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AbstractTheoretical studies from diverse areas of population biology have shown that demographic stochasticity can substantially impact evolutionary dynamics in finite populations, including scenarios where traits that are disfavored by natural selection can nevertheless increase in frequency through the course of evolution. Here, we analytically describe the eco-evolutionary dynamics of finite populations from demographic first principles. We investigate how noise-induced effects can alter the evolutionary fate of populations in which total population size may vary stochastically over time. Starting from a generic birth-death process, we derive a set of stochastic differential equations (SDEs) that describe the eco-evolutionary dynamics of a finite population of individuals bearing discrete traits. Our equations recover well-known descriptions of evolutionary dynamics, such as the replicator-mutator equation, the Price equation, and Fisher's fundamental theorem in the infinite population limit. For finite populations, our SDEs reveal how stochasticity can predictably bias evolutionary trajectories to favor certain traits, a phenomenon we call "noise-induced biasing." We show that noise-induced biasing acts through two distinct mechanisms, which we call the "direct" and "indirect" mechanisms. While the direct mechanism can be identified with classic bet-hedging theory, the indirect mechanism is a more subtle consequence of frequency- and density-dependent demographic stochasticity. Our equations reveal that noise-induced biasing may lead to evolution proceeding in a direction opposite to that predicted by natural selection in the infinite population limit. By extending and generalizing some standard equations of population genetics, we thus describe how demographic stochasticity appears alongside, and interacts with, the more well-understood forces of natural selection and neutral drift to determine the eco-evolutionary dynamics of finite populations of nonconstant size.
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http://dx.doi.org/10.1086/733196 | DOI Listing |
Chaos
December 2024
Department of Electrical and Computer Engineering, the Clarkson Center for Complex Systems Science, Clarkson University, Potsdam, New York 13699, USA.
Artificial Neural Networks (ANNs) have proven to be fantastic at a wide range of machine learning tasks, and they have certainly come into their own in all sorts of technologies that are widely consumed today in society as a whole. A basic task of machine learning that neural networks are well suited to is supervised learning, including when learning orbits from time samples of dynamical systems. The usual construct in ANN is to fully train all of the perhaps many millions of parameters that define the network architecture.
View Article and Find Full Text PDFAbstractTheoretical studies from diverse areas of population biology have shown that demographic stochasticity can substantially impact evolutionary dynamics in finite populations, including scenarios where traits that are disfavored by natural selection can nevertheless increase in frequency through the course of evolution. Here, we analytically describe the eco-evolutionary dynamics of finite populations from demographic first principles. We investigate how noise-induced effects can alter the evolutionary fate of populations in which total population size may vary stochastically over time.
View Article and Find Full Text PDFBeilstein J Nanotechnol
December 2024
Centro de Investigaciones en Dispositivos Semiconductores (CIDS-ICUAP), Benemérita Universidad Autónoma de Puebla (BUAP). Col. San Manuel, Cd. Universitaria, Av. San Claudio y 14 sur, Edif. IC5 y IC6. Puebla, Pue., 72507 México.
In this study, a simulation of the elementary chemical reactions during SiO film growth in a hot filament chemical vapor deposition (HFCVD) reactor was carried out using a 2D model. For the 2D simulation, the continuity, momentum, heat, and diffusion equations were solved numerically by the software COMSOL Multiphysics based on the finite element method. The model allowed for the simulation of the key parameters of the HFCVD reactor.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, 00187 Roma, Italy.
Hydrogen hydrates exhibit a rich phase diagram influenced by both pressure and temperature, with the so-called C_{2} phase emerging prominently above 2.5 GPa. In this phase, hydrogen molecules are densely packed within a cubic icelike lattice and the interaction with the surrounding water molecules profoundly affects their quantum rotational dynamics.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
The dynamics of open quantum systems can be simulated by unraveling it into an ensemble of pure state trajectories undergoing nonunitary monitored evolution, which has recently been shown to undergo measurement-induced entanglement phase transition. Here, we show that, for an arbitrary decoherence channel, one can optimize the unraveling scheme to lower the threshold for entanglement phase transition, thereby enabling efficient classical simulation of the open dynamics for a broader range of decoherence rates. Taking noisy random unitary circuits as a paradigmatic example, we analytically derive the optimum unraveling basis that on average minimizes the threshold.
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