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How does the spatial heterogeneity of landscapes interact with the adaptive evolution of populations to influence their spreading speed? This question arises in agricultural contexts where a pathogen population spreads in a landscape composed of several types of crops, as well as in epidemiological settings where a virus spreads among individuals with distinct immune profiles. To address it, we introduce an analytical method based on reaction-diffusion models. We focus on spatially periodic environments with two distinct patches, where the dispersing population consists of two specialized morphs, each potentially mutating to the other. We present new formulas for the speed together with criteria for persistence, accounting for both rapidly and slowly varying environments, as well as small and large mutation rates. Altogether, our analytical and numerical results yield a comprehensive understanding of persistence and spreading dynamics. In particular, compared to a situation without mutations or to a single morph spreading in a heterogeneous landscape, the introduction of mutations to a second morph with reverse specialization, while consistently impeding persistence, can significantly increase speed, even if the mutation rate between the two morphs is very small. Additionally, we find that the amplitude of the spatial fragmentation effect is significantly increased in this case. This has implications for agroecology, emphasizing the higher importance of landscape structure in influencing adaptation-driven population dynamics., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this article, we propose a method for analyzing the spatial variations in the range expansion of the pine processionary moth (PPM), an invasive species in France. Based on binary measurements - the presence or absence of PPM nests - the proposed method allows us to infer the local effect of the environment on PPM population expansion. This effect is estimated at each position x using a parameter F(x) that corresponds to the local PPM fitness. The data type and the two stage PPM life cycle make estimating this parameter difficult. To overcome these difficulties we adopt a mechanistic-statistical approach that combines a statistical model for the observation process with a hierarchical,reaction-diffusion based mechanistic model for the expansion process. Bayesian inference of the parameter F(x) reveals that PPM fitness is spatially heterogeneous and highlights the existence of large regions associated with lower fitness. The factors underlying this lower fitness are yet to be determined., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

The presence of multiple foci in population patterns may be due to various processes arising in the population dynamics. Group dispersal, which has been lightly investigated for airborne species, is one of these processes. We built a stochastic model generating the dispersal of groups of particles. This model may be viewed as an extension of classical dispersal models based on parametric kernels. It has a hierarchical structure: at the first stage group centers are drawn under a classical dispersal kernel; at the second stage the particles are diffused around their group centers. Analytic and simulation results show that group dispersal is a sufficient condition to generate patterns with multiple foci, i.e. patchy patterns, even if the population can remain particularly concentrated., (Copyright © 2010 Elsevier Ltd. All rights reserved.)