Distinct Spatiotemporal Scales Research Articles

Exploring the interplay between small and large scales movements in a neotropical small mammal

We record and analyze the movement patterns of the marsupial Didelphis aurita at different temporal scales. Animals trajectories are collected at a daily scale by using spool-and-line techniques and, with the help of radio-tracking devices, animals traveled distances are estimated at intervals of weeks. Small-scale movements are well described by truncated Lévy flight, while large-scale movements produce a distribution of distances which is compatible with a Brownian motion. A model of the movement behavior of these animals, based on a truncated Lévy flight calibrated on the small scale data, converges towards a Brownian behavior after a short time interval of the order of 1 week. These results show that whether Lévy flight or Brownian motion behaviors apply, will depend on the scale of aggregation of the animals paths. In this specific case, as the effect of the rude truncation present in the daily data generates a fast convergence towards Brownian behaviors, Lévy flights become of scarce interest for describing the local dispersion properties of these animals, which result well approximated by a normal diffusion process and not a fast, anomalous one. Interestingly, we are able to describe two movement phases as the consequence of a statistical effect generated by aggregation, without the necessity of introducing ecological constraints or mechanisms operating at different spatio-temporal scales. This result is of general interest, as it can be a key element for describing movement phenomenology at distinct spatio-temporal scales across different taxa and in a variety of systems.

Integrating pH into the metabolic theory of ecology to predict bacterial diversity in soil.

Microorganisms play essential roles in soil ecosystem functioning and maintenance, but methods are currently lacking for quantitative assessments of the mechanisms underlying microbial diversity patterns observed across disparate systems and scales. Here we established a quantitative model to incorporate pH into metabolic theory to capture and explain some of the unexplained variation in the relationship between temperature and soil bacterial diversity. We then tested and validated our newly developed models across multiple scales of ecological organization. At the species level, we modeled the diversification rate of the model bacterium Pseudomonas fluorescens evolving under laboratory media gradients varying in temperature and pH. At the community level, we modeled patterns of bacterial communities in paddy soils across a continental scale, which included natural gradients of pH and temperature. Last, we further extended our model at a global scale by integrating a meta-analysis comprising 870 soils collected worldwide from a wide range of ecosystems. Our results were robust in consistently predicting the distributional patterns of bacterial diversity across soil temperature and pH gradients-with model variation explaining from 7 to 66% of the variation in bacterial diversity, depending on the scale and system complexity. Together, our study represents a nexus point for the integration of soil bacterial diversity and quantitative models with the potential to be used at distinct spatiotemporal scales. By mechanistically representing pH into metabolic theory, our study enhances our capacity to explain and predict the patterns of bacterial diversity and functioning under current or future climate change scenarios.

A theoretical understanding of mammalian preimplantation development.

The blastocyst has long been a hallmark system of study in developmental biology due to its importance in mammalian development and clinical relevance for assisted reproductive technologies. In recent years, the blastocyst is emerging as a system of study for mathematical modelling. In this review, we compile, to our knowledge, all models describing preimplantation development. Coupled with experiments, these models have provided insight regarding the morphogenesis and cell-fate specification throughout preimplantation development. In the case of cell-fate specification, theoretical models have provided mechanisms explaining how proportion of cell types are established and maintained when confronted to perturbations. For cell-shape based models, they have described quantitatively how mechanical forces sculpt the blastocyst and even predicted how morphogenesis could be manipulated. As theoretical biology develops, we believe the next critical stage in modelling involves an integration of cell fate and mechanics to provide integrative models of development at distinct spatiotemporal scales. We discuss how, building on a balanced base of mechanical and chemical models, the preimplantation embryo will play a key role in integrating these two faces of the same coin.

Multimodal Integration Across Spatiotemporal Scales to Guide Invertebrate Locomotion.

Locomotion is a hallmark of organisms which has enabled adaptive radiation to an extraordinarily diverse class of ecological niches, and allows animals to move across vast distances. Sampling from multiple sensory modalities enables animals to acquire rich information to guide locomotion. Locomotion without sensory feedback is haphazard; therefore, sensory and motor systems have evolved complex interactions to generate adaptive behavior. Notably, sensory-guided locomotion acts over broad spatial and temporal scales to permit goal-seeking behavior, whether to localize food by tracking an attractive odor plume or to search for a potential mate. How does the brain integrate multimodal stimuli over different temporal and spatial scales to effectively control behavior? In this review, we classify locomotion into three ordinally ranked hierarchical layers that act over distinct spatiotemporal scales: stabilization, motor primitives, and higher-order tasks, respectively. We discuss how these layers present unique challenges and opportunities for sensorimotor integration. We focus on recent advances in invertebrate locomotion due to their accessible neural and mechanical signals from the whole brain, limbs, and sensors. Throughout, we emphasize neural-level description of computations for multimodal integration in genetic model systems, including the fruit fly, Drosophila melanogaster, and the yellow fever mosquito, Aedes aegypti. We identify that summation (e.g., gating) and weighting-which are inherent computations of spiking neurons-underlie multimodal integration across spatial and temporal scales, therefore suggesting collective strategies to guide locomotion.

Phase‐amplitude coupling profiles differ in frontal and auditory cortices of bats

Neural oscillations are at the core of important computations in the mammalian brain. Interactions between oscillatory activities in different frequency bands, such as delta (1-4Hz), theta (4-8Hz) or gamma (>30Hz), are a powerful mechanism for binding fundamentally distinct spatiotemporal scales of neural processing. Phase-amplitude coupling (PAC) is one such plausible and well-described interaction, but much is yet to be uncovered regarding how PAC dynamics contribute to sensory representations. In particular, although PAC appears to have a major role in audition, the characteristics of coupling profiles in sensory and integration (i.e. frontal) cortical areas remain obscure. Here, we address this question by studying PAC dynamics in the frontal-auditory field (FAF; an auditory area in the bat frontal cortex) and the auditory cortex (AC) of the bat Carollia perspicillata. By means of simultaneous electrophysiological recordings in frontal and auditory cortices examining local-field potentials (LFPs), we show that the amplitude of gamma-band activity couples with the phase of low-frequency LFPs in both structures. Our results demonstrate that the coupling in FAF occurs most prominently in delta/high-gamma frequencies (1-4/75-100Hz), whereas in the AC the coupling is strongest in the delta-theta/low-gamma (2-8/25-55Hz) range. We argue that distinct PAC profiles may represent different mechanisms for neuronal processing in frontal and auditory cortices, and might complement oscillatory interactions for sensory processing in the frontal-auditory cortex network.

A second-order test to detect spatio-temporal anisotropic effects in point patterns

ABSTRACTSecond-order spatio-temporal orientation methods provide a natural tool for the analysis of anisotropic spatio-temporal point process data. In this paper, we generalize a method based on the spatial point pair orientation distribution function to test for evidence of spatio-temporal anisotropy, by exploring the fact that the space–time orientation function is the distribution function of a uniform random variable on for any second-order intensity reweighted stationary and isotropic spatio-temporal point process. We present a numerical procedure based on this result to test for anisotropic effects, illustrated through a simulation study considering several space–time structures including Poisson and cluster configurations. The resulting testing procedure is applied to analyse the spatio-temporal distribution of earthquakes in Southern California for the period 1984–2004. Our results confirm that our approach is able to detect directional components at distinct spatio-temporal scales.

Current Challenges in Plant Eco-Metabolomics.

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant–organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Dynamic fragmentation of ice spheres: Two specific fracture patterns

Although ice materials are commonly found not only in our daily life on the earth but also in space, e.g. in the rings of Saturn, their fracture mechanisms, especially those in a dynamic range, have not been fully clarified. In our previous investigation, therefore, collision of a brittle ice sphere (diameter 25, 50 or 60 mm) that is free falling against a fixed plate of ice or polycarbonate has been experimentally observed using high-speed digital video cameras at a frame rate of up to 150,000 frames per second. It has been recognized that there exist basically only two specific fracture patterns generated by this dynamic impact: (i) “Top”-type fracture, normally at a relatively smaller impact speed, where only the bottom surface areas of a sphere are fragmented into small pieces by the impact at bottom and a relatively large top-shaped portion is left unbroken; and (ii) “orange segments”-type in which rather flat fracture planes extend approximately along the central axis of the sphere and split the sphere into three or four larger segments of comparable size. Preliminary comparison of the experimental findings with linear elastic wave fields obtained numerically by three-dimensional finite difference calculations suggests two distinct spatiotemporal scales: (1) The “top” fracture pattern is induced by the propagation of surface waves with relatively shorter wavelengths from the bottom along the free surface of the sphere, quickly producing fracture only near the bottom; (2) In the “orange segments”-type fracture, a longer contact time makes largely stressed regions enlarge more slowly and widely (quasi-statically-like) and larger fracture planes develop along the axis of the sphere. The above speculations seem to be supported by recent computations adopting the Discontinuous Galerkin (DG) method that treat more rigorously the mechanics of dynamic contact between the sphere and plate.

Seasonal controls of aragonite saturation states in the Gulf of Maine

AbstractThe Gulf of Maine (GoME) is a shelf region especially vulnerable to ocean acidification (OA) due to natural conditions of low pH and aragonite saturation states (Ω‐Ar). This study is the first to assess the major oceanic processes controlling seasonal variability of the carbonate system and its linkages with pteropod abundance in Wilkinson Basin in the GoME. Two years of seasonal sampling cruises suggest that water‐column carbonate chemistry in the region undergoes a seasonal cycle, wherein the annual cycle of stratification‐overturn, primary production, respiration‐remineralization and mixing all play important roles, at distinct spatiotemporal scales. Surface production was tightly coupled with remineralization in the benthic nepheloid layer during high production seasons, which results in occasional aragonite undersaturation. From spring to summer, carbonate chemistry in the surface across Wilkinson Basin reflects a transition from a production‐respiration balanced system to a net autotropic system. Mean water‐column Ω‐Ar and abundance of large thecosomatous pteropods show some correlation, although patchiness and discrete cohort reproductive success likely also influence their abundance. Overall, photosynthesis‐respiration is the primary driving force controlling Ω‐Ar variability during the spring‐to‐summer transition as well as over the seasonal cycle. However, calcium carbonate (CaCO3) dissolution appears to occur near bottom in fall and winter when bottom water Ω‐Ar is generally low but slightly above 1. This is accompanied by a decrease in pteropod abundance that is consistent with previous CaCO3 flux trap measurements. The region might experience persistent subsurface aragonite undersaturation in 30–40 years under continued ocean acidification.

A decade of seascape genetics: contributions to basic and applied marine connectivity

Seascape genetics, a term coined in 2006, is a fast growing area of population genetics that draws on ecology, oceanography and geography to address challenges in basic understanding of mar- ine connectivity and applications to management. We provide an accessible overview of the latest develop- ments in seascape genetics that merge exciting new ideas from the field of marine population connectivity with statistical and technical advances in population genetics. After summarizing the historical context leading to the emergence of seascape genetics, we detail questions and methodological approaches that are evolving the discipline, highlight applications to conservation and management, and conclude with a summary of the field's transition to seascape ge- nomics. From 100 seascape genetic studies, we assess trends in taxonomic and geographic coverage, sam- pling and statistical design, and dominant seascape drivers. Notably, temperature, oceanography and geo - graphy show equal prevalence of influence on spatial genetic patterns, and tests of over 20 other seascape factors suggest that a variety of forces impact connec- tivity at distinct spatio-temporal scales. A new level of rigor in statistical analysis is critical for disentangling multiple drivers and spurious effects. Coupled with GIS data and genomic scale sequencing methods, this rigor is taking seascape genetics beyond an initial focus on identifying correlations to hypothesis-driven insights into patterns and processes of population

Management applications of discontinuity theory

Summary Human impacts on the environment are multifaceted and can occur across distinct spatiotemporal scales. Ecological responses to environmental change are therefore difficult to predict, and entail large degrees of uncertainty. Such uncertainty requires robust tools for management to sustain ecosystem goods and services and maintain resilient ecosystems. We propose an approach based on discontinuity theory that accounts for patterns and processes at distinct spatial and temporal scales, an inherent property of ecological systems. Discontinuity theory has not been applied in natural resource management and could therefore improve ecosystem management because it explicitly accounts for ecological complexity. Synthesis and applications. We highlight the application of discontinuity approaches for meeting management goals. Specifically, discontinuity approaches have significant potential to measure and thus understand the resilience of ecosystems, to objectively identify critical scales of space and time in ecological systems at which human impact might be most severe, to provide warning indicators of regime change, to help predict and understand biological invasions and extinctions and to focus monitoring efforts. Discontinuity theory can complement current approaches, providing a broader paradigm for ecological management and conservation.

Stochasticity, periodicity and localized light structures in partially mode-locked fibre lasers.

Physical systems with co-existence and interplay of processes featuring distinct spatio-temporal scales are found in various research areas ranging from studies of brain activity to astrophysics. The complexity of such systems makes their theoretical and experimental analysis technically and conceptually challenging. Here, we discovered that while radiation of partially mode-locked fibre lasers is stochastic and intermittent on a short time scale, it exhibits non-trivial periodicity and long-scale correlations over slow evolution from one round-trip to another. A new technique for evolution mapping of intensity autocorrelation function has enabled us to reveal a variety of localized spatio-temporal structures and to experimentally study their symbiotic co-existence with stochastic radiation. Real-time characterization of dynamical spatio-temporal regimes of laser operation is set to bring new insights into rich underlying nonlinear physics of practical active- and passive-cavity photonic systems.

Second‐order analysis of anisotropic spatiotemporal point process data

Second‐order orientation methods provide a natural tool for the analysis of spatial point process data. In this paper, we extend to the spatiotemporal setting the spatial point pair orientation distribution function. The new space–time orientation distribution function is used to detect space–time anisotropic configurations. An edge‐corrected estimator is defined and illustrated through a simulation study. We apply the resulting estimator to data on the spatiotemporal distribution of fire ignition events caused by humans in a square area of 30 × 30 km2 for 4 years. Our results confirm that our approach is able to detect directional components at distinct spatiotemporal scales. © 2014 The Authors. Statistica Neerlandica © 2014 VVS.

Detecting event-related changes of multivariate phase coupling in dynamic brain networks.

Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171-189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474-480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506-515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110-113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107-3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194-208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.

Single-neuron spike timing depends on global brain dynamics

Event Abstract Back to Event Single-neuron spike timing depends on global brain dynamics Canolty Ryan1*, Karunesh Ganguly1, Steven Kennerley1, Kilian Koepsell1, Charles Cadieu1, Jonathan Wallis1 and Jose Carmena1 1 University of California, United States Brain rhythms have been suggested to play a role in both local cortical computation and long-range communication between areas, with a dynamic hierarchy of neuronal oscillations modulating activity at a variety of levels within the multi-scale brain network. Evidence supporting this view includes the micro-scale dependence of neuronal spiking upon the proximal local field potential (LFP), the meso-scale dependence of spiking upon spatial voltage patterns, and the macro-scale dependence of spiking upon phase coherence between distinct cortical areas. However, few previous studies examined multiple scales simultaneously and therefore the relative importance of these distinct spatiotemporal scales upon spike timing remains unknown. To investigate this issue, multiple single-unit spike times and LFPs were recorded via electrode arrays implanted bilaterally in either motor or prefrontal regions (primary motor and dorsal premotor sites in two subjects; cingulate, orbitofrontal, dorsolateral and ventrolateral prefrontal sites in two subjects). Here we show that many neurons exhibit a preference for distinct, frequency-specific multi-scale patterns of LFP phase coupling. In particular, using a probabilistic multivariate phase model, we show that the large-scale phase coupling structure present during the spike times of a given neuron is similar over repetitions of the same experimental task but is significantly different from the baseline phase coupling structure. Interestingly, however, the preferred phase coupling structure for two different neurons located in the same cortical area can be quite dissimilar, and the pattern of phase coupling for one given neuron can shift as a function of the behavior task. We discuss the implications these findings have for different models of neuronal computation and long-range communication, and consider ways in which future work may use this multi-scale dependence to improve decoding in brain-machine interfaces. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session III Citation: Ryan C, Ganguly K, Kennerley S, Koepsell K, Cadieu C, Wallis J and Carmena J (2010). Single-neuron spike timing depends on global brain dynamics. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00264 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 05 Mar 2010; Published Online: 05 Mar 2010. * Correspondence: Canolty Ryan, University of California, Berkeley, United States, rcanolty@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Canolty Ryan Karunesh Ganguly Steven Kennerley Kilian Koepsell Charles Cadieu Jonathan Wallis Jose Carmena Google Canolty Ryan Karunesh Ganguly Steven Kennerley Kilian Koepsell Charles Cadieu Jonathan Wallis Jose Carmena Google Scholar Canolty Ryan Karunesh Ganguly Steven Kennerley Kilian Koepsell Charles Cadieu Jonathan Wallis Jose Carmena PubMed Canolty Ryan Karunesh Ganguly Steven Kennerley Kilian Koepsell Charles Cadieu Jonathan Wallis Jose Carmena Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Documenting the advantages and limitations of different classes of molecular markers in a well-established phylogeographic context: lessons from the Iberian endemic Golden-striped salamander, Chioglossa lusitanica (Caudata: Salamandridae)

Previous analyses of mitochondrial (mt)DNA and allozymes covering the range of the Iberian endemic golden-striped salamander, Chioglossa lusitanica, suggested a Pleistocene split of the historical species distribution into two population units (north and south of the Mondego river), postglacial expansion into the northernmost extant range, and secondary contact with neutral diffusion of genes close to the Mondego river. We extended analysis of molecular variation over the species range using seven microsatellite loci and the nuclear β-fibrinogen intron 7 (β-fibint7). Both microsatellites and β-fibint7 showed moderate to high levels of population structure, concordant with patterns detected with mtDNA and allozymes; and a general pattern of isolation-by-distance, contrasting the marked differentiation of two population groups suggested by mtDNA and allozymes. Bayesian multilocus analyses showed contrasting results as populations north and south of the Douro river were clearly differentiated based on microsatellites, whereas allozymes revealed differentiation north and south of the Mondego river. Additionally, decreased microsatellite variability in the north supported the hypothesis of postglacial colonization of this region. The well-documented evolutionary history of C. lusitanica provides an excellent framework within which the advantages and limitations of different classes of markers can be evaluated in defining patterns of population substructure and inferring evolutionary processes across distinct spatio-temporal scales. The present study serves as a cautionary note for investigations that rely on a single type of molecular marker, especially when the organism under study exhibits a widespread distribution and complex natural history. © 2008 The Linnean Society of London, Biological Journal of the Linnean Society, 2008, 95, 371–387.