Temporal Variance Research Articles

Invariant antagonistic network structure despite high spatial and temporal turnover of interactions

Recent work has suggested that emergent ecological network structure exhibits very little spatial or temporal variance despite changes in community composition. However, the changes in network interactions associated with turnover in community composition have seldom been assessed. Here we examine whether changes in ecological networks are best detected by standard emergent network metrics or by assessing internal network changes (i.e. interaction and composition turnover). To eliminate possible spatial or phylogenetic effects, that in large‐scale studies may obscure mechanisms structuring networks and interactions, we sampled multiple antagonistic (plant–herbivore) networks for a single diverse plant family (the Restionaceae) in the hyperdiverse Cape Floristic Region. These are the first plant–herbivore networks constructed for this global biodiversity hotspot. We found invariant emergent network structure despite considerable changes in insect and plant composition across communities over time and space. In contrast, there was high interaction turnover between networks. Seasonally, this was driven by turnover in insect species and insect host switching. Spatially, this was driven by simultaneous turnover in plant and insect species, suggesting that many insects are host specific or that both groups exhibit parallel responses to environmental gradients. Spatial interaction turnover was also driven by turnover in plants, showing that many insects can utilise multiple (possibly closely related) hosts and this may create divergent selection gradients that promote insect speciation. Thus we show highly variable interaction fidelity, despite invariant emergent network structure. We suggest that evaluating internal network changes may be more effective at elucidating the processes structuring networks, and many fine‐scale changes may be obscured when only calculating emergent network metrics.

Taylor’s power law and fixed-precision sampling: application to abundance of fish sampled by gillnets in an African lake

Taylor’s power law (TPL) describes the variance of population abundance as a power-law function of the mean abundance for a single or a group of species. Using consistently sampled long-term (1958–2001) multimesh capture data of Lake Kariba in Africa, we showed that TPL robustly described the relationship between the temporal mean and the temporal variance of the captured fish assemblage abundance (regardless of species), separately when abundance was measured by numbers of individuals and by aggregate weight. The strong correlation between the mean of abundance and the variance of abundance was not altered after adding other abiotic or biotic variables into the TPL model. We analytically connected the parameters of TPL when abundance was measured separately by the aggregate weight and by the aggregate number, using a weight–number scaling relationship. We utilized TPL to find the number of samples required for fixed-precision sampling and compared the number of samples when sampling was performed with a single gillnet mesh size and with multiple mesh sizes. These results facilitate optimizing the sampling design to estimate fish assemblage abundance with specified precision, as needed in stock management and conservation.

ON THE NON-KOLMOGOROV NATURE OF FLARE-PRODUCTIVE SOLAR ACTIVE REGIONS

ABSTRACT A magnetic power spectral analysis is performed on 53 solar active regions, observed from 2011 August to 2012 July. Magnetic field data obtained from the Helioseismic and Magnetic Imager, inverted as Active Region Patches, are used to study the evolution of the magnetic power index as each region rotates across the solar disk. Active regions are classified based on the numbers and sizes of solar flares they produce in order to study the relationship between flare productivity and the magnetic power index. The choice of window size and inertial range plays a key role in determining the correct magnetic power index. The overall distribution of magnetic power indices has a range of 1.0–2.5. Flare-quiet regions peak at a value of 1.6. However, flare-productive regions peak at a value of 2.2. Overall, the histogram of the distribution of power indices of flare-productive active regions is well separated from flare-quiet active regions. Only 12% of flare-quiet regions exhibit an index greater than 2, whereas 90% of flare-productive regions exhibit an index greater than 2. Flare-quiet regions exhibit a high temporal variance (i.e., the index fluctuates between high and low values), whereas flare-productive regions maintain an index greater than 2 for several days. This shows the importance of including the temporal evolution of active regions in flare prediction studies, and highlights the potential of a 2–3 day prediction window for space weather applications.

Land Cover Classification in Complex and Fragmented Agricultural Landscapes of the Ethiopian Highlands

Ethiopia is a largely agrarian country with nearly 85% of its employment coming from agriculture. Nevertheless, it is not known how much land is under cultivation. Mapping land cover at finer resolution and global scales has been particularly difficult in Ethiopia. The study area falls in a region of high mapping complexity with environmental challenges which require higher quality maps. Here, remote sensing is used to classify a large area of the central and northwestern highlands into eight broad land cover classes that comprise agriculture, grassland, woodland/shrub, forest, bare ground, urban/impervious surfaces, water, and seasonal water/marsh areas. We use data from Landsat spectral bands from 2000 to 2011, the Normalized Difference Vegetation Index (NDVI) and its temporal mean and variance, together with a digital elevation model, all at 30-m spatial resolution, as inputs to a supervised classifier. A Support Vector Machines algorithm (SVM) was chosen to deal with the size, variability and non-parametric nature of these data stacks. In post-processing, an image segmentation algorithm with a minimum mapping unit of about 0.5 hectares was used to convert per pixel classification results into an object based final map. Although the reliability of the map is modest, its overall accuracy is 55%—encouraging results for the accuracy of agricultural uses at 85% suggest that these methods do offer great utility. Confusion among grassland, woodland and barren categories reflects the difficulty of classifying savannah landscapes, especially in east central Africa with monsoonal-driven rainfall patterns where the ground is obstructed by clouds for significant periods of time. Our analysis also points out the need for high quality reference data. Further, topographic analysis of the agriculture class suggests there is a significant amount of sloping land under cultivation. These results are important for future research and environmental monitoring in agricultural land use, soil erosion, and crop modeling of the Abay basin.

Uncertainty analysis focusing on the variance of energy intensity of vehicle materials

This paper serves as both a literature review and a preliminary study of the quantitative effects of temporality relating to the age of data as well as spatiality in Life Cycle Assessment. Temporality and spatiality due to age of data have not been the subject of formal analysis. In this study, temporality and spatiality is studied in relation to the input stages of manufacture as opposed to emissions-related studies. To this end, the Greenhouse gases, Regulatory Emissions, and Energy use in Transportation 2 model is used. Created by the Argonne National Laboratory, this model offers information on the life cycle of sedans, sport-utility vehicles and pick-up trucks with a variety of different power train configurations and manufacturing of materials. Historic data on net energy intensity for materials of manufacture were analyzed using Monte Carlo simulations in order to explore temporal and spatial variability which can be attributed to age of data. Subsequent analysis showed that, depending on the mix of materials used, temporal and spatial variation has quantified effects on total energy calculations for all vehicle types. However, this variation is dependent on the type of material in question and its corresponding weight. A set of variability analysis tools previously unused in Life Cycle Assessments was used to establish a platform for future analysis in this area, including previous precepts regarding the use of predefined probability distributions (normal, lognormal, etc.) in modeling. The temporal and spatial variance due to the age of data found here have significance in the inventory stage and for the robustness of Life Cycle Assessment findings. In doing so, this study brought attention to a subject which could have consequences across a wide variety of industrial manufacturing activities.

Sub-optimality in motor planning is retained throughout 9 days practice of 2250 trials.

Optimality in motor planning, as well as accuracy in motor execution, is required to maximize expected gain under risk. In this study, we tested whether humans are able to update their motor planning. Participants performed a coincident timing task with an asymmetric gain function, in which optimal response timing to gain the highest total score depends on response variability. Their behaviours were then compared using a Bayesian optimal decision model. After 9 days of practicing 2250 trials, the total score increased, and temporal variance decreased. On the other hand, the participants showed consistent risk-seeking or risk-averse behaviour, preserving suboptimal motor planning. These results suggest that a human’s computational ability to calculate an optimal motor plan is limited, and it is difficult to improve it through repeated practice with a score feedback.

Genetic variation assessed with microsatellites in mass selection lines of the Pacific oyster (Crassostrea gigas) in China

Four successive mass selection lines of the Pacific oyster, Crassostrea gigas, selected for faster growth in breeding programs in China were examined at ten polymorphic microsatellite loci to assess the level of allelic diversity and estimate the effective population size. These data were compared with those of their base population. The results showed that the genetic variation of the four generations were maintained at high levels with an average allelic richness of 18.8–20.6, and a mean expected heterozygosity of 0.902–0.921. They were not reduced compared with those of their base population. Estimated effective population sizes based on temporal variances in microsatellite frequencies were smaller to that of sex ratio-corrected broodstock count estimates. Using a relatively large number of broodstock and keeping an equal sex ratio in the broodstock each generation may have contributed to retaining the original genetic diversity and maintaining relatively large effective population size. The results obtained in this study showed that the genetic variation was not affected greatly by mass selection progress and high genetic variation still existed in the mass selection lines, suggesting that there is still potential for increasing the gains in future generations of C. gigas. The present study provided important information for future genetic improvement by selective breeding, and for the design of suitable management guidelines for genetic breeding of C. gigas.

A Consistent AVHRR Visible Calibration Record Based on Multiple Methods Applicable for the NOAA Degrading Orbits. Part I: Methodology

AbstractThe 35-yr NOAA Advanced Very High Resolution Radiometer (AVHRR) observation record offers an excellent opportunity to study decadal climate variability, provided that all participating AVHRR instruments are calibrated on a consistent radiometric scale. Because of the lack of onboard calibration systems, the solar imaging channels of the AVHRR must be vicariously calibrated using invariant Earth targets as a calibrated reference source. The greatest challenge in calibrating the AVHRR dataset is the orbit degradation of the NOAA satellites, which eventually drift into a terminator orbit several years after launch. Therefore, the invariant targets must be characterized over the full range of solar zenith angles (SZAs) sampled by the satellite instrument.This study outlines a multiple invariant Earth target calibration approach specifically designed to account for the degrading NOAA orbits. The desert, polar ice, and deep convective cloud (DCC) invariant targets are characterized over all observed SZAs using NOAA-16 AVHRR measurements, which are referenced to the Aqua MODIS Collection 6 calibration via direct transfer of the MODIS calibration to the NOAA-16 AVHRR instrument using simultaneous nadir overpass (SNO) observations over the North Pole. The multiple invariant target calibrations are combined using the inverse of their temporal variance to optimize the resulting calibration stability. The NOAA-18 AVHRR gains derived using the desert, polar ice, and DCC targets, as well as from SNO, were found consistent within 1%, thereby validating that the Aqua MODIS calibration is effectively transferred to the reference calibration targets. The companion paper, Part II, applies the methodology across the AVHRR record to derive the sensor-specific calibration coefficients.

A Novel Dynamic Time Wrapping Similarity Algorithm Optimized by Multi-Granularity

Dynamic time warping algorithm (DTW) is a method of measuring the similarity oftime series. Concerning the problem that DTW cannot keep high classification accuracywhen the computation speed improved, a FG-DTW method based on the idea of naivegranular computing is proposed. In this method, firstly, better temporal granularity isacquired by calculating temporal variance feature and it is used to replace original timeseries; Secondly, the elastic size of under comparing time series granularity allowdynamic adjustment through DTW algorithm and optimal time series correspondinggranularity is obtained; Finally, DTW distance is calculated by optimal correspondinggranularity model. At the same time, the early termination strategy of infimum function isintroduced to improve the efficiency of FG-DTW algorithm. Experiments show that theproposed algorithm improves the running rate and accuracy effectively.

Assessing operative natural and anthropogenic forcing factors from long-term climate time series of Uttarakhand (India) in the backdrop of recurring extreme rainfall events over northwest Himalaya

The entire Indo-Himalayan region from northwest (Kashmir) to northeast (Assam) is facing prevalence of floods and landslides in recent years causing massive loss of property, human and animal lives, infrastructure, and eventually threatening tourist activities substantially. Extremely intense rainfall event of 2013C.E. (between 15 and 17 June) kicked off mammoth flash floods in the Kedarnath area of Uttarakhand state, resulting in huge socioeconomic losses to the state and country. Uttarakhand is an important hilly region attracting thousands of tourists every year owing to numerous shrines and forested mountainous tourist spots. Though recent studies indicate a plausible weakening of Indian summer monsoon rainfall overall, recurrent anomalous high rainfall events over northwest Himalaya (e.g. -2010, 2013, and 2016) point out the need for a thorough reassessment of long-term time series data of regional rainfall and ambient temperatures in order to trace signatures of a shifting pattern in regional meteorology, if any. Accordingly, here we investigate ~100-year-long monthly rainfall and air temperature time series data for a selected grid (28.5°N, 31.25°N; 78.75°E, 81.25°E) covering most parts of Uttarakhand state. We also examined temporal variance in interrelationships among regional meteorological data (temperature and precipitation) and key global climate variability indices using advance statistical methods. Major findings are (i) significant increase in pre-monsoon air temperature over Uttarakhand after 1997, (ii) increasing upward trend in June–July rainfall and its relationship with regional May temperatures (iii) monsoonal rainfall (June, July, August, and September; JJAS) showing covariance with interannual variability in Eurasian snow cover (ESC) extent during the month of March, and (iv) enhancing tendency of anomalous high rainfall events during negative phases of Arctic Oscillation. Obtained results indicate that under warming scenario, JJ rainfall (over AS) may further increase with occasional extreme rainfall spells when AO index (March) is negative.

Using satellite-based measurements to explore spatiotemporal scales and variability of drivers of new particle formation.

New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently spatially averaged) measurements of atmospheric conditions to diagnose the occurrence of NPF and NPF characteristics. We demonstrate the potential for using satellite-based measurements of insolation (UV), trace gas concentrations (sulfur dioxide (SO2), nitrogen dioxide (NO2), ammonia (NH3), formaldehyde (HCHO), and ozone (O3)), aerosol optical properties (aerosol optical depth (AOD) and Ångström exponent (AE)), and a proxy of biogenic volatile organic compound emissions (leaf area index (LAI) and temperature (T)) as predictors for NPF characteristics: formation rates, growth rates, survival probabilities, and ultrafine particle (UFP) concentrations at five locations across North America. NPF at all sites is most frequent in spring, exhibits a one-day autocorrelation, and is associated with low condensational sink (AOD × AE) and HCHO concentrations, and high UV. However, there are important site-to-site variations in NPF frequency and characteristics, and in which of the predictor variables (particularly gas concentrations) significantly contribute to the explanatory power of regression models built to predict those characteristics. This finding may provide a partial explanation for the reported spatial variability in skill of simple generalized nucleation schemes in reproducing observed NPF. In contrast to more simple proxies developed in prior studies (e.g., based on AOD, AE, SO2, and UV), use of additional predictors (NO2, NH3, HCHO, LAI, T, and O3) increases the explained temporal variance of UFP concentrations at all sites.

Repertoire of mesoscopic cortical activity is not reduced during anesthesia

Consciousness has been linked to the repertoire of brain states at various spatiotemporal scales. Anesthesia is thought to modify consciousness by altering information integration in cortical and thalamocortical circuits. At a mesoscopic scale, neuronal populations in the cortex form synchronized ensembles whose characteristics are presumably state-dependent but this has not been rigorously tested. In this study, spontaneous neuronal activity was recorded with 64-contact microelectrode arrays in primary visual cortex of chronically instrumented, unrestrained rats under stepwise decreasing levels of desflurane anesthesia (8%, 6%, 4%, and 2% inhaled concentrations) and wakefulness (0% concentration). Negative phases of the local field potentials formed compact, spatially contiguous activity patterns (CAPs) that were not due to chance. The number of CAPs was 120% higher in wakefulness and deep anesthesia associated with burst-suppression than at intermediate levels of consciousness. The frequency distribution of CAP sizes followed a power–law with slope −1.5 in relatively deep anesthesia (8–6%) but deviated from that at the lighter levels. Temporal variance and entropy of CAP sizes were lowest in wakefulness (76% and 24% lower at 0% than at 8% desflurane, respectively) but changed little during recovery of consciousness. CAPs categorized by K-means clustering were conserved at all anesthesia levels and wakefulness, although their proportion changed in a state-dependent manner. These observations yield new knowledge about the dynamic landscape of ongoing population activity in sensory cortex at graded levels of anesthesia. The repertoire of population activity and self-organized criticality at the mesoscopic scale do not appear to contribute to anesthetic suppression of consciousness, which may instead depend on large-scale effects, more subtle dynamic properties, or changes outside of primary sensory cortex.

A promising new mechanism of ionizing radiation detection for positron emission tomography: modulation of optical properties

Using conventional scintillation detection, the fundamental limit in positron emission tomography (PET) time resolution is strongly dependent on the inherent temporal variances generated during the scintillation process, yielding an intrinsic physical limit for the coincidence time resolution of around 100 ps. On the other hand, modulation mechanisms of the optical properties of a material exploited in the optical telecommunications industry can be orders of magnitude faster. In this paper we borrow from the concept of optics pump-probe measurement to for the first time study whether ionizing radiation can produce modulations of optical properties, which can be utilized as a novel method for radiation detection. We show that a refractive index modulation of approximately is induced by interactions in a cadmium telluride (CdTe) crystal from a 511 keV photon source. Furthermore, using additional radionuclide sources, we show that the amplitude of the optical modulation signal varies linearly with both the detected event rate and average photon energy of the radiation source.

Developmental Control and Plasticity of Fruit and Seed Dimorphism in Aethionema arabicum.

Understanding how plants cope with changing habitats is a timely and important topic in plant research. Phenotypic plasticity describes the capability of a genotype to produce different phenotypes when exposed to different environmental conditions. In contrast, the constant production of a set of distinct phenotypes by one genotype mediates bet hedging, a strategy that reduces the temporal variance in fitness at the expense of a lowered arithmetic mean fitness. Both phenomena are thought to represent important adaptation strategies to unstable environments. However, little is known about the underlying mechanisms of these phenomena, partly due to the lack of suitable model systems. We used phylogenetic and comparative analyses of fruit and seed anatomy, biomechanics, physiology, and environmental responses to study fruit and seed heteromorphism, a typical morphological basis of a bet-hedging strategy of plants, in the annual Brassicaceae species Aethionema arabicum Our results indicate that heteromorphism evolved twice within the Aethionemeae, including once for the monophyletic annual Aethionema clade. The dimorphism of Ae. arabicum is associated with several anatomic, biomechanical, gene expression, and physiological differences between the fruit and seed morphs. However, fruit ratios and numbers change in response to different environmental conditions. Therefore, the life-history strategy of Ae. arabicum appears to be a blend of bet hedging and plasticity. Together with the available genomic resources, our results pave the way to use this species in future studies intended to unravel the molecular control of heteromorphism and plasticity.

Coarse climate change projections for species living in a fine-scaled world.

Accurately predicting biological impacts of climate change is necessary to guide policy. However, the resolution of climate data could be affecting the accuracy of climate change impact assessments. Here, we review the spatial and temporal resolution of climate data used in impact assessments and demonstrate that these resolutions are often too coarse relative to biologically relevant scales. We then develop a framework that partitions climate into three important components: trend, variance, and autocorrelation. We apply this framework to map different global climate regimes and identify where coarse climate data is most and least likely to reduce the accuracy of impact assessments. We show that impact assessments for many large mammals and birds use climate data with a spatial resolution similar to the biologically relevant area encompassing population dynamics. Conversely, impact assessments for many small mammals, herpetofauna, and plants use climate data with a spatial resolution that is orders of magnitude larger than the area encompassing population dynamics. Most impact assessments also use climate data with a coarse temporal resolution. We suggest that climate data with a coarse spatial resolution is likely to reduce the accuracy of impact assessments the most in climates with high spatial trend and variance (e.g., much of western North and South America) and the least in climates with low spatial trend and variance (e.g., the Great Plains of the USA). Climate data with a coarse temporal resolution is likely to reduce the accuracy of impact assessments the most in the northern half of the northern hemisphere where temporal climatic variance is high. Our framework provides one way to identify where improving the resolution of climate data will have the largest impact on the accuracy of biological predictions under climate change.

Air pressure effects on sea level changes during the twentieth century

AbstractInterpretation of tide gauge data in terms sea level (η) and ocean dynamics requires estimates of air pressure (pa) to determine the ocean's isostatic response—the inverted barometer effect ( ). Three gridded pa products (HadSLP2, NOAA‐20CRv2, and ERA‐20C) are used alongside meteorological station pa and tide gauge η records to evaluate the contribution of to η changes over the twentieth century. Agreement between gridded products is better during more recent periods and over regions with good historical data coverage, whereas it is worse for earlier time periods or in ocean areas with poor observational data coverage. Comparison against station data reveals the presence of systematic errors in the gridded products, for example, such that uncertainties estimated through differencing the gridded products underestimate the true errors by roughly 40% on interannual and decadal time scales. Notwithstanding such correlated errors, gridded products are still useful for interpretation of tide gauge data. Removing gridded estimates of from η records reduces spatial variance in centennial trends across tide gauges by 10–30%, formal errors in centennial trends from individual gauges by ∼5%, and the temporal variance in detrended records by 10–15% on average (depending on choice of gridded product). Results here advocate for making the correction to tide gauge records in studies of ocean circulation and global η over long, multidecadal, and centennial time scales using an ensemble mean taken across several gridded products.

Detection of long term trends in underwater noise levels

The risk of adverse impact to marine ecosystems from underwater noise pollution is increasingly recognised by scientists, policymakers, and wider society. Deep water measurements from the Northeast Pacific indicate that ocean noise has increased substantially over recent decades. Policymakers are now considering establishing noise monitoring programs to determine noise levels and trends in their waters. However, the ability of noise monitoring to detect statistically significant trends is a function of the temporal extent, variance, and autocorrelation of the time series. This has implications for the feasibility of evaluating quantitative policy targets within prescribed time frames and hence should inform the formulation of such targets. The present work demonstrates that methods developed in other environmental science disciplines (e.g., atmospheric temperature measurement) to design long-term monitoring networks and assess their statistical power can be applied to noise monitoring programmes. Example datasets are used to show the application of these methods both to assess the significance of long-term trends in ambient noise, and the required monitoring period to detect a given magnitude of trend (e.g., 3 dB per decade). The implications for the design of noise monitoring networks and target setting for policy purposes are then discussed.

Compensatory dynamics stabilize aggregate community properties in response to multiple types of perturbations.

Compensatory dynamics are an important suite of mechanisms that can stabilize community and ecosystem attributes in systems subject to environmental fluctuations. However, few experimental investigations of compensatory dynamics have addressed these mechanisms in systems of real-world complexity, and existing evidence relies heavily on correlative analyses, retrospective examination, and experiments in simple systems. We investigated the potential for compensatory dynamics to stabilize plankton communities in plankton mesocosm systems of real-world complexity. We employed four types of perturbations including two types of nutrient pulses, shading, and acidification. To quantify how communities responded to these perturbations, we used a measure of community-wide synchrony combined with spectral analysis that allowed us to assess timescale-specific community dynamics, for example, whether dynamics were synchronous at some timescales but compensatory at others. The 150-d experiment produced 32-point time series of all zooplankton taxa in the mesocosms. We then used those time series to evaluate total zooplankton biomass as an aggregate property and to evaluate community dynamics. For three of our four perturbation types, total zooplankton biomass was significantly less variable in systems with environmental variation than in constant environments. For the same three perturbation types, community-wide synchrony was much lower in fluctuating environments than in the constant environment, particularly at longer timescales (periods ≈ 60d). Additionally, there were strong negative correlations between population temporal variances and the level of community-wide synchrony. Taken together, these results strongly imply that compensatory interactions between species stabilized total biomass in response to perturbations. Diversity did not differ significantly across either treatments or perturbation types, thus ruling out several classes of mechanisms driven by changes in diversity. We also used several pieces of secondary evidence to evaluate the particular mechanism behind compensatory responses since a wide variety of mechanisms are hypothesized to produce compensatory dynamics. We concluded that fluctuation dependent endogenous cycles that occur as a consequence of consumer-resource interactions in competitive communities were the most likely explanation for the compensatory dynamics observed in our experiment. As with our previous work, scale-dependent dynamics were also a key to understanding compensatory dynamics in these experimental communities.

The chronnectome: Evaluating replicability of dynamic connectivity patterns in 7500 resting fMRI datasets.

Functional fMRI data are typically analyzed under the assumption that participants experience one long, continuous connectivity state throughout rest scan sessions. The chronnectome is a model that takes into account the temporal variance in connectivity throughout a scan session. In this work, we evaluate the repeatability of properties of functional network connectivity (FNC) dynamics assessed using sliding-windowed correlations in 28 independent age-matched large samples of 250 subjects. This approach revealed that multiple discrete, reoccurring connectivity states arise during rest, and that subjects tend to remain in one connectivity state for long periods of time before transitioning to another. Occurrence time spent in certain states tends to increase as participants spend more time resting, while less time is spent in other states as time goes on. Overall, results show distinct connectivity states that are similar across groups during rest.

Simultaneous radio and X-ray observations of PSR B0611+22

We report results from simultaneous radio and X-ray observations of PSR B0611+22 which is known to exhibit bursting in its single-pulse emission. The pulse phase of the bursts vary with radio frequency. The bursts are correlated in 327/150~MHz datasets while they are anti-correlated, with bursts at one frequency associated with normal emission at the other, in 820/150~MHz datasets. Also, the flux density of this pulsar is lower than expected at 327~MHz assuming a power law. We attribute this unusual behaviour to the pulsar itself rather than absorption by external astrophysical sources. Using this dataset over an extensive frequency range, we show that the bursting phenomenon in this pulsar exhibits temporal variance over a span of few hours. We also show that the bursting is quasi-periodic over the observed band. The anti-correlation in the phase offset of the burst mode at different frequencies suggests that the mechanisms responsible for phase offset and flux enhancement have different dependencies on the frequency. We did not detect the pulsar with XMM-Newton and place a 99$\%$ confidence upper limit on the X-ray efficiency of $ 10^{-5}$.