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

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.

Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal

While deforestation has traditionally been the focus for forest canopy disturbance detection, forest degradation must not be overlooked. Both deforestation and forest degradation influence carbon loss and greenhouse gas emissions and thus must be included in activity data reporting estimates, such as for the Reduced Emissions from Deforestation and Degradation (REDD+) program. Here, we report on efforts to develop forest degradation mapping capacity in Nepal based on a pilot project in the country’s Terai region, an ecologically complex physiographic area. To strengthen Nepal’s estimates of deforestation and forest degradation, we applied the Continuous Degradation Detection (CODED) algorithm, which uses a time series of the Normalized Degradation Fraction Index (NDFI) to monitor forest canopy disturbances. CODED can detect low-grade degradation events and provides an easy-to-use graphical user interface in Google Earth Engine (GEE). Using an iterative process, we were able to create a model that provided acceptable accuracy and area estimates of forest degradation and deforestation in Terai that can be applied to the whole country. We found that between 2010 and 2020, the area affected by disturbance was substantially larger than the deforested area, over 105,650 hectares compared to 2753 hectares, respectively. Iterating across multiple parameters using the CODED algorithm in the Terai region has provided a wealth of insights not only for detecting forest degradation and deforestation in Nepal in support of activity data estimation but also for the process of using tools like CODED in applied settings. We found that model performance, measured using producer’s and user’s accuracy, varied dramatically based on the model parameters specified. We determined which parameters most altered the results through an iterative process; those parameters are described here in depth. Once CODED is combined with the description of each parameter and how it affects disturbance monitoring in a complex environment, this degradation-sensitive detection process has the potential to be highly attractive to other developing countries in the REDD+ program seeking to accurately monitor their forests.

Spatial Patterns of Canopy Disturbance and Shortleaf Pine in a Mixedwood Forest

The spatial structure of forest ecosystems is dominated by the horizontal and vertical distribution of trees and their attributes across space. Canopy disturbance is a primary regulator of forest spatial structure. Although the importance of tree spatial pattern is widely acknowledged as it affects important ecosystem processes such as regeneration and recruitment into the overstory, quantitative reference spatial conditions to inform silvicultural systems are lacking. This is especially true for mixedwood forests, defined as those that contain hardwoods and softwoods in the canopy. We used data from a preexisting network of plots in a complex-stage mixedwood stand to investigate the influence of canopy disturbance on stand and neighborhood-scale spatial patterns. We reconstructed canopy disturbance history and linked detected stand-wide and gap-scale disturbance events to establishment and spatial patterns of shortleaf pine. The majority of shortleaf pine establishment coincided with stand-wide or gap-scale disturbance. Shortleaf pine was clustered at the stand scale but was randomly distributed at the neighborhood scale (i.e. five tree clusters), which was a legacy of the historical disturbance regime. These results may be used to improve natural disturbance-based silvicultural systems to restore and maintain mixedwood forests for enhanced resilience and provisioning of ecosystem goods and services. Study Implications: Shortleaf pine was clustered into compositionally distinct patches within the oak-pine stand. Based on our findings, we recommend managers of stands with patchy species composition consider silvicultural systems that focus on patches. This approach acknowledges the effects of intrastand spatial variability of biophysical conditions and interactions with stochastically occurring canopy disturbances on regeneration and recruitment. Patch clearcuts with reserves could be implemented with the openings correspondent to microsites that favor regeneration of shortleaf pine. Similar potential approaches could be seedtree, irregular shelterwood, and other regeneration methods suited to stand conditions and the silvics of the species of interest.

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

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.

Seed Rain and Disturbance Impact Recruitment of Invasive Plants in Upland Forest

A critical question in invasion biology involves the relative importance of propagule rain and community invasibility. For plant invasions, invasibility is often related to disturbance, but few studies of forest invaders have simultaneously investigated both canopy and ground-level disturbance. We investigated the relative importance of seed rain, canopy disturbance, and soil disturbance in a mature forest in Maryland on the recruitment of four invasive species: wine raspberry (Rubus phoenicolasiusMaxim.), Japanese barberry (Berberis thunbergiiDC), multiflora rose (Rosa multifloraThunb.), and Japanese stiltgrass [Microstegium vimineum(Trin.) A. Camus]. Using complete censuses of a 9-ha plot at two points in time (2011–12 and 2014), we mapped new recruits, and related their locations to canopy and soil disturbance, as well as to a seed rain index based on locations of reproducing plants and seed-dispersal kernels.We found that propagule rain, as measured by the seed rain index, was a significant predictor of recruitment forB. thunbergii,R. phoenicolasius, andM. vimineum. ForR. multiflora, seed sources were not located, precluding assessment of propagule rain, but recruitment was linked to canopy disturbance, as was recruitment ofM. vimineum. However, because reproduction ofR. phoenicolasiusand, in some years, ofB. thunbergiiis higher in treefall gaps, these gaps experience higher propagule rain, with the result that recruitment is indirectly associated with these gaps. Ground-layer disturbance was an important predictor of recruitment only forB. thunbergii. Our findings reveal that the importance of propagule rain is the most consistent driver of recruitment, but canopy or ground-layer disturbance promotes recruitment of some invasive plant species.