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010   $a3-319-07728-7
024 7 $a10.1007/978-3-319-07728-4
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035   $a(DE-He213)978-3-319-07728-4
035   $a(PPN)179768182
035   $a(EXLCZ)993710000000125777
100   $a20140604d2014      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 14$aThe Influence of Demographic Stochasticity on Population Dynamics $eA Mathematical Study of Noise-Induced Bistable States and Stochastic Patterns /$fby Tommaso Biancalani
205   $a1st ed. 2014.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014.
215   $a1 online resource (123 p.)
225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
300   $aDescription based upon print version of record.
311   $a1-322-13687-4 
311   $a3-319-07727-9 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Methods -- Noise-Induced Bistability -- Stochastic Waves on Regular Lattices -- Stochastic Waves on Complex Network -- Conclusions.
330   $aThe dynamics of population systems cannot be understood within the framework of ordinary differential equations, which assume that the number of interacting agents is infinite. With recent advances in ecology, biochemistry and genetics it is becoming increasingly clear that real systems are in fact subject to a great deal of noise. Relevant examples include social insects competing for resources, molecules undergoing chemical reactions in a cell and a pool of genomes subject to evolution. When the population size is small, novel macroscopic phenomena can arise, which can be analyzed using the theory of stochastic processes. This thesis is centered on two unsolved problems in population dynamics: the symmetry breaking observed in foraging populations, and the robustness of spatial patterns. We argue that these problems can be resolved with the help of two novel concepts: noise-induced bistable states and stochastic patterns.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aSociophysics
606   $aEconophysics
606   $aProbabilities
606   $aBiotic communities
606   $aBiophysics
606   $aBiophysics
606   $aData-driven Science, Modeling and Theory Building$3https://scigraph.springernature.com/ontologies/product-market-codes/P33030
606   $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004
606   $aCommunity & Population Ecology$3https://scigraph.springernature.com/ontologies/product-market-codes/L19120
606   $aBiological and Medical Physics, Biophysics$3https://scigraph.springernature.com/ontologies/product-market-codes/P27008
615  0$aSociophysics.
615  0$aEconophysics.
615  0$aProbabilities.
615  0$aBiotic communities.
615  0$aBiophysics.
615  0$aBiophysics.
615 14$aData-driven Science, Modeling and Theory Building.
615 24$aProbability Theory and Stochastic Processes.
615 24$aCommunity & Population Ecology.
615 24$aBiological and Medical Physics, Biophysics.
676   $a530.15950285
700   $aBiancalani$b Tommaso$4aut$4http://id.loc.gov/vocabulary/relators/aut$0791863
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910300389403321
996   $aThe Influence of Demographic Stochasticity on Population Dynamics$92525348
997   $aUNINA