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. A mechanistic understanding of how tropical-tree mortalityresponds to climate variation is urgently needed to predict how tropical-forest carbon pools will respond to anthropogenic global change, whichis altering the frequency and intensity of storms, droughts, and otherclimate extremes in tropical forests. We used 5 years of approximatelymonthly drone-acquired RGB (red–green–blue) imagery for 50 ha of mature tropical forest onBarro Colorado Island, Panama, to quantify spatial structure; temporalvariation; and climate correlates of canopy disturbances, i.e., sudden andmajor drops in canopy height due to treefalls, branchfalls, or the collapse ofstanding dead trees. Canopy disturbance rates varied strongly over time andwere higher in the wet season, even though wind speeds were lower in the wetseason. The strongest correlate of monthly variation in canopy disturbancerates was the frequency of extreme rainfall events. The size distribution ofcanopy disturbances was best fit by a Weibull function and was close to apower function for sizes above 25 m2. Treefalls accounted for 74 %of the total area and 52 % of the total number of canopy disturbances intreefalls and branchfalls combined. We hypothesize that extremely highrainfall is a good predictor because it is an indicator of storms havinghigh wind speeds, as well as saturated soils that increase uprooting risk.These results demonstrate the utility of repeat drone-acquired data forquantifying forest canopy disturbance rates at fine temporal and spatialresolutions over large areas, thereby enabling robust tests of how temporalvariation in disturbance relates to climate drivers. Further insights couldbe gained by integrating these canopy observations with high-frequencymeasurements of wind speed and soil moisture in mechanistic models to betterevaluate proximate drivers and with focal tree observations to quantify thelinks to tree mortality and woody turnover.

biogeosciences

Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50 ha plot