RESUMEN
Faster growth in tropical trees is usually associated with higher mortality rates, but the mechanisms underlying this relationship are poorly understood. In this study, we investigate how tree growth patterns are linked with environmental conditions and hydraulic traits, by monitoring the cambial growth of 9 tropical cloud forest tree species coupled with numerical simulations using an optimization model. We find that fast-growing trees have lower xylem safety margins than slow-growing trees and this pattern is not necessarily linked to differences in stomatal behaviour or environmental conditions when growth occurs. Instead, fast-growing trees have xylem vessels that are more vulnerable to cavitation and lower density wood. We propose the growth - xylem vulnerability trade-off represents a wood hydraulic economics spectrum similar to the classic leaf economic spectrum, and show through numerical simulations that this trade-off can emerge from the coordination between growth rates, wood density, and xylem vulnerability to cavitation. Our results suggest that vulnerability to hydraulic failure might be related with the growth-mortality trade-off in tropical trees, determining important life history differences. These findings are important in furthering our understanding of xylem hydraulic functioning and its implications on plant carbon economy.
Asunto(s)
Árboles/crecimiento & desarrollo , Xilema/fisiología , Brasil , Modelos Biológicos , Estomas de Plantas/fisiología , Árboles/fisiología , Clima Tropical , Madera/anatomía & histología , Madera/químicaRESUMEN
Native vegetation across the Brazilian Cerrado is highly heterogeneous and biodiverse and provides important ecosystem services, including carbon and water balance regulation, however, land-use changes have been extensive. Conservation and restoration of native vegetation is essential and could be facilitated by detailed landcover maps. Here, across a large case study region in Goiás State, Brazil (1.1 Mha), we produced physiognomy level maps of native vegetation (n = 8) and other landcover types (n = 5). Seven different classification schemes using different combinations of input satellite imagery were used, with a Random Forest classifier and 2-stage approach implemented within Google Earth Engine. Overall classification accuracies ranged from 88.6-92.6% for native and non-native vegetation at the formation level (stage-1), and 70.7-77.9% for native vegetation at the physiognomy level (stage-2), across the seven different classifications schemes. The differences in classification accuracy resulting from varying the input imagery combination and quality control procedures used were small. However, a combination of seasonal Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (surface reflectance) imagery resulted in the most accurate classification at a spatial resolution of 20 m. Classification accuracies when using Landsat-8 imagery were marginally lower, but still reasonable. Quality control procedures that account for vegetation burning when selecting vegetation reference data may also improve classification accuracy for some native vegetation types. Detailed landcover maps, produced using freely available satellite imagery and upscalable techniques, will be important tools for understanding vegetation functioning at the landscape scale and for implementing restoration projects.