Wavelet analysis of ecological time series

Are population cycles recovering?

The dynamics of populations of small mammals of Central Siberia was analyzed. The studies were carried out at the Yenisei ecological station "Mirnoye" of the A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences. The time series analysis was performed by the wavelet transform using the statistical data processing language R. In the 20th century, the dynamics of the population of the community and some of its constituent species (Sorex araneus; S. caecutiens; S. isodon; S. tundrensis; S. minutus; Craseomys rufocanus; Clethrionomys rutilus; Microtus oeconomus; M. agrestis) were characterized by a 4-year periodicity. The type of dynamics changed to noncyclic by the nineties, but by 2022, four species (S. araneus, S. isodon, C. rutilus, and M. oeconomus) and the community as a whole showed a tendency toward recovery of population cycles. The remaining species were characterized by consistently low numbers with irregular low amplitude fluctuations.

The effect of cave ventilation on carbon and oxygen isotopic fractionation between calcite and drip water

Rapid CO2 degassing and calcite precipitation driven by cave ventilation influence the speleothem δ18O and δ13C. However, the drivers of cave ventilation are not completely understood due to the lack of monitoring of multiple environmental factors. Furthermore, the understanding of isotope fractionation caused by the dissolution of speleothem in undersaturated drip water is limited during the cave air stagnation. In this study, we displayed four years of cave microenvironment monitoring in Shawan Cave, Southwestern China, and analyzed the δ13CDIC and δ18O of drip water, and calcite precipitation δ18O and δ13C. The results show that the ventilation process is attributed to buoyancy airflow between external atmosphere, fissure air, and cave air. This causes that the higher (lower) cave air pCO2 in the summer (winter) is associated with upward airflow mode (downward airflow mode). Furthermore, cave ventilation could control the isotopic fractionation. Specifically, when cave air pCO2 is lower, the carbon isotopic disequilibrium between calcite and dissolved inorganic carbon (DIC) is controlled by the degassing of CO2 associated with calcite precipitation. The disequilibrium fractionation in carbon isotopes is less pronounced at slower drip-rate sites. The oxygen isotope fractionation between calcite and the drip water is found to be close to equilibrium. However, the high cave air pCO2 (exceeding 10,000 ppm) may result in drip water undersaturation to drive the dissolution of speleothem calcite. The δ18O values of drip water are pulled away from their original values to disequilibrate to the calcite because the exchange time of oxygen in the dissolved carbonates with the oxygen in the water is sufficiently long. Hence, the dissolution of speleothems may be a new mechanism to explain the oxygen isotopic disequilibrium between the calcite and drip water during the cave air stagnation. The carbon isotope fractionation between calcite and drip water is close to equilibrium.

Recent global climate change effects: A study of Eleutheronema rhadinum in Chang-Yuen Ridge, Taiwan

This study investigated the potential effects of climatic oscillations on CPUE of Eleutheronema rhadinum (East Asian fourfinger threadfin), a commercially valuable fish species in East Asia. Fishery data from Chang-Yuen Ridge between 2015 and 2022 was analyzed in conjunction with four climatic oscillation indices that were lagged by up to 5 years. The results revealed a fluctuating CPUE associated with the 1-year-lagged Ocean Niño Index (ONI lag 1) and 1-year-lagged Southern Oscillation Index (SOI lag 1) suggesting a potential effect between climatic oscillation indices and East Asian fourfinger threadfin CPUE. These findings can provide insights into the association between East Asian fourfinger threadfin abundance and climatic oscillations in Chang-Yuen Ridge, Taiwan; the insights are valuable for fishery management amidst changing climate conditions.

Taking the beat of the Arctic: are lemming population cycles changing due to winter climate?

Reports of fading vole and lemming population cycles and persisting low populations in some parts of the Arctic have raised concerns about the spread of these fundamental changes to tundra food web dynamics. By compiling 24 unique time series of lemming population fluctuations across the circumpolar region, we show that virtually all populations displayed alternating periods of cyclic/non-cyclic fluctuations over the past four decades. Cyclic patterns were detected 55% of the time (n = 649 years pooled across sites) with a median periodicity of 3.7 years, and non-cyclic periods were not more frequent in recent years. Overall, there was an indication for a negative effect of warm spells occurring during the snow onset period of the preceding year on lemming abundance. However, winter duration or early winter climatic conditions did not differ on average between cyclic and non-cyclic periods. Analysis of the time series shows that there is presently no Arctic-wide collapse of lemming cycles, even though cycles have been sporadic at most sites during the last decades. Although non-stationary dynamics appears a common feature of lemming populations also in the past, continued warming in early winter may decrease the frequency of periodic irruptions with negative consequences for tundra ecosystems.

Impact of climatic oscillations on marlin catch rates of Taiwanese long-line vessels in the Indian Ocean

This study explored the influence of climatic oscillations on the striped, blue, and silver marlin catch rates in the Indian Ocean by using logbook data from Taiwanese large-scale fishing vessels and climate records from 1994 to 2016. Only the Madden-Julian oscillation (MJO) and the subtropical Indian Ocean dipole (SIOD) had immediate effects on the striped and silver marlin catch rates. The positive and negative phases of the IOD at the lags of 7 and 3 years corresponded to increased and decreased catch rates, respectively, for both the striped and blue marlin, contrasting to the reverse pattern for the silver marlin. Similarly, all three marlin species experienced decreased and increased catch rates respectively during the positive and negative phases of the Pacific decadal oscillation. The striped and blue marlin catch rates decreased and increased during the positive and negative phases, respectively, of the SIOD and MJO with various lags. Our results suggest that the impacts of climatic oscillations on fish species are crucial for policymakers and coastal communities for managing marine resources, forecasting changes in marine ecosystems, and developing strategies to adapt to and mitigate the effects of climate variability.

Daily variability of pH and temperature in seawater from a near-pristine oceanic atoll, Southwest Atlantic

The study of pH and temperature variability in reef environments, and the underlying processes that control this variability, is of great importance for ocean acidification research. Therefore, in the reef environment of Rocas Atoll, we conducted continuous monitoring of pH and temperature and periodic sampling of carbonate chemistry, and we hypothesize that seawater temperature is not the determining factor in the daily variability of pH at this atoll. Our results showed that the seawater of the atoll presented a high daily variability in pH, [H+], and temperature. The cycles of variations occurred primarily with a periodicity of ∼24 h, related to the daily light cycle, and secondarily with a periodicity of ∼12 h, associated with the semi-diurnal tidal cycles of the atoll. The results indicate that the relative balance of net organic carbon metabolism is the main process modulating carbonate chemistry on the atoll throughout the day.

A combination of annual and nonannual forces drive respiratory disease in the tropics

INTRODUCTION

It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics is less well understood. In this study, we aimed to characterise the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City, Vietnam.

METHODS

We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodic signals in the system. We evaluated the contribution of these periodic signals to predicting ILI and influenza patterns through lognormal and gamma hurdle models.

RESULTS

During 10 years of community surveillance, 66 799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected, 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI 8.8% to 9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC=183) compared with all annual covariates (ΔAIC=263) in lognormal regression. Near-annual signals were observed for PCR-confirmed influenza but were not consistent over time or across influenza (sub)types. The explanatory power of climate factors for ILI and influenza virus trends was weak.

CONCLUSION

Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers, with influenza dynamics showing near-annual periodicities. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach.

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach

Dengue transmission poses significant challenges for public health authorities worldwide due to its susceptibility to various factors, including environmental and climate variability, affecting its incidence and geographic spread. This study focuses on Costa Rica, a country characterized by diverse microclimates nearby, where dengue has been endemic since its introduction in 1993. Using wavelet coherence and clustering analysis, we performed a time-series analysis to uncover the intricate connections between climate, local environmental factors, and dengue occurrences. The findings indicate that multiannual dengue frequency (3 yr) is correlated with the Oceanic Niño Index and the Tropical North Atlantic Index. This association is particularly prominent in cantons located along the North and South Pacific Coast, as well as in the Central cantons of the country. Furthermore, the time series of these climate indices exhibit a leading phase of approximately nine months ahead of dengue cases. Additionally, the clustering analysis uncovers non-contiguous groups of cantons that exhibit similar correlation patterns, irrespective of their proximity or adjacency. This highlights the significance of climate factors in influencing dengue dynamics across diverse regions, regardless of spatial closeness or distance between them. On the other hand, the annual dengue frequency was correlated with local environmental indices. A persistent correlation between dengue cases and local environmental variables is observed over time in the North Pacific and the Central Region of the country's Northwest, with environmental factors leading by less than three months. These findings contribute to understanding dengue transmission's spatial and temporal dynamics in Costa Rica, highlighting the importance of climate and local environmental factors in dengue surveillance and control efforts.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

Circadian rhythms and diurnal patterns in the feed intake behaviour of growing-finishing pigs

The feeding behaviour of growing-finishing pigs is an important indicator of performance, health and welfare, but this use is limited by its large, poorly-understood variation. We explored the variation in basal feed intake of individual pigs by detecting circadian rhythms, extracting features of diurnal patterns and assessing consistency over time, from day-to-day and across age. Hourly feed intake data of individual pigs (n = 110) was obtained during one growing-finishing phase, using electronic feeding stations. We applied wavelet analysis to assess rhythms and a hurdle generalised additive model to extract features of diurnal patterns. We found that circadian rhythms could be detected during 58 ± 3% (mean ± standard error) of days in the growing-finishing phase (range 0-100%), predominantly at older ages. Although the group diurnal intake pattern was alternans (small morning peak, larger afternoon peak), individual pigs showed a range of diurnal patterns that changed with age, differing mostly in the extent of night fasting and day-to-day consistency. Our results suggest that the type, day-to-day consistency and age development of diurnal patterns in feed intake show general group patterns but also differ between pigs. Using this knowledge, promising features may be selected to compare against production, health and welfare parameters.

The changing relationship between Cholera and interannual climate variables in Kolkata over the past century

BACKGROUND

In the Bengal Delta, research has shown that climate and cholera are linked. One demonstration of this is the relationship between interannual ocean-atmospheric oscillations such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). What remains unclear in the present literature is the nature of this relationship in the specific context of Kolkata, and how this relationship may have changed over time.

RESULTS

In this study, we analyse the changing relationship between ENSO and IOD with cholera in Kolkata over recent (1999-2019) and historical (1897-1941) time intervals. Wavelet coherence analysis revealed significant non-stationary association at 2-4 year and 4-8 year periods between cholera and both interannual timeseries during both time intervals. However, coherence was notably weakened in the recent interval, particularly with regards to ENSO, a result supported by a complementary SARIMA analysis. Similar coherence patterns with temperature indicate it could be an important mediating factor in the relationship between cholera and oscillating climate phenomena in Kolkata.

CONCLUSIONS

This study reveals a shifting relationship between cholera and climate variables (ENSO and IOD) in Kolkata, suggesting a decoupling between environmental influences and cholera transmission in recent years. Our results therefore do not suggest that an intensification of ENSO is likely to significantly influence cholera in the region. We also find that the relationship between cholera and interannual climate variables is distinct to Kolkata, highlighting the spatial heterogeneity of the climate-cholera relationship even within the Bengal Delta.

Assessment of global fish footprint reveals growing challenges for sustainable production and consumption

Globalization faces a tradeoff between meeting fish consumption demand for nutritious & healthy living and reducing the ecological footprint to achieve sustainable development. Here, we document drivers, historical trends, and mitigation options for global fish footprint using unevenly spaced data spanning 1961 to 2021 from over 200 economies while accounting for income classifications. We report a decline in fish production in developed countries, yet, their increased consumption demand per capita is met through overexploited stocks of fish imported from developing economies. Besides, global fish price volatility has no effect on fish distribution in high-income nations but highly influences fish production, consumption, import, and export in developing nations. The evidence of purchasing economies of scale in urbanized countries and the potential threat of embodied price in fish distribution and trade affect global fish footprint. The persistent increase in fish footprint can be attributed to affluence, choice of technology, urbanization, human development, marine trophic levels, emission intensity, and time-invariant & unobserved country-specificities. We highlight that aligning development and choices along the targets of sustainable development goals augments the achievement of sustainable fish production and consumption.

Disrupted dynamic network reconfiguration of the executive and reward networks in internet gaming disorder

BACKGROUND

Studies have shown that people with internet gaming disorder (IGD) exhibit impaired executive control of gaming cravings; however, the neural mechanisms underlying this process remain unknown. In addition, these conclusions were based on the hypothesis that brain networks are temporally static, neglecting dynamic changes in cognitive processes.

METHODS

Resting-state fMRI data were collected from 402 subjects [162 subjects with IGD and 240 recreational game users (RGUs)]. The community structure (recruitment and integration) of the executive control network (ECN) and the basal ganglia network (BGN), which represents the reward network, of patients with IGD and RGUs were compared. Mediation effects among the different networks were analyzed.

RESULTS

Compared to RGUs, subjects with IGD had a lower recruitment coefficient within the right ECN. Further analysis showed that only male subjects had a lower recruitment coefficient. Mediation analysis showed that the integration coefficient of the right ECN mediated the relationship between the recruitment coefficients of both the right ECN and the BGN in RGUs.

CONCLUSIONS

Male subjects with IGD had a lower recruitment coefficient than RGUs, which impairing their impulse control. The mediation results suggest that top-down executive control of the ECN is absent in subjects with IGD. Together, these findings could explain why subjects with IGD exhibit impaired executive control of gaming cravings; these results have important therapeutic implications for developing effective interventions for IGD.

Acoustic accelerometer transmitters and their growing relevance to aquatic science

There has recently been great interest in the use of accelerometers onboard electronic transmitters to characterise various aspects of the ecology of wild animals. We review use cases and outline how these tools can provide opportunities for studying activity and survival, exercise physiology of wild animals, the response to stressors, energy landscapes and conservation planning tools, and the means with which to identify behaviours remotely from transmitted data. Accelerometer transmitters typically send data summaries to receivers at fixed intervals after filtering out static acceleration and calculating root-mean square error or overall dynamic body action of 2- or 3-axis acceleration values (often at 5-12.5 Hz) from dynamic acceleration onboard the tag. Despite the popularity of these transmitters among aquatic ecologists, we note that there is wide variation in the sampling frequencies and windows used among studies that will potentially affect the ability to make comparisons in the future. Accelerometer transmitters will likely become increasingly popular tools for studying finer scale details about cryptic species that are difficult to recapture and hence not suitable for studies using data loggers. We anticipate that there will continue to be opportunities to adopt methods used for analysing data from loggers to datasets generated from acceleration transmitters, to generate new knowledge about the ecology of aquatic animals.

Wavelet coherence phase analysis decodes the universal switching mechanism of Ras GTPase superfamily

The Ras superfamily of GTPases regulate critical cellular processes by shuttling between GTP-bound ON and GDP-bound OFF states. This switching mechanism is attributed to the conformational changes in two loops, SWI and SWII, upon GTP binding and hydrolysis. Since these conformational changes vary across the Ras superfamily, there is no generic parameter to define their functional states. A unique wavelet coherence (WC) analysis-based approach developed here shows that the structural changes in switch regions could be mapped onto the wavelet coherence phase couplings (WPCs). Thus, WPCs could serve as unique parameters to define their functional states. Disentanglement of WPCs in oncogenic GTPases shows how breakdown of structural allostery leads to their aberrant function. These observations stand out even for simulated ensemble of switch region conformers. Overall, for the first time, we show that WPCs could unravel the latent structural deviations in Ras proteins to decode their universal switching mechanism.

Excess mortality associated with the COVID-19 pandemic during the 2020 and 2021 waves in Antananarivo, Madagascar

INTRODUCTION

COVID-19-associated mortality remains difficult to estimate in sub-Saharan Africa because of the lack of comprehensive systems of death registration. Based on death registers referring to the capital city of Madagascar, we sought to estimate the excess mortality during the COVID-19 pandemic and calculate the loss of life expectancy.

METHODS

Death records between 2016 and 2021 were used to estimate weekly excess mortality during the pandemic period. To infer its synchrony with circulation of SARS-CoV-2, a cross-wavelet analysis was performed. Life expectancy loss due to the COVID-19 pandemic was calculated by projecting mortality rates using the Lee and Carter model and extrapolating the prepandemic trends (1990-2019). Differences in life expectancy at birth were disaggregated by cause of death.

RESULTS

Peaks of excess mortality in 2020-21 were associated with waves of COVID-19. Estimates of all-cause excess mortality were 38.5 and 64.9 per 100 000 inhabitants in 2020 and 2021, respectively, with excess mortality reaching ≥50% over 6 weeks. In 2021, we quantified a drop of 0.8 and 1.0 years in the life expectancy for men and women, respectively attributable to increased risks of death beyond the age of 60 years.

CONCLUSION

We observed high excess mortality during the pandemic period, in particular around the peaks of SARS-CoV-2 circulation in Antananarivo. Our study highlights the need to implement death registration systems in low-income countries to document true toll of a pandemic.

Divergent spatiotemporal variability of terrestrial water storage and eight hydroclimatic components over three different scales of the Yangtze River basin

Terrestrial water storage anomaly (TWSA) from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on was first exacted by using the forward modeling (FM) method at three different scales over the Yangtze River basin (YRB): whole basin, three middle sub-basins, and eleven small sub-basins (total 15 basins). The spatiotemporal variability of eight hydroclimatic variables, snow water storage change (SnWS), canopy water storage change (CnWS), surface water storage anomaly (SWSA), soil moisture storage anomaly (SMSA), groundwater storage anomaly (GWSA), precipitation (P), evapotranspiration (ET), and runoff (R), and their contribution to TWSA were comprehensively investigated over the YRB. The results showed that the root mean square error of TWS change after FM improved by 17 %, as validated by in situ P, ET, and R data. The seasonal, inter-annual, and trend revealed that TWSA over the YRB increased during 2003-2018. The seasonal TWSA signal increased from the lower to the upper of YRB, but the trend, sub-seasonal, and inter-annual signals receded from the lower to the upper of YRB. The contribution of CnWS to TWSA was small over the YRB. The contribution of SnWS to TWSA occurs mainly in the upper of YRB. The main contributors to TWSA were SMSA (~36 %), SWSA (~33 %), and GWSA (~30 %). GWSA can be affected by TWSA, but other hydrological elements may have a slight impact on groundwater in the YRB. The primary driver of TWSA over the YRB was P (~46 %), followed by ET and R (both ~27 %). The contribution of SMSA, SWSA, and P to TWSA increased from the upper to the lower of YRB. R was the key driver of TWSA in the lower of YRB. The proposed approaches and results of this study can provide valuable new insights for water resource management in the YRB and can be applied globally.

Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome

Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.

Environmental reverberations of geopolitical risk and economic policy uncertainty resulting from the Russia-Ukraine conflict: A wavelet based approach for sectoral CO2 emissions

After the COVID-19 pandemic, Russia invaded Ukraine in February 2022, and a natural gas crisis between the European Union (EU) and Russia has begun. These events have negatively affected humanity and resulted in economic and environmental consequences. Against this background, this study examines the impact of geopolitical risk (GPR) and economic policy uncertainty (EPU) caused by the Russia-Ukraine conflict, on sectoral carbon dioxide (CO₂) emissions. To this end, the study analyzes data from January 1997 to October 2022 by using wavelet transform coherence (WTC) and time-varying wavelet causality test (TVWCT) approaches. The WTC results show that GPR and EPU reduce CO₂ emissions in the residential, commercial, industrial, and electricity sectors, while GPR increases CO₂ emissions in the transportation sector during the period from January 2019 to October 2022, which includes Russia-Ukraine conflict. The WTC analysis also indicates that the reduction in CO₂ emissions provided by the EPU is higher than that of the GPR for several periods. According to the TVWCT, there are causal impacts of the GPR and the EPU on sectoral CO₂ emissions, but the timing of the causal impacts differs between the raw and decomposed data. The results suggest that the EPU has a larger impact on reducing sectoral CO₂ emissions during the Ukraine-Russia crisis and that production disruptions due to uncertainty have the greatest impact on reducing CO₂ emissions in the electric power and transportation sectors.

A combination of annual and nonannual forces drive respiratory disease in the tropics

Background

It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics remains elusive. In this study, we aimed to characterize the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City (HCMC), Vietnam.

Methods

We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodicities in the system. We evaluated the contribution of these periodicities to predicting ILI and influenza patterns through lognormal and gamma hurdle models.

Findings

During ten years of community surveillance, 66,799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI: 8.8%-9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC = 183) compared to all annual covariates (ΔAIC = 263). Near-annual signals were observed for PCR-confirmed influenza but were not consistent along in time or across influenza (sub)types.

Interpretation

Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach.

A real-time prediction interval correction method with an unscented Kalman filter for settlement monitoring of a power station dam

A prediction interval (PI) method is developed to quantify the model uncertainty of embankment settlement prediction. Traditional PIs are constructed based on specific past period information and remain unchanged; hence, they neglect discrepancies between previous calculations and new monitoring data. In this paper, a real-time prediction interval correction method is proposed. Time-varying PIs are built by continuously incorporating new measurements into model uncertainty calculations. The method consists of trend identification, PI construction, and real-time correction. Primarily, trend identification is carried out by wavelet analysis to eliminate early unstable noise and determine the settlement trend. Then, the Delta method is applied to construct PIs based on the characterized trend, and a comprehensive evaluation index is introduced. The model output and the upper and lower bounds of the PIs are updated by the unscented Kalman filter (UKF). The effect of the UKF is compared with that of the Kalman filter (KF) and extended Kalman filter (EKF). The method was demonstrated in the Qingyuan power station dam. The results show that the time-varying PIs based on trend data are smoother than those based on original data with better evaluation index scores. Also, the PIs are not affected by local anomalies. The proposed PIs are consistent with the actual measurements, and the UKF performs better than the KF and EKF. The approach has the potential to provide more reliable embankment safety assessments.

Cold winters drive consistent and spatially synchronous 8-year population cycles of cabbage stem flea beetle

Population cycles have been observed in mammals as well as insects, but consistent population cycling has rarely been documented in agroecosystems and never for a beetle. We analysed the long-term population patterns of the cabbage stem flea beetle Psylliodes chrysocephala in winter oilseed rape over 50 years. Psylliodes chrysocephala larval density from 3045 winter oilseed rape fields in southern Sweden showed strong 8-year population cycles in regional mean density. Fluctuations in larval density were synchronous over time across five subregional populations. Subregional mean environmental variables explained 90.6% of the synchrony in P. chrysocephala populations at the 7-11 year time-scale. The number of days below -10°C showed strong anti-phase coherence with larval densities in the 7-11 year time-scale, such that more cold days resulted in low larval densities. High levels of the North Atlantic Oscillation weather system are coherent and anti-phase with cold weather in Scania, Sweden. At the field-scale, later crop planting date and more cold winter days were associated with decreased overwintering larval density. Warmer autumn temperatures, resulting in greater larval accumulated degree days early in the season, increased overwintering larval density. Despite variation in environmental conditions and crop management, 8-year cycles persisted for cabbage stem flea beetle throughout the 50 years of data collection. Moran effects, influenced by the North Atlantic Oscillation weather patterns, are the primary drivers of this cycle and synchronicity. Insect pest data collected in commercial agriculture fields is an abundant source of long-term data. We show that an agricultural pest can have the same periodic population cycles observed in perennial and unmanaged ecosystems. This unexpected finding has implications for sustainable pest management in agriculture and shows the value of long-term pest monitoring projects as an additional source of time-series data to untangle the drivers of population cycles.

Phenomics enables measurement of complex responses of developing animals to global environmental drivers

Phenomics offers technological advances for high-dimensional phenotyping, facilitating rapid, high-throughput assessment of physiological performance and has proven invaluable in global research challenges including drug discovery and food security. However, this rapidly growing discipline has remained largely inaccessible to the increasingly urgent challenge of assessing organismal functional sensitivity to global change drivers. Here, we investigate the response of an ecologically important marine invertebrate to multiple environmental drivers using Energy Proxy Traits (EPTs), a new approach for measuring complex phenotypes captured on video as a spectrum of energy levels across different temporal frequencies in fluctuating pixel values. We imaged three developmental stages of the common prawn Palaemon serratus at different salinities and temperatures, and measured EPTs and heart rate, a major proxy of physiological performance in ectotherms present across stages. Significant interactions were detected between temperature, developmental stage and salinity in frequency-specific energy levels. Despite cardiac activity being a significant contributor to the EPT spectra, treatment interactions were different from those observed on EPTs, highlighting additional phenotypic drivers of EPTs. Elevated temperature resulted in a shift of the EPT spectra towards higher frequency signals, indicating a reallocation of resources within the phenome. Using a non-linear dimensionality reduction, we interrogated the responses of EPT spectra in high-dimensional space. We discovered complex developmental-stage specific sensitivities, highlighting both the complexity of phenotypic responses, and the limits of using univariate approaches with pre-selected traits to assess responses to multiple global environmental drivers. EPTs are a high-dimensional, transferrable method of phenotyping, and are therefore highly relevant to addressing the current limitations of traditional methods of phenotyping applied to assessing biological sensitivity to drivers of global change. We predict that EPTs will become an important tool for indiscriminate phenotyping, transferrable between species, developmental stages and experimental designs.

Recent Tianshan warming in relation to large-scale climate teleconnections

On the alpine areas such as Tianshan Mountains, snow and glaciers are widely distributed, which are sensitive to temperature changes. However, due to high altitude and scarcity of observed stations, the temperature changes and their causes in Tianshan are unclear. To address this issue, this study integrated Thiel-Sen trend test, Pearson correlation, and wavelet analysis methods to analyze the driving factors of temperature changes in Tianshan. We draw the following conclusions: (1) In the past 40 years, Tianshan warmed at a rate of 0.30 °C/decade. Seasonally, the temperature increased the most in spring and summer; spatially, the east Tianshan experienced the most warming. (2) Climate change has affected significant warming in the Tianshan. (3) The large-scale climate teleconnections found to be associated with warming in the Tianshan include North Pacific pattern, Atlantic Multidecadal Variability (AMV), North Atlantic Oscillation, and Western Hemisphere Warm Pool (WHWP). During the study period, the temperature changes lagged AMV and WHWP by 1.5 months, North Tropical Atlantic Index and Tropical Northern Atlantic Index by 3 months, and Arctic Oscillation by 4 months. This research contributes to understanding the response of dry mountains to global warming and atmospheric circulation changes.

Spatial-temporal dynamics and time series prediction of HFRS in mainland China: A long-term retrospective study

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China. The current study aims to characterize the spatial-temporal dynamics of HFRS in mainland China during a long-term period (1950-2018). A total of 1 665 431 cases of HFRS were reported with an average annual incidence of 54.22 cases/100 000 individuals during 1950-2018. The joint regression model was used to define the global trend of the HFRS cases with an increasing-decreasing-slightly increasing-decreasing-slightly increasing trend during the 68 years. Then spatial correlation analysis and wavelet cluster analysis were used to identify four types of clusters of HFRS cases located in central and northeastern China. Lastly, the prophet model outperforms auto-regressive integrated moving average model in the HFRS modeling. Our findings will help reduce the knowledge gap on the transmission dynamics and distribution patterns of the HFRS in mainland China and facilitate to take effective preventive and control measures for the high-risk epidemic area.

Exploring links between climatic predictability and the evolution of within- and transgenerational plasticity

In variable environments, phenotypic plasticity can increase fitness by providing tight environment-phenotype matching. However, adaptive plasticity is expected to evolve only when the future selective environment can be predicted based on the prevailing conditions. That is, the juvenile environment should be predictive of the adult environment (within-generation plasticity) or the parental environment should be predictive of the offspring environment (transgenerational plasticity). Moreover, the environmental predictability can also shape transient responses such as stress response in an adaptive direction. Here, we test links between environmental predictability and the evolution of adaptive plasticity by combining time series analyses and a common garden experiment using temperature as a stressor in a temperate butterfly (Melitaea cinxia). Time series analyses revealed that across season fluctuations in temperature over 48 years are overall predictable. However, within the growing season, temperature fluctuations showed high heterogeneity across years with low autocorrelations and the timing of temperature peaks were asynchronous. Most life-history traits showed strong within-generation plasticity for temperature and traits such as body size and growth rate broke the temperature-size rule. Evidence for transgenerational plasticity, however, was weak and detected for only two traits each in an adaptive and non-adaptive direction. We suggest that the low predictability of temperature fluctuations within the growing season likely disfavors the evolution of adaptive transgenerational plasticity but instead favors strong within-generation plasticity.

Exploiting the Cone of Influence for Improving the Performance of Wavelet Transform-Based Models for ERP/EEG Classification

Features extracted from the wavelet transform coefficient matrix are widely used in the design of machine learning models to classify event-related potential (ERP) and electroencephalography (EEG) signals in a wide range of brain activity research and clinical studies. This novel study is aimed at dramatically improving the performance of such wavelet-based classifiers by exploiting information offered by the cone of influence (COI) of the continuous wavelet transform (CWT). The COI is a boundary that is superimposed on the wavelet scalogram to delineate the coefficients that are accurate from those that are inaccurate due to edge effects. The features derived from the inaccurate coefficients are, therefore, unreliable. In this study, it is hypothesized that the classifier performance would improve if unreliable features, which are outside the COI, are zeroed out, and the performance would improve even further if those features are cropped out completely. The entire, zeroed out, and cropped scalograms are referred to as the "same" (S)-scalogram, "zeroed out" (Z)-scalogram, and the "valid" (V)-scalogram, respectively. The strategy to validate the hypotheses is to formulate three classification approaches in which the feature vectors are extracted from the (a) S-scalogram in the standard manner, (b) Z-scalogram, and (c) V-scalogram. A subsampling strategy is developed to generate small-sample ERP ensembles to enable customized classifier design for single subjects, and a strategy is developed to select a subset of channels from multiple ERP channels. The three scalogram approaches are implemented using support vector machines, random forests, k-nearest neighbor, multilayer perceptron neural networks, and deep learning convolution neural networks. In order to validate the performance hypotheses, experiments are designed to classify the multi-channel ERPs of five subjects engaged in distinguishing between synonymous and non-synonymous word pairs. The results confirm that the classifiers using the Z-scalogram features outperform those using the S-scalogram features, and the classifiers using the V-scalogram features outperform those using the Z-scalogram features. Most importantly, the relative improvement of the V-scalogram classifiers over the standard S-scalogram classifiers is dramatic. Additionally, enabling the design of customized classifiers for individual subjects is an important contribution to ERP/EEG-based studies and diagnoses of patient-specific disorders.

El Niño-Southern Oscillation affects the species-level temporal variation in seed and leaf fall in a mixed temperate forest

El Niño-Southern Oscillation (ENSO), the variation between anomalously cold (La Niña) and warm conditions (El Niño), is one of the most prominent large-scale climate patterns with worldwide effects. Elevated seed and leaf fall has been found at the positive phase of ENSO (El Niño) in tropical forests. However, how seed and leaf fall respond to ENSO at species level is understudied, especially in temperate forests. In this study, we monitored seed and leaf fall at the species-level at 150 points across a 25-ha temperate forest in northeastern China over a span of 12 years. Using time series and wavelet analyses, we assessed three hypotheses: 1) temperate tree species' seed and leaf fall are strongly, but differently, correlated with ENSO and, 2) community synchrony in seed and leaf occurred both at seasonal and ENSO scales; finally, 3) local climatic modulated the effects of ENSO on seed and leaf fall. We found that ENSO was significantly correlated with seed and leaf fall of all species, although correlation strength varied across species (r = 0.206-0.658). Specifically, ENSO indices (ENSO12 or ENSO34) accounted for the most variation in seed and leaf fall of Acer pseudo-sieboldianum (40 % and 34 %, respectively) and ranged 4 %-31 % in all other species. Leaf fall was synchronous with ENSO cycles with a period of 2-7 years, but community synchrony of seed fall was only detected at seasonal scales. ENSO influenced seed fall of Fraxinus mandshurica and Tilla amurensis by mediating rainfall and relative humidity, respectively, highlighting the interactive effects of local climate and ENSO. Our findings highlight the potential effects of ENSO on ecosystems outside of tropical regions and improve our ability to predict regeneration dynamics and nutrient cycling of temperate forests under the context of global change.

Coastal upwelling generates cryptic temperature refugia

Understanding the effects of climate-mediated environmental variation on the distribution of organisms is critically important in an era of global change. We used wavelet analysis to quantify the spatiotemporal (co)variation in daily water temperature for predicting the distribution of cryptic refugia across 16 intertidal sites that were characterized as 'no', 'weak' or 'strong' upwelling and spanned 2000 km of the European Atlantic Coast. Sites experiencing weak upwelling exhibited high synchrony in temperature but low levels of co-variability at monthly to weekly timescales, whereas the opposite was true for sites experiencing strong upwelling. This suggests upwelling generates temporal thermal refugia that can promote organismal performance by both supplying colder water that mitigates thermal stress during hot Summer months and ensuring high levels of fine-scale variation in temperature that reduce the duration of thermal extremes. Additionally, pairwise correlograms based on the Pearson-product moment correlation coefficient and wavelet coherence revealed scale dependent trends in temperature fluctuations across space, with a rapid decay in strong upwelling sites at monthly and weekly timescales. This suggests upwelling also generates spatial thermal refugia that can 'rescue' populations from unfavorable conditions at local and regional scales. Overall, this study highlights the importance of identifying cryptic spatiotemporal refugia that emerge from fine-scale environmental variation to map potential patterns of organismal performance in a rapidly changing world.

From January to June: Birth seasonality across two centuries in a rural Polish community

Seasonality of births is a worldwide phenomenon, but the mechanisms behind it remain insufficiently explored. Birth seasonality is likely to be driven by seasonal changes in women's fecundity (i.e. ability to conceive), which is strongly influenced by their energetic status. We tested whether birth seasonality is driven by high workload and/or low access to food using 200 years of birth data, from 1782 until 2004, in an agricultural rural Polish community. First, we analysed the time series of births and within-annual variance in births, a proxy for the extent of seasonality. Secondly, we tested the hypothesis that a high agricultural workload and/or low access to food decreases number of births. We found seasonality of births throughout more than 200 years of observation in an agricultural Polish population, with a dominant birth seasonality in January and February which gradually shifted towards June in the late twentieth century. The observed pattern does not support the hypothesis that birth seasonality resulted from women's energetic status. We discuss the possible reasons why our results do not support the tested hypothesis and some implications for our understanding of the birth seasonality.

Temporal heterogeneity in photosystem II photochemistry in Artemisia ordosica under a fluctuating desert environment

Acclimation strategies in xerophytic plants to stressed environmental conditions vary with temporal scales. Our understanding of environmentally-induced variation in photosystem II (PSII) processes as a function of temporal scales is limited, as most studies have thus far been based on short-term, laboratory-controlled experiments. In a study of PSII processes, we acquired near-continuous, field-based measurements of PSII-energy partitioning in a dominant desert-shrub species, namely Artemisia ordosica, over a six-year period from 2012-2017. Continuous-wavelet transformation (CWT) and wavelet coherence analyses (WTC) were employed to examine the role of environmental variables in controlling the variation in the three main PSII-energy allocation pathways, i.e., photochemical efficiency and regulated and non-regulated thermal dissipation, i.e., Φ _PSII, Φ _NPQ, and Φ _NO, respectively, across a time-frequency domain from hours to years. Convergent cross mapping (CCM) was subsequently used to isolate cause-and-effect interactions in PSII-energy partitioning response. The CWT method revealed that the three PSII-energy allocation pathways all had distinct daily periodicities, oscillating abruptly at intermediate timescales from days to weeks. On a diurnal scale, WTC revealed that all three pathways were influenced by photosynthetically active radiation (PAR), air temperature (T _a), and vapor pressure deficit (VPD). By comparing associated time lags for the three forms of energy partitioning at diurnal scales, revealed that the sensitivity of response was more acutely influenced by PAR, declining thereafter with the other environmental variables, such that the order of influence was greatest for T _a, followed by VPD, and then soil water content (SWC). PSII-energy partitioning on a seasonal scale, in contrast, displayed greater variability among the different environmental variables, e.g., Φ _PSII and Φ _NO being more predisposed to changes in T _a, and Φ _NPQ to changes in VPD. CCM confirmed the causal relationship between pairings of PSII-energy allocation pathways, according to shrub phenology. A. ordosica is shown to have an innate ability to (i) repair damaged PSII-photochemical apparatus (maximum quantum yield of PSII photochemistry, with F _v/F _m > 0.78), and (ii) acclimatize to excessive PAR, dry-air conditions, and prolonged drought. A. ordosica is relatively sensitive to extreme temperature and exhibits photoinhibition.

Ikhnos: A Novel Software to Register and Analyze Bone Surface Modifications Based on Three-Dimensional Documentation

Simple Summary

Here we present a novel open-access 3D software called Ikhnos to register and analyze bone surface modifications in bone assemblages using a sample of wild and captive wolf populations as an example to demonstrate the possibilities offered by this newly developed toolkit. The study of bone surface modifications has been proven crucial in understanding archaeological and paleontological site formation processes, including the identification of the assemblage causal agent. The use of the tools provided by Ikhnos will help interpret the fossil record by allowing the generation of accurate biological and physicochemical referential models as well as the precise documentation of bone surface modifications in archaeological and paleontological assemblages.

Abstract

The study of bone surface modifications (BSM) is crucial in understanding site formation processes and the identification of the causal agent behind bone assemblages in the fossil record. In that line, many efforts have been made to generate referential models based on feeding experiments and human butchery simulations that can then be used to interpret the patterns observed in archaeological and paleontological sites. Considering these needs, we developed a novel open-access three-dimensional (3D) software called Ikhnos for the study of BSM distribution patterns on limb long bones. This software is comprised of all the necessary tools for the 3D documentation of BSM and bone breakage patterns, as well as the subsequent statistical analysis of this data due to the integration of an exclusive R library, the IkhnosToolBox. Additionally, Ikhnos integrates tools for bone survivorship calculations that could facilitate the estimation of the minimum number of elements (MNE) and minimum number of individuals (MNI). As a demonstration of its precision, here we present a case study analyzing the modifications produced by wild and captive wolf (Canis lupus signatus) populations of the Iberian Peninsula on deer carcasses.

Connecting single-cell ATP dynamics to overflow metabolism, cell growth, and the cell cycle in Escherichia coli

Adenosine triphosphate (ATP) is an abundant and essential metabolite that cells consume and regenerate in large amounts to support growth. Although numerous studies have inferred the intracellular concentration of ATP in bacterial cultures, what happens in individual bacterial cells under stable growth conditions is less clear. Here, we use the QUEEN-2m biosensor to quantify ATP dynamics in single Escherichia coli cells in relation to their growth rate, metabolism, cell cycle, and cell lineage. We find that ATP dynamics are more complex than expected from population studies and are associated with growth-rate variability. Under stable nutrient-rich condition, cells can display large fluctuations in ATP level that are partially coordinated with the cell cycle. Abrogation of aerobic acetate fermentation (overflow metabolism) through genetic deletion considerably reduces both the amplitude of ATP level fluctuations and the cell-cycle trend. Similarly, growth in media in which acetate fermentation is lower or absent results in the reduction of ATP level fluctuation and cell-cycle trend. This suggests that overflow metabolism exhibits temporal dynamics, which contributes to fluctuating ATP levels during growth. Remarkably, at the single-cell level, growth rate negatively correlates with the amplitude of ATP fluctuation for each tested condition, linking ATP dynamics to growth-rate heterogeneity in clonal populations. Our work highlights the importance of single-cell analysis in studying metabolism and its implication to phenotypic diversity and cell growth.

Quantifying phenological diversity: a framework based on Hill numbers theory

Background

Despite the great concern triggered by the environmental crisis worldwide, the loss of temporal key functions and processes involved in biodiversity maintenance has received little attention. Species are restricted in their life cycles by environmental variables because of their physiological and behavioral properties; thus, the timing and duration of species' presence and their activities vary greatly between species within a community. Despite the ecological relevance of such variation, there is currently no measure that summarizes the key temporal aspects of biological diversity and allows comparisons of community phenological patterns. Here, we propose a measure that synthesizes variability of phenological patterns using the Hill numbers-based attribute diversity framework.

Methods

We constructed a new phenological diversity measure based on the aforementioned framework through pairwise overlapping distances, which was supplemented with wavelet analysis. The Hill numbers approach was chosen as an adequate way to define a set of diversity values of different order q, a parameter that determines the sensitivity of the diversity measure to abundance. Wavelet transform analysis was used to model continuous variables from incomplete data sets for different phenophases. The new measure, which we call Phenological Hill numbers (PD), considers the decouplings of phenophases through an overlapping area value between pairs of species within the community. PD was first tested through simulations with varying overlap in phenophase magnitude and intensity and varying number of species, and then by using one real data set.

Results

PD maintains the diversity patterns of order q as in any other diversity measure encompassed by the Hill numbers framework. Minimum PD values in the simulated data sets reflect a lack of differentiation in the phenological curves of the community over time; by contrast, the maximum PD values reflected the most diverse simulations in which phenological curves were equally distributed over time. PD values were consistent with the homogeneous distribution of the intensity and concurrence of phenophases over time, both in the simulated and the real data set.

Discussion

PD provides an efficient, readily interpretable and comparable measure that summarizes the variety of phenological patterns observed in ecological communities. PD retains the diversity patterns of order q characteristic of all diversity measures encompassed by the distance-based Hill numbers framework. In addition, wavelet transform analysis proved useful for constructing a continuous phenological curve. This methodological approach to quantify phenological diversity produces simple and intuitive values for the examination of phenological diversity and can be widely applied to any taxon or community's phenological traits.

Aeroscapes and the Sensory Ecology of Olfaction in a Tropical Dry Forest

Aeroscapes—dynamic patterns of air speed and direction—form a critical component of landscape ecology by shaping numerous animal behaviors, including movement, foraging, and social and/or reproductive interactions. Aeroecology is particularly critical for sensory ecology: air is the medium through which many sensory signals and cues propagate, inherently linking sensory perception to variables such as air speed and turbulence. Yet, aeroscapes are seldom explicitly considered in studies of sensory ecology and evolution. A key first step towards this goal is to describe the aeroscapes of habitats. Here, we quantify the variation in air movement in two successional stages (early and late) of a tropical dry forest in Costa Rica. We recorded air speeds every 10 seconds at five different heights simultaneously. Average air speeds and turbulence increased with height above the ground, generally peaked midday, and were higher overall at the early successional forest site. These patterns of lower air speed and turbulence at ground level and overnight have important implications for olfactory foraging niches, as chemotaxis is most reliable when air movement is low and steady. We discuss our results in the context of possible selective pressures and observed variation in the foraging ecology, behaviors, and associated morphologies of resident vertebrates, with a focus on mammals. However, these data also have relevance to researchers studying socioecology, invertebrate biology, plant evolution, community ecology and more. Further investigation into how animals use different forest types, canopy heights and partition activities across different times of day will further inform our understanding of how landscape and sensory ecology are interrelated. Finally, we emphasize the timeliness of monitoring aeroecology as global wind patterns shift with climate change and human disturbance alters forest structure, which may have important downstream consequences for biological conservation.

Timescale analyses of fluctuations in coexisting populations of a native and invasive tree squirrel

Competition from invasive species is an increasing threat to biodiversity. In Southern California, the western gray squirrel (Sciurus griseus, WGS) is facing competition from the fox squirrel (Sciurus niger, FS), an invasive congener.

We used spectral methods to analyze 140 consecutive monthly censuses of WGS and FS within a 11.3 ha section of the California Botanic Garden. Variation in the numbers for both species and their synchrony was distributed across long timescales (>15 months).

After filtering out annual changes, concurrent mean monthly temperatures from nearby Ontario Airport yielded a spectrum with a large semi‐annual peak and significant spectral power at long timescales (>28 months). The cospectrum between WGS numbers and temperature revealed a significant negative correlation at long timescales (>35 months). Cospectra also revealed significant negative correlations with temperature at a six‐month timescale for both WGS and FS.

Simulations from a model of two competing species indicate that the risk of extinction for the weaker competitor increases quickly as environmental noise shifts from short to long timescales.

We analyzed the timescales of fluctuations in detrended mean annual temperatures for the time period 1915–2014 from 1218 locations across the continental USA. In the last two decades, significant shifts from short to long timescales have occurred, from <3 years to 4–6 years.

Our results indicate that (i) population fluctuations in co‐occurring native and invasive tree squirrels are synchronous, occur over long timescales, and may be driven by fluctuations in environmental conditions; (ii) long timescale population fluctuations increase the risk of extinction in competing species, especially for the inferior competitor; and (iii) the timescales of interannual environmental fluctuations may be increasing from recent historical values. These results have broad implications for the impact of climate change on the maintenance of biodiversity.

Long-term ecological research and the COVID-19 anthropause: A window to understanding social-ecological disturbance

The period of disrupted human activity caused by the COVID-19 pandemic, coined the "anthropause," altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long-term social-ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic-related delays in data availability and ecological response lags, we detail three case studies that show how long-term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long-term data in separating disturbance impacts from natural variation and long-term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.

Differences in the temporal scale of reproductive investment across the slow-fast continuum in a passerine

Life-history strategies differ with respect to investment in current versus 'future' reproduction, but when is this future? Under the novel 'temporality in reproductive investment hypothesis', we postulate variation should exist in the time frame over which reproductive costs are paid. Slow-paced individuals should pay reproductive costs over short (e.g. inter-annual) time scales to prevent reproductive costs accumulating, whereas fast-paced individuals should allow costs to accumulate (i.e. senescence). Using Fourier transforms, we quantify adjustments in clutch size with age, across four populations of blue tits (Cyanistes caeruleus). Fast populations had more prevalent and stronger long-term changes in reproductive investment, whereas slower populations had more prevalent short-term adjustments. Inter-annual environmental variation partly accounted for short-, but not long-term changes in reproductive investment. Our study reveals individuals differ in when they pay the cost of reproduction and that failure to partition this variation across different temporal scales and environments could underestimate reproductive trade-offs.

Tradeoffs of managing cod as a sustainable resource in fluctuating environments

Sustainable human exploitation of living marine resources stems from a delicate balance between yield stability and population persistence to achieve socioeconomic and conservation goals. But our imperfect knowledge of how oceanic oscillations regulate temporal variation in an exploited species can obscure the risk of missing management targets. We illustrate how applying a management policy to suppress fluctuations in fishery yield in variable environments (prey density and regional climate) can present unintended outcomes in harvested predators and the sustainability of harvesting. Using Atlantic cod (Gadus morhua, an apex predatory fish) in the Barents Sea as a case study we simulate age-structured population and harvest dynamics through time-varying, density-dependent and density-independent processes with a stochastic, process-based model informed by 27-year monitoring data. In this model, capelin (Mallotus villosus, a pelagic forage fish), a primary prey of cod, fluctuations modulate the strength of density-dependent regulation primarily through cannibalistic pressure on juvenile cod survival; sea temperature fluctuations modulate thermal regulation of cod feeding, growth, maturation, and reproduction. We first explore how capelin and temperature fluctuations filtered through cod intrinsic dynamics modify catch stability and then evaluate how management to suppress short-term variability in catch targets alters overharvest risk. Analyses revealed that suppressing year-to-year catch variability impedes management responses to adjust fishing pressure, which becomes progressively out of sync with variations in cod abundance. This asynchrony becomes amplified in fluctuating environments, magnifying the amplitudes of both fishing pressure and cod abundance and then intensifying the density-dependent regulation of juvenile survival through cannibalism. Although these transient dynamics theoretically give higher average catches, emergent, quasicyclic behaviors of the population would increase long-term yield variability and elevate overharvest risk. Management strategies that overlook the interplay of extrinsic (fishing and environment) and intrinsic (life history and demography) fluctuations thus can inadvertently destabilize fish stocks, thereby jeopardizing the sustainability of harvesting. These policy implications underscore the value of ecosystem approaches to designing management measures to sustainably harvest ecologically connected resources while achieving socioeconomic security.

North to south gradient and local waves of influenza in Chile

Influenza seasonality is caused by complex interactions between environmental factors, viral mutations, population crowding, and human travel. To date, no studies have estimated the seasonality and latitudinal patterns of seasonal influenza in Chile. We obtained influenza-like illness (ILI) surveillance data from 29 Chilean public health networks to evaluate seasonality using wavelet analysis. We assessed the relationship between the start, peak, and latitude of the ILI epidemics using linear and piecewise regression. To estimate the presence of incoming and outgoing traveling waves (timing vs distance) between networks and to assess the association with population size, we used linear and logistic regression. We found a north to south gradient of influenza and traveling waves that were present in the central, densely populated region of Chile. Our findings suggest that larger populations in central Chile drive seasonal influenza epidemics.

Malaria elimination on Hainan Island despite climate change

Background

Rigorous assessment of the effect of malaria control strategies on local malaria dynamics is a complex but vital step in informing future strategies to eliminate malaria. However, the interactions between climate forcing, mass drug administration, mosquito control and their effects on the incidence of malaria remain unclear.

Methods

Here, we analyze the effects of interventions on the transmission dynamics of malaria (Plasmodium vivax and Plasmodium falciparum) on Hainan Island, China, controlling for environmental factors. Mathematical models were fitted to epidemiological data, including confirmed cases and population-wide blood examinations, collected between 1995 and 2010, a period when malaria control interventions were rolled out with positive outcomes.

Results

Prior to the massive scale-up of interventions, malaria incidence shows both interannual variability and seasonality, as well as a strong correlation with climatic patterns linked to the El Nino Southern Oscillation. Based on our mechanistic model, we find that the reduction in malaria is likely due to the large scale rollout of insecticide-treated bed nets, which reduce the infections of P. vivax and P. falciparum malaria by 93.4% and 35.5%, respectively. Mass drug administration has a greater contribution in the control of P. falciparum (54.9%) than P. vivax (5.3%). In a comparison of interventions, indoor residual spraying makes a relatively minor contribution to malaria control (1.3%–9.6%).

Conclusions

Although malaria transmission on Hainan Island has been exacerbated by El Nino Southern Oscillation, control methods have eliminated both P. falciparum and P. vivax malaria from this part of China.

Several malaria control strategies have been implemented on Hainan Island, China, and it is important to determine which of these have been effective to guide future efforts to control malaria. Here, we use mathematical and statistical methods to assess the effectiveness of control methods using data on malaria cases on Hainan, considering the impact of climate change simultaneously, since malaria transmission is affected by the climate. We observe time-related trends in malaria incidence and a strong relationship with climate before the large-scale rollout of malaria control interventions. We find that insecticide-treated bed nets are the most effective strategy in decreasing malaria incidence, while mass drug administration and indoor residual spraying also contribute to malaria control. Our findings provide evidence that a combination of strategies reduces the burden of malaria in affected regions.

Tian et al. use mathematical modelling to estimate the impact of various interventions on malaria incidence on Hainan Island, also taking into account climate change. They find that although malaria transmission has been exacerbated by climate change, insecticide-treated bed nets and other interventions were effective in controlling the disease.

Extensive oceanic mesopelagic habitat use of a migratory continental shark species

The identification of movement and behaviour patterns, as well as inter- and intra-population connectivity is crucial in order to implement effective and functional management and conservation measures for threatened migratory species such as tope (Galeorhinus galeus). Yet, previous studies struggled to elucidate clear and consistent movement and depth usage patterns of adult tope in the Northeast Atlantic, suggesting a high plasticity in the migration and behaviour. We deployed pop-up satellite archival tags on adult tope during their seasonal summer aggregations in the inner German Bight of the south-eastern North Sea and near a presumed mating site in southwest Scotland. Depth distribution and migration pathways were derived from time series data with location processing. Four individuals followed migration trajectories leaving coastal areas and crossed the European shelf slope into oceanic areas of the Northeast Atlantic, remaining fully pelagic for the rest of the deployment duration. These sharks showed far-ranging migration trajectories and undertook regular and frequent diel vertical migrations, reaching daytime depths of over 700 m. Vertical migration patterns closely overlapped with biological mesopelagic habitat structures and closely tracked the diel migration of organisms from deep scattering layers derived from hydroacoustic recordings. It is hypothesized that adult tope regularly utilize oceanic habitats, foraging on mesopelagic layers in an environment generally considered of low prey density.

Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (Rs) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET.

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.