The role of fluctuations in ecological patterns and processes

Fluctuations are ubiquitous in nature and are relevant for nearly every ecological process. The main sources of fluctuations in population abundances are demographic and environmental stochasticity, whose effect on local population dynamics, metapopulations and metacommunities have attracted much interest in the ecological literature. A third source of stochasticity is demographic heterogeneity, which is the variability of demographic traits within a population. Despite the large body of literature dedicated to fluctuations in ecology, their role in some relevant ecological patterns and processes is still rather unexplored. For example, the effect of demographic and environmental stochasticity on species spread is poorly understood, mostly due to a scarcity of experimentation linking theoretical models with replicated experiments. Additionally, environmental stochasticity can induce population fluctuations and has been shown theoretically to determine the exponent of one of the most widespread scaling laws in nature, Taylor's law of fluctuation scaling. However, empirical observations point towards the existence of a single universal Taylorâs law exponent, in contrast with such model predictions. Here, experiments with protist microcosms and methods from statistical physics are used to investigate the role of fluctuations and heterogeneity on relevant ecological patterns and processes. The effect of demographic and environmental stochasticity on the propagation of biological invasions is studied in microcosm experiments with Tetrahymena sp. and Euglena gracilis and with stochastic generalizations of the Fisher-Kolmogorov equation. Demographic stochasticity is shown to induce fluctuations in the position of the propagating front and the statistical structure of the environmental heterogeneity is shown to cause a slowing-down of the invasion front at large autocorrelation lengths. The investigation of biological invasions in environments with heterogeneous distribution of resources is performed experimentally by manipulating light, the energy resource for photosynthetic organisms. Such experimental setup is further used to study phototaxis, the directed motion of phytoplankton towards or against light sources, a process that is important for relevant ecological phenomena such as diel vertical migration. A model for phototaxis is derived from the experiments in the generalized Keller-Segel framework. Large deviations theory is used to derive a generalized Taylor's law and to elucidate the origin of a universal scaling exponent as due to sampling rather than to the population growth process. The framework of finite-size scaling is used to characterize the demographic heterogeneity in a relevant ecological trait, the body size of individuals. Intra-specific body size distributions measured experimentally are shown to be described by a universal scaling distribution across different taxa and over four orders of magnitude in body size. Mathematical models of cell growth and division are shown to be compatible with the observed universal body size distribution

Connecting the Micro-dynamics to the Emergent Macro-variables: Self-Organized Criticality and Absorbing Phase Transitions in the Deterministic Lattice Gas

We reinvestigate the Deterministic Lattice Gas introduced as a paradigmatic model of the 1/f spectra (Phys. Rev. Lett. V26, 3103 (1990)) arising according to the Self-Organized Criticality scenario. We demonstrate that the density fluctuations exhibit an unexpected dependence on systems size and relate the finding to effective Langevin equations. The low density behavior is controlled by the critical properties of the gas at the absorbing state phase transition. We also show that the Deterministic Lattice Gas is in the Manna universality class of absorbing state phase transitions. This is in contrast to expectations in the literature which suggested that the entirely deterministic nature of the dynamics would put the model in a different universality class. To our knowledge this is the first fully deterministic member of the Manna universality class.Comment: 8 pages, 12 figures. Changes in the new version: Reference list has been correcte

Sample and population exponents of generalized Taylor's law

Taylor's law (TL) states that the variance $V$ of a non-negative random variable is a power function of its mean $M$, i.e. $V=a M^b$. The ubiquitous empirical verification of TL, typically displaying sample exponents $b \simeq 2$, suggests a context-independent mechanism. However, theoretical studies of population dynamics predict a broad range of values of $b$. Here, we explain this apparent contradiction by using large deviations theory to derive a generalized TL in terms of sample and populations exponents $b_{jk}$ for the scaling of the $k$-th vs the $j$-th cumulant (conventional TL is recovered for $b=b_{12}$), with the sample exponent found to depend predictably on the number of observed samples. Thus, for finite numbers of observations one observes sample exponents $b_{jk}\simeq k/j$ (thus $b\simeq2$) independently of population exponents. Empirical analyses on two datasets support our theoretical results.Comment: 41 pages, 10 figures, 6 table

On the probabilistic nature of the species-area relation

The Species-Area Relation (SAR), which describes the increase in the number of species S with increasing area A, is under intense scrutiny in contemporary ecology, in particular to probe its reliability in predicting the number of species going extinct as a direct result of habitat loss. Here, we focus on the island SAR, which is measured across a set of disjoint habitat patches, and we argue that the SAR portrays an average trend around which fluctuations are to be expected due to the stochasticity of community dynamics within the patches, external perturbations, and habitat heterogeneity across different patches. This probabilistic interpretation of the SAR, though already implicit in the theory of island biogeography and manifest in the scatter of data points in plots of empirical SAR curves, has not been investigated systematically from the theoretical point of view. Here, we show that the two main contributions to SAR fluctuations, which are due to community dynamics within the patches and to habitat heterogeneity between different patches, can be decoupled and analyzed independently. To investigate the community dynamics contribution to SAR fluctuations, we explore a suite of theoretical models of community dynamics where the number of species S inhabiting a patch emerges from diverse ecological and evolutionary processes, and we compare stationary predictions for the coefficient of variation of S, i.e. the fluctuations of S with respect to the mean. We find that different community dynamics models diverge radically in their predictions. In island biogeography and in neutral frameworks, where fluctuations are only driven by the stochasticity of diversification and extinction events, relative fluctuations decay when the mean increases. Computational evidence suggests that this result is robust in the presence of competition for space or resources. When species compete for finite resources, and mass is introduced as a trait determining species' birth, death and resource consumption rates based on empirical allometric scalings, relative fluctuations do not decay with increasing mean S due to the occasional introduction of new species with large resource demands causing mass extinctions in the community. Given this observation, we also investigate the contribution of external disturbance events to fluctuations of S in neutral community dynamics models and compare this scenario with the community dynamics in undisturbed non-neutral models. Habitat heterogeneity within a single patch, in the context of metapopulation models, causes variability in the number of coexisting species which proves negligible with respect to that caused by the stochasticity of the community dynamics. The second contribution to SAR fluctuations, which is due to habitat heterogeneity among different patches, introduces corrections to the coefficient of variation of S. Most importantly, inter-patches heterogeneity introduces a constant, lower bound on the relative fluctuations of S equal to the coefficient of variation of a habitat variable describing the heterogeneity among patches. Because heterogeneity across patches is inevitably present in natural ecosystems, we expect that the relative fluctuations of S always tend to a constant in the limit of large mean S or large patch area A, with contributions from community dynamics, inter-patches heterogeneity or both. We provide a theoretical framework for modelling these two contributions and we show that both can affect significantly the fluctuations of the SAR. (C) 2018 The Authors. Published by Elsevier Ltd

The scaling structure of the global road network

Because of increasing global urbanization and its immediate consequences, including changes in patterns of food demand, circulation and land use, the next century will witness a major increase in the extent of paved roads built worldwide. To model the effects of this increase, it is crucial to understand whether possible self-organized patterns are inherent in the global road network structure. Here, we use the largest updated database comprising all major roads on the Earth, together with global urban and cropland inventories, to suggest that road length distributions within croplands are indistinguishable from urban ones, once rescaled to account for the difference in mean road length. Such similarity extends to road length distributions within urban or agricultural domains of a given area. We find two distinct regimes for the scaling of the mean road length with the associated area, holding in general at small and at large values of the latter. In suitably large urban and cropland domains, we find that mean and total road lengths increase linearly with their domain area, differently fromearlier suggestions. Scaling regimes suggest that simple and universal mechanisms regulate urban and cropland road expansion at the global scale. As such, our findings bear implications for global road infrastructure growth based on land-use change and for planning policies sustaining urban expansions

Sensitivity of three commercial tests for SARS-CoV-2 serology in children: an Italian multicentre prospective study

US Food and Drug Administration has issued Emergency Use Authorizations for hundreds of serological assays to support Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) diagnosis. The aim of this study is to evaluate, for the first time in children, the performance of three widely utilized SARS-CoV-2 serology commercial assays, Diesse Diagnostics (IgG, IgA, IgM) and Roche Diagnostics, both Roche Nucleocapsid (N) IgG and Roche Spike (S) IgG assays. Methods: Sensitivity and 95% confidence intervals (CIs) were estimated for each of the three different serological tests and mixed and direct comparison were performed. Univariate and multivariate Poisson regression models were fitted to calculate incidence rate ratios and 95% CIs as estimate of the effects of age, gender, time on the serology title. A p-value < 0.05 indicated statistical significance. Results: Overall, 149 children were enrolled in the study. A low sensitivity was found for Diesse IgA, IgM and IgG. Compare to Diesse, Roche S had a higher sensitivity at 15-28 days from infection (0.94, 95%CI: 0.73-1.0) and Roche N at 28-84 days (0.78, 95%CI: 0.58-0.91). When a direct comparison of IgG tests sensitivity was feasible for patients with pairwise information, Roche S and Roche N showed a statistically significant higher sensitivity compared to Diesse in all the study periods, whereas there was no difference between the two Roche tests. Conclusion: Roche S and Roche N serology tests seem to better perform in children. Large prospective studies are needed to better define the characteristics of those tests

Therapeutic Value and Innovation of a Drug and Criteria for Evaluation: A Multidisciplinary Experience in the Veneto Region

n/

Mean front position and standard error, large autocorrelation treatment

This is a Mathematica list. It contains five elements, each of which corresponds to the mean front positions measured at a different threshold density, respectively: 45, 60, 75, 90 and 105 1/cm. The mean is computed across the replicates belonging to the large autocorrelation length treatment. Each element of the list contains a list of triplets: the first element of each pair is the mean time of measurement (in days), the second element is the mean front position (in cm), the third element is the standard error of the mean (in cm)