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The balance between trees and grasses is a key aspect of savanna ecosystem functioning, and so far, believed to be regulated by resource availability, fire frequency and consumption by mammalian herbivores. Herbivores, however, also impact plant communities through the deposition of growth‐limiting nutrients in their dung and urine. Little attention has been paid to the fact that savanna herbivores produce dung containing different concentrations of nutrients and it remains unknown what the effect of this variation is on tree–grass interactions.Here, we investigate if stoichiometric differences in dung from browsers and grazers from an African savanna are large enough to influence competitive interactions between N2‐fixing trees and grasses. We performed a competition experiment with seedlings of three common N2‐fixing tree species and three common C4 grass species from a Kenyan savanna. Plants were grown in mesocosms as monocultures or mixtures with dung from either zebra (grazer) or giraffe (browser).We show that variation in dung nitrogen (N) to phosphorus (P) ratio between these herbivores was large enough to drive the competitive outcome between tree seedlings and grasses. Under zebra dung with a low N:P ratio (3.5), tree seedlings had an advantage due to increased nodulation and N2 fixation. Under giraffe dung with a high N:P ratio (6.4), grasses suppressed nodulation and tree seedling growth.Synthesis. We identify another potentially important mechanism by which mammalian herbivores can stimulate ecosystem stability, namely through the N:P ratio of their dung. This illustrates how connected browsing and grazing herbivores are in African savannas, through the forage quality they consume, the N:P stoichiometry of their dung and subsequent effects on plant competition. [ABSTRACT FROM AUTHOR]

Disentangling species strategies that confer resilience to natural disturbances is key to conserving and restoring savanna ecosystems. Fire is a recurrent disturbance in savannas, and savanna vegetation is highly adapted to and often dependent on fire. However, although the woody component of tropical savannas is well studied, we still do not understand how ground‐layer plant communities respond to fire, limiting conservation and management actions.We investigated the effects of prescribed fire on community structure and composition, and evaluated which traits are involved in plant community regeneration after fire in the cerrado ground layer. We assessed traits related to species persistence and colonization capacity after fire, including resprouter type, underground structure, fire‐induced flowering, regeneration strategy and growth form. We searched for functional groups related to response to fire, to shed light on the main strategies of post‐fire recovery among species in the ground layer.Fire changed ground‐layer community structure and composition in the short term, leading to greater plant species richness, population densities and increasing bare soil, compared with unburned communities. Eight months after fire, species abundance did not differ from pre‐disturbance values for 86% of the species, demonstrating the resilience of this layer to fire. Only one ruderal species was disadvantaged by fire and 13% of the species benefited. Rapid recovery of soil cover by native vegetation in burned areas was driven by species with high capacity to resprout and spread vegetatively. Recovery of the savanna ground‐layer community, as a whole, resulted from a combination of different species traits. We summarized these traits into five large groups, encompassing key strategies involved in ground‐layer regeneration after fire.Synthesis. Fire dramatically changes the ground layer of savanna vegetation in the short term, but the system is highly resilient, quickly recovering the pre‐fire state. Recovery involves different strategies, which we categorized into five functional groups of plant species: grasses, seeders, bloomers, undergrounders and resprouters. Knowledge of these diverse strategies should be used as a tool to assess conservation and restoration status of fire‐resilient ecosystems in the cerrado. [ABSTRACT FROM AUTHOR]

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Climate change is expected to lead to more frequent, intense and longer droughts in the future, with major implications for ecosystem processes and human livelihoods. The impacts of such droughts are already evident, with vegetation dieback reported from a range of ecosystems, including savannas, in recent years.Most of our insights into the mechanisms governing vegetation drought responses have come from forests and temperate grasslands, while responses of savannas have received less attention. Because the two life forms that dominate savannas—C3 trees and C4 grasses—respond differently to the same environmental controls, savanna responses to droughts can differ from those of forests and grasslands.Drought‐driven mortality of savanna vegetation is not readily predicted by just plant drought‐tolerance traits alone, but is the net outcome of multiple factors, including drought‐avoidance strategies, landscape and neighborhood context, and impacts of past and current stressors including fire, herbivory and inter‐life form competition.Many savannas currently appear to have the capacity to recover from moderate to severe short‐term droughts, although recovery times can be substantial. Factors facilitating recovery include the resprouting ability of vegetation, enhanced flowering and seeding and post‐drought amelioration of herbivory and fire. Future increases in drought severity, length and frequency can interrupt recovery trajectories and lead to compositional shifts, and thus pose substantial threats, particularly to arid and semi‐arid savannas.Synthesis. Our understanding of, and ability to predict, savanna drought responses is currently limited by availability of relevant data, and there is an urgent need for campaigns quantifying drought‐survival traits across diverse savannas. Importantly, these campaigns must move beyond reliance on a limited set of plant functional traits to identifying suites of physiological, morphological, anatomical and structural traits or "syndromes" that encapsulate both avoidance and tolerance strategies. There is also a critical need for a global network of long‐term savanna monitoring sites as these can provide key insights into factors influencing both resistance and resilience of different savannas to droughts. Such efforts, coupled with site‐specific rainfall manipulation experiments that characterize plant trait–drought response relationships, and modelling efforts, will enable a more comprehensive understanding of savanna drought responses. [ABSTRACT FROM AUTHOR]

Simple mathematical models can exhibit rich and complex behaviors. Prototypical examples of these drawn from biology and other disciplines have provided insights that extend well beyond the situations that inspired them. Here, we explore a set of simple, yet realistic, models for savanna-forest vegetation dynamics based on minimal ecological assumptions. These models are aimed at understanding how vegetation interacts with both climate (a primary global determinant of vegetation structure) and feedbacks with chronic disturbances from fire. The model includes three plant functional types-grasses, savanna trees and forest trees. Grass and (when they allow grass to persist in their subcanopy) savanna trees promote the spread of fires, which in turn, demographically limit trees. The model exhibits a spectacular range of behaviors. In addition to bistability, analysis reveals (i) that diverse cyclic behaviors (including limit and homo and heteroclinic cycles) occur for broad ranges of parameter space, (ii) that large shifts in landscape structure can result from endogenous dynamics and not just from external drivers or from noise, and (iii) that introducing noise into this system induces resonant and inverse resonant phenomena, some of which have never been previously observed in ecological models. Ecologically, these results raise questions about how to evaluate complicated dynamics with data. Mathematically, they lead to classes of behaviors that are likely to occur in other models with similar structure. [ABSTRACT FROM AUTHOR]