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Plants with the C₄photosynthesis pathway typically respond to climate change differently from more common C₃-type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C₄and C₃vegetation distributions. However, current C₄vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C₄vegetation. We find that global C₄vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C₄natural grass cover due to elevated CO₂favoring C₃-type photosynthesis, and an increase in C₄crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C₄vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C₄plants in the contemporary global carbon cycle.

 

Abstract Aim Understanding the considerable variability and drivers of global leaf photosynthetic capacity [indicated by the maximum carboxylation rate standardized to 25°C ( V c,max25 )] is an essential step for accurate modelling of terrestrial plant photosynthesis and carbon uptake under climate change. Although current environmental conditions have often been connected with empirical and theoretical models to explain global V c,max25 variability through acclimatization and adaptation, long‐term evolutionary history has largely been neglected, but might also explicitly play a role in shaping the V c,max25 variability. Location Global. Time period Contemporary. Major taxa studied Terrestrial plants. Methods We compiled a geographically comprehensive global dataset of V c,max25 for C 3 plants ( n  = 6917 observations from 2157 species and 425 sites covering all major biomes world‐wide), explored the biogeographical and phylogenetic patterns of V c,max25 , and quantified the relative importance of current environmental factors and evolutionary history in driving global V c,max25 variability. Results We found that V c,max25 differed across different biomes, with higher mean values in relatively drier regions, and across different life‐forms, with higher mean values in non‐woody relative to woody plants and in legumes relative to non‐leguminous plants. The values of V c,max25 displayed a significant phylogenetic signal and diverged in a contrasting manner across phylogenetic groups, with a significant trend along the evolutionary axis towards a higher V c,max25 in more modern clades. A Bayesian phylogenetic linear mixed model revealed that evolutionary history (indicated by phylogeny and species) explained nearly 3‐fold more of the variation in global V c,max25 than present‐day environment (53 vs. 18%). Main conclusions These findings contribute to a comprehensive assessment of the patterns and drivers of global V c,max25 variability, highlighting the importance of evolutionary history in driving global V c,max25 variability, hence terrestrial plant photosynthesis. 

Abstract Plant productivity varies due to environmental heterogeneity, and theory suggests that plant diversity can reduce this variation. While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here we determine the relationship between plant diversity and spatial variability of productivity in 83 grasslands, and quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. We found that communities with higher plant species richness (alpha and gamma diversity) have lower spatial variability of productivity as reduced abundance of some species can be compensated for by increased abundance of other species. In contrast, high species dissimilarity among local communities (beta diversity) is positively associated with spatial variability of productivity, suggesting that changes in species composition can scale up to affect productivity. Experimentally increased spatial environmental heterogeneity weakens the effect of plant alpha and gamma diversity, and reveals that beta diversity can simultaneously decrease and increase spatial variability of productivity. Our findings unveil the generality of the diversity-stability theory across space, and suggest that reduced local diversity and biotic homogenization can affect the spatial reliability of key ecosystem functions. 

Abstract Plants invest a considerable amount of leaf nitrogen in the photosynthetic enzyme ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO), forming a strong coupling of nitrogen and photosynthetic capacity. Variability in the nitrogen-photosynthesis relationship indicates different nitrogen use strategies of plants (i.e., the fraction nitrogen allocated to RuBisCO; fLNR), however, the reason for this remains unclear as widely different nitrogen use strategies are adopted in photosynthesis models. Here, we use a comprehensive database of in situ observations, a remote sensing product of leaf chlorophyll and ancillary climate and soil data, to examine the global distribution in fLNR using a random forest model. We find global fLNR is 18.2 ± 6.2%, with its variation largely driven by negative dependence on leaf mass per area and positive dependence on leaf phosphorus. Some climate and soil factors (i.e., light, atmospheric dryness, soil pH, and sand) have considerable positive influences on fLNR regionally. This study provides insight into the nitrogen-photosynthesis relationship of plants globally and an improved understanding of the global distribution of photosynthetic potential.