Preprints
https://doi.org/10.5194/bg-2021-102
https://doi.org/10.5194/bg-2021-102

  20 Apr 2021

20 Apr 2021

Review status: this preprint is currently under review for the journal BG.

Strong temporal variation in treefall and branchfall rates in a tropical forest is explained by rainfall: results from five years of monthly drone data for a 50-ha plot

Raquel Fernandes Araujo1, Samuel Grubinger2, Carlos Henrique Souza Celes1, Robinson I. Negrón-Juárez3, Milton Garcia1, Jonathan P. Dandois4, and Helene C. Muller-Landau1 Raquel Fernandes Araujo et al.
  • 1Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Balboa, Ancon, PO Box 0843-03092, Panama
  • 2Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
  • 3Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
  • 4Johns Hopkins University, Facilities and Real Estate, 3910 Keswick Rd. Suite N3100 Baltimore, MD 21211, USA

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 five years of approximately monthly drone-acquired RGB 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 collapse of standing dead trees. Treefalls accounted for 77 % of the total area and 60 % of the total number of canopy disturbances in treefalls and branchfalls combined. The size distribution of canopy disturbances was close to a power function for sizes above 25 m2, and best fit by a Weibull function overall. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though windspeeds were lower in the wet season.  The strongest correlate of temporal variation in canopy disturbance rates was the frequency of 1-hour rainfall events above the 99.4th percentile (here 35.7 mm hour−1, r = 0.67). We hypothesize that extreme high rainfall is associated with both saturated soils, increasing risk of uprooting, and with gusts having high horizontal and vertical windspeeds that increase stresses on tree crowns. These results demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates over large spatial scales at fine temporal and spatial resolution, thereby enabling strong tests of linkages to drivers. Future studies should include high frequency measurements of vertical and horizontal windspeeds and soil moisture to better capture proximate drivers, and incorporate additional image analyses to quantify standing dead trees in addition to treefalls.

Raquel Fernandes Araujo et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-102', Ricardo Dal Agnol da Silva, 02 May 2021
    • AC1: 'Reply on RC1', Raquel Araujo, 26 Aug 2021
  • RC2: 'Comment on bg-2021-102', Anonymous Referee #2, 02 Jul 2021
    • AC2: 'Reply on RC2', Raquel Araujo, 26 Aug 2021

Raquel Fernandes Araujo et al.

Model code and software

gap_dynamics_BCI50ha Raquel Araujo https://github.com/forestgeo/gap_dynamics_BCI50ha

Raquel Fernandes Araujo et al.

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Short summary
Our study contributed to improving the understanding of temporal variation and climate correlates of canopy disturbances mainly caused by treefalls and branchfalls. We used a unique dataset of five years of approximately monthly drone-acquired RGB imagery for 50 ha of mature tropical forest on Barro Colorado Island, Panama. We found that canopy disturbance rates were highly temporally variable, were higher in the wet season, and were well-predicted by extreme rainfall events.
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