Eddy covariance measurements are commonly used to quantify ecosystem flux exchange rates through time, but their interpretation depends on the spatial representativeness of measurements and environmental conditions that influence flux variability. 

This Study first evaluates whether eddy covariance measurements at the Kakubari site in Santa Rosa National Park, Costa Rica, were representative of that ecosystem under different aspects. Flux variability was evaluated across wind speed, wind direction, and footprint extension (X80) to determine the consistency when the source was different, considering a one-kilometer buffer around the tower. Secondly, an analysis was conducted to examine how environmental variables are associated with variability in ecosystem fluxes was conducted, including Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Latent Heat Flux (LE), Intrinsic Water Use Efficiency (iWUE), and Water Use Efficiency (WUE). 

Exploratory patterns were observed using Principal Component Analysis (PCA), followed by a Canonical Correlation Analysis to evaluate relationships between predictor variables (Vapor Pressure Deficit, Air temperature, Photosynthetic Active Radiation, NDVI, and EVI) and response variables (GPP, NEE, LE, iWUE, and WUE). 

The results indicate that flux variability is relatively consistent across the forest surrounding the Kakubari tower, with a coefficient of variation of 3.75% for the Gross Primary Productivity when the footprint extension (X80) changed. In contrast, a higher variability was observed when the wind speed and direction were changed (17.4% and 19.7% respectively). The canonical correlation analysis showed a strong statistical association between environmental variables and ecosystem fluxes (Rc = 0.926), with the Vapor Pressure Deficit and Air temperature as main contributors to covariation. However, these relationships reflect patterns of association rather than causality. 

Overall, the results highlight the importance of evaluating footprint representativeness when interpreting Eddy Covariance data and show that multiple environmental factors that vary together are associated with ecosystem flux variability in a tropical dry forest.

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