Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth’s climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface.

Blue carbon (C) ecosystems are among the most effective C sinks of the biosphere, but methane (CH₄) emissions can offset their climate cooling effect. Drivers of CH₄ emissions from blue C ecosystems and effects of global change are poorly understood. Here we test for the effects of sea level rise (SLR) and its interactions with elevated atmospheric CO₂, eutrophication, and plant community composition on CH₄ emissions from an estuarine tidal wetland. Changes in CH₄ emissions with SLR are primarily mediated by shifts in plant community composition and associated plant traits that determine both the direction and magnitude of SLR effects on CH₄ emissions. We furthermore show strong stimulation of CH₄ emissions by elevated atmospheric CO₂, whereas effects of eutrophication are not significant. Overall, our findings demonstrate a high sensitivity of CH₄ emissions to global change with important implications for modeling greenhouse-gas dynamics of blue C ecosystems.

Global change is impacting plant community composition, but the mechanisms underlying these changes are unclear. Using a dataset of 58 global change experiments, we tested the five fundamental mechanisms of community change: changes in evenness and richness, reordering, species gains and losses. We found 71% of communities were impacted by global change treatments, and 88% of communities that were exposed to two or more global change drivers were impacted. Further, all mechanisms of change were equally likely to be affected by global change treatments—species losses and changes in richness were just as common as species gains and reordering. We also found no evidence of a progression of community changes, for example, reordering and changes in evenness did not precede species gains and losses. We demonstrate that all processes underlying plant community composition changes are equally affected by treatments and often occur simultaneously, necessitating a wholistic approach to quantifying community changes.

While short-term plant responses to global change are driven by physiological mechanisms, which are represented relatively well by models, long-term ecosystem responses to global change may be determined by shifts in plant community structure resulting from other ecological phenomena such as interspecific interactions, which are represented poorly by models. In single-factor scenarios, plant communities often adjust to increase ecosystem response to that factor. For instance, some early global change experiments showed that elevated CO₂ favours plants that respond strongly to elevated CO₂, generally amplifying the response of ecosystem productivity to elevated CO₂, a positive community feedback. However, most ecosystems are subject to multiple drivers of change, which can complicate the community feedback effect in ways that are more difficult to generalize. Recent studies have shown that (i) shifts in plant community structure cannot be reliably predicted from short-term plant physiological response to global change and (ii) that the ecosystem response to multi-factored change is commonly less than the sum of its parts. Here, we survey results from long-term field manipulations to examine the role community shifts may play in explaining these common findings. We use a simple model to examine the potential importance of community shifts in governing ecosystem response. Empirical evidence and the model demonstrate that with multi-factored change, the ecosystem response depends on community feedbacks, and that the magnitude of ecosystem response will depend on the relationship between plant response to one factor and plant response to another factor. Tradeoffs in the ability of plants to respond positively to, or to tolerate, different global change drivers may underlie generalizable patterns of covariance in responses to different drivers of change across plant taxa. Mechanistic understanding of these patterns will help predict the community feedbacks that determine long-term ecosystem responses.