Identifying the factors modulating range expansion is essential to accurately predict changes in the spatial distribution of populations. By preventing population growth after dispersal, Allee effects can lead to front stops in discrete space, called ’pinning’ if permanent. However, other mechanisms, such as positive density-dependent dispersal, have also been shown to affect the rate of range expansion and generate discrete-space front stops, albeit temporarily. In this study, we investigated the stability of the front stops generated by such mechanisms in relation to the carrying capacity of the environment. To this end, we performed artificial range expansions in discrete space using stochastic simulations and microcosm experiments. Simulation results confirmed that density-dependent dispersal alone can generate sustained front stops, albeit for a limited range of carrying capacities. We also highlighted the synergy between Allee effects and density-dependent dispersal on pinning emergence. Experimental results, obtained using a model species known to exhibit density-dependent dispersal, but without Allee effects, confirmed the model results. Furthermore, our study raises the issue of carefully considering the conditions for pinning stability, in a stochastic context and depending on the time-scale considered.

For "results_experiment.csv": this dataset corresponds to the experiment described in the article. All the information concerning the collection or processing of the data is present in the article.

For "model.zip": the files correspond to the code used to run the simulations presented in the article. They have been specifically and entirely written for this article. The functioning of the underlying model is fully described in the article.

For "results_experiment.csv": The complete dataset is available, columns from P1 to P7 correspond to the population sizes in patches 1 to 7. The numbering used in the dataset is the same as the one presented in the article.

For "model.zip" : the .R files placed in the "script" folder call functions from the file "simulation.r" in the "src" folder. They should be therefore used together. Each file in the "script" folder can be run independently.

Simulate 100 generations by running the following from the "model.zip" folder: Rscript script/simul_100_gen.r <r> <rho> <tau> <alpha>. The values between "<>" should be indicated, and are explained in the article.

Simulate 10k generations by running the following from the "model.zip" folder: Rscript script/simul_10k_gen.r <r> <rho> <tau> <alpha>. The values between "<>" should be indicated, and are explained in the article.