Using multivariate cross correlations, Granger causality and graphical models to quantify spatiotemporal synchronization and causality between pest populations

On formal limitations of causal ecological networks

Causal multivariate time-series analysis, combined with network theory, provide a powerful tool for studying complex ecological interactions. However, these methods have limitations often underestimated when used in graphical modelling of ecological systems. In this opinion article, I examine the relationship between formal logic methods used to describe causal networks and their inherent statistical and epistemological limitations. I argue that while these methods offer valuable insights, they are restricted by axiomatic assumptions, statistical constraints and the incompleteness of our knowledge. To prove that, I first consider causal networks as formal systems, define causality and formalize their axioms in terms of modal logic and use ecological counterexamples to question the axioms. I also highlight the statistical limitations when using multivariate time-series analysis and Granger causality to develop ecological networks, including the potential for spurious correlations among other data characteristics. Finally, I draw upon Gödel's incompleteness theorems to highlight the inherent limits of fully understanding complex networks as formal systems and conclude that causal ecological networks are subject to initial rules and data characteristics and, as any formal system, will never fully capture the intricate complexities of the systems they represent. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Dynamics of Noctiluca scintillans blooms: A 20-year study in Jangmok Bay, Korea

This 20-year study (2001-2020) conducted in Jangmok Bay, Korea, assessed the intricate relationships between environmental factors and Noctiluca scintillans blooms. Granger causality tests and PCA analysis were used to assess the impact of sea surface temperature (SST), salinity, dissolved oxygen (DO) concentration, wind patterns, rainfall, and chlorophyll-a (Chl-a) concentration on bloom dynamics. The results revealed significant, albeit delayed, influences of these variables on bloom occurrence, with SST exhibiting a notable 2-month lag and salinity a 1-month lag in their impact. Additionally, the analysis highlighted the significant roles of phosphate, ammonium, and silicate, which influenced N. scintillans blooms with lags of 1 to 3 months. The PCA demonstrates how SST and wind speed during spring and summer, along with wind direction and salinity in winter, significantly impact N. scintillans blooms. We noted not only an increase in large-scale N. scintillans blooms but also a cyclical pattern of occurrence every 3 years. These findings underscore the synergistic effects of environmental factors, highlighting the complex interplay between SST, salinity, DO concentration, and weather conditions to influence bloom patterns. This research enhances our understanding of harmful algal blooms (HABs), emphasizing the importance of a comprehensive approach that considers multiple interconnected environmental variables for predicting and managing N. scintillans blooms.

Environmentally induced functional shifts in phytoplankton and their potential consequences for ecosystem functioning

Phytoplanktonic organisms are particularly sensitive to environmental change, and, as they represent a direct link between abiotic and biotic compartments within the marine food web, changes in the functional structure of phytoplankton communities can result in profound impacts on ecosystem functioning. Using a trait-based approach, we examined changes in the functional structure of the southern North Sea phytoplankton over the past five decades in relation to environmental conditions. We identified a shift in functional structure between 1998 and 2004 which coincides with a pronounced increase in diatom and decrease in dinoflagellate abundances, and we provide a mechanistic explanation for this taxonomic change. Early in the 2000s, the phytoplankton functional structure shifted from slow growing, autumn blooming, mixotrophic organisms, towards earlier blooming and faster-growing microalgae. Warming and decreasing dissolved phosphorus concentrations were linked to this rapid reorganization of the functional structure. We identified a potential link between this shift and dissolved nutrient concentrations, and we hypothesise that organisms blooming early and displaying high growth rates efficiently take up nutrients which then are no longer available to late bloomers. Moreover, we identified that the above-mentioned functional change may have bottom-up consequences, through a food quality-driven negative influence on copepod abundances. Overall, our study highlights that, by altering the phytoplankton functional composition, global and regional changes may have profound long-term impacts on coastal ecosystems, impacting both food-web structure and biogeochemical cycles.

Implementation of biohybrid olfactory bulb on a high-density CMOS-chip to reveal large-scale spatiotemporal circuit information

Large-scale multi-site biosensors are essential to probe the olfactory bulb (OB) circuitry for understanding the spatiotemporal dynamics of simultaneous discharge patterns. Current ex-vivo biosensing techniques are limited to recording a small set of neurons and cannot provide an adequate resolution, which hinders revealing the fast dynamic underlying the information coding mechanisms in the OB circuit. Here, we demonstrate a novel biohybrid OB-CMOS biosensing platform to decipher the cross-scale dynamics of the OB electrogenesis and quantify the distinct neuronal coding properties. The approach with 4096-microelectrodes offers a non-invasive, label-free, bioelectrical imaging to decode simultaneous firing patterns from thousands of connected neuronal ensembles in acute OB slices. The platform can measure spontaneous and drug-induced extracellular field potential activity with substantially improved spatiotemporal resolution over conventional OB-based biosensors. Also, we employ our OB-CMOS recordings to perform multidimensional analysis to instantiate specific neurophysiological metrics underlying the olfactory spatiotemporal coding that emerged from the OB interconnected layers. Our results delineate the computational implications of large-scale activity patterns in functional olfactory processing. The systematic interplay of the experimental CMOS-base platform architecture and the high-content characterization of the olfactory circuit with various computational analyses endow significant functional interrogations of the OB information processing, high-spatiotemporal connectivity mapping, and global circuit dynamics. Thus, our study can inspire the design of advanced biomimetic olfactory-based biosensors and neuromorphic approaches for diagnostic biomarkers and drug discovery applications.

Soft Computing of a Medically Important Arthropod Vector with Autoregressive Recurrent and Focused Time Delay Artificial Neural Networks

Simple Summary

Arthropod vectors are responsible for transmitting a large number of diseases, and for most, there are still not available effective vaccines. Vector disease control is mostly achieved by a sustained prediction of vector populations to maintain support for surveillance and control activities. Mathematical models may assist in predicting arthropod population dynamics. However, arthropod dynamics, and mosquitoes particularly, due their complex life cycle, often exhibit an abrupt and non-linear occurrence. Therefore, there is a growing interest in describing mosquito population dynamics using new methodologies. In this work, we made an effort to gain insights into the non-linear population dynamics of Culex sp. adults, aiming to introduce straightforward soft-computing techniques based on artificial neural networks (ANNs). We propose two kind of models, one autoregressive, handling temperature as an exogenous driver and population as an endogenous one, and a second based only on the exogenous factor. To the best of our knowledge, this is the first study using recurrent neural networks and the most influential environmental variable for prediction of the WNv vector Culex sp. population dynamics, providing a new framework to be used in arthropod decision-support systems.

Abstract

A central issue of public health strategies is the availability of decision tools to be used in the preventive management of the transmission cycle of vector-borne diseases. In this work, we present, for the first time, a soft system computing modeling approach using two dynamic artificial neural network (ANNs) models to describe and predict the non-linear incidence and time evolution of a medically important mosquito species, Culex sp., in Northern Greece. The first model is an exogenous non-linear autoregressive recurrent neural network (NARX), which is designed to take as inputs the temperature as an exogenous variable and mosquito abundance as endogenous variable. The second model is a focused time-delay neural network (FTD), which takes into account only the temperature variable as input to provide forecasts of the mosquito abundance as the target variable. Both models behaved well considering the non-linear nature of the adult mosquito abundance data. Although, the NARX model predicted slightly better (R = 0.623) compared to the FTD model (R = 0.534), the advantage of the FTD over the NARX neural network model is that it can be applied in the case where past values of the population system, here mosquito abundance, are not available for their forecasting.

Spatiotemporal filtering method for detecting kinematic waves in a connected environment

Backward-moving kinematic waves (KWs) (e.g., stop-and-go traffic conditions and a shock wave) cause unsafe driving conditions, decreases in the capacities of freeways, and increased travel time. In this paper, a sequential filtering method is proposed to detect KWs using data collected in a connected environment, which can aid in developing a traffic control strategy for connected vehicles to stop or dampen the propagation of these KWs. The proposed method filters out random fluctuation in the data using ensemble empirical mode decomposition that considers the spectral features of KWs. Then, the spatial movements of KWs are considered using cross-correlation to identify potential candidate KWs. Asynchronous changes in the denoised flow and speed are used to evaluate candidate KWs using logistic regression to identify the KWs from localized reductions in speed that are not propagated upstream. The findings from an empirical evaluation of the proposed method showed strong promise for detecting KWs using data in a connected environment, even at 30% of the market penetration rates. This paper also addresses how data resolution of the connected environment affects the performance in detecting KWs.

Metric of Highlighting the Synchronicity of Time Series and Its Application in Analyzing the Fundamental Frequencies of the Speaker’s Speech Signal

As part of the study, the problem of developing and applying a metric for assessing the degree of similarity of time series is considered, which makes it possible to consider the assumptions about the model of a series when comparing, as well as to compare the values of the corresponding characteristics of the series. Characteristics can be values that describe the structure of a series, or directly the values of the approximating function, which can be obtained using nonparametric statistics methods. One of the directions in which this approach can be applied to assessing the similarity of time series is the study of vocal performances. In practice, the degree of similarity in the performance of melodies by several speakers was analyzed. It was determined that, using the synchronicity metric, it is possible to implement an exercise in which students need to repeat the melody after the teacher. In addition, this approach was applied in the segment identification module with an abrupt change in the sounding of the fundamental frequency. This work is devoted to the modification of the program complex for vocal recognition in order to identify notes with a sharp change in the fundamental frequency. The complex is aimed at carrying out additional independent training in teaching vocals. The use of the software package will allow, in real time, providing feedback to the user with an assessment of the quality of their singing. This should allow students to study not only under the supervision of a teacher, but also independently in the early stages of learning. The basic algorithm of the program recognizes notes without sharp changes in frequencies with high accuracy, which is confirmed by experiments. In order to recognize by the algorithms of the program notes sung vibrato and glissando in singing, a new analysis method based on the metric of time series synchronicity is proposed.

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.