Spatio-Temporal Organization Mediated by a Hierarchy in Time Scales in Ensembles of Predator-Prey Pairs

Author(s):  
Claudia Pahl-Wostl
Facies ◽  
2021 ◽  
Vol 67 (3) ◽  
Author(s):  
Markus Wilmsen ◽  
Udita Bansal

AbstractCenomanian strata of the Elbtal Group (Saxony, eastern Germany) reflect a major global sea-level rise and contain, in certain intervals, a green authigenic clay mineral in abundance. Based on the integrated study of five new core sections, the environmental background and spatio-temporal patterns of these glauconitic strata are reconstructed and some general preconditions allegedly needed for glaucony formation are critically questioned. XRD analyses of green grains extracted from selected samples confirm their glauconitic mineralogy. Based on field observations as well as on the careful evaluation of litho- and microfacies, 12 glauconitc facies types (GFTs), broadly reflecting a proximal–distal gradient, have been identified, containing granular and matrix glaucony of exclusively intrasequential origin. When observed in stratigraphic succession, GFT-1 to GFT-12 commonly occur superimposed in transgressive cycles starting with the glauconitic basal conglomerates, followed up-section by glauconitic sandstones, sandy glauconitites, fine-grained, bioturbated, argillaceous and/or marly glauconitic sandstones; glauconitic argillaceous marls, glauconitic marlstones, and glauconitic calcareous nodules continue the retrogradational fining-upward trend. The vertical facies succession with upwards decreasing glaucony content demonstrates that the center of production and deposition of glaucony in the Cenomanian of Saxony was the nearshore zone. This time-transgressive glaucony depocenter tracks the regional onlap patterns of the Elbtal Group, shifting southeastwards during the Cenomanian 2nd-order sea-level rise. The substantial development of glaucony in the thick (60 m) uppermost Cenomanian Pennrich Formation, reflecting a tidal, shallow-marine, nearshore siliciclastic depositional system and temporally corresponding to only ~ 400 kyr, shows that glaucony formation occurred under wet, warm-temperate conditions, high accumulation rates and on rather short-term time scales. Our new integrated data thus indicate that environmental factors such as great water depth, cool temperatures, long time scales, and sediment starvation had no impact on early Late Cretaceous glaucony formation in Saxony, suggesting that the determining factors of ancient glaucony may be fundamentally different from recent conditions and revealing certain limitations of the uniformitarian approach.


2020 ◽  
Vol 1861 (1) ◽  
pp. 148091 ◽  
Author(s):  
Kirill Salewskij ◽  
Bettina Rieger ◽  
Frances Hager ◽  
Tasnim Arroum ◽  
Patrick Duwe ◽  
...  

2005 ◽  
Vol 12 (1) ◽  
pp. 31-40 ◽  
Author(s):  
B. S. Daya Sagar

Abstract. Spatio-temporal patterns of small water bodies (SWBs) under the influence of temporally varied stream flow discharge are simulated in discrete space by employing geomorphologically realistic expansion and contraction transformations. Cascades of expansion-contraction are systematically performed by synchronizing them with stream flow discharge simulated via the logistic map. Templates with definite characteristic information are defined from stream flow discharge pattern as the basis to model the spatio-temporal organization of randomly situated surface water bodies of various sizes and shapes. These spatio-temporal patterns under varied parameters (λs) controlling stream flow discharge patterns are characterized by estimating their fractal dimensions. At various λs, nonlinear control parameters, we show the union of boundaries of water bodies that traverse the water body and non-water body spaces as geomorphic attractors. The computed fractal dimensions of these attractors are 1.58, 1.53, 1.78, 1.76, 1.84, and 1.90, respectively, at λs of 1, 2, 3, 3.46, 3.57, and 3.99. These values are in line with general visual observations.


2020 ◽  
Vol 10 (15) ◽  
pp. 5326
Author(s):  
Xiaolei Diao ◽  
Xiaoqiang Li ◽  
Chen Huang

The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called “Multi-Term Attention Networks” (MTANs), which solves the above problem by extracting temporal features with different time scales. The network consists of a Multi-Term Attention Recurrent Neural Network (MTA-RNN) and a Spatio-Temporal Convolutional Neural Network (ST-CNN). In MTA-RNN, a method for fusing multi-term temporal features are proposed to extract the temporal dependence of different time scales, and the weighted fusion temporal feature is recalibrated by the attention mechanism. Ablation research proves that this network has powerful spatio-temporal dynamic modeling capabilities for actions with different time scales. We perform extensive experiments on four challenging benchmark datasets, including the NTU RGB+D dataset, UT-Kinect dataset, Northwestern-UCLA dataset, and UWA3DII dataset. Our method achieves better results than the state-of-the-art benchmarks, which demonstrates the effectiveness of MTANs.


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