Carbon declines along tropical forest edges correspond to heterogeneous effects on canopy structure and function

Abstract

Nearly 20% of tropical forests are within 100 m of a nonforest edge, a consequence of rapid deforestation for agriculture. Despite widespread conversion, roughly 1.2 billion ha of tropical forest remain, constituting the largest terrestrial component of the global carbon budget. Effects of deforestation on carbon dynamics in remnant forests, and spatial variation in underlying changes in structure and function at the plant scale, remain highly uncertain. Using airborne imaging spectroscopy and light detection and ranging (LiDAR) data, we mapped and quantified changes in forest structure and foliar characteristics along forest/oil palm boundaries in Malaysian Borneo to understand spatial and temporal variation in the influence of edges on aboveground carbon and associated changes in ecosystem structure and function. We uncovered declines in aboveground carbon averaging 22% along edges that extended over 100 m into the forest. Aboveground carbon losses were correlated with significant reductions in canopy height and leaf mass per area and increased foliar phosphorus, three plant traits related to light capture and growth. Carbon declines amplified with edge age. Our results indicate that carbon losses along forest edges can arise from multiple, distinct effects on canopy structure and function that vary with edge age and environmental conditions, pointing to a need for consideration of differences in ecosystem sensitivity when developing land-use and conservation strategies. Our findings reveal that, although edge effects on ecosystem structure and function vary, forests neighboring agricultural plantations are consistently vulnerable to long-lasting negative effects on fundamental ecosystem characteristics controlling primary productivity and carbon storage.

Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses

“…S13). This pattern is corroborated by similar results found in Sabah, Malaysian Borneo (31). The losses observed in our study are greater in the first 5 years after the edge creation, which are consistent with field observations in controlled experiments in the Brazilian Central Amazon ( fig.…”

“…Specifically, we (i) analyzed 16 years (2000–2015) of readily available 30-m spatial resolution Landsat-based forest cover and change datasets ( 28 ) to quantify the dynamics and age distribution of forest edges in Amazonia, (ii) processed an airborne LiDAR dataset collected across several locations in the studied area to build an empirical carbon loss model as a function of forest edge age, and last, (iii) modeled the edge-induced carbon loss across the entire Amazonia by applying the LiDAR-based carbon loss model across all pixels of the forest edge age maps. Our model is grounded on the observation ( 31 ) and concept ( 32 ) that tropical forest edges formed by deforestation continuously reduce their carbon stocks with age. Thus, we hypothesize that direct carbon losses by deforestation are followed by incremental indirect carbon losses induced by the aging of forest edges in Amazonia.…”

Drivers of carbon stocks in forest edges across Europe

“…higher wind speed and reduced moisture availability), leading to an increased tree mortality and a change in species composition near edges (i.e. more pioneer species with a lower C-storage potential) (Laurance et al, 1997;Chaplin-Kramer et al, 2015;Brinck et al, 2017;Ordway and Asner, 2020). On the contrary, most temperate broadleaved tree species seem to have a higher resilience against increased wind speeds and could profit from the improved light conditions near edges (Smith et al, 2018).…”

Airborne Lidar Sampling Pivotal for Accurate Regional AGB Predictions from Multispectral Images in Forest-Savanna Landscapes

“…Despite known limitations, multispectral spaceborne data remain widely used for AGB extrapolations. A widely held assumption is that Airborne LiDAR scanning (ALS) data ensure better model calibration, and hence partly compensates signal limitations [14,16,18,55]. Our results indeed show that model fit and error can be drastically improved, with an R 2 of 0.7 and a RMSE decrease of 30 %, when using a Random Forest model calibrated with AGB ALS reference data (RF ALS ) instead of field plots (RF FIELD ).…”

“…Due to its ability to accurately characterize the vegetation's three-dimensional structure, ALS has indeed emerged as the reference technology for mapping vegetation AGB variations at landscape scales [12,[15][16][17], although cost still prevents wall-to-wall mapping at regional or national levels. The currently held assumption seems to be that the calibration of AGB mapping models based on MS imageries is improved when using the larger calibration dataset allowed by an ALS sampling of the territory [7,18]. Table 1 presents a synthesis of a selection of previous studies that used ALS sampling to calibrate wall-to-wall vegetation AGB models from MS satellite imagery, and their performances.…”