期刊
BIOGEOSCIENCES
卷 18, 期 24, 页码 6517-6531出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/bg-18-6517-2021
关键词
-
资金
- US Department of Energy, Office of Science, Office of Biological and Environmental Research [DE-AC02-05CH11231]
- Smithsonian Institution Competitive Grants Program for Science
- Smithsonian Tropical Research Institute fellowship program
The study utilized drone-acquired imagery data to analyze canopy disturbances in mature tropical forest on Barro Colorado Island. It found that canopy disturbance rates fluctuated significantly over time, with higher rates during the wet season, primarily driven by the frequency of extreme rainfall events.
A mechanistic understanding of how tropical-tree mortality responds to climate variation is urgently needed to predict how tropical-forest carbon pools will respond to anthropogenic global change, which is altering the frequency and intensity of storms, droughts, and other climate extremes in tropical forests. We used 5 years of approximately monthly drone-acquired RGB (red-green-blue) imagery for 50 ha of mature tropical forest on Barro Colorado Island, Panama, to quantify spatial structure; temporal variation; and climate correlates of canopy disturbances, i.e., sudden and major drops in canopy height due to treefalls, branchfalls, or the collapse of standing dead trees. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though wind speeds were lower in the wet season. The strongest correlate of monthly variation in canopy disturbance rates was the frequency of extreme rainfall events. The size distribution of canopy disturbances was best fit by a Weibull function and was close to a power function for sizes above 25 m(2). Treefalls accounted for 74 % of the total area and 52 % of the total number of canopy disturbances in treefalls and branchfalls combined. We hypothesize that extremely high rainfall is a good predictor because it is an indicator of storms having high wind speeds, as well as saturated soils that increase uprooting risk. These results demon-strate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates at fine temporal and spatial resolutions over large areas, thereby enabling robust tests of how temporal variation in disturbance relates to climate drivers. Further insights could be gained by integrating these canopy observations with high-frequency measurements of wind speed and soil moisture in mechanistic models to better evaluate proximate drivers and with focal tree observations to quantify the links to tree mortality and woody turnover.
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