Persistence and Propagation of a PDE and Discrete-Time Map Hybrid Animal Movement Model With Habitat Shift Driven by Climate Change

2020 ◽  
Vol 80 (6) ◽  
pp. 2608-2630
Author(s):  
Zhenkun Wang ◽  
Hao Wang
Keyword(s):  
Climate Change ◽  
Discrete Time ◽  
Animal Movement ◽  
Habitat Shift ◽  
Movement Model ◽  
Time Map ◽  
Hybrid Animal

scholarly journals The likely effects of thermal climate change on vertebrate skeletal muscle mechanics with possible consequences for animal movement and behaviour

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Rob S James ◽  
Jason Tallis
Keyword(s):  
Climate Change ◽  
Skeletal Muscle ◽  
Animal Movement ◽  
Muscle Mechanics ◽  
Local Extinction ◽  
Thermal Climate ◽  
Vertebrate Skeletal Muscle

Changes in temperature, caused by climate change, can alter the amount of power an animal’s muscle produces, which could in turn affect that animal’s ability to catch prey or escape predators. Some animals may cope with such changes, but other species could undergo local extinction as a result.

scholarly journals A note on a discrete time MAP risk model

2017 ◽  
Vol 309 ◽  
pp. 111-121 ◽  
Author(s):  
Chaolin Liu ◽  
Zhimin Zhang ◽  
Hu Yang
Keyword(s):  
Discrete Time ◽  
Risk Model ◽  
Time Map

Construction of Markov chains for discrete time MAP/PH/K queues

2015 ◽  
Vol 93 ◽  
pp. 17-26 ◽  
Author(s):  
Qi-Ming He ◽  
Attahiru Sule Alfa
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Time Map

scholarly journals An animal movement model incorporating home range and habitat selection

2007 ◽  
Vol 15 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Aaron Christ ◽  
Jay Ver Hoef ◽  
Dale L. Zimmerman
Keyword(s):  
Habitat Selection ◽  
Home Range ◽  
Animal Movement ◽  
Movement Model

scholarly journals A FRAMEWORK FOR INTERNEURAL DYNAMICS IN A CEREBRAL CORTEX CENTER

2013 ◽  
Vol 23 (09) ◽  
pp. 1350151
Author(s):  
ABRAHAM BOYARSKY ◽  
ZHENYANG LI ◽  
PAWEŁ GÓRA
Keyword(s):  
Cerebral Cortex ◽  
Steady State ◽  
Probability Measure ◽  
Discrete Time ◽  
Sensory Input ◽  
Invariant Probability Measure ◽  
Chaotic State ◽  
Time Map

We model the innervation dynamics of interneurons in a cerebral cortex center A between the time of initial sensory input and acquisition of a sustained steady state. The model assumes that interneurons in A are heavily interconnected allowing synchronization. This invites modeling the dynamics by means of a discrete time map. The model takes into account the influence of excitatory and inhibitory cells and reflects the architecture of synapses along the axons. The acquisition of a sustained chaotic state is characterized by means of a natural invariant probability measure. The time to attain this probability measure can be estimated.

Discrete-time MAP/G/1/∞ system with inversive probabilistic servicing discipline

2010 ◽  
Vol 71 (12) ◽  
pp. 2547-2557
Author(s):  
A. Cascone ◽  
R. Manzo ◽  
A. V. Pechinkin ◽  
S. Salerno
Keyword(s):  
Discrete Time ◽  
Time Map

Scheduling of high-speed rail traffic based on discrete-time movement model

2013 ◽  
Vol 22 (12) ◽  
pp. 120501 ◽  
Author(s):  
Ya-Hua Sun ◽  
Cheng-Xuan Cao ◽  
Yan Xu ◽  
Chao Wu
Keyword(s):  
Discrete Time ◽  
High Speed ◽  
Movement Model ◽  
High Speed Rail ◽  
Time Movement ◽  
Rail Traffic
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