Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

Reliability analysis of dynamic systems by translating temporal fault trees into Bayesian networks

Classical combinatorial fault trees can be used to assess combinations of failures but are unable to capture sequences of faults, which are important in complex dynamic systems. A number of proposed techniques extend fault tree analysis for dynamic systems. One of such technique, Pandora, introduces temporal gates to capture the sequencing of events and allows qualitative analysis of temporal fault trees. Pandora can be easily integrated in model-based design and analysis techniques. It is, therefore, useful to explore the possible avenues for quantitative analysis of Pandora temporal fault trees, and we identify Bayesian Networks as a possible framework for such analysis. We describe how Pandora fault trees can be translated to Bayesian Networks for dynamic dependability analysis and demonstrate the process on a simplified fuel system model. The conversion facilitates predictive reliability analysis of Pandora fault trees, but also opens the way for post-hoc diagnostic analysis of failures

iDNA from terrestrial haematophagous leeches as a wildlife surveying and monitoring tool - prospects, pitfalls and avenues to be developed

Invertebrate-derived DNA (iDNA) from terrestrial haematophagous leeches has recently been proposed as a powerful non-invasive tool with which to detect vertebrate species and thus to survey their populations. However, to date little attention has been given to whether and how this, or indeed any other iDNA-derived data, can be combined with state-of-the-art analytical tools to estimate wildlife abundances, population dynamics and distributions. In this review, we discuss the challenges that face the application of existing analytical methods such as site-occupancy and spatial capture-recapture (SCR) models to terrestrial leech iDNA, in particular, possible violations of key assumptions arising from factors intrinsic to invertebrate parasite biology. Specifically, we review the advantages and disadvantages of terrestrial leeches as a source of iDNA and summarize the utility of leeches for presence, occupancy, and spatial capture-recapture models. The main source of uncertainty that attends species detections derived from leech gut contents is attributable to uncertainty about the spatio-temporal sampling frame, since leeches retain host-blood for months and can move after feeding. Subsequently, we briefly address how the analytical challenges associated with leeches may apply to other sources of iDNA. Our review highlights that despite the considerable potential of leech (and indeed any) iDNA as a new survey tool, further pilot studies are needed to assess how analytical methods can overcome or not the potential biases and assumption violations of the new field of iDNA. Specifically we argue that studies to compare iDNA sampling with standard survey methods such as camera trapping, and those to improve our knowledge on leech (and other invertebrate parasite) physiology, taxonomy, and ecology will be of immense future value