No Arabic abstract
Movement tracks of wild animals frequently fit models of anomalous rather than simple diffusion, mostly reported as ergodic superdiffusive motion combining area-restricted search within a local patch and larger-scale commuting between patches, as highlighted by the Levy walk paradigm. Since Levy walks are scale invariant, superdiffusive motion is also expected within patches, yet investigation of such local movements has been precluded by the lack of accurate high-resolution data at this scale. Here, using rich high-resolution movement datasets ($>! 7 times 10^7$ localizations) from 70 individuals and continuous-time random walk modeling, we found subdiffusive behavior and ergodicity breaking in the localized movement of three species of avian predators. Small-scale, within-patch movement was qualitatively different, not inferrable and separated from large-scale inter-patch movement via a clear phase transition. Local search is characterized by long power-law-distributed waiting times with diverging mean, giving rise to ergodicity breaking in the form of considerable variability uniquely observed at this scale. This implies that wild animal movement is scale specific rather than scale free, with no typical waiting time at the local scale. Placing these findings in the context of the static-ambush to mobile-cruise foraging continuum, we verify predictions based on the hunting behavior of the study species and the constraints imposed by their prey.
The study of record statistics of correlated series is gaining momentum. In this work, we study the records statistics of the time series of select stock market data and the geometric random walk, primarily through simulations. We show that the distribution of the age of records is a power law with the exponent $alpha$ lying in the range $1.5 le alpha le 1.8$. Further, the longest record ages follow the Fr{e}chet distribution of extreme value theory. The records statistics of geometric random walk series is in good agreement with that from the empirical stock data.
A symmetry-guided definition of time may enhance and simplify the analysis of historical series with recurrent patterns and seasonalities. By enforcing simple-scaling and stationarity of the distributions of returns, we identify a successful protocol of time definition in Finance. The essential structure of the stochastic process underlying the series can thus be analyzed within a most parsimonious symmetry scheme in which multiscaling is reduced in the quest of a time scale additive and independent of moment-order in the distribution of returns. At the same time, duration of periods in which markets remain inactive are properly quantified by the novel clock, and the corresponding (e.g., overnight) returns are consistently taken into account for financial applications.
The goal of developing a firmer theoretical understanding of inhomogenous temporal processes -- in particular, the waiting times in some collective dynamical system -- is attracting significant interest among physicists. Quantifying the deviations in the waiting-time distribution away from one generated by a random process, may help unravel the feedback mechanisms that drive the underlying dynamics. We analyze the waiting-time distributions of high frequency foreign exchange data for the best executable bid-ask prices across all major currencies. We find that the lognormal distribution yields a good overall fit for the waiting-time distribution between currency rate changes if both short and long waiting times are included. If we restrict our study to long waiting-times, each currency pairs distribution is consistent with a power law tail with exponent near to 3.5. However for short waiting times, the overall distribution resembles one generated by an archetypal complex systems model in which boundedly rational agents compete for limited resources. Our findings suggest a gradual transition arises in trading behavior between a fast regime in which traders act in a boundedly rational way, and a slower one in which traders decisions are driven by generic feedback mechanisms across multiple timescales and hence produce similar power-law tails irrespective of currency type.
Non-transitive dominance and the resulting cyclic loop of three or more competing species provide a fundamental mechanism to explain biodiversity in biological and ecological systems. Both Lotka-Volterra and May-Leonard type model approaches agree that heterogeneity of invasion rates within this loop does not hazard the coexistence of competing species. While the resulting abundances of species become heterogeneous, the species who has the smallest invasion power benefits the most from unequal invasions. Nevertheless, the effective invasion rate in a predator and prey interaction can also be modified by breaking the direction of dominance and allowing reversed invasion with a smaller probability. While this alteration has no particular consequence on the behavior within the framework of Lotka-Volterra models, the reactions of May-Leonard systems are highly different. In the latter case, not just the mentioned survival of the weakest effect vanishes, but also the coexistence of the loop cannot be maintained if the reversed invasion exceeds a threshold value. Interestingly, the extinction to a uniform state is characterized by a non-monotonous probability function. While the presence of reversed invasion does not fully diminish the evolutionary advantage of the original predator species, but this weakened effective invasion rate helps the related prey species to collect larger initial area for the final battle between them. The competition of these processes determines the likelihood in which uniform state the system terminates.
Empirical observations in marine ecosystems have suggested a balance of biological and advection time scales as a possible explanation of species coexistence. To characterise this scenario, we measure the time to fixation in neutrally evolving populations in chaotic flows. Contrary to intuition the variation of time scales does not interpolate straightforwardly between the no-flow and well-mixed limits; instead we find that fixation is the slowest at intermediate Damkohler numbers, indicating long-lasting coexistence of species. Our analysis shows that this slowdown is due to spatial organisation on an increasingly modularised network. We also find that diffusion can either slow down or speed up fixation, depending on the relative time scales of flow and evolution.