Processes driving short-term temporal dynamics of small mammal distribution in human-disturbed environments

Oecologia ◽  
2016 ◽  
Vol 181 (3) ◽  
pp. 831-840 ◽  
Author(s):  
Julie Martineau ◽  
David Pothier ◽  
Daniel Fortin
Keyword(s):  
Small Mammal ◽  
Temporal Dynamics ◽  
Short Term ◽  
Mammal Distribution

Economic conditions and perceptions of immigrants as an economic threat in Europe: Temporal dynamics and mediating processes

2021 ◽  
pp. 002071522199352
Author(s):  
Boris Heizmann ◽  
Nora Huth
Keyword(s):  
Temporal Dynamics ◽  
European Social Survey ◽  
Primary Objective ◽  
Conflict Theory ◽  
Economic Conditions ◽  
Political Climate ◽  
Short Term ◽  
Economic Threat ◽  
Party Positions

This article addresses the extent to which economic downturns influence the perception of immigrants as an economic threat and through which channels this occurs. Our primary objective is an investigation of the specific mechanisms that connect economic conditions to the perception of immigrants as a threat. We therefore also contribute to theoretical discussions based on group threat and realistic group conflict theory by exposing the dominant source of competition relevant to these relationships. Furthermore, we investigate whether people react more sensitive to short-term economic dynamics within countries than to the long-term economic circumstances. Our database comprises all waves of the European Social Survey from 2002 to 2017. The macro-economic indicators we use include GDP per capita, unemployment, and national debt levels, covering the most salient economic dimensions. We furthermore control for the country’s migration situation and aggregate party positions toward cultural diversity. Our results show that the dynamic short-term developments of the economy and migration within countries are of greater relevance for perceived immigrant threat than the long-term situation. In contrast, the long-term political climate appears to be more important than short-term changes in the aggregate party positions. Further mediation analyses show that objective economic conditions influence anti-immigrant attitudes primarily through individual perceptions of the country’s economic performance and that unemployment rates are of primary importance.

Specialist resources are key to improving small mammal distribution models

2011 ◽  
Vol 37 (2) ◽  
pp. 216-226 ◽  
Author(s):  
NERISSA A. HABY ◽  
STEVEN DELEAN ◽  
BARRY W. BROOK
Keyword(s):  
Small Mammal ◽  
Distribution Models ◽  
Mammal Distribution

The role of short-term weather conditions in temporal dynamics of fire regime features in mainland Spain

2019 ◽  
Vol 241 ◽  
pp. 575-586 ◽  
Author(s):  
Adrián Jiménez-Ruano ◽  
Marcos Rodrigues Mimbrero ◽  
W. Matt Jolly ◽  
Juan de la Riva Fernández
Keyword(s):  
Temporal Dynamics ◽  
Fire Regime ◽  
Weather Conditions ◽  
Short Term

Short-term temporal dynamics of yeast abundance on the tall fescue phylloplane

2008 ◽  
Vol 54 (4) ◽  
pp. 299-304 ◽  
Author(s):  
Shannon S. Nix ◽  
Leon L. Burpee ◽  
Kimberly L. Jackson ◽  
James W. Buck
Keyword(s):  
Tall Fescue ◽  
Festuca Arundinacea ◽  
Temporal Dynamics ◽  
Early Morning ◽  
Water Control ◽  
Short Term ◽  
Yeast Community ◽  
Early Afternoon ◽  
Colonization Model ◽  
Phylloplane Yeasts

Six replicate trials were conducted to determine the short-term temporal dynamics and the effects of foliar applications of nutrients on the phylloplane yeast community of tall fescue ( Festuca arundinacea Schreb.). In each trial, 2% sucrose + 0.5% yeast extract solution or sterile deionized water (control) was applied to the experiment plots. Twelve hours post-treatment (at 0600 hours), leaf samples were collected and yeast colony-forming units (cfu) were enumerated by dilution plating. This process was repeated at 1200, 1800, and 2400 hours in each trial. Significant differences were observed between the number of yeast cfu and the time at which the samples were collected. On average, the number of yeast cfu recovered was significantly less at 1800 hours and significantly greatest at 2400 hours when compared with all other sampling times. Averaged over all time intervals, we observed a trend of increased yeast abundance in turf treated with the nutrient solution compared with control treatments. In a separate investigation, atmospheric yeast abundance above the canopy of tall fescue was assessed in the morning (0900) and in the afternoon (1500) using a Thermo Andersen single stage viable particle sampler. In 5 of the 6 trials of this experiment, atmospheric yeast abundance was significantly greater in the morning than in the afternoon. Results suggest the following colonization model: phylloplane yeasts on tall fescue reproduce during the late evening and early morning, stabilize during the late morning and early afternoon through exchange of immigrants and emigrants, and decline during the late afternoon and (or) early evening.

FREEMAN'S K MODELS AS RESERVOIR COMPUTING ARCHITECTURES

2009 ◽  
Vol 05 (01) ◽  
pp. 265-286
Author(s):  
MUSTAFA C. OZTURK ◽  
JOSE C. PRINCIPE
Keyword(s):  
Computational Models ◽  
Temporal Dynamics ◽  
Temporal Structure ◽  
Reservoir Computing ◽  
Short Term ◽  
System Parameters ◽  
History Of ◽  
Spatio Temporal ◽  
Universal Approximators ◽  
Theoretical Results

Walter Freeman in his classic 1975 book "Mass Activation of the Nervous System" presented a hierarchy of dynamical computational models based on studies and measurements done in real brains, which has been known as the Freeman's K model (FKM). Much more recently, liquid state machine (LSM) and echo state network (ESN) have been proposed as universal approximators in the class of functionals with exponential decaying memory. In this paper, we briefly review these models and show that the restricted K set architecture of KI and KII networks share the same properties of LSM/ESNs and is therefore one more member of the reservoir computing family. In the reservoir computing perspective, the states of the FKM are a representation space that stores in its spatio-temporal dynamics a short-term history of the input patterns. Then at any time, with a simple instantaneous read-out made up of a KI, information related to the input history can be accessed and read out. This work provides two important contributions. First, it emphasizes the need for optimal readouts, and shows how to adaptively design them. Second, it shows that the Freeman model is able to process continuous signals with temporal structure. We will provide theoretical results for the conditions on the system parameters of FKM satisfying the echo state property. Experimental results are presented to illustrate the validity of the proposed approach.

scholarly journals The Dimension of Time in Host-Microbiome Interactions

mSystems ◽  
2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Giulia T. Uhr ◽  
Lenka Dohnalová ◽  
Christoph A. Thaiss
Keyword(s):  
Microbial Community ◽  
Environmental Factors ◽  
Temporal Dynamics ◽  
Therapeutic Strategies ◽  
Intestinal Microbiome ◽  
Short Term ◽  
Intestinal Microbial Community ◽  
Intermediate States ◽  
Different Time Scales

ABSTRACT The intestinal microbiota contains trillions of commensal microorganisms that shape multiple aspects of host physiology and disease. In contrast to the host’s genome, the microbiome is amenable to change over the course of an organism’s lifetime, providing an opportunity to therapeutically modulate the microbiome’s impact on human pathophysiology. In this Perspective, we highlight environmental factors that regulate the temporal dynamics of the intestinal microbiome, with a particular focus on the different time scales at which they act. We propose that the identification of transient and intermediate states of microbiome responses to perturbations is essential for understanding the rules that govern the behavior of this ecosystem. The delineation of microbiome dynamics is also helpful for distinguishing cause and effect in microbiome responses to environmental stimuli. Understanding the dimension of time in host-microbiome interactions is therefore critical for therapeutic strategies that aim at short-term or long-term engineering of the intestinal microbial community.

Comparative Analysis of Gut Microbiota Following Changes in Training Volume Among Swimmers

2020 ◽  
Vol 41 (05) ◽  
pp. 292-299 ◽  
Author(s):  
Jarrad Timothy Hampton-Marcell ◽  
Tifani W. Eshoo ◽  
Marc D. Cook ◽  
Jack A. Gilbert ◽  
Craig A. Horswill ◽  
...  
Keyword(s):  
Body Composition ◽  
Community Structure ◽  
Microbial Community ◽  
Microbial Community Structure ◽  
Temporal Dynamics ◽  
Fecal Microbiota ◽  
Future Research ◽  
Training Volume ◽  
Short Term ◽  
Microbial Composition

AbstractExercise can influence gut microbial community structure and diversity; however, the temporal dynamics of this association have rarely been explored. Here we characterized fecal microbiota in response to short term changes in training volume. Fecal samples, body composition, and training logs were collected from Division I NCAA collegiate swimmers during peak training through their in-season taper in 2016 (n=9) and 2017 (n=7), capturing a systematic reduction in training volume near the conclusion of their athletic season. Fecal microbiota were characterized using 16S rRNA V4 amplicon sequencing and multivariate statistical analysis, Spearman rank correlations, and random forest models. Peak training volume, measured as swimming distance, decreased significantly during the study period from 32.6±4.8 km/wk to 11.3±8.1 km/wk (ANOVA, p<0.05); however, body composition showed no significant changes. Coinciding with the decrease in training volume, the microbial community structure showed a significant decrease in overall microbial diversity, a decrease in microbial community structural similarity, and a decrease in the proportion of the bacterial genera Faecalibacterium and Coprococcus. Together these data demonstrate a significant association between short-term changes in training volume and microbial composition and structure in the gut; future research will establish whether these changes are associated with energy balance or nutrient intake.

scholarly journals Stable maintenance of multiple representational formats in human visual short-term memory

2020 ◽  
Vol 117 (51) ◽  
pp. 32329-32339
Author(s):  
Jing Liu ◽  
Hui Zhang ◽  
Tao Yu ◽  
Duanyu Ni ◽  
Liankun Ren ◽  
...  
Keyword(s):  
Visual Information ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Low Frequency ◽  
Higher Order ◽  
Semantic Representations ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Stable Maintenance

Visual short-term memory (VSTM) enables humans to form a stable and coherent representation of the external world. However, the nature and temporal dynamics of the neural representations in VSTM that support this stability are barely understood. Here we combined human intracranial electroencephalography (iEEG) recordings with analyses using deep neural networks and semantic models to probe the representational format and temporal dynamics of information in VSTM. We found clear evidence that VSTM maintenance occurred in two distinct representational formats which originated from different encoding periods. The first format derived from an early encoding period (250 to 770 ms) corresponded to higher-order visual representations. The second format originated from a late encoding period (1,000 to 1,980 ms) and contained abstract semantic representations. These representational formats were overall stable during maintenance, with no consistent transformation across time. Nevertheless, maintenance of both representational formats showed substantial arrhythmic fluctuations, i.e., waxing and waning in irregular intervals. The increases of the maintained representational formats were specific to the phases of hippocampal low-frequency activity. Our results demonstrate that human VSTM simultaneously maintains representations at different levels of processing, from higher-order visual information to abstract semantic representations, which are stably maintained via coupling to hippocampal low-frequency activity.

scholarly journals Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 203
Author(s):  
Benjamin Plaster ◽  
Gautam Kumar
Keyword(s):  
Neural Network ◽  
Time Horizon ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Brain Dynamics ◽  
Short Term ◽  
Term Memory ◽  
Brain Functions ◽  
Long Short Term Memory

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions have been of interest to engineers, mathematicians and physicists over the last several decades. With the motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

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