Examining the effect of plant traits on moisture recycling in the Amazon Basin

Moisture recycling is an important process in the hydrological system, as well as an important ecosystem service being responsible for more than 10% of precipitation in the majority of terrestrial areas. Changes in land use are known to affect this process, however, detailed understanding on how vegetation characteristics, i.e., plant traits, are seldom included in modeling this important process. To overcome this knowledge gap, we conduct a first order examination of the effect of plant traits on recycling, where we examine how variation in plant traits influences moisture recycling properties (average and standard deviation) in the Amazon Basin. More specifically, we used remotely-sensed estimates of trait values for: Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Phosphorus Content (LPC), Leaf Nitrogen Content (LNC), as well as information on Normalised Difference Vegetation Index (NDVI), and Leaf Area Index (LAI) for 10 years (2001-2010). We link this data on plant traits to six parameters that relevant for moisture recycling, namely Evapotranspiration (ET), Potential Evapotranspiration (PET), Land Surface Temperature (LST) Day and Night, Soil Moisture (SM) and Vapour Pressure Deficit (VPD). We used multivariate regression to analyse how plant traits explain the variance of moisture recycling parameters and find that NDVI (10- 40%), LAI (10-50%) and SLA (5-20%) exert the strongest effects on moisture recycling parameters suggesting that leaf gas exchange traits are most important in comparison to the other traits. We find, however, that the strength and the directionality of the effect while variable, it matches the expectations: NDVI positively correlates with ET, PET and negatively correlates with SM and LST Night; SLA positively correlates with VPD and LST Day. These results suggest that leaf gas exchange properties operate differently during the day and night-time, likely constrained by SM availability, and are linked with VPD and ET exchanges in the direction expected. We then examined whether these patterns were exacerbated or attenuated at the extremes of plant trait values using quantile regression (5th, 50th and 95th), to find that indeed some relationships became stronger (e.g. NDVI and LST Night, LAI and PET, ET and LST Day), while others became more attenuated (e.g. LPC and VPD, NDVI and ET). Finally, we examined whether the effect of traits would be related to the sub-basin processes due to the found control of SM on trait effects and founnd that nutrient and dry matter traits became more important, mostly for the extremes of trait distributions. These results show a promising first approach to include trait distributions in modeling hydrological processes. Indeed, we find some relationships in the direction expected, exacerbated in some cases at the extremes of trait distributions, and at local scale we show that different processes control hydrological parameters in comparison to the whole basin. While promising, more and better estimates of traits through remote sensing or in situ data acquisition are necessary to gain a better understanding of which traits might need to be managed to maintain this important ecosystem service and to understand its links with the overall hydrological cycle.