invasively than the true endpoint for a health outcome and is used to make conclusions about the effect of intervention on the true endpoint. In this approach, each previous trial with surrogate and true endpoints contributes an estimated predicted effect of intervention on true endpoint in the trial of interest based on the surrogate endpoint in the trial of interest. These predicted quantities are combined in a simple random-effects meta-analysis to estimate the predicted effect of intervention on true endpoint in the trial of interest. Validation involves comparing the average prediction error of the aforementioned approach with (i) the average prediction error of a standard meta-analysis using only true endpoints in the other trials and (ii) the average clinically meaningful difference in true endpoints implicit in the trials. Validation is illustrated using data from multiple randomized trials of patients with advanced colorectal cancer in which the surrogate endpoint was tumor response and the true endpoint was median survival time.     

When Is Hub Gene Selection Better than Standard Meta-Analysis?

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to gene expression data and presents novel R functions for carrying out consensus network analysis, network based screening, and meta analysis.     

Statistical issues arising in the Women's Health Initiative

A brief overview of the design of the Women's Health Initiative (WHI) clinical trial and observational study is provided along with a summary of results from the postmenopausal hormone therapy clinical trial components. Since its inception in 1992, the WHI has encountered a number of statistical issues where further methodology developments are needed. These include measurement error modeling and analysis procedures for dietary and physical activity assessment; clinical trial monitoring methods when treatments may affect multiple clinical outcomes, either beneficially or adversely; study design and analysis procedures for high-dimensional genomic and proteomic data; and failure time data analysis procedures when treatment group hazard ratios are time dependent. This final topic seems important in resolving the discrepancy between WHI clinical trial and observational study results on postmenopausal hormone therapy and cardiovascular disease.     

Improving efficiency of inference in clinical trials with external control data

Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are often available from historical studies. Motivated by an application to Helicobacter pylori infection, we show how to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both the clinical trial data and external controls. We then derive a doubly robust and locally efficient estimator. The improvement in efficiency is prominent especially when the external control data set has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains a treated group. We also develop doubly robust and locally efficient approaches that extrapolate the causal effect in the clinical trial to the external population and the overall population. Our results also offer a meaningful implication for trial design and data collection. We evaluate the finite-sample performance of the proposed estimators via simulation. In the Helicobacter pylori infection application, our approach shows that the combination treatment has potential efficacy advantages over the triple therapy.     

Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying “causal” rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.     

The Relationship between Serum Lipids and Sudden Sensorineural Hearing Loss: A Systematic Review and Meta-Analysis

Background

Sudden sensorineural hearing loss (SSNHL) is a relatively common condition that is usually of unknown etiology. A number of individual studies have investigated the association between various serum lipids and SSNHL; however, the findings have been inconsistent. In an attempt to obtain more definitive information on the relationship between serum lipids and SSNHL, we carried out a systematic review and meta-analysis.

Methods

Medline, the Cochrane Library, and EMBASE were searched using the following key words: lipid, cholesterol, triglyceride, fat, serum, blood, sudden hearing loss, hearing loss, hearing disorders. Randomized controlled trials, prospective cohort studies, and retrospective case-control studies involving patients with SSNHL and healthy controls that examined the relationship (reported as odds ratios [OR]) between lipid profiles and SSNHL were included. Primary outcomes were total cholesterol and low-density lipoprotein cholesterol (LDL-C) concentrations. Secondary outcomes were triglyceride, high-density lipoprotein cholesterol, and lipoprotein(a) concentrations.

Results

A total of 6 case-control studies were included in this systematic review/meta-analysis. The total number of participants ranged from 30 to 250 in the case group and from 43 to 271 in the control group. Meta-analysis revealed no significant difference in total cholesterol levels between the case and control groups (pooled OR = 1.79, 95% confidence interval [CI] = 0.98 to 3.26, P = 0.057). Likewise, meta-analysis revealed no significant difference in LDL-C concentrations between the case and control groups (pooled OR = 1.15, 95% CI = 0.64 to 2.07, P = 0.639). Since there were an insufficient number of studies reporting data for the secondary outcomes, meta-analysis was not possible.

Conclusions

Our results do not provide evidence for serum lipids being associated with SSNHL, nor do they definitively rule out such an association. Additional studies are needed to ascertain the relationship, or lack thereof, between serum lipids and SSNHL.     

Registered clinical trials investigating treatment with cell-derived extracellular vesicles: a scoping review

Background aimsInterest in cell-based therapy using extracellular vesicles (EVs) is intensifying, building upon promising preclinical research and a handful of published clinical studies. Registered clinical trials remain small, heterogeneous in design and underpowered to determine safety and efficacy on their own. A scoping review of registered studies can identify opportunities to pool data and perform meta-analysis.MethodsRegistered trials were identified by searching clinical trial databases (Clinicaltrials.gov, the World Health Organization International Clinical Trials Registry Platform and the Chinese Clinical Trial Registry) on June 10, 2022.ResultsSeventy-three trials were identified and included for analysis. Mesenchymal stromal cells (MSCs) were the most common cell type from which EVs were derived (49 studies, 67%). Among the 49 identified MSC-EV studies, 25 were controlled trials (51%) with a combined total of 3094 participants anticipated to receive MSC-derived EVs (2225 in controlled studies). Although EVs are being administered to treat a broad range of conditions, trials treating patients with coronavirus disease-2019 and/or acute respiratory distress syndrome were observed most commonly. Despite heterogeneity between studies, we anticipate that at least some of the studies could be combined in meaningful meta-analysis and that a combined sample size of 1000 patients would provide the ability to detect a ≥5% difference in mortality with MSC-EVs compared to controls and could be achieved by December 2023.ConclusionsThis scoping review identifies potential barriers that may stall clinical translation of EV-based treatment, and our analysis calls for more standardized product characterization, use of quantifiable product quality attributes and consistent outcome reporting in future clinical trials.     

细菌高通量数据荟萃分析的可行性研究

细菌作为重要的分解者，对于生态系统功能至关重要，其群落多样性一直是生态研究的热点。通过荟萃分析，整理已发表的细菌高通量数据，已经取得一些重要的全球性研究结果。这些荟萃分析的核心是将不同引物来源序列提取统一区段，进行细菌群落分析，但该方法的可行性和准确性还一直未被探究。本研究使用了含有三种引物扩增的同一细菌16S rRNA高通量数据，分析了不同引物和提取统一区段后细菌群落情况。结果显示，不同引物以及提取统一区段后，得到细菌群落组成和多样性显著不同，且提取同一区段后的群落主要受原始引物的影响较大。该研究表明，细菌高通量数据荟萃分析中，引物差异会影响其统一分析的群落结果，荟萃方法需要慎重使用。     

Safety and tolerability of sitagliptin in clinical studies: a pooled analysis of data from 10,246 patients with type 2 diabetes

Background

In a previous pooled analysis of 12 double-blind clinical studies that included data on 6,139 patients with type 2 diabetes, treatment with sitagliptin, a dipeptidyl peptidase-4 (DPP-4) inhibitor, was shown to be generally well tolerated compared with treatment with control agents. As clinical development of sitagliptin continues, additional studies have been completed, and more patients have been exposed to sitagliptin. The purpose of the present analysis is to update the safety and tolerability assessment of sitagliptin by pooling data from 19 double-blind clinical studies.

Methods

The present analysis included data from 10,246 patients with type 2 diabetes who received either sitagliptin 100 mg/day (N = 5,429; sitagliptin group) or a comparator agent (placebo or an active comparator) (N = 4,817; non-exposed group). The 19 studies from which this pooled population was drawn represent the double-blind, randomized studies that included patients treated with the usual clinical dose of sitagliptin (100 mg/day) for between 12 weeks and 2 years and for which results were available as of July 2009. These 19 studies assessed sitagliptin taken as monotherapy, initial combination therapy with metformin or pioglitazone, or as add-on combination therapy with other antihyperglycemic agents (metformin, pioglitazone, a sulfonylurea ± metformin, insulin ± metformin, or rosiglitazone + metformin). Patients in the non-exposed group were taking placebo, metformin, pioglitazone, a sulfonylurea ± metformin, insulin ± metformin, or rosiglitazone + metformin. The analysis used patient-level data from each study to evaluate between-group differences in the exposure-adjusted incidence rates of adverse events.

Results

Summary measures of overall adverse events were similar in the sitagliptin and non-exposed groups, except for an increased incidence of drug-related adverse events in the non-exposed group. Incidence rates of specific adverse events were also generally similar between the two groups, except for increased incidence rates of hypoglycemia, related to the greater use of a sulfonylurea, and diarrhea, related to the greater use of metformin, in the non-exposed group and constipation in the sitagliptin group. Treatment with sitagliptin was not associated with an increased risk of major adverse cardiovascular events.

Conclusions

In this updated pooled safety analysis of data from 10,246 patients with type 2 diabetes, sitagliptin 100 mg/day was generally well tolerated in clinical trials of up to 2 years in duration.     

A Resolution to the Blue Whiting (Micromesistius poutassou) Population Paradox?

We provide the strongest evidence to date supporting the existence of two independent blue whiting (Micromesistius poutassou (Risso, 1827)) populations in the North Atlantic. In spite of extensive data collected in conjunction with the fishery, the population structure of blue whiting is poorly understood. On one hand, genetic, morphometric, otolith and drift modelling studies point towards the existence of two populations, but, on the other hand, observations of adult distributions point towards a single population. A paradox therefore arises in attempting to reconcile these two sets of information. Here we analyse 1100 observations of blue whiting larvae from the Continuous Plankton Recorder (CPR) from 1948–2005 using modern statistical techniques. We show a clear spatial separation between a northern spawning area, in the Rockall Trough, and a southern one, off the Porcupine Seabight. We further show a difference in the timing of spawning between these sites of at least a month, and meaningful differences in interannual variability. The results therefore support the two-population hypothesis. Furthermore, we resolve the paradox by showing that the acoustic observations cited in support of the single-population model are not capable of resolving both populations, as they occur too late in the year and do not extend sufficiently far south to cover the southern population: the confusion is the result of a simple observational artefact. We conclude that blue whiting in the North Atlantic comprises two populations.     

Fish assemblages as indicators of estuary ecosystem health

Understanding and managing increasing threat from diverse anthropogenic pressures on estuaries requires impact assessment and monitoring indices that provide accurate quantification of change and are readily communicable. Although indices based on nekton assemblage structure have obvious appeal to managers, the imperative to produce the most accurate measures possible has seen a move away from simple composite measures (such as diversity indices) towards complex multivariate approaches. However, complex methods often provide a poor basis for reporting because they can be difficult to report in terms that are meaningful to the end user. Effective indices should be simple to construct and communicate, relate directly to definable biological attributes, fall within predictable ranges for unimpacted systems and show demonstrable responses to known impacts. We use published nekton data for 30 natural and two artificial estuaries to develop a set of nekton assemblage-based summary measures that fit these criteria. We evaluated a suite of simple parallel measures based on both catch per unit effort (CPUE) and probability of encounter (PoE). Parallel measures provide complementary information thus a more robust assessments of change. Three measures fell within consistent bounds as long as comparisons were confined to the same time of year to remove the influence of seasonal variability, and were efficient at differentiating degraded from unimpacted estuaries. Because the successful approaches rely on PoE rather than CPUE they have considerable tactical advantages in that they are less destructive, allow for the collection of many more samples per unit time, and treat schooling and non-schooling species equivalently.