Information use shapes the dynamics of range expansions into environmental gradients

Abstract

Aim Globally, the geographical distributions of species are dynamic and strongly influenced by dispersal. At the same time, range dynamics feed back and may select for increased dispersal at expanding range fronts. This interplay between macroecological and evolutionary dynamics happens almost universally across environmental gradients and such gradients can have a direct impact on the fitness of organisms due to the match or mismatch between an individual's environmental optimum and the current conditions along the gradient. Importantly, gradients also provide individuals with information on environmental changes because dispersing individuals may sense whether environmental conditions improve or deteriorate. However, the ability of organisms to use this information on the environment and to subsequently adjust dispersal decisions plastically, that is, deciding to further disperse into the gradient or not, has been largely ignored and the macroecological consequences remain unclear. We here aim to demonstrate the impact of informed dispersal on the eco‐evolutionary dynamics of ranges. Location Laboratory and theoretical. Methods We used individual‐based simulations and controlled experiments in replicated microcosm landscapes. Range expansions of the protist model organism Tetrahymena were tracked using video recording and analysis. Results We show that information on environmental gradients had a severe impact on range dynamics and inverted the spatial distribution of population densities in comparison with controls where this information was not provided. Additionally, the use of information on gradients prevented evolutionary changes in dispersal rates and an acceleration of range expansion. Main conclusions We demonstrate the strong impact of informed dispersal and subsequent behavioural changes on range dynamics in environmental gradients. More generally, our findings highlight the importance of informed dispersal for spatial ecological and evolutionary dynamics.

Evolution of dispersal syndrome and its corresponding metabolomic changes

“…Furthermore, dispersal is known to be linked with several other ecological and evolutionary processes, such as, population stability (Dey and Joshi 2006), local adaptation (Gandon et al 1996;Lenormand 2002), speciation (reviewed in Barton 2001), coopera-tion and sociality (Le Galliard et al 2005), community dynamics (reviewed in Leibold et al 2004), species invasion (Shaw and Kokko 2015), range expansion (Travis and Dytham 2002), and disease spread (Rappole et al 2006). Not surprisingly, the causes and consequences of dispersal evolution have been a major focus of investigation for the past decade (Fronhofer and Altermatt 2015;Williams et al 2016;Fronhofer et al 2017;Ochocki and Miller 2017;Weiss-Lehman et al 2017). Not surprisingly, the causes and consequences of dispersal evolution have been a major focus of investigation for the past decade (Fronhofer and Altermatt 2015;Williams et al 2016;Fronhofer et al 2017;Ochocki and Miller 2017;Weiss-Lehman et al 2017).…”

Eusociality outcompetes egalitarian and solitary strategies when resources are limited and reproduction is costly

“…A minimization of (Figure ) thus allows us to determine the optimal group size N opt (Fronhofer, Pasurka, Mitesser, et al., ; Fronhofer, Pasurka, Poitrineau, et al., ). Clearly, it is well known that evolution does not generally maximize carrying capacity (e.g., Fronhofer & Altermatt, ; Fronhofer, Nitsche, & Altermatt, ; Matessi & Gatto, ; Reznick, Bryant, & Bashey, ). In order to show that, under the model assumptions outlined above, optimal strategies that maximize carrying capacity are indeed continuously stable strategies, we compare the results of our optimality approach with an invasibility analysis in the Appendix .…”

Evolution under pH stress and high population densities leads to increased density‐dependent fitness in the protistTetrahymena thermophila

“…Our population sampling method is adapted from well‐established protocols (Fronhofer and Altermatt ; Fronhofer et al. ). Briefly, 200 μL of culture was sampled from the population, and if cell density was too high for video analysis, diluted 1/10 or 1/100, because excessive cell density decreases the accuracy of cell recognition during video analysis.…”