While Amazonian forests are extraordinarily diverse, the abundance of trees is skewed strongly towards relatively few ‘hyperdominant' species. In addition to their diversity, Amazonian trees are a key component of the global carbon cycle, assimilating and storing more carbon than any other ecosystem on Earth. Here we ask, using a unique data set of 530 forest plots, if the functions of storing and producing woody carbon are concentrated in a small number of tree species, whether the most abundant species also dominate carbon cycling, and whether dominant species are characterized by specific functional traits. We find that dominance of forest function is even more concentrated in a few species than is dominance of tree abundance, with only ≈1% of Amazon tree species responsible for 50% of carbon storage and productivity. Although those species that contribute most to biomass and productivity are often abundant, species maximum size is also influential, while the identity and ranking of dominant species varies by function and by region.

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basinwide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

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Both the rate and the vertical distribution of soil disturbance modify soil properties such as porosity, particle size, chemical composition and age structure; all of which play an important role in a soil's biogeochemical functioning. Whereas rates of mixing have been previously quantified, the nature of bioturbation's depth dependence remains poorly constrained. Here we constrain, for the first time, the relationship between mixing rate and depth in a bioturbated soil in northeast Queensland, Australia using a novel method combining OSL (optically-stimulated luminescence) ages and meteoric beryllium-10 ( 10 Be) inventories. We find that the best fit mixing rate decreases non-linearly with increasing soil depth in this soil and the characteristic length scale of 0.28 m over which the mixing coefficient decays is comparable to reported rooting depth coefficients. In addition we show that estimates of surface mixing rates from OSL data are highly dependent on erosion rate and that erosion rate must be constrained if accurate mixing rates are to be quantified. We calculate surface diffusion-like mixing coefficients of 1.8 × 10 À4 and 2.1 × 10 À4 m 2 yr À1 for the studied soil for two different estimates of soil erosion.

Abstract. Through interpretations of remote-sensing data and/or theoretical propositions, the idea that forest and savanna represent "alternative stable states" is gaining increasing acceptance. Filling an observational gap, we present detailed stratified floristic and structural analyses for forest and savanna stands located mostly within zones of transition (where both vegetation types occur in close proximity) in Africa, South America and Australia. Woody plant leaf area index variation was related to tree canopy cover in a similar way for both savanna and forest with substantial overlap between the two vegetation types. As total woody plant canopy cover increased, so did the relative contribution of middle and lower strata of woody vegetation. Herbaceous layer cover declined as woody cover increased. This pattern of understorey grasses and herbs progressively replaced by shrubs as the canopy closes over was found for both savanna and forests and on all continents. Thus, once subordinate woody canopy layers are taken into account, a less marked transition in woody plant cover across the savanna–forest-species discontinuum is observed compared to that inferred when trees of a basal diameter > 0.1 m are considered in isolation. This is especially the case for shrub-dominated savannas and in taller savannas approaching canopy closure. An increased contribution of forest species to the total subordinate cover is also observed as savanna stand canopy closure occurs. Despite similarities in canopy-cover characteristics, woody vegetation in Africa and Australia attained greater heights and stored a greater amount of above-ground biomass than in South America. Up to three times as much above-ground biomass is stored in forests compared to savannas under equivalent climatic conditions. Savanna–forest transition zones were also found to typically occur at higher precipitation regimes for South America than for Africa. Nevertheless, consistent across all three continents coexistence was found to be confined to a well-defined edaphic–climate envelope with soil and climate the key determinants of the relative location of forest and savanna stands. Moreover, when considered in conjunction with the appropriate water availability metrics, it emerges that soil exchangeable cations exert considerable control on woody canopy-cover extent as measured in our pan-continental (forest + savanna) data set. Taken together these observations do not lend support to the notion of alternate stable states mediated through fire feedbacks as the prime force shaping the distribution of the two dominant vegetation types of the tropical lands.

Abstract. Through interpretations of remote sensing data and/or theoretical propositions, the idea that forest and savanna represent "alternative stable states" is gaining increasing acceptance. Filling an observational gap, we present detailed stratified floristic and structural analyses for forest and savanna stands mostly located within zones of transition (where both vegetation types occur in close proximity) in Africa, South America and Australia. Woody plant leaf area index variation was related in a similar way to tree canopy cover for both savanna and forest with substantial overlap between the two vegetation types. As total woody plant canopy cover increased, so did the contribution of middle and lower strata of woody vegetation to this total. Herbaceous layer cover also declined as woody cover increased. This pattern of understorey grasses and herbs being progressively replaced by shrubs as canopy closure occurs was found for both savanna and forests and on all continents. Thus, once subordinate woody canopy layers are taken into account, a less marked transition in woody plant cover across the savanna-forest species discontinuum is observed compared to that implied when trees of a basal diameter > 0.1m are considered in isolation. This is especially the case for shrub-dominated savannas and in taller savannas approaching canopy closure. An increased contribution of forest species to the total subordinate cover is also observed as savanna stand canopy closure occurs. Despite similarities in canopy cover characteristics, woody vegetation in Africa and Australia attained greater heights and stored a greater concentration of above ground biomass than in South America. Up to three times as much aboveground biomass is stored in forests compared to savannas under equivalent climatic conditions. Savanna/forest transition zones were also found to typically occur at higher precipitation regimes for South America than for Africa. Nevertheless, coexistence was found to be confined to a well-defined edaphic/climate envelope consistent across all three continents with both soil and climate playing a role as the key determinants of the relative location of forest and savanna. Taken together these observations do not lend support the notion of alternate stable states mediated through fire-feedbacks as the prime force shaping the distribution of the two dominant vegetation types of the tropical lands.

Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under-and overestimate aboveground biomass by 25 % and up to 60 %, respectively. Our comparison metrics provide a quantitative measure for model-data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the Published by Copernicus Publications on behalf of the European Geosciences Union. 5204A. Rammig et al.: Pixel-to-point comparison for simulated large-scale ecosystem properties relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biomass distribution are necessary to improve DGVMs. Our approach provides robust statistical measures for any pixel-to-point comparison, which is applicable for evaluation of models and remote-sensing products.

Strong El Niño events alter tropical climates and may lead to a negative carbon balance in tropical forests and consequently a disruption to the global carbon cycle. The complexity of tropical forests and the lack of data from these regions hamper the assessment of the spatial distribution of El Niño impacts on these ecosystems. Typically, maps of climate anomaly are used to detect areas of greater risk, ignoring baseline climate conditions and forest cover. Here, we integrated climate anomalies from the 1982–1983, 1997–1998, and 2015–2016 El Niño events with baseline climate and forest edge extent, using a risk assessment approach to hypothetically assess the spatial and temporal distributions of El Niño risk over tropical forests under several risk scenarios. The drivers of risk varied temporally and spatially. Overall, the relative risk of El Niño has been increasing driven mainly by intensified forest fragmentation that has led to a greater chance of fire ignition and increased mean annual air temperatures. We identified areas of repeated high risk, where conservation efforts and fire control measures should be focused to avoid future forest degradation and negative impacts on the carbon cycle.