## 5 Conclusions  
This study utilized a time series of eddy covariance data to elucidate the dynamics of energy and carbon fluxes at the ecosystem, overstory and understory levels within an Araucaria forest site in southern Brazil. Our analysis revealed that the Araucaria forest can act as a significant carbon sink, where the specific site absorbed, on average, 298.96 g C m\({}^{-2}\) yr\({}^{-1}\), with greater CO\({}_{2}\) absorption occurring during the SS seasons compared to AW. The overstory is identified as the primary contributor to this process, accounting for 63% of the total ecosystem's GPP, while the understory, despite being a smaller component, is responsible for 94% of ecosystem's total respiration. The study also highlights that the majority of energy fluxes, including H and LE, are driven by the overstory. The overstory contributed to 75% of the total LE of the ecosystem, emphasizing its role in regulating the microclimate and water dynamics within this forest system. Although the study spans a relatively short period, the Araucaria forest exhibits high water-use efficiency for carbon assimilation, displaying variability patterns similar to those observed in other subtropical forests. However, it is important to acknowledge that our interpretations are limited to this specific dataset and that the long-term functioning of the ecosystem should be further investigated through extended observations and/or modeling. In particular, we emphasize the importance of conserving Araucaria forests in the face of ongoing land use and land cover changes. Given the current challenges posed by deforestation in Brazil, the preservation of these ecosystems is crucial for climate change mitigation and the maintenance of biodiversity. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/f16061008/s1](https://www.mdpi.com/article/10.3390/f16061008/s1)]([https://www.mdpi.com/article/10.3390/f16061008/s1](https://www.mdpi.com/article/10.3390/f16061008/s1)), Figure S1: Daily integrated values of (a) NEE, (b) GPP, and (c) RE in g C m\({}^{-2}\) d\({}^{-1}\); (d) LE and (e) H in MJ m\({}^{-2}\) d\({}^{-1}\) for the understory and ecosystem; and (f) LAI (m\({}^{2}\) m\({}^{-2}\)) and EVI. The non-shaded and shaded areas represent the spring-summer (SS) and autumn-winter (AW) periods, respectively. Table S1: Seasonality of atmospheric variables and fluxes. Daily mean for Tair (\({}^{\circ}\)C) and VPD (hPa). Accumulated in the period for Rg (MJ m\({}^{-2}\) Season\({}^{-1}\) for AW and SS and MJ m\({}^{-2}\) yr\({}^{-1}\) for Annual); Prec (mm Season\({}^{-1}\) for AW and SS and mm yr\({}^{-1}\) for Annual); NEE, RE, and GPP (g C m\({}^{-2}\) Season\({}^{-1}\) for AW and SS and g C m\({}^{-2}\) yr\({}^{-1}\) for Annual); H and LE (MJ m\({}^{-2}\) Season\({}^{-1}\) for AW and SS and MJ m\({}^{-2}\) yr\({}^{-1}\) for Annual); and WUE (GPP/ET)(g C (kg H\({}_{2}\)O)\({}^{-1}\). Table S2: Coefficient of determination (\(r^{2}\)) between the fluxes and meteorological variables for monthly averages. M.B.D., conceptualization, data curation, formal analysis, investigation, methodology, software, validation, and writing--original draft; P.E.S.d.O., data curation, investigation, methodology, software, and writing--review and editing; V.d.A.S., formal analysis, methodology, writing--original draft, and writing--review and editing; C.A.T., data curation and investigation; H.R.Z., data curation and investigation; G.P.V., data curation and software; A.M., data curation and software; M.E.O.P., visualization and writing--review and editing; M.B.S., visualization and writing--review and editing; O.L.L.d.M., funding acquisition, project administration, and resources; G.d.O., visualization and writing--review and editing; C.A.G.S., visualization and writing--review and editing; D.R.R., formal analysis, funding acquisition, investigation, project administration, resources, supervision, writing--original draft, and writing--review and editing.