Martinelli, L. A. and Nardoto, G. B. and Soltangheisi, A. and Reis, G. R.G. and Abdalla-Filho, A. L. and Camargo, P. B. and Domingues, T. F. and Faria, D. and Figueira, A. M. and Gomes, T. F. and Lins, S. R.M. and Mardegan, S. F. and Mariano, E. and Miatto, R. C. and Moraes, R. and Moreira, M. Z. and Oliveira, R. S. and Ometto, J. P.H.B. and Santos, F. L.S. and Sena-Souza, J. and Silva, D. M.L. and Silva, J. C.S.S. and Vieira, A. S. (2021) Determining ecosystem functioning in Brazilian biomes through foliar carbon and nitrogen concentrations and stable isotope ratios. Biogeochemistry, 154. pp. 405-423. ISSN 0168-2563
Full text not available from this repository.Abstract
By analyzing 6,480 tree leaf samples from 57 sites within Brazilian biomes, we considered whether vegetation types in terrestrial ecosystems reflect biogeochemical diversity and whether they fit into a leaf economics spectrum (LES). To achieve this, we investigated the relations among leaf carbon (C) and nitrogen (N) concentrations, their isotope natural abundance and C:N ratio. In addition, we tested their correlations with mean annual temperature (MAT) and precipitation (MAP), as climatic factors. We found consistent differences in the C and N concentrations and their isotopic composition among the vegetation types. MAP is the main climatic driver of changes in N, C:N ratio, δ15N, and δ13C, correlating negatively with N and positively with C:N ratio. These relations show that these biomes follow an LES. The Caatinga had the highest δ15N values, suggesting that N residence time in soil is longer due to low leaching and plant uptake. We observed that MAP is not the only factor influencing δ13C values in different biomes; instead canopy effect probably explains the highest values observed in the Cerrado. Our results reinforce earlier findings that life diversity in the tropics reflects biogeochemistry diversity and leaf δ15N opens the possibility for investigating plant trade-offs dictated by the LES. Finally, we expect our findings to contribute to a better understanding of the tropics in global climate models.