Front localization in a ballistic annihilation model

We study the possibility of localization of the front present in a one-dimensional ballistically-controlled annihilation model in which the two annihilating species are initially spatially separated. We construct two different classes of initial conditions, for which the front remains localized.Comment: Using elsart (Elsevier Latex macro) and epsf. 12 Pages, 2 epsf figures. Submitted to Physica

Ballistic annihilation kinetics for a multi-velocity one-dimensional ideal gas

Ballistic annihilation kinetics for a multi-velocity one-dimensional ideal gas is studied in the framework of an exact analytic approach. For an initial symmetric three-velocity distribution, the problem can be solved exactly and it is shown that different regimes exist depending on the initial fraction of particles at rest. Extension to the case of a n-velocity distribution is discussed.Comment: 19 pages, latex, uses Revtex macro

Search for universality in one-dimensional ballistic annihilation kinetics

We study the kinetics of ballistic annihilation for a one-dimensional ideal gas with continuous velocity distribution. A dynamical scaling theory for the long time behavior of the system is derived. Its validity is supported by extensive numerical simulations for several velocity distributions. This leads us to the conjecture that all the continuous velocity distributions \phi(v) which are symmetric, regular and such that \phi(0) does not vanish, are attracted in the long time regime towards the same Gaussian distribution and thus belong to the same universality class. Moreover, it is found that the particle density decays as n(t)~t^{-\alpha}, with \alpha=0.785 +/- 0.005.Comment: 8 pages, needs multicol, epsf and revtex. 8 postscript figures included. Submitted to Phys. Rev. E. Also avaiable at http://mykonos.unige.ch/~rey/publi.html#Secon

Front formation in a ballistic annihilation model

We study a simple one-dimensional model of ballisticaly-controlled annihilation in which the two annihilating species are initially spatially separated. The time dependent properties of the annihilation front are exactly derived. It is shown that the front wanders in a brownian fashion around its average value.Comment: Typeset using Latex, with Elsevier macros (elsart); 17 pages with one Latex figure and two Encapsulated Postscript figures (need epsf

Kinetics of ballistic annihilation and branching

We consider a one-dimensional model consisting of an assembly of two-velocity particles moving freely between collisions. When two particles meet, they instantaneously annihilate each other and disappear from the system. Moreover each moving particle can spontaneously generate an offspring having the same velocity as its mother with probability 1-q. This model is solved analytically in mean-field approximation and studied by numerical simulations. It is found that for q=1/2 the system exhibits a dynamical phase transition. For q<1/2, the slow dynamics of the system is governed by the coarsening of clusters of particles having the same velocities, while for q>1/2 the system relaxes rapidly towards its stationary state characterized by a distribution of small cluster sizes.Comment: 10 pages, 11 figures, uses multicol, epic, eepic and eepicemu. Also avaiable at http://mykonos.unige.ch/~rey/pubt.htm

Too many candidates: Embedded covariate selection procedure for species distribution modelling with the covsel R package

1. Selecting the best subset of covariates out of a panel of many candidates is a key and highly influential stage of the species distribution modelling process. Yet, there is currently no commonly accepted and widely adopted standard approach by which to perform this selection. 2. We introduce a two-step “embedded” covariate selection procedure aimed at optimizing the predictive ability and parsimony of species distribution models fitted in a context of high-dimensional candidate covariate space. The procedure combines a collinearity-filtering algorithm (Step A) with three model-specific embedded regularization techniques (Step B), including generalized linear model with elastic net regularization, generalized additive model with null-space penalization, and guided regularized random forest. 3. We evaluated the embedded covariate selection procedure through an example application aimed at modelling the habitat suitability of 50 species in Switzerland from a suite of 123 candidate covariates. We demonstrated the ability of the embedded covariate selection procedure to provide significantly more accurate species distribution models as compared to models obtained with alternative procedures. Model performance was independent of the characteristics of the species data, such as the number of occurrence records or their spatial distribution across the study area. 4. We implemented and streamlined our embedded covariate selection procedure in the covsel R package, paving the way for a ready-to-use, automated, covariate selection tool that was missing in the field of species distribution modelling. All the information required for installing and running the covsel R package is openly available on the GitHub repository https://github.com/N-SDM/covsel

A renormalization group study of a class of reaction-diffusion model, with particles input

We study a class of reaction-diffusion model extrapolating continuously between the pure coagulation-diffusion case ($A+A\to A$) and the pure annihilation-diffusion one ($A+A\to\emptyset$) with particles input ($\emptyset\to A$) at a rate $J$. For dimension $d\leq 2$, the dynamics strongly depends on the fluctuations while, for $d >2$, the behaviour is mean-field like. The models are mapped onto a field theory which properties are studied in a renormalization group approach. Simple relations are found between the time-dependent correlation functions of the different models of the class. For the pure coagulation-diffusion model the time-dependent density is found to be of the form $c(t,J,D) = (J/D)^{1/\delta}{\cal F}[(J/D)^{\Delta} Dt]$, where $D$ is the diffusion constant. The critical exponent $\delta$ and $\Delta$ are computed to all orders in $\epsilon=2-d$, where $d$ is the dimension of the system, while the scaling function $\cal F$ is computed to second order in $\epsilon$. For the one-dimensional case an exact analytical solution is provided which predictions are compared with the results of the renormalization group approach for $\epsilon=1$.Comment: Ten pages, using Latex and IOP macro. Two latex figures. Submitted to Journal of Physics A. Also available at http://mykonos.unige.ch/~rey/publi.htm

N‐SDM: a high‐performance computing pipeline for Nested Species Distribution Modelling

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high‐resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high‐performance computing (HPC) pipeline, we developedN‐SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments.N‐SDMwas built around a spatially‐nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales.N‐SDMallows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state‐of‐the‐art SDM features embodied inN‐SDMincludes a newly devised covariate selection procedure, five modelling algorithms, an algorithm‐specific hyperparameter grid search, and the ensemble of small‐models approach.N‐SDMis designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and runningN‐SDMis openly available on the GitHub repositoryhttps://github.com/N‐SDM/N‐SDM

N‐SDM: a high‐performance computing pipeline for Nested Species Distribution Modelling

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high-resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm-specific hyperparameter grid search, and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N-SDM is openly available on the GitHub repository https://github.com/N-SDM/N-SDM

Integrating ecosystem services within spatial biodiversity conservation prioritization in the Alps

As anthropogenic degradation of biodiversity and ecosystems increases, so does the potential threat to the supply of ecosystem services, a key contribution of nature to people. Biodiversity has often been used in spatial conservation planning and has been regarded as one among multiple services delivered by ecosystems. Hence, biodiversity conservation planning should be integrated in a framework of prioritizing services in order to inform decision-making. Here, we propose a prioritization approach based on scenarios maximising both the provision of ecosystem services and the conservation of biodiversity hotspots. Different weighting scenarios for the α-diversity in four taxonomic groups and 10 mapped ecosystem services were used to simulate varying priorities of policymakers in a mountain region. Our results illustrate how increasing priorities to ecosystem services can be disadvantageous to biodiversity. Moreover, the analysis to identify priority areas that best compromise the conservation of α-diversity and ecosystem services are predominantly not located within the current protected area network. Our analyses stress the need for an appropriate weighting of biodiversity within decision making that seek to integrate multiple ecosystem services. Our study paves the way toward further integration of multiple biodiversity groups and components, ecosystem services and various socio-economic scenarios, ultimately fuelling the development of more informed, evidence-based spatial planning decisions for conservation