An improved methodology for quantifying causality in complex ecological systems

This paper provides a statistical methodology for quantifying causality in complex dynamical systems, based on analysis of multidimensional time series data of the state variables. The methodology integrates Granger’s causality analysis based on the log-likelihood function expansion (Partial pair-wise causality), and Akaike’s power contribution approach over the whole frequency domain (Total causality). The proposed methodology addresses a major drawback of existing methodologies namely, their inability to use time series observation of state variables to quantify causality in complex systems. We first perform a simulation study to verify the efficacy of the methodology using data generated by several multivariate autoregressive processes, and its sensitivity to data sample size. We demonstrate application of the methodology to real data by deriving inter-species relationships that define key food web drivers of the Barents Sea ecosystem. Our results show that the proposed methodology is a useful tool in early stage causality analysis of complex feedback systems.publishedVersio

INTEGRATING HAWKES PROCESS- ND BIOMASS MODELS TO CAPTURE IMPULSIVE POPULATION DYNAMICS

This paper presents a modeling framework that captures the impulsive biomass dynamics (bust-boom) of a fish stock. The framework is based on coupling a Hawkes-process model to a discrete-time, ages-structured population dynamics model. Simulation results are presented to demonstrate the efficacy of the framework in capturing impulsive events in the population trajectory. The results presented in this paper are significant in three ways: • A framework has been presented that demonstrates how premonitory information may be extracted from exogenous observations from complex environmental systems • We have demonstrated how exogenous information may be parameterized and incorporated into the modeling process for better understanding of the link between environmental drivers and the population dynamical system • The framework has been successfully applied in modeling and short-term prediction of the population dynamics of an empirical fish stock.publishedVersio

Estimation and classification of temporal trends to support integrated ecosystem assessment

We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend) classes. We use simulations to evaluate the proposed approach and compare it with a relevant dynamic factor analysis in the time domain, which was recently proposed to estimate common trends in fisheries time series. We apply the TREC approach to the multivariate short time series datasets investigated by the ICES integrated assessment working groups for the Norwegian Sea and the Barents Sea. The proposed approach is robust for application to short time series, and it directly identifies and classifies the dominant trends underlying observations. Based on the classified trend classes, we suggest that communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies can be enhanced by finding the common tendency between a biological community in a marine ecosystem and the environmental factors, as well as by the icons produced by generalizing common trend patterns.publishedVersio

trec: An R package for trend estimation and classification to support integrated ecosystem assessment of the marine ecosystem and environmental factors

Solvang and Planque [ICES Journal of Marine Science, 77, pp.2529–2540, (2020)] provided a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. This approach was developed to improve communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies as they investigate the common tendencies between a biological community in a marine ecosystem and the local environmental factors. The tasks of trend estimation and classification in the original computational procedure have been revised, and new features include an automatic icon assignment algorithm using a multinomial logistic discriminator. In this paper, we present R package trec. Implementation of this package involves three partitions corresponding to TREC1) estimating trends from observed time series data; TREC2) classifying two/three rough patterns; and TREC3) generating a table summarizing categories of common configurations (trends) and the automatic icon assignments to them. The proposed trec focuses on investigating mean non-stationary long-term trends of data, and it works for any length of time steps. It is not necessary to apply a stationary Gaussian assumption to the estimated trends to investigate the common trends, which are interpreted as common variations of biological and environmental data.publishedVersio

Consideration of measurement errors for the Norwegian common minke whale (Balaenoptera acutorostrata acutorostrata) surveys

A discrete measurement error model for radial distance and angle to detected objects in line transect surveys is considered. This approach directly quantifies the effect of measurement error on the estimated effective strip half-width. We apply the method to experimental data collected over the period 2008-2013 in North Atlantic both under the assumption of multiplicative and additive measurement errors. Our results indicate that the abundance estimates considering the measurement error are consistently larger than the abundance estimates without any measurement error correction.publishedVersio

Semiparametric Copula Estimation for Spatially Correlated Multivariate Mixed Outcomes: Analyzing Visual Sightings of Fin Whales from Line Transect Survey

Multivariate data having both continuous and discrete variables is known as mixed outcomes and has widely appeared in a variety of fields such as ecology, epidemiology, and climatology. In order to understand the probability structure of multivariate data, the estimation of the dependence structure among mixed outcomes is very important. However, when location information is equipped with multivariate data, the spatial correlation should be adequately taken into account; otherwise, the estimation of the dependence structure would be severely biased. To solve this issue, we propose a semiparametric Bayesian inference for the dependence structure among mixed outcomes while eliminating spatial correlation. To this end, we consider a hierarchical spatial model based on the rank likelihood and a latent multivariate Gaussian process. We develop an efficient algorithm for computing the posterior using the Markov Chain Monte Carlo. We also provide a scalable implementation of the model using the nearest-neighbor Gaussian process under large spatial datasets. We conduct a simulation study to validate our proposed procedure and demonstrate that the procedure successfully accounts for spatial correlation and correctly infers the dependence structure among outcomes. Furthermore, the procedure is applied to a real example collected during an international synoptic krill survey in the Scotia Sea of the Antarctic Peninsula, which includes sighting data of fin whales (Balaenoptera physalus), and the relevant oceanographic data.Comment: 23 pages, 5 figure

トータルパワー寄与率を用いた海洋生態システムにおける因果性推測

要旨あり研究ノー

Distribution of rorquals and Atlantic cod in relation to their prey in the Norwegian high Arctic

Recent warming in the Barents Sea has led to changes in the spatial distribution of both zooplankton and fish, with boreal communities expanding northwards. A similar northward expansion has been observed in several rorqual species that migrate into northern waters to take advantage of high summer productivity, hence feeding opportunities. Based on ecosystem surveys conducted during August–September in 2014–2017, we investigated the spatial associations among the three rorqual species of blue, fin, and common minke whales, the predatory fish Atlantic cod, and their main prey groups (zooplankton, 0-group fish, Atlantic cod, and capelin) in Arctic Ocean waters to the west and north of Svalbard. During the surveys, whale sightings were recorded by dedicated whale observers on the bridge of the vessel, whereas the distribution and abundance of cod and prey species were assessed using trawling and acoustic methods. Based on existing knowledge on the dive habits of these rorquals, we divided our analyses into two depth regions: the upper 200 m of the water column and waters below 200 m. Since humpback whales were absent in the area in 2016 and 2017, they were not included in the subsequent analyses of spatial association. No association or spatial overlap between fin and blue whales and any of the prey species investigated was found, while associations and overlaps were found between minke whales and zooplankton/0-group fish in the upper 200 m and between minke whales and Atlantic cod at depths below 200 m. A prey detection range of more than 10 km was suggested for minke whales in the upper water layers.publishedVersio

Commonly used medications and endometrial cancer survival: a population-based cohort study.

Genomic Identification of Significant Targets (GISTIC) outputs for Circular Binary Segmentation (CBS) - or Piecewise Constant Fit (PCF) - segmented input data. The number of peaks attained by GISTIC on the y-axis is plotted against the two changing parameters α for CBS and γ for PCF on the x-axis. GISTIC peaks of amplification applying CBS-segmented data are illustrated in pink and PCF-segmented data in red, respectively. Deletion peaks are colored in green for CBS-segmented input data and in blue for PCF-segmented data. From top to bottom are shown GISTIC focal peaks for breast, ovarian, endometrial, and cervical cancers, to the left for PCF-segmented input data (A, C, E, and G) and to the right for CBS-segmented input data (B, D, F and H), respectively. For further analysis are the selected α and γ highlighted with a colored square. (PDF 362 kb

DNA methylation profiling in doxorubicin treated primary locally advanced breast tumours identifies novel genes associated with survival and treatment response

<p>Abstract</p> <p>Background</p> <p>Breast cancer is the most frequent cancer in women and consists of a heterogeneous collection of diseases with distinct histopathological, genetic and epigenetic characteristics. In this study, we aimed to identify DNA methylation based biomarkers to distinguish patients with locally advanced breast cancer who may benefit from neoadjuvant doxorubicin treatment.</p> <p>Results</p> <p>We investigated quantitatively the methylation patterns in the promoter regions of 14 genes (<it>ABCB1</it>, <it>ATM</it>, <it>BRCA1</it>, <it>CDH3</it>, <it>CDKN2A</it>, <it>CXCR4</it>, <it>ESR1</it>, <it>FBXW7</it>, <it>FOXC</it>1, <it>GSTP1</it>, <it>IGF2</it>, <it>HMLH1</it>, <it>PPP2R2B</it>, and <it>PTEN</it>) in 75 well-described pre-treatment samples from locally advanced breast cancer and correlated the results to the available clinical and molecular parameters. Six normal breast tissues were used as controls and 163 unselected breast cancer cases were used to validate associations with histopathological and clinical parameters.</p> <p>Aberrant methylation was detected in 9 out of the 14 genes including the discovery of methylation at the <it>FOXC1 </it>promoter. Absence of methylation at the <it>ABCB1 </it>promoter correlated with progressive disease during doxorubicin treatment. Most importantly, the DNA methylation status at the promoters of <it>GSTP1</it>, <it>FOXC1 </it>and <it>ABCB1 </it>correlated with survival, whereby the combination of methylated genes improved the subdivision with respect to the survival of the patients. In multivariate analysis <it>GSTP1 </it>and <it>FOXC1 </it>methylation status proved to be independent prognostic markers associated with survival.</p> <p>Conclusions</p> <p>Quantitative DNA methylation profiling is a powerful tool to identify molecular changes associated with specific phenotypes. Methylation at the <it>ABCB1 </it>or <it>GSTP1 </it>promoter improved overall survival probably due to prolonged availability and activity of the drug in the cell while <it>FOXC1 </it>methylation might be a protective factor against tumour invasiveness. <it>FOXC1 </it>proved to be general prognostic factor, while <it>ABCB1 </it>and <it>GSTP1 </it>might be predictive factors for the response to and efficacy of doxorubicin treatment. Pharmacoepigenetic effects such as the reported associations in this study provide molecular explanations for differential responses to chemotherapy and it might prove valuable to take the methylation status of selected genes into account for patient management and treatment decisions.</p