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

Abstract

Abstract. 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 m2. 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 demonstrate 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.

Soils and topography control natural disturbance rates and thereby forest structure in a lowland tropical landscape

“…Previous studies have shown that mortality rates are important for variation in standing biomass regionally and globally; we show that the same is true on a landscape scale for mature tropical forest and identify abiotic variables that control this variation. Our average canopy disturbance rates across BCI, 1.8% yr −1 to 2.1% yr −1 , are comparable to estimates of woody mortality fluxes on BCI (Meakem et al, 2018; 1.9% yr −1 of total live biomass) and disturbance rates in a previous study using higher resolution drone imagery over the 50 ha plot (Araujo et al, 2021; ~2.2% yr −1 ).…”

“…In general, forces from wind are the proximate cause of most treefalls (Gardiner, 2021), although preconditions such as heartrot and lighting damage can contribute to susceptibility to wind, and saturated soils can increase uprooting risk. Disturbance rates on BCI are highest in wet season periods with extreme precipitation, consistent with the idea that storm‐associated winds and soil saturation contribute to canopy disturbance in this landscape (Araujo et al, 2021).…”

“…For each time interval (2015-2018 and 2018-2020), we defined new canopy disturbances as areas where canopy height decreased by ≥5 m over a contiguous area ≥25 m 2 , a minimum size used in other studies of disturbances on BCI (Araujo et al, 2021;Brokaw, 1982b). We verified the accuracy of this automated canopy disturbance classification through comparison with manual classifications of disturbances from higher-resolution, monthly images over BCI's 50 ha forest dynamics plot (Araujo et al, 2021; Methods S3; Figure S5).…”

“…Drones are a promising tool for studying mortality over tropical forest landscapes, reducing the limitations of traditional remote sensing products. In particular, recent advances in drone photogrammetry allow cost‐efficient 3D reconstruction of forest canopies to monitor canopy disturbances (Araujo et al, 2021). Here, we leverage repeat drone photogrammetry to detect canopy disturbances from tree damage and mortality across a 1500 ha tropical forest landscape of Barro Colorado Island (BCI), Panama, over five years (Figure 1).…”

UAV-Based Forest Health Monitoring: A Systematic Review

“…Repetitive UAV surveys of the same area were designed to collect time series as a database for change analysis. Hence, the scientists analyzed spectral and structural changes over time to assess mechanical crown damage [97,98], fire damage based on pre-and postfire data [99][100][101], phenological differences [89], and different stages of stress-induced symptoms evident in the tree canopy [92,94,[102][103][104][105][106][107][108][109][110][111][112]. The primary period of FHM typically lay within the growing season.…”