Temporal behaviour of marine landings along coastal Karnataka 1. Relative distribution and secular trends

Landing data of 21 fisheries from Karnataka (quarterly landings for the period 1956 to 1978 and annual landings for the period 1979 to 1981) were analysed to study the te111poral behaviour of the landings. The relative distribution and secular trends in each of the fisheries are reported. On an average, mackerel and oil sardine together account for about 70% of the annual landings. The fluctuation in the landings in all the fisheries was very high with the coefficient of variation attaining a minimum value of 61.53%. The inverse relationship between the landings of oil sardine and mackerel was found to be statistically significant. It is suggested that fishing effort be further increased in fisheries that have demonstrated a rising trend (about 73% of the total annual landings) as also in the case of the fisheries whose trends appear stagnant (about 21% of the annual landings). However, there seems to be some evidence of a need for regulating fishing in Hemirhamphus and Belone, Caranx and penaeid prawns. Regulations need to be made authentic with the help of sufficient information on not merely the fishery but on the whole population.

Temporal behaviour of marine landing in Karnataka 2. Seasonal patterns

Seasonal patterns of 21 fisheries in Karnataka (after isolation from time-series components) are presented. Depending on the pattern of seasonal fluctuation in landings, 19 fisheries have been grouped into five patterns, A, B, C, D and E. Ribbon fishes and 'other clupeids' did not exhibit any significant seasonal pattern. Pattern A with highest landings in the 4th quarter (October to December), followed by the 1, 2 and 3 quarters, is the most common in 10 species/groups (comprising 78% of the total landings). Harmonic analysis has been carried out using the seasonal indices.

Temporal behaviour of marine landings along coastal Karnataka 3. Cyclical patterns

Marine landing data for Karnataka during 1956-1978 were subjected to time series analysis and cyclical periodicities isolated in the case of seven fisheries namely, ribbon fish (five year cycle); Caranx spp., Leiognathus spp, and mackerel (six year cycle each); the combined landings of Hemirhamphus spp. and Belone spp.,'Lesser Sardines' and 'Other Clupeids' (seven year cycle each). 'Total' demonstrated an eight-year cyclical periodicity.

Breathers and thermal relaxation as a temporal process: A possible way to detect breathers in experimental situations

Breather stability and longevity in thermally relaxing nonlinear arrays is investigated under the scrutiny of the analysis and tools employed for time series and state reconstruction of a dynamical system. We briefly review the methods used in the analysis and characterize a breather in terms of the results obtained with such methods. Our present work focuses on spontaneously appearing breathers in thermal Fermi-Pasta-Ulam arrays but we believe that the conclusions are general enough to describe many other related situations; the particular case described in detail is presented as another example of systems where three incommensurable frequencies dominate their chaotic dynamics (reminiscent of the Ruelle-Takens scenario for the appearance of chaotic behavior in nonlinear systems). This characterization may also be of great help for the discovery of breathers in experimental situations where the temporal evolution of a local variable (like the site energy) is the only available/measured data. © 2005 American Institute of Physics.

Spatial and temporal sensitivity degradation of primary visual cortical cells in senescent rhesus monkeys

Human visual function declines with age. Much of this decline is mediated by changes in the central visual pathways. In this study we compared the spatial and temporal sensitivities of striate cortical cells in young and old paralysed macaque monkeys. Ext

Temporal and spatial distribution of phytoplankton with emphasis on Skeletonema costatum in the Mathamuhuri river – estuary (Chakaria mangrove ecosystem), Bangladesh

A total of 91 species under 44 genera were identified among the phytoplankton community during the course of one year's investigation between May 1982 and April 1983. Bacillariophyta was the most dominant group with 72 specie, Chlorophyta 11 spp, Cyanophyta 6 spp and Pyrrophyta was represented by 2 species. The yearly percentage composition of 4 groups of phytoplankton in order of abundance were Bacillariophyta 50.77%, Cyanophyta 47.70%, Chlorophyta 1.5% and Pyrrophyta 0.02%. The highest densities of phytoplankton were recorded in monsoon months (June-July) with a peak in July (31550 cells/l) and the minimum in February (770 cells/1). Higher concentration of phytoplankton was recorded at station 2, nearer to the Chakaria Sundarbans (mangroves), but abundance of phytoplankton showed no significant difference in the two stations (Mann Whitney U test, P=0.64, Z=-0.642, U=64). Phytoplankton population in this area were positively correlated with rainfall (r=0.655, P=<0.5, df.22) and water temperature (r=0.523, P=<0.05). Skeletonema costatum was the dominant member of phytoplankton and occupied 35.23% of the annual population and occurred throughout the period of study except in September and January. Its abundance was recorded during the monsoon months (April- July) with a maximum density (24185 cells/l) in July. No significant correlation was found between abundance of S. costatum and the hydro-meteorological parameters recorded in the Chakaria mangrove area.

Image sequence segmentation using mixture-models with uniqueness and spatio-temporal consistency constraints

The use of mixture-model techniques for motion estimation and image sequence segmentation was discussed. The issues such as modeling of occlusion and uncovering, determining the relative depth of the objects in a scene, and estimating the number of objects in a scene were also investigated. The segmentation algorithm was found to be computationally demanding, but the computational requirements were reduced as the motion parameters and segmentation of the frame were initialized. The method provided a stable description, in whichthe addition and removal of objects from the description corresponded to the entry and exit of objects from the scene.

Spatio-temporal clustering of probabilistic region trajectories

We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences. © 2011 IEEE.

Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

The temporal evolution of feedback gains rapidly update to task demands.

Recent theoretical frameworks such as optimal feedback control suggest that feedback gains should modulate throughout a movement and be tuned to task demands. Here we measured the visuomotor feedback gain throughout the course of movements made to "near" or "far" targets in human subjects. The visuomotor gain showed a systematic modulation over the time course of the reach, with the gain peaking at the middle of the movement and dropping rapidly as the target is approached. This modulation depends primarily on the proportion of the movement remaining, rather than hand position, suggesting that the modulation is sensitive to task demands. Model-predictive control suggests that the gains should be continuously recomputed throughout a movement. To test this, we investigated whether feedback gains update when the task goal is altered during a movement, that is when the target of the reach jumped. We measured the visuomotor gain either simultaneously with the jump or 100 ms after the jump. The visuomotor gain nonspecifically reduced for all target jumps when measured synchronously with the jump. However, the visuomotor gain 100 ms later showed an appropriate modulation for the revised task goal by increasing for jumps that increased the distance to the target and reducing for jumps that decreased the distance. We conclude that visuomotor feedback gain shows a temporal evolution related to task demands and that this evolution can be flexibly recomputed within 100 ms to accommodate online modifications to task goals.

Temporal Coherence of Chlorophyll a during a Spring Phytoplankton Bloom in Xiangxi Bay of Three-Gorges Reservoir, China

Algal bloom phenomenon was defined as "the rapid growth of one or more phytoplankton species which leads to a rapid increase in the biomass of phytoplankton", yet most estimates of temporal coherence are based on yearly or monthly sampling frequencies and little is known of how synchrony varies among phytoplankton or of the causes of temporal coherence during spring algal bloom. In this study, data of chlorophyll a and related environmental parameters were weekly gathered at 15 sampling sites in Xiangxi Bay of Three-Gorges Reservoir (TGR, China) to evaluate patterns of temporal coherence for phytoplankton during spring bloom and test if spatial heterogeneity of nutrient and inorganic suspended particles within a single ecosystem influences synchrony of spring phytoplankton dynamics. There is a clear spatial and temporal variation in chlorophyll a across Xiangxi Bay. The degree of temporal coherence for chlorophyll a between pairs of sites located in Xiangxi Bay ranged from -0.367 to 0.952 with mean and median values of 0.349 and 0.321, respectively. Low levels of temporal coherence were often detected among the three stretches of the bay (Down reach, middle reach and upper reach), while high levels of temporal coherence were often found within the same reach of the bay. The relative difference of DIN between pair sites was the strong predictor of temporal coherence for chlorophyll a in down and middle reach of the bay, while the relative difference in Anorganic Suspended Solids was the important factor regulating temporal coherence in middle and upper reach. Contrary to many studies, these results illustrate that, in a small geographic area (a single reservoir bay of approximately 25 km), spatial heterogeneity influence synchrony of phytoplankton dynamics during spring bloom and local processes may override the effects of regional processes or dispersal.

Temporal and Spatial Distributions of Rotifers in Xiangxi Bay of the Three Gorges Reservoir, China

From July 2003 to June 2005, investigations of rotifer temporal and spatial distributions were carried out in a bay of the Three Gorges Reservoir, Xiangxi Bay, which is the downstream segment of the Xiangxi River and the nearest bay to the Three Gorges Reservoir dam in Hubei Province, China. Thirteen sampling sites were selected. The results revealed a high species diversity, with 76 species, and 14 dominant species; i.e., Polyarthra vulgaris, Keratella cochlearis, Keratella valga, Synchaeta tremula, Synchaeta stylata, Trichocerca lophoessa, Trichocerca pusilla, Brachionus angularis, Brachionus calyciflorus, Brachionus forficula forficula, Ascomorpha ovalis, Conochilus unicornis, Ploesoma truncatum and Anuraeopsis fissa. After the first year of the reservoir impoundment, the rotifer community was dominated by ten species; one year later it was dominated by eight species. The community in 2003/2004 was dissimilar to that in 2004/2005, which resulted from the succession of the dominant species. The rotifer community exhibited a patchy distribution, with significant heterogeneity observed along the longitudinal axis. All rotifer communities could be divided into three groups, corresponding to the riverine, the transition and the lacustrine zone, respectively.

Hydroacoustic study of spatial and temporal distribution of Gymnocypris przewalskii (Kessler, 1876) in Qinghai Lake, China

Gymnocypris przewalskii (Kessler 1876) is an endangered and state-protected rare fish species in Qinghai Lake, China. To further understand the life history and distribution of this fish, five surveys were carried out in Qinghai Lake between 2002-2006. Results of these surveys indicate that fishes were predominantly distributed about 2 m under the surface. In July, significant differences in fish density were found between surface and bottom layers (P = 0.001), and/or between middle and bottom layers (P = 0.025). Fish density was the greatest in the surface layer. In August and October, no significant differences were found between the different layers, but the bottom layer had a greater fish density. Furthermore, there were very large differences among different zones in fish distribution density. Differences in horizontal distribution were not significantly correlated to factors such as water depth and inshore distance, possibly because of very low and uniform fish density. Feeding, changes in water temperature, over-wintering and spawning appeared to influence fish distribution. Hydroacoustic estimates of G. przewalskii biomass in Qinghai Lake increased significantly between 2002 and 2006. We attribute this increase to the management measures put in place to protect this species.

Spatial and temporal variations of microcystins in hepatopancreas of a freshwater snail from Lake Taihu

In this paper, spatial and temporal variations of three common microcystins (MC-RR, MC-YR, and MC-LR) in the hepatopancreas of a freshwater snail (Bellamya aeruginosa) were studied monthly in two bays of Lake Taihu. Microcystins (MCs) concentration in hepatopancreas was quantified by liquid chromatography-mass spectrometry (LC-MS). The MCs concentrations in hepatopancreas were higher at Site 1 than those at other sites, which was in agreement with the changes of intracellular MCs concentrations in the water column. There was a significant correlation between MCs concentrations in the hepatopancreas and that in the seston, suggesting that spatial variances of MCs; concentrations in hepatopancreas among the five sites were due to spatial changes of toxic Microcystis cells in the water column. PCCA indicates that in addition to Microcystis, other factors (e.g., water temperature) also substantially affected the accumulation of MCs in hepatopancreas of the snail. (C) 2008 Published by Elsevier Inc.