Wavelet analysis of ecological time series

Computational Approaches and Tools as Applied to the Study of Rhythms and Chaos in Biology

The temporal dynamics in biological systems displays a wide range of behaviors, from periodic oscillations, as in rhythms, bursts, long-range (fractal) correlations, chaotic dynamics up to brown and white noise. Herein, we propose a comprehensive analytical strategy for identifying, representing, and analyzing biological time series, focusing on two strongly linked dynamics: periodic (oscillatory) rhythms and chaos. Understanding the underlying temporal dynamics of a system is of fundamental importance; however, it presents methodological challenges due to intrinsic characteristics, among them the presence of noise or trends, and distinct dynamics at different time scales given by molecular, dcellular, organ, and organism levels of organization. For example, in locomotion circadian and ultradian rhythms coexist with fractal dynamics at faster time scales. We propose and describe the use of a combined approach employing different analytical methodologies to synergize their strengths and mitigate their weaknesses. Specifically, we describe advantages and caveats to consider for applying probability distribution, autocorrelation analysis, phase space reconstruction, Lyapunov exponent estimation as well as different analyses such as harmonic, namely, power spectrum; continuous wavelet transforms; synchrosqueezing transform; and wavelet coherence. Computational harmonic analysis is proposed as an analytical framework for using different types of wavelet analyses. We show that when the correct wavelet analysis is applied, the complexity in the statistical properties, including temporal scales, present in time series of signals, can be unveiled and modeled. Our chapter showcase two specific examples where an in-depth analysis of rhythms and chaos is performed: (1) locomotor and food intake rhythms over a 42-day period of mice subjected to different feeding regimes; and (2) chaotic calcium dynamics in a computational model of mitochondrial function.

Impacts of El Niño Southern Oscillation on the dengue transmission dynamics in the Metropolitan Region of Recife, Brazil

Background:

This research addresses two questions: (1) how El Niño Southern Oscillation (ENSO) affects climate variability and how it influences dengue transmission in the Metropolitan Region of Recife (MRR), and (2) whether the epidemic in MRR municipalities has any connection and synchronicity.

Methods:

Wavelet analysis and cross-correlation were applied to characterize seasonality, multiyear cycles, and relative delays between the series. This study was developed into two distinct periods. Initially, we performed periodic dengue incidence and intercity epidemic synchronism analyses from 2001 to 2017. We then defined the period from 2001 to 2016 to analyze the periodicity of climatic variables and their coherence with dengue incidence.

Results:

Our results showed systematic cycles of 3-4 years with a recent shortening trend of 2-3 years. Climatic variability, such as positive anomalous temperatures and reduced rainfall due to changes in sea surface temperature (SST), is partially linked to the changing epidemiology of the disease, as this condition provides suitable environments for the Aedes aegypti lifecycle.

Conclusion:

ENSO may have influenced the dengue temporal patterns in the MRR, transiently reducing its main way of multiyear variability (3-4 years) to 2-3 years. Furthermore, when the epidemic coincided with El Niño years, it spread regionally and was highly synchronized.

The Time–Frequency Relationship between Oil Price, Stock Returns and Exchange Rate

The concept of time scales is essential for modeling financial decisions. This paper investigates time–frequency relationships across time scales between stock market returns, crude oil prices and exchange rates by applying wavelet analysis technique over the period 1999 to 2021. We find evidence of several strong co-movements between oil price and stock market and between oil price and foreign exchange rate in India. Each of these associations is linked with some important macroeconomic events. This implies economic shocks in developed market have a spillover effect on Indian market. The phase relationships indicate stock returns are in phase with oil prices and exchange rates are in out of phase with oil prices. We find that the impact of volatility at lower scale has a short term effect on the variables. Further, the wavelet coherency at high scale has slower changes with long term effect on the relationship between the variables of our interest. These results are useful for investors aiming specific time horizon of their investment and preferences, for portfolio managers and in risk assessment. Understanding the leading and lagging relationships will also help in business cycle based investing by detecting the subsequent business cycle fluctuations and forecasting the trend.

Did Hydro-climatic Extremes, Positive Checks, and Economic Fluctuations Modulate the Epidemics Outbreaks in Late Imperial China?

Empirical research has shown that climate-related variables, the decline in economic well-being, and the mutual reinforcement of positive checks are the primary drivers of epidemic outbreaks in recent human history. However, their relative importance in causing the outbreak of epidemics is rarely examined quantitatively in a single study. I sought to address this issue by analyzing the 1402 epidemic incidents in China between 1841 and 1911, which partially overlaps partly with the Third Pandemic period. Fine-grained historical big data, multiple regression, and wavelet coherence analysis were employed. Statistical results show that economic fluctuations drove the country-wide epidemics outbreaks in China in inter-annual and decadal time scales. Economic fluctuations could cause short-term hardship and long-term impoverishment to the underprivileged social groups since a large portion of the Chinese population lived at the subsistence level in the past. The fluctuations might have sustained the repeated waves of epidemic outbreaks during the Third Pandemic period.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10745-021-00272-7.

Monitoring of Expansive Clays over Drought-Rewetting Cycles Using Satellite Remote Sensing

New capabilities for measuring and monitoring are needed to prevent the shrink-swell risk caused by drought-rewetting cycles. A clayey soil in the Loire Valley at Chaingy (France) has been instrumented with two extensometers and several soil moisture sensors. Here we show by direct comparison between remote and in situ data that the vertical ground displacements due to clay expansion are well-captured by the Multi-Temporal Synthetic Aperture Radar Interferometry (MT-InSAR) technique. In addition to the one-year period, two sub-annual periods that reflect both average ground shrinking and swelling timeframes are unraveled by a wavelet-based analysis. Moreover, the relative phase difference between the vertical displacement and surface soil moisture show local variations that are interpreted in terms of depth and thickness of the clay layer, as visualized by an electrical resistivity tomography. With regard to future works, a similar treatment relying fully on remote sensing observations may be scaled up to map larger areas in order to better assess the shrink-swell risk.

Regime shift tipping point in hare population collapse associated with climatic and agricultural change during the very early 20th century

Animal populations at northern latitudes may have cyclical dynamics that are degraded by climate change leading to trophic cascade. Hare populations at more southerly latitudes are characterized by dramatic declines in abundance associated with agricultural intensification. We focus on the impact of historical climatic and agricultural change on a mid-latitude population of mountain hares, Lepus timidus hibernicus. Using game bag records from multiple sites throughout Ireland, the hare population index exhibited a distinct regime shift. Contrary to expectations, there was a dynamical structure typical of northern latitude hare populations from 1853 to 1908, during which numbers were stable but cyclic with a periodicity of 8 years. This regime was replaced by dynamics more typical of southern latitude hare populations from 1909 to 1970, in which cycles were lost and numbers declined dramatically. Destabilization of the autumn North Atlantic Oscillation (NAO) led to the collapse of similar cycles in the hare population, coincident with the onset of agricultural intensification (a shift from small-to-large farms) in the first half of the 20th century. Similar, but more recent regime shifts have been observed in Arctic ecosystems and attributed to anthropogenic climate change. The present study suggests such shifts may have occurred at lower latitudes more than a century ago during the very early 20th century. It seems likely that similar tipping points in the population collapse of other farmland species may have occurred similarly early but went undocumented. As northern systems are increasingly impacted by climate change and probable expansion of agriculture, the interaction of these processes is likely to disrupt the pulsed flow of resources from cyclic populations impacting ecosystem function.

Teleconnection between climate oscillations and riverine nutrient dynamics in Southeast China based on wavelet analysis

Nonpoint source (NPS) pollution is mainly driven by hydrological processes; climate oscillation can affect regional water cycle processes. However, the relationship between climate oscillation and NPS pollution is still unclear, which increases the difficulty of water quality prediction and management. In this study, Mann-Kendall test and wavelet transform were adopted to investigate the teleconnection between ENSO (El Niño-Southern Oscillation) phenomenon and riverine NPS load dynamics in an agricultural watershed of Southeast China from 2003 to 2016. Results showed that annual precipitation, streamflow, and riverine nutrient load increased significantly during the study period. The change point for long-term riverine TN load and TP load appeared in 2009 and 2007, respectively. Rainfall, streamflow, nutrient loads, and Niño 3.4 sea temperature (SST) shared a common periodicity of 10-16 months. The southern oscillation index (SOI) and Niño 3.4 SST shared a common periodicity of 28-36 months. Moreover, Niño 3.4 SST showed a positive correlation with riverine nutrient loads at a periodicity of 10-16 months, while SOI showed a weakly negative correlation with riverine nutrient loads at a periodicity of 28-36 months. These findings indicate that the increasing frequency of warm ENSO events would enhance the risk of nutrient export to rivers in Southeast China and more attention should be paid to large-scale climate oscillation in the prediction of agricultural nutrient pollution and management of water quality in agricultural watersheds.

Predictability of temporal variation in climate and the evolution of seasonal polyphenism in tropical butterfly communities

Phenotypic plasticity in heterogeneous environments can provide tight environment-phenotype matching. However, the prerequisite is a reliable environmental cue(s) that enables organisms to use current environmental information to induce the development of a phenotype with high fitness in a forthcoming environment. Here, we quantify predictability in the timing of precipitation and temperature change to examine how this is associated with seasonal polyphenism in tropical Mycalesina butterflies. Seasonal precipitation in the tropics typically results in distinct selective environments, the wet and dry seasons, and changes in temperature can be a major environmental cue. We sampled communities of Mycalesina butterflies from two seasonal locations and one aseasonal location. Quantifying environmental predictability using wavelet analysis and Colwell's indices confirmed a strong periodicity of precipitation over a 12-month period at both seasonal locations compared to the aseasonal one. However, temperature seasonality and periodicity differed between the two seasonal locations. We further show that: (a) most females from both seasonal locations synchronize their reproduction with the seasons by breeding in the wet season but arresting reproduction in the dry season. In contrast, all species breed throughout the year in the aseasonal location and (b) species from the seasonal locations, but not those from the aseasonal location, exhibited polyphenism in wing pattern traits (eyespot size). We conclude that seasonal precipitation and its predictability are primary factors shaping the evolution of polyphenism in Mycalesina butterflies, and populations or species secondarily evolve local adaptations for cue use that depend on the local variation in the environment.

Global hotspots of coherent marine fishery catches

Although different fisheries can be tightly linked to each other by human and ecosystem processes, they are often managed independently. Synchronous fluctuations among fish populations or fishery catches can destabilize ecosystems and economies, respectively, but the degree of synchrony around the world remains unclear. We analyzed 1,092 marine fisheries catch time series over 60 yr to test for the presence of coherence, a form of synchrony that allows for phase-lagged relationships. We found that nearly every fishery was coherent with at least one other fishery catch time series globally and that coherence was strongest in the northeast Atlantic, western central Pacific, and eastern Indian Ocean. Analysis of fish biomass and fishing mortality time series from these hotspots revealed that coherence in biomass or fishing mortality were both possible, though biomass coherence was more common. Most of these relationships were synchronous with no time lags, and across catches in all regions, synchrony was a better predictor of regional catch portfolio effects than catch diversity. Regions with higher synchrony had lower stability in aggregate fishery catches, which can have negative consequences for food security and economic wealth.

Classification of red blood cell aggregation using empirical wavelet transform analysis of ultrasonic radiofrequency echo signals

Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machine learning technique based on an adaptive analysis of ultrasonic radiofrequency (RF) echo signals in blood is proposed, and its feasibility for classifying RBC aggregation is explored. Using an adaptive empirical wavelet transform (EWT) analysis, the ultrasonic RF signals are decomposed into a series of empirical mode functions (EMFs); then, dominant empirical mode functions (DEMFs) are selected from the series. Six statistical characteristics, including the mean, variance, median, kurtosis, root mean square (RMS), and skewness are calculated for the locally normalized DEMFs, aiming to form primary feature vectors. Random forest (RDF) and support vector machine (SVM) classifiers are trained with the given feature vectors to obtain prediction models for RBC classification. Ultrasonic RF echo signals are acquired from five groups of six types of porcine blood samples with average numbers of aggregated RBCs of 1.04, 1.20, 1.83, 2.31, 2.72, and 4.28, respectively, to test the classification performance of the proposed method. The best subset with regard to the variance, kurtosis, and RMS is determined according to the maximum accuracy based on the RDF and SVM classifiers. The classification accuracies are 84.03 ± 3.13% for the RDF classifier, and 85.88 ± 2.99% for the SVM classifier. The mean classification accuracy of the SVM classifier is 1.85% better than that of the RDF classifier. In conclusion, the machine learning method is useful for the discrimination of varying degrees of RBC aggregation, and has potential for use in characterizing and monitoring the RBC aggregation in vessels.

Episodic recruitment in the saguaro cactus is driven by multidecadal periodicities

Each year, an individual mature large saguaro cactus produces about one million seeds in attractive juicy fruits that lure seed predators and seed dispersers to a 3-month feast. From the million seeds produced, however, only a few will persist into mature saguaros. A century of research on saguaro population dynamics has led to the conclusion that saguaro recruitment is an episodic event that depends on the convergence of suitable conditions for survival during the critical early stages. Because most data have been collected in Arizona, particularly in the surroundings of Tucson, most research has relied on a limited amount of environmental variation. In this study, we upscaled this knowledge on saguaro recruitment to a regional scale with a new method that used the inverse-growth modeling of 1,487 saguaros belonging to 13 populations in a latitudinal gradient ranging from arid desert to tropical thornscrub forest in Sonora, Mexico. Using generalized linear and additive mixed models, we created two 110-yr-long saguaro recruitment curves: one driven only by previous size, and the second driven by size, drought, and soil structure. We found evidence that saguaro recruitment is indeed episodic, with periodicities of 20-30 yr, possibly related to strong El Niño Southern Oscillation events. Our results suggest that saguaros rely on multidecadal periodic pulses of good beneficial years to incorporate new individuals into their populations. Inverse-growth modeling can be used in a wide variety of plant species to study their recruitment dynamics.

Temporary Fertility Decline after Large Rubella Outbreak, Japan

Japan experienced 2 large rubella epidemics in 2004 and 2012–2014. Because of suboptimal immunization levels, the country has been experiencing a third major outbreak during 2018–2020. We conducted time series analyses to evaluate the effect of the 2012–2014 nationwide rubella epidemic on prefecture-level natality in Japan. We identified a statistically significant decline in fertility rates associated with rubella epidemic activity and increased Google searches for the term “rubella.” We noted that the timing of fertility declines in 2014 occurred 9–13 months after peak rubella incidence months in 2013 in 4 prefectures with the highest rubella incidence. Public health interventions should focus on enhancing vaccination campaigns against rubella, not only to protect pregnant women from infection but also to mitigate declines in population size and birth rates.

Multiple Wavelet Coherence to Evaluate Local Multivariate Relationships in a Groundwater System

Groundwater level fluctuations are affected by surface properties due to complex correlations of groundwater-surface water interaction and/or other surface processes, which are usually hard to be accurately quantified. Previous studies have assessed the relationship between groundwater level fluctuations and specific controlling factors. However, few studies have been conducted to explore the impact of the combination of multiple factors on the groundwater system. Hence, this paper tries to explore the localized and scale-specific multivariate relationships between the groundwater level and controlling factors (such as hydrologic and meteorological factors) using bivariate wavelet coherence and multiple wavelet coherence. The groundwater level fluctuations of two wells in areas covered by different plant densities (i.e., the riparian zone of the Colorado River, USA) are analyzed. Main findings include three parts. First, barometric pressure and river stage are the best factors to interpret the groundwater level fluctuations at small scales (<1 day) and large scales (>1 day) at the well of low-density plants stand, respectively. Second, at the well of high-density plants stand, the best predictors to control the groundwater level fluctuations include barometric pressure (<1 day), the combination of barometric pressure and temperature (1-7 days), temperature (7-30 days), and the combination of barometric pressure, temperature, and river stage (>30 days). The best predictor of groundwater head fluctuations depends on the variance of the vegetation coverage and hydrological processes. Third, these results provide a suite of factors to explain the groundwater level variations, which is an important topic in water-resource prediction and management.

Multifractal Characteristics of Geomagnetic Field Fluctuations for the Northern and Southern Hemispheres at Swarm Altitude

This paper explores the spatial variations of the statistical scaling features of low to high latitude geomagnetic field fluctuations at Swarm altitude. The data for this study comes from the vector field magnetometer onboard Swarm A satellite, measured at low resolution (1 Hz) for one year (from 9 March 2016, to 9 March 2017). We estimated the structure-function scaling exponents using the p-leaders discrete wavelet multifractal technique, from which we obtained the singularity spectrum related to the magnetic fluctuations in the North-East-Center (NEC) coordinate system. From this estimation, we retain just the maximal fractal subset, associated with the Hurst exponent H. Here we present thresholding for two levels of the Auroral Electrojet index and almost the whole northern and southern hemispheres, the Hurst exponent, the structure-function scaling exponent of order 2, and the multifractal p-exponent width for the geomagnetic fluctuations. The latter quantifies the relevance of the multifractal property. Sometimes, we found negative values of H, suggesting a behavior similar to wave breaking or shocklet-like propagating front. Furthermore, we found some asymmetries in the magnetic field turbulence between the northern and southern hemispheres. These estimations suggest that different turbulent regimes of the geomagnetic field fluctuations exist along the Swarm path.

The delayed effect of cooling reinforced the NAO-plague connection in pre-industrial Europe

Previous studies on the connection between climate and plague were mostly conducted without considering the influence of large-scale atmospheric circulations and long-term historical observations. The current study seeks to reveal the sophisticated role of climatic control on plague by investigating the combined effect of North Atlantic Oscillation (NAO) and temperature on plague outbreaks in Europe from 1347 to 1760 CE. Moving correlation analysis is applied to explore the non-linear relationship between NAO and plague transmission over time. Also, we apply the cross-correlation function to identify the role of temperature in mediating the NAO-plague connection and the lead-lag relationship in between. Our statistical results show that the pathway from climate change to plague incidence is distinctive in its spatial, temporal, and non-linear patterns. The multi-decadal temperature change exerted a 15-22 years lagged impact on the NAO-plague correlation in different European regions. The NAO-plague correlation in Atlantic-Central Europe primarily remained positive, while the correlation in Mediterranean Europe switched between positive and negative alternately. The modulating effect of temperature over the NAO-plague correlation increases exponentially with the magnitude of the temperature anomaly, but the effect is negligible between 0.3 and -0.3 °C anomaly. Our findings show that a lagged influence from the temperature extremes dominantly controls the correlation between NAO and plague incidence. A forecast from our study suggests that large-scale plague outbreaks are unlikely to happen in Europe if NAO remains at its current positive phase during the earth's future warming.

Coupled phase-amplitude dynamics in heterogeneous metacommunities

Spatial synchrony of population fluctuations is an important tool for predicting regional stability. Its application to natural systems is still limited by the complexity of ecological time series displaying great variation in the frequency and amplitude of their fluctuations, which are not fully resolved by current ecological theories of spatial synchrony. In particular, while environmental fluctuations and limited dispersal can each control the dynamics of frequency and amplitude of population fluctuations, ecological theories of spatial synchrony still need to resolve their role on synchrony and stability in heterogeneous metacommunities. Here, we adopt a heterogeneous predator-prey metacommunity model and study the response of dispersal-driven phase locking and frequency modulation to among-patch heterogeneity in carrying capacity. We find that frequency modulation occurs at intermediate values of dispersal and habitat heterogeneity. We also show how frequency modulation can emerge in metacommunities of autonomously oscillating populations as well as through the forcing of local communities at equilibrium. Frequency modulation was further found to produce temporal variation in population amplitudes, promoting local and regional stability through cyclic patterns of local and regional variability. Our results highlight the importance of approaching spatial synchrony as a non-stationary phenomenon, with implications for the assessment and interpretation of spatial synchrony observed in experimental and natural systems.

Viral etiology and seasonal trends of pediatric acute febrile illness in southern Puerto Rico; a seven-year review

BACKGROUND

Acute febrile illness (AFI) is an important cause for seeking health care among children. Knowledge of the most common etiologic agents of AFI and its seasonality is limited in most tropical regions.

METHODOLOGY/PRINCIPAL FINDINGS

To describe the viral etiology of AFI in pediatric patients (≤18 years) recruited through a sentinel enhanced dengue surveillance system (SEDSS) in Southern Puerto Rico, we analyzed data for patients enrolled from 2012 to May 2018. To identify seasonal patterns, we applied time-series analyses to monthly arboviral and respiratory infection case data. We calculated coherence and phase differences for paired time-series to quantify the association between each time series. A viral pathogen was found in 47% of the 14,738 patients. Influenza A virus was the most common pathogen detected (26%). The incidence of Zika and dengue virus etiologies increased with age. Arboviral infections peaked between June and September throughout the times-series. Respiratory infections have seasonal peaks occurring in the fall and winter months of each year, though patterns vary by individual respiratory pathogen.

CONCLUSIONS/SIGNIFICANCE

Distinct seasonal patterns and differences in relative frequency by age groups seen in this study can guide clinical and laboratory assessment in pediatric patients with AFI in Puerto Rico.

Wavelet methods reveal big cat activity patterns and synchrony of activity with preys

Appropriate temporal and spatial scales are important prerequisites for obtaining reliable results in studies of wildlife activity patterns and interspecific interactions. The spread of camera-trap technology has increased interest in and feasibility of studying the activity patterns and interspecific interactions of wildlife. However, such studies are often conducted at arbitrary spatial and temporal scales, and the methods used impose scale on the study rather than determining how activity and species interactions change with spatial scale. In this study, we used a wavelet-based approach to determine the temporal and spatial scales for activity patterns and interspecific interactions on Amur leopard and their ungulate prey species that were recorded using camera traps in the main Amur leopard occurrence region in northeast China. Wavelets identified that Amur leopards were more active in spring and fall than summer, and fluctuated with periodicities of 9 and 17 days, respectively. Synchronous relationships between leopards and their prey commonly occurred in spring and fall, with a periodicity of about 20 days, indicating the appropriate seasons and temporal scales for interspecific interaction research. The influence of human activities on the activity patterns of Amur leopard or prey species often occurred over longer time periods (60-64 days). Two-dimensional wavelet analyses showed that interactions between leopard and prey were more significant at spatial scales of 1 km² . Overall, our study provides a feasible approach to studying the temporal and spatial scales for wildlife activity patterns and interspecific interaction research using camera trap data.

Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model

BACKGROUND

Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models.

MATERIALS AND METHODS

We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)-neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA-NNAR hybrid model were established for comparison and estimation.

RESULTS

The wavelet-based SARIMA-NNAR hybrid model demonstrates excellent performance whether in fitting or forecasting compared with other models. Its fitted and forecasting time series are similar to the actual observed time series.

CONCLUSIONS

The wavelet-based SARIMA-NNAR hybrid model fitted in this study is suitable for forecasting the number of HFMD cases. Hence, it will facilitate the prevention and control of HFMD.

The interrelationship between meteorological parameters and leptospirosis incidence in Hambantota district, Sri Lanka 2008-2017 and practical implications

Background

Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals pass leptospires to the environment with their urine. Leprospires' survival in the environment to infect a new host depends on meteorological factors. El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) modulate the weather in Sri Lanka.

Objectives

The determination of interrelationship between the LI in the Hambantota District, and local meteorological parameters, ENSO and IOD.

Methods

We acquired notified leptospirosis cases in the Hambantota District and population data. We calculated weekly leptospirosis incidences for 2008 to 2017.Weather data from two weather stations was obtained, averaged and converted into weekly data. We plotted time series graphs and observed the correlation between seven aggregated weather parameters and LI. We estimated cross-correlations between those weather parameters and LI. As our principal analysis we determined correlation between LI and seven local weather parameters, Nino 3.4, Nino4 and Dipole Mode Index (DMI) indices using wavelet analysis.

Results

Our wavelet analysis results showed troughs of minimum, maximum, mean temperatures, soil temperature, the evaporation rate, the duration of sunshine were followed by peaks in LI and peaks of rainfall followed by peaks of LI, all after lag periods. Our time series graphs and cross-correlation determination results are generally in agreement with these results. However there was no significant correlation between rainfall and LI in the cross-correlation analysis. There were peaks of LI following both peaks and troughs of DMI. There was no clear correlation between both Nino indices and LI.

Discussion

This may be the first long-term study demonstrating soil temperature, evaporation rate and IOD are correlating with LI. The correlation pattern of LI with temperature parameters differs from similar past studies and we explain the reasons. We propose ways to control high LI we observed after periods of weather favorable for transmission of leptospirosis.

Dynamic wavelet correlation analysis for multivariate climate time series

The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.

Tensor decomposition for infectious disease incidence data

Many demographic and ecological processes generate seasonal and other periodicities. Seasonality in infectious disease transmission can result from climatic forces such as temperature and humidity; variation in contact rates as a result of migration or school calendar; or temporary surges in birth rates. Seasonal drivers of acute immunizing infections can also drive longer-term fluctuations.Tensor decomposition has been used in many disciplines to uncover dominant trends in multi-dimensional data. We introduce tensors as a novel method for decomposing oscillatory infectious disease time series.We illustrate the reliability of the method by applying it to simulated data. We then present decompositions of measles data from England and Wales. This paper leverages simulations as well as much-studied data to illustrate the power of tensor decomposition to uncover dominant epidemic signals as well as variation in space and time. We then use tensor decomposition to uncover new findings and demonstrate the potential power of the method for disease incidence data. In particular, we are able to distinguish between annual and biennial signals across locations and shifts in these signals over time.Tensor decomposition is able to isolate variation in disease seasonality as a result of variation in demographic rates. The method allows us to discern variation in the strength of such signals by space and population size. Tensors provide an opportunity for a concise approach to uncovering heterogeneity in disease transmission across space and time in large datasets.

Patterns of smallpox mortality in London, England, over three centuries

Smallpox is unique among infectious diseases in the degree to which it devastated human populations, its long history of control interventions, and the fact that it has been successfully eradicated. Mortality from smallpox in London, England was carefully documented, weekly, for nearly 300 years, providing a rare and valuable source for the study of ecology and evolution of infectious disease. We describe and analyze smallpox mortality in London from 1664 to 1930. We digitized the weekly records published in the London Bills of Mortality (LBoM) and the Registrar General's Weekly Returns (RGWRs). We annotated the resulting time series with a sequence of historical events that might have influenced smallpox dynamics in London. We present a spectral analysis that reveals how periodicities in reported smallpox mortality changed over decades and centuries; many of these changes in epidemic patterns are correlated with changes in control interventions and public health policies. We also examine how the seasonality of reported smallpox mortality changed from the 17th to 20th centuries in London.

Life in fluctuating environments

Variability in the environment defines the structure and dynamics of all living systems, from organisms to ecosystems. Species have evolved traits and strategies that allow them to detect, exploit and predict the changing environment. These traits allow organisms to maintain steady internal conditions required for physiological functioning through feedback mechanisms that allow internal conditions to remain at or near a set-point despite a fluctuating environment. In addition to feedback, many organisms have evolved feedforward processes, which allow them to adjust in anticipation of an expected future state of the environment. Here we provide a framework describing how feedback and feedforward mechanisms operating within organisms can generate effects across scales of organization, and how they allow living systems to persist in fluctuating environments. Daily, seasonal and multi-year cycles provide cues that organisms use to anticipate changes in physiologically relevant environmental conditions. Using feedforward mechanisms, organisms can exploit correlations in environmental variables to prepare for anticipated future changes. Strategies to obtain, store and act on information about the conditional nature of future events are advantageous and are evidenced in widespread phenotypes such as circadian clocks, social behaviour, diapause and migrations. Humans are altering the ways in which the environment fluctuates, causing correlations between environmental variables to become decoupled, decreasing the reliability of cues. Human-induced environmental change is also altering sensory environments and the ability of organisms to detect cues. Recognizing that living systems combine feedback and feedforward processes is essential to understanding their responses to current and future regimes of environmental fluctuations.

This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

Warm temperatures increase population growth of a nonnative defoliator and inhibit demographic responses by parasitoids

Changes in thermal regimes that disparately affect hosts and parasitoids could release hosts from biological control. When multiple natural enemy species share a host, shifts in host-parasitoid dynamics could depend on whether natural enemies interact antagonistically vs. synergistically. We investigated how biotic and abiotic factors influence the population ecology of larch casebearer (Coleophora laricella), a nonnative pest, and two imported parasitoids, Agathis pumila and Chrysocharis laricinellae, by analyzing (1) temporal dynamics in defoliation from 1962 to 2018, and (2) historical, branch-level data on densities of larch casebearer and parasitism rates by the two imported natural enemies from 1972 to 1995. Analyses of defoliation indicated that, prior to the widespread establishment of parasitoids (1962 to ~1980), larch casebearer outbreaks occurred in 2-6 yr cycles. This pattern was followed by a >15-yr period during which populations were at low, apparently stable densities undetectable via aerial surveys, presumably under control from parasitoids. However, since the late 1990s and despite the persistence of both parasitoids, outbreaks exhibiting unstable dynamics have occurred. Analyses of branch-level data indicated that growth of casebearer populations, A. pumila populations, and within-casebearer densities of C. laricinellae-a generalist whose population dynamics are likely also influenced by use of alternative hosts-were inhibited by density dependence, with high intraspecific densities in one year slowing growth into the next. Casebearer population growth was also inhibited by parasitism from A. pumila, but not C. laricinellae, and increased with warmer autumnal temperatures. Growth of A. pumila populations and within-casebearer densities of C. laricinellae increased with casebearer densities but decreased with warmer annual maximum temperatures. Moreover, parasitism by A. pumila was associated with increased growth of within-casebearer densities of C. laricinellae without adverse effects on its own demographics, indicating a synergistic interaction between these parasitoids. Our results indicate that warming can be associated with opposing effects between trophic levels, with deleterious effects of warming on one natural enemy species potentially being exacerbated by similar impacts on another. Coupling of such parasitoid responses with positive responses of hosts to warming might have contributed to the return of casebearer outbreaks to North America.

Coherence of dengue incidence and climate in the wet and dry zones of Sri Lanka

We studied the dynamics of dengue disease in two epidemic regions in Sri Lanka, the densely populated Colombo district representing the wet zone and the relatively less populated Batticaloa district representing the dry zone. Regional differences in disease dynamics were analysed against regional weather factors. Wavelets, Granger causality and regression methods were used. The difference between the dynamical features of these two regions may be explained by the differences in the climatic characteristics of the two regions. Wavelet analysis revealed that Colombo dengue incidence has 6 months periodicity while Batticaloa dengue incidence has 1 year periodicity. This is well explained by the dominant 6 months periodicity in Colombo rainfall and 1 year periodicity in Batticaloa rainfall. The association between dengue incidence and temperature was negative in dry Batticaloa and was insignificant in wet Colombo. Granger causality results indicated that rainfall, rainy days, relative humidity and wind speed can be used to predict Colombo dengue incidence while only rainfall and relative humidity were significant in Batticaloa. Negative binomial and linear regression models were used to identify the weather variables which best explain the variations in dengue incidence. Most recent available incidence data performed as best explanatory variables, outweighing the importance of past weather data. Therefore we recommend the health authorities to closely monitor the number of cases and to streamline recording procedures so that most recent data are available for early detection of epidemics. We also noted that epidemic responses to weather changes appear quickly in densely populated Colombo compared to less populated Batticaloa. The past dengue incidence and weather variables explain the dengue incidence better in Batticaloa than in Colombo and thus other exogenous factors such as population density and human mobility may be affecting Colombo dengue incidence.

A Method for Tree Detection Based on Similarity with Geometric Shapes of 3D Geospatial Data

This paper presents an approach to detecting patterns in a three-dimensional context, emphasizing the role played by the local geometry of the surface model. The core of the associated algorithm is represented by the cosine similarity computed to sub-matrices of regularly gridded digital surface/canopy models. We developed an accompanying software instrument compatible with a GIS environment which allows, as inputs, locations in the surface/canopy model based on field data, pre-defined geometric shapes, or their combination. We exemplified the approach for a study case dealing with the locations of scattered trees and shrubs previously identified in the field in two study sites. We found that the variation in the pairwise similarities between the trees is better explained by the computation of slopes. Furthermore, we considered a pre-defined shape, the Mexican Hat wavelet. Its geometry is controlled by a single number, for which we found ranges of best fit between the shapes and the actual trees. Finally, a suitable combination of parameters made it possible to determine the potential locations of scattered trees. The accuracy of detection was equal to 77.9% and 89.5% in the two study sites considered. Moreover, a visual check based on orthophotomaps confirmed the reliability of the outcomes.

Coexisting attractors in the context of cross-scale population dynamics: measles in London as a case study

Patterns of measles infection in large urban populations have long been considered the paradigm of synchronized nonlinear dynamics. Indeed, recurrent epidemics appear approximately mass-action despite underlying heterogeneity. However, using a subset of rich, newly digitized mortality data (1897–1906), we challenge that proposition. We find that sub-regions of London exhibited a mixture of simultaneous annual and biennial dynamics, while the aggregate city-level dynamics appears firmly annual. Using a simple stochastic epidemic model and maximum-likelihood inference methods, we show that we can capture this observed variation in periodicity. We identify agreement between theory and data, indicating that both changes in periodicity and epidemic coupling between regions can follow relatively simple rules; in particular we find local variation in seasonality drives periodicity. Our analysis underlines that multiple attractors can coexist in a strongly mixed population and follow theoretical predictions.

Time-Frequency Analysis of Photopic Negative Response in CRVO Patients

PURPOSE

The PhNR is driven by retinal ganglion cells (RGCs). Therefore, the function of RGCs could be objectively evaluated by analyzing the PhNR. The aim of this article is to determine the effect of central retinal vein occlusion (CRVO) on PhNR and RGCs performances.

METHODS

Seventeen patients with CRVO were included. Full-field photopic ERGs, including PhNR, were recorded and compared with the fellow normal eyes. ERG signals were analyzed based on the standard time-domain analyses of the PhNR as well as a continuous wavelet transform (CWT) to extract time-frequency components that correspond to the PhNR using MATLAB. We obtained the main frequencies and their occurrence time from CWT.

RESULTS

All a-wave, b-wave, and PhNR amplitudes of CRVO eyes showed a significant reduction compared to those of the fellow eyes (P < .01, P < .001, and P < .001, respectively). The peak times of a-wave, b-wave, and PhNR were increased significantly in the CRVO eyes (P = .04, P = .04, and P = .003, respectively). The dominant f₃ frequency, which corresponds to the PhNR in CRVO patients, showed a more significant decrease (P < .001) compared to other dominant frequencies (f₀, f₁, and f₂). The occurrence time of f₃ (t₃) was significantly higher in the CRVO eyes (P < .001). Time-domain of the PhNR was also affected in CRVO patients (P < .001).

CONCLUSION

CWT allows quantifications of ERG responses, especially for PhNR. The PhNR was severely affected in CRVO eyes implicating loss of RGCs. CWT might demonstrate the severity of CRVO more precisely and identify diagnostically significant changes of ERG waveforms that are not resolved when the analysis is only limited to the time-domain measurements.

High frequency fluorescence monitoring reveals new insights into organic matter dynamics of an urban river, Birmingham, UK

Natural organic matter (NOM) is fundamental to many biogeochemical processes in river ecosystems. Currently, however, we have limited knowledge of NOM dynamics across the spectrum of flow conditions as previous studies have focused largely on storm events. Field deployable fluorescence technology offers new opportunities to explore both stochastic and predictable diel NOM dynamics at finer time-steps and for longer periods than was hitherto possible, thus yielding new insight into NOM sources, processing, and pathways. Hourly fluorescence data (humic-like fluorescence [Peak C] and tryptophan-like fluorescence [Peak T]) and a suite of hydro-climatological variables were collected from an urban river (Birmingham, UK). We explored monthly concentration-discharge (C-Q) patterns using segmented regression and assessed hysteretic and flushing behaviour for Peak C, T and turbidity to infer source zone activation. Diel patterns were assessed during low flow periods. Wavelet analysis identified strong diurnal variations in Peak C with early morning peaks while no diel dynamics were apparent for Peak T. Using generalised linear modelling relationships between Peak C periodicity and both solar radiation and time since previous storm/scouring event were identified. Breakpoints and positive slopes for C-Q relationship highlighted chemodynamic behaviour for NOM over most of the monitoring period, with Peak T mobilised more relative to Peak C during high Q. Hysteresis patterns were highly variable but flushing behaviour of Peak T and C suggested exhaustion of humic compounds during long duration events and following successive rainfall events. Peak T flushing was correlated with Q magnitude highlighting the potential for combined sewer overflows to act as important NOM sources despite significant dilution potential. This research highlights the potential of real-time, field deployable fluorescence spectroscopy as a viable method for providing insight into diel and transport driven NOM dynamics.

Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy

Remote sensing reflectance (Rrs) values measured by satellite sensors involve large amounts of uncertainty leading to non-negligible noise in remote Chlorophyll-a (Chl-a) concentration estimation. This work distinguished between two main stages in the case of estimating distributions of Chl-a within the Gulf of St. Lawrence (Canada). At the model building stage, the retrieval algorithm used both in-situ Chl-a measurements and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) L2-level data estimated Rrs at 412, 443, 469, 488, 531, 547, 555, 645, 667, 678 nm at a 1 km spatial resolution during 2004-2013. Through the training and validation of various models and Rrs combinations of the considered eight techniques (including support vector regression, artificial neural networks, gradient boosting machine, random forests, standard CI-OC3M, multiple linear regression, generalized addictive regression, principal component regression), the support vector regression (SVR) technique was shown to have the best performance in Chl-a concentration estimation using Rrs at 412, 443, 488, 531 and 678 nm. The accuracy indicators for both the training (850) and the validation (213) datasets were found to be very good to excellent (e.g., the R² value varied between 0.7058 and 0.9068). At the space-time estimation stage, this work took a step forward by using the Bayesian maximum entropy (BME) theory to further process the SVR estimated Chl-a concentrations by incorporating the inherent spatiotemporal dependency of physical Chl-a distribution. A 56% improvement was achieved in the reduction of the mean uncertainty of the validation data decreased considerably (from 1.2222 to 0.5322 mg/m³). Then, this novel BME/SVR framework was employed to estimate the daily Chl-a concentrations in the Gulf of St. Lawrence during Jan 1-Dec 31 of 2017 (1 km spatial resolution). The results showed that the daily mean Chl-a concentration varied from 1.6630 to 3.3431 mg/m³, and that the daily mean Chl-a uncertainty reduction of the composite BME/SVR vs. the SVR estimation had a maximum reduction value of 1.0082 and an average reduction value of 0.6173 mg/m³. The monthly spatial Chl-a distribution covariances showed that the highest Chl-a concentration variability occurred during November and that the spatiotemporal Chl-a concentration pattern changed a lot during the period August to November. In conclusion, the proposed BME/SVR was shown to be a promising remote Chl-a retrieval approach that exhibited a significant ability in reducing the non-negligible uncertainty and improving the accuracy of remote sensing Chl-a concentration estimates.

Assessment of environmental variability on malaria transmission in a malaria-endemic rural dry zone locality of Sri Lanka: The wavelet approach

Malaria is a global public health concern and its dynamic transmission is still a complex process. Malaria transmission largely depends on various factors, including demography, geography, vector dynamics, parasite reservoir, and climate. The dynamic behaviour of malaria transmission has been explained using various statistical and mathematical methods. Of them, wavelet analysis is a powerful mathematical technique used in analysing rapidly changing time-series to understand disease processes in a more holistic way. The current study is aimed at identifying the pattern of malaria transmission and its variability with environmental factors in Kataragama, a malaria-endemic dry zone locality of Sri Lanka, using a wavelet approach. Monthly environmental data including total rainfall and mean water flow of the “Menik Ganga” river; mean temperature, mean minimum and maximum temperatures and mean relative humidity; and malaria cases in the Kataragama Medical Officer of Health (MOH) area were obtained from the Department of Irrigation, Department of Meteorology and Malaria Research Unit (MRU) of University of Colombo, respectively, for the period 1990 to 2005. Wavelet theory was applied to analyze these monthly time series data. There were two significant periodicities in malaria cases during the period of 1992–1995 and 1999–2000. The cross-wavelet power spectrums revealed an anti-phase correlation of malaria cases with mean temperature, minimum temperature, and water flow of “Menik Ganga” river during the period 1991–1995, while the in-phase correlation with rainfall is noticeable only during 1991–1992. Relative humidity was similarly associated with malaria cases between 1991–1992. It appears that environmental variables have contributed to a higher incidence of malaria cases in Kataragama in different time periods between 1990 and 2005.

Relationship between Sunspot Numbers and Mean Annual Precipitation: Application of Cross-Wavelet Transform—A Case Study

Observations show that the Sun, which is the primary source of energy for the Earth’s climate system, is a variable star. In order to understand the influence of solar variability on the Earth’s climate, knowledge of solar variability and solar–terrestrial interactions is required. Knowledge of the Sun’s cyclic behavior can be used for future prediction purposes on Earth. In this study, the possible connection between sunspot numbers (SSN) as a proxy for the 11-year solar cycle and mean annual precipitation (MAP) in Iran were investigated, with the motivation of contributing to the controversial issue of the relationship between SSN and MAP. Nine locations throughout Iran were selected, representing different climatic conditions in the country. Cross-wavelet transform (XWT) analysis was employed to investigate the temporal relationship between cyclicities in SSN and MAP. Results indicated that a distinct 8–12-year correlation exists between the two time series of SSN and MAP, and peaks in precipitation mostly occur one to three years after the SSN maxima. The findings of this study can be beneficial for policymakers, to consider future potential droughts and wet years based on sunspot activities, so that water resources can be more properly managed.

The Dynamic Shift Detector: An algorithm to identify changes in parameter values governing populations

Environmental factors interact with internal rules of population regulation, sometimes perturbing systems to alternate dynamics though changes in parameter values. Yet, pinpointing when such changes occur in naturally fluctuating populations is difficult. An algorithmic approach that can identify the timing and magnitude of parameter shifts would facilitate understanding of abrupt ecological transitions with potential to inform conservation and management of species. The “Dynamic Shift Detector” is an algorithm to identify changes in parameter values governing temporal fluctuations in populations with nonlinear dynamics. The algorithm examines population time series data for the presence, location, and magnitude of parameter shifts. It uses an iterative approach to fitting subsets of time series data, then ranks the fit of break point combinations using model selection, assigning a relative weight to each break. We examined the performance of the Dynamic Shift Detector with simulations and two case studies. Under low environmental/sampling noise, the break point sets selected by the Dynamic Shift Detector contained the true simulated breaks with 70–100% accuracy. The weighting tool generally assigned breaks intentionally placed in simulated data (i.e., true breaks) with weights averaging >0.8 and those due to sampling error (i.e., erroneous breaks) with weights averaging <0.2. In our case study examining an invasion process, the algorithm identified shifts in population cycling associated with variations in resource availability. The shifts identified for the conservation case study highlight a decline process that generally coincided with changing management practices affecting the availability of hostplant resources. When interpreted in the context of species biology, the Dynamic Shift Detector algorithm can aid management decisions and identify critical time periods related to species’ dynamics. In an era of rapid global change, such tools can provide key insights into the conditions under which population parameters, and their corresponding dynamics, can shift.

Populations naturally fluctuate in abundance, and the rules governing these fluctuations are a result of both internal (density dependent) and external (environmental) processes. For these reasons, pinpointing when changes in populations occur is difficult. In this study, we develop a novel break-point analysis tool for population time series data. Using a density dependent model to describe a population’s underlying dynamic process, our tool iterates through all possible break point combinations (i.e., abrupt changes in parameter values) and applies information-theoretic decision tools (i.e. Akaike's Information Criterion corrected for small sample sizes) to determine best fits. Here, we develop the approach, simulate data under a variety of conditions to demonstrate its utility, and apply the tool to two case studies: an invasion of multicolored Asian ladybeetle and declining monarch butterflies. The Dynamic Shift Detector algorithm identified parameter changes that correspond to known environmental change events in both case studies.

Hidden similarities in the dynamics of a weakly synchronous marine metapopulation

Populations of many marine species are only weakly synchronous, despite coupling through larval dispersal and exposure to synchronous environmental drivers. Although this is often attributed to observation noise, factors including local environmental differences, spatially variable dynamics, and chaos might also reduce or eliminate metapopulation synchrony. To differentiate spatially variable dynamics from similar dynamics driven by spatially variable environments, we applied hierarchical delay embedding. A unique output of this approach, the "dynamic correlation," quantifies similarity in intrinsic dynamics of populations, independently of whether their abundance is correlated through time. We applied these methods to 17 populations of blue crab (Callinectes sapidus) along the US Atlantic coast and found that their intrinsic dynamics were broadly similar despite largely independent fluctuations in abundance. The weight of evidence suggests that the latitudinal gradient in temperature, filtered through a unimodal response curve, is sufficient to decouple crab populations. As unimodal thermal performance is ubiquitous in ectotherms, we suggest that this may be a general explanation for the weak synchrony observed at large distances in many marine species, although additional studies are needed to test this hypothesis.

Long-term cyclic persistence in an experimental predator-prey system

Predator-prey cycles rank among the most fundamental concepts in ecology, are predicted by the simplest ecological models and enable, theoretically, the indefinite persistence of predator and prey^1-4. However, it remains an open question for how long cyclic dynamics can be self-sustained in real communities. Field observations have been restricted to a few cycle periods^5-8 and experimental studies indicate that oscillations may be short-lived without external stabilizing factors^9-19. Here we performed microcosm experiments with a planktonic predator-prey system and repeatedly observed oscillatory time series of unprecedented length that persisted for up to around 50 cycles or approximately 300 predator generations. The dominant type of dynamics was characterized by regular, coherent oscillations with a nearly constant predator-prey phase difference. Despite constant experimental conditions, we also observed shorter episodes of irregular, non-coherent oscillations without any significant phase relationship. However, the predator-prey system showed a strong tendency to return to the dominant dynamical regime with a defined phase relationship. A mathematical model suggests that stochasticity is probably responsible for the reversible shift from coherent to non-coherent oscillations, a notion that was supported by experiments with external forcing by pulsed nutrient supply. Our findings empirically demonstrate the potential for infinite persistence of predator and prey populations in a cyclic dynamic regime that shows resilience in the presence of stochastic events.

Complex life histories discovered in a critically endangered fish

Effective conservation of endangered species requires knowledge of the full range of life-history strategies used to maximize population resilience within a stochastic and ever-changing environment. California’s endemic Delta Smelt (Hypomesus transpacificus) is rapidly approaching extinction in the San Francisco Estuary, placing it in the crossfire between human and environmental uses of limited freshwater resources. Though managed as a semi-anadromous species, recent studies have challenged this lifecycle model for Delta Smelt, suggesting the species is an estuarine resident with several localized “hot-spots” of abundance. Using laser-ablation otolith strontium isotope microchemistry, we discovered three distinct life-history phenotypes including freshwater resident (FWR), brackish-water resident (BWR), and semi-anadromous (SA) fish. We further refined life-history phenotypes using an unsupervised algorithm and hierarchical clustering and found that in the last resilient year-class, the FWR (12%) and BWR (7%) comprised a small portion of the population, while the majority of fish were SA (81%). Furthermore, the semi-anadromous fish could be clustered into at least four additional life-history phenotypes that varied by natal origin, dispersal age and adult salinity history. These diverse life-history strategies should be incorporated into future conservation and management efforts aimed at preventing the extinction of Delta Smelt in the wild.

Holocene vegetation patterns in southern Lithuania indicate astronomical forcing on the millennial and centennial time scales

The Earth’s biota originated and developed to its current complex state through interacting with multilevel physical forcing of our planet’s climate and near and outer space phenomena. In the present study, we focus on the time scale of hundreds to thousands of years in the most recent time interval – the Holocene. Using a pollen paleocommunity dataset from southern Lithuania (Čepkeliai bog) and applying spectral analysis techniques, we tested this record for the presence of statistically significant cyclicities, which can be observed in past solar activity. The time series of non-metric multidimensional scaling (NMDS) scores, which in our case are assumed to reflect temperature variations, and Tsallis entropy-related community compositional diversity estimates q* revealed the presence of cycles on several time scales. The most consistent periodicities are characterized by periods lasting between 201 and 240 years, which is very close to the DeVries solar cycles (208 years). A shorter-term periodicity of 176 years was detected in the NMDS scores that can be putatively linked to the subharmonics of the Gleissberg solar cycle. In addition, periodicities of ≈3,760 and ≈1,880 years were found in both parameters. These periodic patterns could be explained either as originating as a harmonic nonlinear response to precession forcing, or as resulting from the long-term solar activity quasicycles that were reported in previous studies of solar activity proxies.

Intrinsic and extrinsic drivers of transmission dynamics of hemorrhagic fever with renal syndrome caused by Seoul hantavirus

Seoul hantavirus (SEOV) has recently raised concern by causing geographic range expansion of hemorrhagic fever with renal syndrome (HFRS). SEOV infections in humans are significantly underestimated worldwide and epidemic dynamics of SEOV-related HFRS are poorly understood because of a lack of field data and empirically validated models. Here, we use mathematical models to examine both intrinsic and extrinsic drivers of disease transmission from animal (the Norway rat) to humans in a SEOV-endemic area in China. We found that rat eradication schemes and vaccination campaigns, but below the local elimination threshold, could diminish the amplitude of the HFRS epidemic but did not modify its seasonality. Models demonstrate population dynamics of the rodent host were insensitive to climate variations in urban settings, while relative humidity had a negative effect on the seasonality in transmission. Our study contributes to a better understanding of the epidemiology of SEOV-related HFRS, demonstrates asynchronies between rodent population dynamics and transmission rate, and identifies potential drivers of the SEOV seasonality.

Seoul hantavirus (SEOV) infections are common in Europe and Asia where a considerably high seroprevalence among the population is found. However, only relatively few hemorrhagic fever with renal syndrome (HFRS) cases are reported. Comprehensive epidemiological data is necessary to study the patterns and drivers of this underestimated disease. Here, we analyzed rodent host surveillance and seroprevalence data from 1998 to 2015 for disease outbreaks in Huludao City, one of the typical SEOV-endemic areas for HFRS in China. Our mathematical models quantified the drivers on HFRS transmission and estimated the epidemiological parameters. Our study provides an understanding of its ecological process between intrinsic and extrinsic factors, human-rodent interface and disease dynamics.

Long-term dynamics of measles in London: Titrating the impact of wars, the 1918 pandemic, and vaccination

A key question in ecology is the relative impact of internal nonlinear dynamics and external perturbations on the long-term trajectories of natural systems. Measles has been analyzed extensively as a paradigm for consumer-resource dynamics due to the oscillatory nature of the host-pathogen life cycle, the abundance of rich data to test theory, and public health relevance. The dynamics of measles in London, in particular, has acted as a prototypical test bed for such analysis using incidence data from the pre-vaccination era (1944–1967). However, during this timeframe there were few external large-scale perturbations, limiting an assessment of the relative impact of internal and extra demographic perturbations to the host population. Here, we extended the previous London analyses to include nearly a century of data that also contains four major demographic changes: the First and Second World Wars, the 1918 influenza pandemic, and the start of a measles mass vaccination program. By combining mortality and incidence data using particle filtering methods, we show that a simple stochastic epidemic model, with minimal historical specifications, can capture the nearly 100 years of dynamics including changes caused by each of the major perturbations. We show that the majority of dynamic changes are explainable by the internal nonlinear dynamics of the system, tuned by demographic changes. In addition, the 1918 influenza pandemic and World War II acted as extra perturbations to this basic epidemic oscillator. Our analysis underlines that long-term ecological and epidemiological dynamics can follow very simple rules, even in a non-stationary population subject to significant perturbations and major secular changes.

The impact of intrinsic versus external drivers of transmission on long-term dynamics is an open question in complex systems studies. In particular, when and where dynamics become chaotic has crucial implications for control efforts. Here, we extended the well-studied London measles data to include nearly a century of novel data (1897–1991) that also contains five major demographic changes: the First and Second World Wars, the wartime evacuation of London, the 1918 influenza pandemic, and the start of a measles mass vaccination program. We found that a simple stochastic epidemic model, with minimal historical specifications, can capture the nearly 100 years of dynamics including changes caused by each of the major perturbations. We further illustrated that the majority of dynamic changes are explainable by the internal nonlinear dynamics of the system, tuned by demographic changes. Notably however, the 1918 influenza pandemic and evacuation acted as external perturbations to this basic epidemic oscillator. Yet, in the wake of these massive shifts, the overall system remained stable (Lyapunov exponent < 0), underlining how long-term ecological and epidemiological dynamics can follow very simple rules, even in a non-stationary population subject to significant perturbations and major secular changes.