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

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

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

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

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

Panel-based Assessment of Ecosystem Condition of the Norwegian Sea Pelagic Ecosystem

The System for Assessment of Ecological Condition, coordinated by the Norwegian Environment Agency, is intended to form the foundation for evidence-based assessments of the ecological condition of Norwegian terrestrial and marine ecosystems not covered by the EU Water Framework Directive. The reference condition is defined as “intact ecosystems”, i.e., a condition that is largely unimpacted by modern industrial activities. An ecosystem in good ecological condition does not deviate substantially from this reference condition in structure, functions or productivity. This report describes the first operational assessment of the ecological condition of the pelagic ecosystem in the Norwegian Sea. The assessment method employed is the Panel-based Assessment of Ecosystem Condition (PAEC1) and the current assessment has considered to what extent the Norwegian Sea pelagic ecosystem deviates from the reference condition2 by evaluating change in trajectories.Panel-based Assessment of Ecosystem Condition of the Norwegian Sea Pelagic EcosystempublishedVersio

Panel-based Assessment of Ecosystem Condition of the North Sea Shelf Ecosystem

The System for Assessment of Ecological Condition, coordinated by the Norwegian Environment Agency, is intended to form the foundation for evidence-based assessments of the ecological condition of Norwegian terrestrial and marine ecosystems not covered by the EU Water Framework Directive. The reference condition is defined as “intact ecosystems”, i.e., a condition that is largely unimpacted by modern industrial anthropogenic activities. An ecosystem in good ecological condition does not deviate substantially from this reference condition in structure, functions or productivity. This report describes the first operational assessment of the ecological condition of the marine shelf ecosystem in the Norwegian sector of the North Sea and Skagerrak. The assessment method employed is the Panel-based Assessment of Ecosystem Condition (PAEC1) and the current assessment has considered to what extent the North Sea and Skagerrak shelf ecosystem deviates from the reference condition2 by evaluating change trajectories.Panel-based Assessment of Ecosystem Condition of the North Sea Shelf EcosystempublishedVersio

Panel-based Assessment of Ecosystem Condition of the North Sea Shelf Ecosystem - Appendices

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