AUTHOR=Cicuéndez Víctor , Litago Javier , Sánchez-Girón Víctor , Román-Cascón Carlos , Recuero Laura , Saénz César , Yagüe Carlos , Palacios-Orueta Alicia TITLE=Dynamic relationships between gross primary production and energy partitioning in three different ecosystems based on eddy covariance time series analysis JOURNAL=Frontiers in Forests and Global Change VOLUME=6 YEAR=2023 URL=https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2023.1017365 DOI=10.3389/ffgc.2023.1017365 ISSN=2624-893X ABSTRACT=

Ecosystems are responsible for strong feedback processes that affect climate. The mechanisms and consequences of this feedback are uncertain and must be studied to evaluate their influence on global climate change. The main objective of this study is to assess the gross primary production (GPP) dynamics and the energy partitioning patterns in three different European forest ecosystems through time series analysis. The forest types are an Evergreen Needleleaf Forest in Finland (ENF_FI), a Deciduous Broadleaf Forest in Denmark (DBF_DK), and a Mediterranean Savanna Forest in Spain (SAV_SP). Buys-Ballot tables were used to study the intra-annual variability of meteorological data, energy fluxes, and GPP, whereas the autocorrelation function was used to assess the inter-annual dynamics. Finally, the causality of GPP and energy fluxes was studied with Granger causality tests. The autocorrelation function of the GPP, meteorological variables, and energy fluxes revealed that the Mediterranean ecosystem is more irregular and shows lower memory in the long term than in the short term. On the other hand, the Granger causality tests showed that the vegetation feedback to the atmosphere was more noticeable in the ENF_FI and the DBF_DK in the short term, influencing latent and sensible heat fluxes. In conclusion, the impact of the vegetation on the atmosphere influences the energy partitioning in a different way depending on the vegetation type, which makes the study of the vegetation dynamics essential at the local scale to parameterize these processes with more detail and build improved global models.