Quantifying tropical forest disturbances using canopy structural traits derived from terrestrial laser scanning

Forest disturbances can reduce the potential of ecosystems to provide resources and services. Despite the urgent need to understand the effects of logging on tropical ecosystems, the quantification of disturbances arising from selective logging remains a challenge. Here, we used canopy-three-dimensional information retrieved from Terrestrial Laser Scanner (TLS) measurements to investigate the impacts of logging on key structural traits relevant to forest functioning. We addressed the following questions: 1) Which canopy structural traits were mostly affected by logging? 2) Can remotely-sensed canopy structural traits be used to quantify forest distur-bances? Fourteen canopy structural traits were applied as input to machine learning models, which were trained to quantify the intensity of logging disturbance. The plots were located in Malaysian Borneo, over a gradient of logging intensity, ranging from forest not recently disturbed by logging, to forest at the early stage of recovery following logging. Our results showed that using the Random Forest regression approach, the Plant Area Index (PAI) between 0 m -5 m aboveground, Relative Height at 50 %, and metrics describing plant allocation in the middle-higher canopy layer were the strongest predictors of disturbance. In particular, PAI between 35 m and 40 m explained 12 % to 19 % of the structural variability between plots, followed by the relative height at 50 %, (10.5 % -18.6 %), and the foliage height diversity (7.5 % -16.9 %). The approach presented in this study allowed a spatially explicitly characterization of disturbances, providing a novel approach for quantifying and monitoring the integrity of tropical forests. Our results indicate that canopy structural traits can provide a robust indication of disturbances, with strong potential to be applied at regional or global scales. The data used in this study are openly available and we encourage other researchers to use them as a benchmark data set to test larger scale approaches based on satellite and airborne platforms.Peer reviewe

Structural changes caused by selective logging undermine the thermal buffering capacity of tropical forests

Selective logging is responsible for approximately 50 % of human-induced disturbances in tropical forests. The magnitude of disturbances from logging on the structure of forests varies widely and is associated with a multitude of impacts on the forest microclimate. However, it is still unclear how changes in the spatial arrangement of vegetation arising from selective logging affect the capacity of forests to buffer large-scale climate (i.e., macroclimate) variability. In this study, we leveraged hundreds of terrestrial LiDAR measurements across tropical forests in Malaysian Borneoto quantify the impacts of logging on canopy structural traits, using a space-for-time approach. This information was combined with locally measured microclimate temperatures of the forest understory to evaluate how logging disturbances alter the capacity of tropical forests to buffer macroclimate variability. We found that heavily logged forests were approximately 12 m shorter and had 65 % lower plant area density than unlogged forests, with most plant material allocated in the first 10 m above ground. Heavily logged forests were on average 1.5 °C warmer than unlogged forests. More strikingly, we show that subtle changes in the forest structure were sufficient to reduce the cooling capacity of forests during extremely warm days (e.g., anomalies > 2σ), while understory temperatures in heavily logged forests were often warmer than the macroclimate under the same conditions. Our results thus demonstrate that selective logging is associated with substantial changes in the fine-scale thermal regime of the understory. Hence, mitigating and managing logging disturbances will be critical for maintaining niches and thermal limits within tropical forests in the future

Patterns of tropical forest understory temperatures

Abstract Temperature is a fundamental driver of species distribution and ecosystem functioning. Yet, our knowledge of the microclimatic conditions experienced by organisms inside tropical forests remains limited. This is because ecological studies often rely on coarse-gridded temperature estimates representing the conditions at 2 m height in an open-air environment (i.e., macroclimate). In this study, we present a high-resolution pantropical estimate of near-ground (15 cm above the surface) temperatures inside forests. We quantify diurnal and seasonal variability, thus revealing both spatial and temporal microclimate patterns. We find that on average, understory near-ground temperatures are 1.6 °C cooler than the open-air temperatures. The diurnal temperature range is on average 1.7 °C lower inside the forests, in comparison to open-air conditions. More importantly, we demonstrate a substantial spatial variability in the microclimate characteristics of tropical forests. This variability is regulated by a combination of large-scale climate conditions, vegetation structure and topography, and hence could not be captured by existing macroclimate grids. Our results thus contribute to quantifying the actual thermal ranges experienced by organisms inside tropical forests and provide new insights into how these limits may be affected by climate change and ecosystem disturbances