Amazonia is well known for its high natural regeneration capacity; for this reason, passive restoration is normally recommended for the recovery of its degraded forests. However, highly deforested landscapes in southern Amazonia require active restoration. Since restoration methods can shape the quality and speed of early forest recovery, this study aimed to verify how active restoration pushes sites stably covered with exotic grasses towards forest recovery. We evaluated early forest succession at active restoration sites, i.e., soil plowing, direct seeding of pioneer species, and seedling stock planting at low density. We analyzed forest structure, diversity, and species composition in two age classes, 0.5–3.5 and 4.5–7.5 years old. As reference, we evaluated sites able to naturally regenerate in the same region. We sampled 36 active restoration and 31 natural regeneration sites along the Madeira River, southern Amazonia. Active restoration triggered succession to similar or higher levels of forest structure than sites where natural regeneration was taking place. The most dominant species did not overlap between active restoration and natural regeneration sites. The overall composition of species was different between the two restoration methods. Dominant species and size class distribution show that active restoration is performing successfully. Soil preparation combined with a high availability of seeds of pioneer trees resulted in a high stem density and basal area of facilitative pioneer trees. Planted seedlings added species diversity and increased density of large trees. Interventions to increase the odds of natural regeneration can be effective for non-regenerating sites in resilient landscapes.

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The reaction-diffusion model constitutes one of the most influential mathematical models to study the distribution of morphogens spreading within tissues. Despite its widespread appearance, the role that the finitude of the tissue plays in the spatiotemporal morphogen distribution predicted by the model has not been unveiled so far. In this study, we investigated the spatiotemporal distribution of a morphogen predicted by a reaction-diffusion model in a 1D finite domain as a proxy to simulate a biological tissue. We analytically solved, for the first time to our knowledge, the model assuming morphogen produced de novo within a finite domain and compared it with the scenario considering an infinite domain, which was previously solved. We explored the only relevant parameter in the reduced model, the tissue length in units of a characteristic reaction-diffusion length, and fully characterized the model behavior in terms of: i) geometrical aspects of the spatial distributions and ii) kinetic features derived from the time elapsed to reach the steady state. We found a critical tissue size that we estimated as ~3.3 characteristic reaction-diffusion lengths, above which the model assuming the infinite domain could suffice as a reasonable approximation. In contrast, for tissues smaller than the critical size, the error of assuming an infinite domain could rapidly accumulate, indicating that the model assuming finite domains is a better description. This new solution could replace the one used to estimate diffusion coefficients and degradation constants during the analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments and it could also help to improve the performance of multiscale computational approaches, which involve a morphogen dynamics scale, typically modeled with a reaction diffusion scheme. These findings could drive new modeling strategies to understand tissue morphogenesis as well as cancer invasion, among many other relevant problems in biology and medicine.