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Choosing restoration strategies may depend on ecosystem's stability properties. When degraded ecosystems do not self‐perpetuate, natural regeneration can lead to system recovery, and restoration interventions are often designed to accelerate the natural regeneration process. However, when degraded systems self‐perpetuate, reestablishing functional ecosystems depends on overcoming resistance thresholds that impede recovery. In both scenarios, concentrating restoration efforts in patches of the desired state may enhance ecosystem recovery. Introducing patches of a desired state has been motivated by two frameworks: autocatalytic nucleation and the analogy to nucleation. When restoration depends on overcoming resistance thresholds, autocatalytic nucleation lowers restoration barriers by initiating a local positive feedback mechanism that is only successful when desired patches are introduced above a critical patch size. In contrast, the analogy to nucleation accelerates natural regeneration whereby desired patches interact with landscape scale factors often through directed dispersal. We compare nucleation frameworks, and discuss their applications for restoration practices. 

Abstract Meeting restoration targets may require active strategies to accelerate natural regeneration rates or overcome the resilience associated with degraded ecosystem states. Introducing desired ecosystem patches in degraded landscapes constitutes a promising active restoration strategy, with various mechanisms potentially causing these patches to become foci from which desired species can re‐establish throughout the landscape. This study considers three mechanisms previously identified as potential drivers of introduced patch dynamics: autocatalytic nucleation, directed dispersal, and resource concentration. These mechanisms reflect qualitatively different positive feedbacks. We developed an ecological model framework that compared how the occurrence of each mechanism was reflected in spatio‐temporal patch dynamics. We then analyzed the implications of these relationships for optimal restoration design. We found that patch expansion accelerated over time when driven by the autocatalytic nucleation mechanism, while patch expansion driven by the directed dispersal or resource concentration mechanisms decelerated over time. Additionally, when driven by autocatalytic nucleation, patch expansion was independent of patch position in the landscape. However, the proximity of other patches affected patch expansion either positively or negatively when driven by directed dispersal or resource concentration. For autocatalytic nucleation, introducing many small patches was a favorable strategy, provided that each individual patch exceeded a critical patch size. Introducing a single patch or a few large patches was the most effective restoration strategy to initiate the directed dispersal mechanism. Introducing many small patches was the most effective strategy for reaching restored ecosystem states driven by a resource concentration mechanism. Our model results suggest that introducing desirable patches can substantially accelerate ecosystem restoration, or even induce a critical transition from an otherwise stable degraded state toward a desired ecosystem state. However, the potential of this type of restoration strategy for a particular ecosystem may strongly depend on the mechanism driving patch dynamics. In turn, which mechanism drives patch dynamics may affect the optimal spatial design of an active restoration strategy. Each of the three mechanisms considered reflects distinct spatio‐temporal patch dynamics, providing novel opportunities for empirically identifying key mechanisms, and restoration designs that introduce desired patches in degraded landscapes according to these patch dynamics. 

Abstract Plant, soil, and aquatic microbiomes interact, but scientists often study them independently. Integrating knowledge across these traditionally separate subdisciplines will generate better understanding of microbial ecological properties. Interactions among plant, soil, and aquatic microbiomes, as well as anthropogenic factors, influence important ecosystem processes, including greenhouse gas fluxes, crop production, nonnative species control, and nutrient flux from terrestrial to aquatic habitats. Terrestrial microbiomes influence nutrient retention and particle movement, thereby influencing the composition and functioning of aquatic microbiomes, which, themselves, govern water quality, and the potential for harmful algal blooms. Understanding how microbiomes drive links among terrestrial (plant and soil) and aquatic habitats will inform management decisions influencing ecosystem services. In the present article, we synthesize knowledge of microbiomes from traditionally disparate fields and how they mediate connections across physically separated systems. We identify knowledge gaps currently limiting our abilities to actualize microbiome management approaches for addressing environmental problems and optimize ecosystem services. 

Abstract The theory of alternate stable states provides an explanation for rapid ecosystem degradation, yielding important implications for ecosystem conservation and restoration. However, utilizing this theory to initiate transitions from degraded to desired ecosystem states remains a significant challenge. Applications of the alternative stable states framework may currently be impeded by a mismatch between local‐scale driving processes and landscape‐scale emergent system transitions. We show how nucleation theory provides an elegant bridge between local‐scale positive feedback mechanisms and landscape‐scale transitions between alternate stable ecosystem states. Geometrical principles can be used to derive a critical patch radius: a spatially explicit, local description of an unstable equilibrium point. This insight can be used to derive an optimal patch size that minimizes the cost of restoration, and to provide a framework to measure the resilience of desired ecosystem states to the synergistic effects of disturbance and environmental change.