Experimental Time Series Research Articles

Modeling Temperature-Dependent Photoluminescence Dynamics of Colloidal CdS Quantum Dots Using Long Short-Term Memory (LSTM) Networks

This study addresses the challenge of modeling temperature-dependent photoluminescence (PL) in CdS colloidal quantum dots (QD), where PL properties fluctuate with temperature, complicating traditional modeling approaches. The objective is to develop a predictive model capable of accurately capturing these variations using Long Short-Term Memory (LSTM) networks, which are well suited for managing temporal dependencies in time-series data. The methodology involved training the LSTM model on experimental time-series data of PL intensity and temperature. Through numerical simulation, the model’s performance was assessed. Results demonstrated that the LSTM-based model effectively predicted PL trends under different temperature conditions. This approach could be applied in optoelectronics and quantum dot-based sensors for enhanced forecasting capabilities.

Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.

Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy

To accurately capture the dynamic behavior of small nanoparticles in scanning transmission electron microscopy, high-quality data and advanced data processing is needed. The fast scan rate required to observe structural dynamics inherently leads to very noisy data where machine learning tools are essential for unbiased analysis. In this study, we develop a workflow based on two U-Net architectures to automatically localize and classify atomic columns at particle-support interfaces. The model is trained on non-physical image simulations, achieves sub-pixel localization precision, high classification accuracy, and generalizes well to experimental data. We test our model on both in situ and ex situ experimental time series recorded at 5 frames per second of small Pt nanoparticles supported on CeO2(111). The processed movies show sub-second dynamics of the nanoparticles and reveal site-specific movement patterns of individual atomic columns.

Risk factors for pressure injuries in individuals with spinal cord injuries who have sarcopenic obesity: A comparison of time-dependent changes in sacral region pressure between individuals with and without sarcopenic obesity

Objective To identify the risk factors for pressure injuries in individuals with spinal cord injuries (SCIs) who have sarcopenic obesity, comparing time-dependent changes in sacral region pressure in individuals with and without sarcopenic obesity. Design An experimental time series study. Setting Single-center hospital. Participants Twenty-five adult participants with subacute and chronic paraplegia who visited our rehabilitation center, Republic of Korea, between May 2021 and June 2022. Interventions Whole-body dual-energy X-ray absorptiometry was performed to diagnose sarcopenic obesity. After the participants were placed in the supine position for 1 hour, the average pressure (mmHg), peak pressure (mmHg), and total contact area (cm2) of the sacral region were measured using the pressure-mapping system. Results Compared with the non-sarcopenic obesity group, the sarcopenic obesity group showed significant before-and-after differences in peak pressure. Furthermore, the risk factors that were significantly associated with peak pressure in the sarcopenic obesity group were the American Spinal Injury Association Impairment Scale score and the fat mass index. Conclusion Among participants with SCIs, the risk of pressure injuries is higher in the sarcopenic obesity group than in the non-sarcopenic obesity group. Notably, the risk of pressure injuries increases in participants who have complete injury and an increased fat mass index, indicating the importance of close monitoring and more active management to prevent pressure injuries in this subpopulation.

Efficient, nonparametric removal of noise and recovery of probability distributions from time series using nonlinear-correlation functions: Photon and photon-counting noise.

A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green's function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region.

Derivation of the nonequilibrium generalized Langevin equation from a time-dependent many-body Hamiltonian.

It has become standard practice to describe systems that remain far from equilibrium even in their steady state by Langevin equationswith colored noise which is chosen independently from the friction contribution. Since these Langevin equationsare typically not derived from first-principle Hamiltonian dynamics, it is not clear whether they correspond to physically realizable scenarios. By exact Mori projection in phase space we derive the nonequilibrium generalized Langevin equation(GLE) for an arbitrary phase-space dependent observable A from a generic many-body Hamiltonian with a time-dependent external force h(t) acting on the same observable A. This is the same Hamiltonian from which the standard fluctuation-dissipation theorem is derived, which reflects the generality of our approach. The observable A could, for example, be the position of an atom, of a molecule or of a macroscopic object, the distance between two such entities or a more complex phase-space function such as the reaction coordinate of a chemical reaction or of the folding of a protein. The Hamiltonian could, for example, describe a fluid, a solid, a viscoelastic medium, or even a turbulent inhomogeneous environment. The GLE, which is a closed-form equationof motion for the observable A, is obtained in explicit form to all orders in h(t) and without restrictions on the type of many-body Hamiltonian or the observable A. If the dynamics of the observable A corresponds to a Gaussian process, the resultant GLE has a similar form as the equilibrium Mori GLE, and in particular the friction memory kernel is given by the two-point autocorrelation function of the sum of the complementary and the external force h(t). This is a nontrivial and useful result, as many observables that characterize nonequilibrium systems display Gaussian statistics. For non-Gaussian nonequilibrium observables correction terms appear in the GLE and in the relation between the force autocorrelation and the friction memory kernel, which are explicitly given in terms of cubic correlation functions of A. Interpreting the external force h(t) as a stochastic process, we derive nonequilibrium corrections to the fluctuation-dissipation theorem and present methods to extract all GLE parameters from experimental or simulation time-series data, thus making our nonequilibrium GLE a practical tool to study and model general nonequilibrium systems.

Cross-Recurrence Quantification Of Cardiovascular Signals In Newborns Is A Sensitive Marker Of Health Status

Background — Analysis of the state of complex biological systems requires the use of sensitive methods for diagnosing interactions using experimental time series. Objective — To evaluate the possibility of using the technique of cross-recurrence quantification (CRQ) in studying the health status of newborns using low-frequency components of RR-interval signals and photoplethysmograms, reflecting the dynamics of the circuits in autonomic regulation of blood circulation. Methods — The study included two groups of neonates: 10 full-term newborns and 10 preterm neonates. We carried out simultaneous recording of electrocardiographic and photoplethysmographic signals. CRQ analysis was employed as the primary tool. Results — We established that some indices of CRQ analysis, characterizing the degree of interaction of the studied circuits, act as sensitive markers. They make it possible to distinguish the dynamics of the studied contours between healthy newborns and preterm neonates. Conclusion — The results of our study confirmed that CRQ is a promising tool in creating methods for diagnosing health conditions, including in newborns.

Gut-associated functions are favored during microbiome assembly across a major part of C. elegans life.

The microbiome plays a crucial role in host biology. Its functions depend on the microbiome composition that can change during a host's lifetime. To date, the dynamics of microbiome assembly and the resulting functions still need to be better understood. This study introduces a new approach to characterize the functional consequences of microbiome assembly by modeling both the relevance of stochastic processes and metabolic characteristics of microbial community changes. The approach was applied to experimental time-series data obtained for the microbiome of the nematode Caenorhabditis elegans across the major part of its lifetime. Stochastic processes played a minor role, whereas beneficial bacteria as well as gut-associated functions enriched in hosts. This indicates that the host might actively shape the composition of its microbiome. Overall, this study provides a framework for studying microbiome assembly dynamics and yields new insights into C. elegans microbiome functions.

Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families.

The nematode Caenorhabditis elegans is a widely used model organism for neuroscience. Although its nervous system has been fully reconstructed, the physiological bases of single-neuron functioning are still poorly explored. Recently, many efforts have been dedicated to measuring signals from C. elegans neurons, revealing a rich repertoire of dynamics, including bistable responses, graded responses, and action potentials. Still, biophysical models able to reproduce such a broad range of electrical responses lack. Realistic electrophysiological descriptions started to be developed only recently, merging gene expression data with electrophysiological recordings, but with a large variety of cells yet to be modeled. In this work, we contribute to filling this gap by providing biophysically accurate models of six classes of C. elegans neurons, the AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. We test our models by comparing computational and experimental time series and simulate knockout neurons, to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. Our models represent a step forward toward the modeling of C. elegans neuronal networks and virtual experiments on the nematode nervous system.

Efficient Estimates of Surface Diffusion Parameters for Spatio-Temporally Resolved Virus Replication Dynamics.

Advanced methods of treatment are needed to fight the threats of virus-transmitted diseases and pandemics. Often, they are based on an improved biophysical understanding of virus replication strategies and processes in their host cells. For instance, an essential component of the replication of the hepatitis C virus (HCV) proceeds under the influence of nonstructural HCV proteins (NSPs) that are anchored to the endoplasmatic reticulum (ER), such as the NS5A protein. The diffusion of NSPs has been studied by in vitro fluorescence recovery after photobleaching (FRAP) experiments. The diffusive evolution of the concentration field of NSPs on the ER can be described by means of surface partial differential equations (sufPDEs). Previous work estimated the diffusion coefficient of the NS5A protein by minimizing the discrepancy between an extended set of sufPDE simulations and experimental FRAP time-series data. Here, we provide a scaling analysis of the sufPDEs that describe the diffusive evolution of the concentration field of NSPs on the ER. This analysis provides an estimate of the diffusion coefficient that is based only on the ratio of the membrane surface area in the FRAP region to its contour length. The quality of this estimate is explored by a comparison to numerical solutions of the sufPDE for a flat geometry and for ten different 3D embedded 2D ER grids that are derived from fluorescence z-stack data of the ER. Finally, we apply the new data analysis to the experimental FRAP time-series data analyzed in our previous paper, and we discuss the opportunities of the new approach.

Larval precompetency and settlement behaviour in 25 Indo-Pacific coral species

Knowledge of coral larval precompetency periods and maximum competency windows is fundamental to understanding coral population dynamics, informing biogeography and connectivity patterns, and predicting reef recovery following disturbances. Yet for many species, estimates of these early-life history metrics are scarce and vary widely. Furthermore, settlement cues for many taxa are not known despite consequences to habitat selection. Here we performed a comprehensive experimental time-series investigation of larval settlement behaviour, for 25 Indo-Pacific broadcast-spawning species. To investigate the duration of precompetency, improve predictions of the competency windows, and compare settlement responses within and amongst species, we completed replicated and repeated 24-hour assays that exposed larvae to five common settlement cues. Our study revealed that larval competency in some broadcast-spawning species begins as early as two days post fertilization, but that the precompetency period varies within and between species from about two to six days, with consequences for local retention and population connectivity. We also found that larvae of some species are competent to settle beyond 70 days old and display complex temporal settlement behaviour, challenging the assumption that competency gradually wanes over time and adding to the evidence that larval longevity can support genetic connectivity and long-distance dispersal. Using these data, we grouped coral taxa by short, mid and long precompetency periods, and identified their preferred settlement cues. Taken together, these results inform our understanding of larval dynamics across a broad range of coral species and can be applied to investigations of population dynamics, connectivity, and reef recovery.

Impact of discontinuation of contact precautions on surveillance- and whole genome sequencing-defined methicillin-resistant Staphylococcus aureus healthcare-associated infections.

Prior studies evaluating the impact of discontinuation of contact precautions (DcCP) on methicillin-resistant Staphylococcus aureus (MRSA) outcomes have characterized all healthcare-associated infections (HAIs) rather than those likely preventable by contact precautions. We aimed to analyze the impact of DcCP on the rate of MRSA HAI including transmission events identified through whole genome sequencing (WGS) surveillance. Quasi experimental interrupted time series. Acute care medical center. Inpatients. The effect of DcCP (use of gowns and gloves) for encounters among patients with MRSA carriage was evaluated using time series analysis of MRSA HAI rates from January 2019 through December 2022, compared to WGS-defined attributable transmission events before and after DcCP in December 2020. The MRSA HAI rate was 4.22/10,000 patient days before and 2.98/10,000 patient days after DcCP (incidence rate ratio [IRR] 0.71 [95% confidence interval 0.56-0.89]) with a significant immediate decrease (P = .001). There were 7 WGS-defined attributable transmission events before and 11 events after DcCP (incident rate ratio 0.90 [95% confidence interval 0.30-2.55]). DcCP did not result in an increase in MRSA HAI or, in WGS-defined attributable transmission events. Comprehensive analyses of the effect of transmission prevention measures should include outcomes specifically measuring transmission-associated HAI.

A practical software package for estimating the periodicities in time series by least-squares spectral analysis

The researchers investigate some phenomena by continuously observing physical variables, i.e., time series. Nowadays, the Least-Squares Spectral Analysis (LSSA) technique has been preferred for the analysis of time series to conduct more reliable analysis. This technique uses the least-squares principle to estimate the hidden periodicities in the time series. Based on the previous investigations, LSSA gives more reasonable results in the experimental time series that have disturbing effects such as the datum shifts, linear trend, unequally spaced data and etc. The LSSA method is a unique method that can overcome these problems without pre-processing the original series. However, a practical and user-friendly software package in C programming language is not available for scientific purposes to implement the LSSA method. In this paper, we review the computational scheme of the LSSA method, then a software (LSSASOFT) package in the C programming language is developed in the view of the simplicity of the method and compatibility of all types of data. Finally, LSSASOFT is applied in two sample studies for the determining hidden periods in the synthetic data and sea level observations. Consequently, the numerical results indicate that LSSASOFT is a useful tool that can efficiently predicting hidden periodicity for the experimental time series that have disturbing effects.

Correlation dimension of high-dimensional and high-definition experimental time series

The correlation dimension (CD) is a nonlinear measure of the complexity of invariant sets. First introduced for describing low-dimensional chaotic attractors, it has been later extended to the analysis of experimental electroencephalographic (EEG), magnetoencephalographic (MEG), and local field potential (LFP) recordings. However, its direct application to high-dimensional (dozens of signals) and high-definition (kHz sampling rate) 2HD data revealed a controversy in the results. We show that the need for an exponentially long data sample is the main difficulty in dealing with 2HD data. Then, we provide a novel method for estimating CD that enables orders of magnitude reduction of the required sample size. The approach decomposes raw data into statistically independent components and estimates the CD for each of them separately. In addition, the method allows ongoing insights into the interplay between the complexity of the contributing components, which can be related to different anatomical pathways and brain regions. The latter opens new approaches to a deeper interpretation of experimental data. Finally, we illustrate the method with synthetic data and LFPs recorded in the hippocampus of a rat.

A Pilot Study On “A Study To Assess The Effectiveness Of Comprehensive Nursing Interventions (Hydrotherapy, Rice Heat Pad, Turmeric Therapy, Acupressure And Self Learning Booklet) On The Level Of Pain Perception And Dysmenorrhea Symptoms Among Adolescent

Background: Menstruation is a natural phenomenon in women after puberty and is often associated with dysmenorrhea (Garg, 2015). Dysmenorrhea is very common, painful menstrual cramps of uterine origin, which is commonly divided into primary dysmenorrhea (pain without organic pathology) and secondary dysmenorrhea (pelvic pain with an identifiable pathologic condition, such as endometriosis or ovarian cysts) (Nagy, 2021). Doctors and other women give a lot of conflicting advice to reduce the pain during dysmenorrhea. To handle menstrual pain, we can utilize either the pharmacological or non-pharmacological methods. Pharmacologically, menstrual pain can be treated with medication. However, Non-pharmacologically, menstrual pain can be reduced by adequate rest, regular exercise (especially walking), water immersion, and pressing pressure points and by drinking traditional herbs such as turmeric water (Armour et al., 2019). Alleviation of pain during menstruation will result in good productivity and confidence among women. Aim of the study: To evaluate the effectiveness of comprehensive nursing interventions (Hydrotherapy, Rice heat pad, Turmeric therapy, Acupressure and Self-learning booklet) on pain perception and dysmenorrhea symptoms among adolescent girls. Methods and design: The study was conducted using a Quantitative approach with Quasi Experimental Time Series with 2 group pretest and posttest design. Forty-Eight adolescent girls in the age group of 13-17 years were selected by using random sampling technique for both experimental and control group. In phase I, Data about background variables was collected before pretest, using socio demographic Performa and menstrual Performa. The researcher has conducted pretest level of Dysmenorrheal symptoms were assessed during first menstrual cycle for the first three days (1st day, 2nd day, 3rd day) by using Numerical Rating scale (NRS) and modified menstrual symptoms questionnaire (MMSQ) to the control group and experimental group. Comprehensive Nursing Interventions ((Hydrotherapy, Rice heat pad, Turmeric therapy, Acupressure and Self-learning booklet) were given to adolescent girls in experimental group on individual basis. Assessment of Effectiveness of Comprehensive Nursing Interventions on level of pain perception and dysmenorrheal symptoms were measured by using same pretest scale. The major findings of the study: Comprehensive Nursing Interventions ((Hydrotherapy, Rice heat pad, Turmeric therapy, Acupressure and Self-learning booklet) is an intervention in reducing Pain perception and dysmenorrheal symptoms as the pre-test mean score, SD and Mean percentage of experimental group shows that Mean (7.42±1.8) and control group Mean (7 ±2.34) on level of pain perception among adolescents with dysmenorrhea. So, the difference is 0.42, not statistically significant. Pretest mean score, SD, and Mean percentage of dysmenorrheal symptoms in experimental group shows that Mean (127.38±8.88) and control group Mean (125.25± 7.66) among adolescents with dysmenorrhea. So, the difference is 2.13, not statistically significant, assessed using student independent t-test. In posttest, there was a significant difference between experimental and control group of adolescent girls. The significant p- values 0.001 indicates, the level of pain perception and dysmenorrheal symptoms score were not similar in both groups, calculated using chi square test. Experimental groups are reduced the 44.20% of pain perception score and 20.67% of dysmenorrheal symptoms score after having comprehensive nursing interventions and control group are reduced only 2.90% of pain perception score and 1.61% of dysmenorrheal symptoms after routine care. There is significant association between posttest level of pain perception and demographic variables of adolescent girls with the age group of 13-14 years and 10th standard and posttest level of pain perception and menstrual variables of adolescent girls with 28 days menstrual cycle and moderate extent of blood loss in experimental group. There is significant association between posttest level of dysmenorrheal symptoms and demographic variables of adolescent girls with the type of family and family history and posttest level of pain perception and menstrual variables of adolescent girls with day of flow and material used in experimental group. Discussion: The findings of the study enlighten the fact that comprehensive nursing interventions can be used as cost-effective interventions and showed higher participant satisfaction during intervention by reducing the level of pain perception and dysmenorrheal symptoms score in adolescent girls with primary dysmenorrhea.

Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

LoRa’s biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel desig n. In this research, a Bayesian surrogate Gaussian pr ocess based bidirectional LSTM stacked autoencoder model (BSGP BLSTMSAE) is proposed to estimate the channel perfor-mance indicators such as received signal strength indicator (RSSI) and signal to noise ratio (SNR) and to determine the correlation between the ambient weather conditions and per-formance indicators for the LoRaWAN network. Bayesian op-timization algorithm has been used to optimize the hyper parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evalua-tion of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental Lo-RaWAN network. The mean absolu te error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real time optimization of the transmission parameters to enhance the network performance in LoRaWAN based systems at different ambient conditions

Analysis of Roll Decay for Surface-Ship Model Experiments With Uncertainty Estimates

Abstract Roll decay of David Taylor model basin (DTMB) Model 5720, a 23rd-scale free-running model of the research vessel (R/V) Melville, is evaluated with uncertainty estimates. Experimental roll-decay time series was accurately modeled as an exponentially decaying cosine function, which is the solution of a second-order ordinary differential equation for damping coefficient of less than one (N < 1). The curve-fit provides damping coefficient (N), period (T), and offset. Roll period in calm water was dependent on Froude number (Fr) and initial roll angle (a). Roll decay data are from 76 runs for three nominal Froude numbers, Fr = 0, 0.15, and 0.22. The initial roll angle variation was 3 deg–25 deg. The natural roll period was 2.139 ± 0.041 s (±1.9%). The decay coefficient data were approximated by a plane in three dimensions with Fr and initial roll amplitudes (a) as the independent variables. Curve-fit results are compared to decay coefficient by log decrement and period from time between zero crossings. Examples demonstrate average values for a single roll decay event from log decrement are the same as values by the curve-fitting method within uncertainty estimates. The uncertainty estimate for the decay coefficient is significantly less by curve-fit method in comparison to log-decrement method. By log decrement, the relative uncertainty increases with decreasing roll amplitude peak; consequently, focus should be on the damping coefficient at the largest peaks, where the uncertainty is the smallest.

Analysis of the fractal characteristics for combustion instability in a premixed natural gas engine

To investigate the influence of gas injection timing (GIT) on the combustion instability of a premixed natural gas engine, experiments were conducted under low load conditions using various GITs. Multifractal and multiscale entropy analyses were employed to examine the fractal characteristics and complexity of the experimental time series for indicated mean effective pressure (IMEP) and heat release (Q) at different scales. Statistical analysis and return maps of the IMEP and Q time series were utilized to verify the results. The findings revealed that the combustion process of the natural gas engine demonstrates clear fractal characteristics at different scales. A strong correlation is found between the combustion instability and the fractal characteristics. Furthermore, the probability densities of the IMEP and Q time series exhibit super-Gaussian distributions. Retarding the GIT results in an initial increase, followed by a decrease in the difference value of the Hurst index and singular spectrum width. The mapping point distributions of the IMEP and Q time series initially disperse and subsequently concentrate. The fractal complexity and chaotic characteristics of combustion instability initially strengthen and then gradually diminish. Moreover, under lower load conditions, the anti-persistent correlation becomes more pronounced, and the intermittence and complexity of the fractal characteristics also intensify, signifying a more significant impact of GIT on the combustion instability of the natural gas engine. Notably, when the GIT is approximately 60°CA after top dead center, the combustion process exhibits stronger fractal characteristics, accompanied by a greater dispersion degree of the mapping points. This study provides a theoretical basis for enhancing the lean-burn stability of natural gas engines.

Understanding the state-dependent impact of species correlated responses on community sensitivity to perturbations.

Understanding how communities respond to perturbations requires us to consider not only changes in the abundance of individual species but also correlated changes that can emerge through interspecific effects. However, our knowledge of this phenomenon is mostly constrained to situations where interspecific effects are fixed. Here, we introduce a framework to disentangle the impact of species correlated responses on community sensitivity to perturbations when interspecific effects change over time due to cyclic or chaotic population dynamics. We partition the volume expansion rate of perturbed abundances (community sensitivity) into contributions of individual species and of species correlated responses by converting the time-varying Jacobian matrix containing interspecific effects into a time-varying covariance matrix. Using population dynamics models, we demonstrate that species correlated responses change considerably across time and continuously alternate between reducing and having no impact on community sensitivity. Importantly, these alternating impacts depend on the abundance of particular species and can be detected even from noisy time series. We showcase our framework using two experimental predator-prey time series and find that the impact of species correlated responses is modulated by prey abundance-as theoretically expected. Our results provide new insights into how and when species interactions can dampen community sensitivity when abundances fluctuate over time.

Time series analysis showing how different environmental conditions affect the interspecific interactions of Callosobruchus maculatus and Callosobruchus chinensis

AbstractEnvironmental changes alter the strength of interspecific interactions. However, because it is difficult to quantify interaction strength, empirical evidence remains limited on the relationships among environmental change, interaction strength, and consequences on population dynamics. Here, we evaluated how the interactions of two species of Callosobruchus seed beetles changed with increasing the cage size of the experiments and then affected the coexistence period as a property of population dynamics. Specifically, competition experiments were conducted on Callosobruchus maculatus (C. maculatus) and Callosobruchus chinensis (C. chinensis) using cages of different sizes, which altered the density of adults. We focused on two modes of interspecific interactions between these two species: larval resource competition and adult reproductive interference. Convergent cross mapping (CCM) was implemented to the experimental time series of the two species to assess how environmental change altered their interaction strength and the coexistence period (as a proxy of population dynamics). In most replications, C. maculatus persisted, whereas C. chinensis became extinct. The coexistence periods were longer with increasing cage size. However, there was no statistically significant relationship between cage size and interaction strength. Nevertheless, the stronger (or weaker) interaction strength of the competitively inferior (superior) species on competitively superior (inferior) was associated with longer coexistence periods. Overall, this study demonstrated that interaction strength affected population dynamics; however, changing interaction strength by altering environmental conditions did not necessarily mean that increasing habitat size reduces competition strength.