Intermediate Temporal Scales Research Articles

Interannual Response of Reef Islands to Climate-Driven Variations in Water Level and Wave Climate

Coral reef islands are among the most vulnerable landforms to climate change. However, our understanding of their morphodynamics at intermediate (seasonal to interannual) timescales remains poor, limiting our ability to forecast how they will evolve in the future. Here, we applied a semi-automated shoreline detection technique (CoastSat.islands) to 20 years of publicly available satellite imagery to investigate the evolution of a group of reef islands located in the eastern Indian Ocean. At interannual timescales, island changes were characterized by the cyclical re-organization of island shorelines in response to the variability in water levels and wave conditions. Interannual variability in forcing parameters was driven by El Niño Southern Oscillation (ENSO) cycles, causing prolonged changes to water levels and wave conditions that established new equilibrium island morphologies. Our results present a new opportunity to measure intermediate temporal scale changes in island morphology that can complement existing short-term (weekly to seasonal) and long-term (decadal) understanding of reef island evolution.

Autoregressive Modeling of Forest Dynamics

In this work, we employ autoregressive models developed in financial engineering for modeling of forest dynamics. Autoregressive models have some theoretical advantage over currently employed forest modeling approaches such as Markov chains and individual-based models, as autoregressive models are both analytically tractable and operate with continuous state space. We performed a time series statistical analysis of forest biomass and basal areas recorded in Quebec provincial forest inventories from 1970 to 2007. The geometric random walk model adequately describes the yearly average dynamics. For individual patches, we fit an autoregressive process (AR) of order 1 capable to model negative feedback (mean-reversion). Overall, the best fit also turned out to be geometric random walk; however, the normality tests for residuals failed. In contrast, yearly means were adequately described by normal fluctuations, with annual growth on average of 2.3%, but with a standard deviation of order of 40%. We used a Bayesian analysis to account for the uneven number of observations per year. This work demonstrates that autoregressive models represent a valuable tool for the modeling of forest dynamics. In particular, they quantify the stochastic effects of environmental disturbances and develop predictive empirical models on short and intermediate temporal scales.

Drosophila melanogaster grooming possesses syntax with distinct rules at different temporal scales

Mathematical modeling of behavioral sequences yields insight into the rules and mechanisms underlying sequence generation. Grooming in Drosophila melanogaster is characterized by repeated execution of distinct, stereotyped actions in variable order. Experiments demonstrate that, following stimulation by an irritant, grooming progresses gradually from an early phase dominated by anterior cleaning to a later phase with increased walking and posterior cleaning. We also observe that, at an intermediate temporal scale, there is a strong relationship between the amount of time spent performing body-directed grooming actions and leg-directed actions. We then develop a series of data-driven Markov models that isolate and identify the behavioral features governing transitions between individual grooming bouts. We identify action order as the primary driver of probabilistic, but non-random, syntax structure, as has previously been identified. Subsequent models incorporate grooming bout duration, which also contributes significantly to sequence structure. Our results show that, surprisingly, the syntactic rules underlying probabilistic grooming transitions possess action duration-dependent structure, suggesting that sensory input-independent mechanisms guide grooming behavior at short time scales. Finally, the inclusion of a simple rule that modifies grooming transition probabilities over time yields a generative model that recapitulates the key features of observed grooming sequences at several time scales. These discoveries suggest that sensory input guides action selection by modulating internally generated dynamics. Additionally, the discovery of these principles governing grooming in D. melanogaster demonstrates the utility of incorporating temporal information when characterizing the syntax of behavioral sequences.

Abnormal neural hierarchy in processing of verbal information in patients with schizophrenia

Previous research indicates abnormal comprehension of verbal information in patients with schizophrenia. Yet the neural mechanism underlying the breakdown of verbal information processing in schizophrenia is poorly understood. Imaging studies in healthy populations have shown a network of brain areas involved in hierarchical processing of verbal information over time. Here, we identified critical aspects of this hierarchy, examining patients with schizophrenia. Using functional magnetic resonance imaging, we examined various levels of information comprehension elicited by naturally presented verbal stimuli; from a set of randomly shuffled words to an intact story. Specifically, patients with first episode schizophrenia (N = 15), their non-manifesting siblings (N = 14) and healthy controls (N = 15) listened to a narrated story and randomly scrambled versions of it. To quantify the degree of dissimilarity between the groups, we adopted an inter-subject correlation (inter-SC) approach, which estimates differences in synchronization of neural responses within and between groups. The temporal topography found in healthy and siblings groups were consistent with our previous findings – high synchronization in responses from early sensory toward high order perceptual and cognitive areas. In patients with schizophrenia, stimuli with short and intermediate temporal scales evoked a typical pattern of reliable responses, whereas story condition (long temporal scale) revealed robust and widespread disruption of the inter-SCs. In addition, the more similar the neural activity of patients with schizophrenia was to the average response in the healthy group, the less severe the positive symptoms of the patients. Our findings suggest that system-level neural indication of abnormal verbal information processing in schizophrenia reflects disease manifestations.

Shifts in sediment provenance across a hierarchy of bounding surfaces: A sequence-stratigraphic perspective from bulk-sediment geochemistry

Geochemical differentiation of sediment packages framed by surfaces of chronostratigraphic significance represents an effective tool to unravel the stratigraphic architecture of multi-sourced sediment-supply systems on a variety of temporal scales and through a wide spectrum of lithofacies assemblages. Sediment provenance shifts were examined across three-orders of bounding surfaces, arranged in a hierarchy of 105 to 103years sediment packages, from late Quaternary alluvial, deltaic, coastal and shallow-marine strata of the Po Basin, where controlling factors of sedimentary evolution are firmly constrained by strong age control. A total of 150 samples were analyzed for bulk-sediment geochemistry by X-ray fluorescence spectrometry (XRF). Geochemically unique catchment lithologies, such as ultramafic rocks and dolostones, were used as end-member grain assemblages to assess clear geochemical signatures, and chemostratigraphic correlations were generated on the basis of key element abundances and ratios.Sequence boundaries originating in response to tectonic uplift and related to 105 years sediment packages are associated with marked, basin-wide changes in sediment composition that reflect phases of substantial basin rearrangement, with strong modification of the drainage patterns. On an intermediate temporal scale (104years depositional units), large magnitude sea-level fluctuations had a major impact on sediment pathways, producing sharp provenance shifts on a systems tract scale. The Holocene transgressive surface records a substantial change in clastic sediment flux, as the alluvial system turned into a wave-dominated estuary. On the other hand, no notable changes in composition were observed across the maximum flooding surface. Finally, identifiable provenance variations developed on a short-term, facies tract scale (103years sediment packages) and revealed by lower-rank bounding surfaces denote dramatic changes in sediment dispersal patterns in response to simple autogenic processes, such as channel avulsion and delta lobe switching. In the stratigraphic context provided by the ancient record, such provenance shifts could easily be misinterpreted as the sedimentary expression of higher-rank bounding surfaces.This study supports the robustness of a comprehensive geochemical approach to an improved interpretation of the subsurface geological record across a hierarchy of depositional elements. It shows, however, that provenance shifts at chemofacies boundaries alone cannot be used to infer allogenic or autogenic controls on sedimentation. High-resolution facies analysis within stratigraphically constrained time intervals needs to be considered before promoting predictive relationships between provenance shifts and basin evolution.

Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961–1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network’s local density) and degree distribution (a measure of the network’s spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are weak, especially when more stringent conditions are imposed (i.e. when T is very high), except at the monthly scale.

Neural modulations in processing of natural information in patients with schizophrenia and their unaffected siblings

Recent studies in healthy populations have shown a hierarchical network of brain areas to process information over time. Specifically, we revealed that the capacity to accumulate information changes gradually from the early sensory areas toward high-order perceptual and cognitive areas. Previous research in schizophrenia pointed to impairment in comprehension of information. Yet, the neural mechanisms underlying the breakdown of information processing are poorly known. Better understanding of the neural circuits involved in information processing may assist in early identification of predisposition to the disease. Using fMRI, we examined different levels of information comprehension elicited by naturally presented stimuli. Healthy participants, patients with first episode schizophrenia and their undiagnosed siblings listened to a real-life narrated story and scrambled versions of it. To estimate the level of synchronization in response time courses, we calculated inter-subject correlation (inter-SC) across the entire stimuli within each group. The time-scale gradients found in healthy and siblings groups were consistent with our previous findings. Within the schizophrenia group, the reliability patterns obtained for the shortest and intermediate temporal scales were similar to patterns observed in healthy groups. However, the analysis of responses to story condition (long temporal scale) revealed robust and widespread disruption of the inter-SC. In comparison to healthy groups, the response time courses to the story were highly variable within the schizophrenia group, although some significant inter-SCs in the TPJ and precuneus were found. The hierarchical temporal deficit is a fundamental trait that may be a better target for the study of the etiology and pathophysiology of the disease.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Temporal Scale Selection in Time-Causal Scale Space

When designing and developing scale selection mechanisms for generating hypotheses about characteristic scales in signals, it is essential that the selected scale levels reflect the extent of the underlying structures in the signal. This paper presents a theory and in-depth theoretical analysis about the scale selection properties of methods for automatically selecting local temporal scales in time-dependent signals based on local extrema over temporal scales of scale-normalized temporal derivative responses. Specifically, this paper develops a novel theoretical framework for performing such temporal scale selection over a time-causal and time-recursive temporal domain as is necessary when processing continuous video or audio streams in real time or when modelling biological perception. For a recently developed time-causal and time-recursive scale-space concept defined by convolution with a scale-invariant limit kernel, we show that it is possible to transfer a large number of the desirable scale selection properties that hold for the Gaussian scale-space concept over a non-causal temporal domain to this temporal scale-space concept over a truly time-causal domain. Specifically, we show that for this temporal scale-space concept, it is possible to achieve true temporal scale invariance although the temporal scale levels have to be discrete, which is a novel theoretical construction. The analysis starts from a detailed comparison of different temporal scale-space concepts and their relative advantages and disadvantages, leading the focus to a class of recently extended time-causal and time-recursive temporal scale-space concepts based on first-order integrators or equivalently truncated exponential kernels coupled in cascade. Specifically, by the discrete nature of the temporal scale levels in this class of time-causal scale-space concepts, we study two special cases of distributing the intermediate temporal scale levels, by using either a uniform distribution in terms of the variance of the composed temporal scale-space kernel or a logarithmic distribution. In the case of a uniform distribution of the temporal scale levels, we show that scale selection based on local extrema of scale-normalized derivatives over temporal scales makes it possible to estimate the temporal duration of sparse local features defined in terms of temporal extrema of first- or second-order temporal derivative responses. For dense features modelled as a sine wave, the lack of temporal scale invariance does, however, constitute a major limitation for handling dense temporal structures of different temporal duration in a uniform manner. In the case of a logarithmic distribution of the temporal scale levels, specifically taken to the limit of a time-causal limit kernel with an infinitely dense distribution of the temporal scale levels towards zero temporal scale, we show that it is possible to achieve true temporal scale invariance to handle dense features modelled as a sine wave in a uniform manner over different temporal durations of the temporal structures as well to achieve more general temporal scale invariance for any signal over any temporal scaling transformation with a scaling factor that is an integer power of the distribution parameter of the time-causal limit kernel. It is shown how these temporal scale selection properties developed for a pure temporal domain carry over to feature detectors defined over time-causal spatio-temporal and spectro-temporal domains.

Untangling climate signals from autogenic changes in long‐term peatland development

AbstractPeatlands represent important archives of Holocene paleoclimatic information. However, autogenic processes may disconnect peatland hydrological behavior from climate and overwrite climatic signals in peat records. We use a simulation model of peatland development driven by a range of Holocene climate reconstructions to investigate climate signal preservation in peat records. Simulated water‐table depths and peat decomposition profiles exhibit homeostatic recovery from prescribed changes in rainfall, whereas changes in temperature cause lasting alterations to peatland structure and function. Autogenic ecohydrological feedbacks provide both high‐ and low‐pass filters for climatic information, particularly rainfall. Large‐magnitude climatic changes of an intermediate temporal scale (i.e., multidecadal to centennial) are most readily preserved in our simulated peat records. Simulated decomposition signals are offset from the climatic changes that generate them due to a phenomenon known as secondary decomposition. Our study provides the mechanistic foundations for a framework to separate climatic and autogenic signals in peat records.

Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields

We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, based on a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about (i) parameterizing the intermediate temporal scale levels, (ii) analysing the resulting temporal dynamics, (iii) transferring the theory to a discrete implementation, (iv) computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision. We show that by distributing the intermediate temporal scale levels according to a logarithmic distribution, we obtain much faster temporal response properties (shorter temporal delays) compared to a uniform distribution. Specifically, these kernels converge very rapidly to a limit kernel possessing true self-similar scale-invariant properties over temporal scales, thereby allowing for true scale invariance over variations in the temporal scale, although the underlying temporal scale-space representation is based on a discretized temporal scale parameter. We show how scale-normalized temporal derivatives can be defined for these time-causal scale-space kernels and how the composed theory can be used for computing basic types of scale-normalized spatio-temporal derivative expressions in a computationally efficient manner.

Weathering instability and landscape evolution

The argument in this paper is that the fundamental control on landscape evolution in erosional landscapes is weathering. The possibility of and evidence for instability in weathering at four scales is examined. The four scales are concerned with weathering processes, allocation of weathered products, the interrelations of weathering and denudation, and the topographic and isostatic responses to weathering-limited denudation (the regolith, hillslope, landscape unit, and landscape scales, respectively). The stability conditions for each model, and the circumstances under which the models themselves are relevant, are used to identify scale-related domains of stability and instability. At the regolith scale, the interactions among weathering rates, resistance, and moisture are unstable, but there are circumstances—over long timescales and where weathering is well advanced—under which the instability is irrelevant. At the hillslope scale, the system is stable when denudation is transport rather than weathering limited and where no renewal of exposure via regolith stripping occurs. At the level of landscape units, the stability model is based entirely on the mutual reinforcements of weathering and erosion. While this should generally lead to instability, the model would be stable where other, external controls of both weathering and erosion rates are stronger than the weathering–erosion feedbacks. At the broadest landscape scale, the inclusion of isostatic responses destabilizes erosion–topography–uplift relationships. Thus, if the spatial or temporal scale is such that isostatic responses are not relevant, the system may be stable. Essentially, instability is prevalent at local spatial scales at all but the longest timescales. Stability at intermediate spatial scales is contingent on whether weathering–erosion feedbacks are strong or weak, with stability being more likely at shorter and less likely at longer timescales. At the broadest spatial scales, instability is likely; although stability may be present at intermediate temporal scales if weathering–erosion feedbacks are weak. The distinction is important because stability is associated with convergent evolution whereby the effects of initial variations or disturbances are reduced over time as the landscape converges toward a stable equilibrium state. Instability, by contrast, indicates divergent evolution, increasing differentiation over time, and the persistence and growth of disturbance effects and initial variations.