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The complexity of forest structures plays a crucial role in regulating forest ecosystem functions and strongly influences biodiversity. Yet, knowledge of the global patterns and determinants of forest structural complexity remains scarce. Using a stand structural complexity index based on terrestrial laser scanning, we quantify the structural complexity of boreal, temperate, subtropical and tropical primary forests. We find that the global variation of forest structural complexity is largely explained by annual precipitation and precipitation seasonality (R² = 0.89). Using the structural complexity of primary forests as benchmark, we model the potential structural complexity across biomes and present a global map of the potential structural complexity of the earth´s forest ecoregions. Our analyses reveal distinct latitudinal patterns of forest structure and show that hotspots of high structural complexity coincide with hotspots of plant diversity. Considering the mechanistic underpinnings of forest structural complexity, our results suggest spatially contrasting changes of forest structure with climate change within and across biomes. Forest structure depends both on extrinsic factors such as climate and on intrinsic properties such as community composition and diversity. Here, the authors use a dataset of stand structural complexity based on LiDAR measurements to build a global map of structural complexity for primary forests, and find that precipitation variables best explain global patterns of forest structural complexity.

Logged and structurally degraded tropical forests are fast becoming one of the most prevalent land-use types throughout the tropics and are routinely assumed to be a net carbon sink because they experience rapid rates of tree regrowth. Yet this assumption is based on forest biomass inventories that record carbon stock recovery but fail to account for the simultaneous losses of carbon from soil and necromass. Here, we used forest plots and an eddy covariance tower to quantify and partition net ecosystem CO₂ exchange in Malaysian Borneo, a region that is a hot spot for deforestation and forest degradation. Our data represent the complete carbon budget for tropical forests measured throughout a logging event and subsequent recovery and found that they constitute a substantial and persistent net carbon source. Consistent with existing literature, our study showed a significantly greater woody biomass gain across moderately and heavily logged forests compared with unlogged forests, but this was counteracted by much larger carbon losses from soil organic matter and deadwood in logged forests. We estimate an average carbon source of 1.75 ± 0.94 Mg C ha−1 yr−1 within moderately logged plots and 5.23 ± 1.23 Mg C ha−1 yr−1 in unsustainably logged and severely degraded plots, with emissions continuing at these rates for at least one-decade post-logging. Our data directly contradict the default assumption that recovering logged and degraded tropical forests are net carbon sinks, implying the amount of carbon being sequestered across the world’s tropical forests may be considerably lower than currently estimated.

Monitoring tropical ecosystem services such as carbon stock and biodiversity with satellite remote sensing is essential for addressing climate change and biodiversity loss, but collecting ground truth data is costly. We investigated whether Unmanned Aerial Vehicles (UAVs) can reduce the costs. First, we developed a method to estimate Above-Ground Carbon (AGC) and biodiversity index (mixing ratio of pioneer and late-successional species) based on data derived from UAV-RGB images in four Forest Management Units (FMUs) in Malaysia. Second, we tested whether adding UAV-based ground truth (i.e., estimated carbon and biodiversity index) improves satellite-based models. We built machine learning models to estimate AGC and biodiversity index based on Landsat metrics and inventory data across Malaysia and Indonesia (395 plots). Accuracy was low without local inventory data (287 plots outside the four FMUs; R 2  = 0.43 and 0.46 for AGC and biodiversity, respectively). Adding UAV-based data ( n  = 934) significantly increased the accuracy ( R 2  = 0.51 and 0.48), which was comparable to the model with the full dataset including local inventory data ( R 2  = 0.53 and 0.60). These results underscore that integrating UAV and satellite analyses facilitates the monitoring of ecosystem services in tropical forests by reducing costs while maintaining accuracy.

Tropical forests have been recognized as important global carbon sinks and sources. However, many uncertainties about the spatial distribution of live tree above-ground biomass (AGB) remain, mostly due to limited availability of AGB field data. Recent studies in the Amazon have already shown the importance of large sample size for accurate AGB gradient analysis. Here we use a large stem density, basal area, community wood density and AGB dataset to study and explain their spatial patterns in an Asian tropical forest. Borneo, Southeast Asia. We combined stem density, basal area, community wood density and AGB data from 83 locations in Borneo with an environmental database containing elevation, climate and soil variables. The Akaike information criterion was used to select models and environmental variables that best explained the observed values of stem density, basal area, community wood density and AGB. These models were used to extrapolate these parameters across Borneo. We found that wood density, stem density, basal area and AGB respond significantly, but differentially, to the environment. AGB was only correlated with basal area, but not with stem density and community wood specific gravity. Unlike results from Amazonian forests, soil fertility was an important positive correlate for AGB in Borneo while community wood density, which is a main driver of AGB in the Neotropics, did not correlate with AGB in Borneo. Also, Borneo's average AGB of 457.1 Mg ha⁻¹ was c. 60% higher than the Amazonian average of 288.6 Mg ha⁻¹. We find evidence that this difference might be partly explained by the high density of large wind-dispersed Dipterocarpaceae in Borneo, which need to be tall and emergent to disperse their seeds. Our results emphasize the importance of Bornean forests as carbon sinks and sources due to their high carbon storage capacity.

Tropical forest above‐ground wood production (AGWP) varies substantially along environmental gradients. Some evidence suggests that AGWP may vary between regions and specifically that Asian forests have particularly high AGWP. However, comparisons across biogeographic regions using standardized methods are lacking, limiting our assessment of pan‐tropical variation in AGWP and potential causes. We sampled AGWP in NW Amazon (17 long‐term forest plots) and N Borneo (11 plots), both with abundant year‐round precipitation. Within each region, forests growing on a broad range of edaphic conditions were sampled using standardized soil and forest measurement techniques. Plot‐level AGWP was 49% greater in Borneo than in Amazonia (9.73 ± 0.56 vs. 6.53 ± 0.34 Mg dry mass ha⁻¹ a⁻¹, respectively; regional mean ± 1 SE). AGWP was positively associated with soil fertility (PCA axes, sum of bases and total P). After controlling for the edaphic environment, AGWP remained significantly higher in Bornean plots. Differences in AGWP were largely attributable to differing height–diameter allometry in the two regions and the abundance of large trees in Borneo. This may be explained, in part, by the greater solar radiation in Borneo compared with NW Amazonia. Trees belonging to the dominant SE Asian family, Dipterocarpaceae, gained woody biomass faster than otherwise equivalent, neighbouring non‐dipterocarps, implying that the exceptional production of Bornean forests may be driven by floristic elements. This dominant SE Asian family may partition biomass differently or be more efficient at harvesting resources and in converting them to woody biomass. Synthesis. N Bornean forests have much greater AGWP rates than those in NW Amazon when soil conditions and rainfall are controlled for. Greater resource availability and the highly productive dipterocarps may, in combination, explain why Asian forests produce wood half as fast again as comparable forests in the Amazon. Our results also suggest that taxonomic groups differ in their fundamental ability to capture carbon and that different tropical regions may therefore have different carbon uptake capacities due to biogeographic history.

Logged tropical forests represent a major opportunity for preserving biodiversity and sequestering carbon, playing a large role in meeting global forest restoration targets. Left alone, these ecosystems have been expected to undergo natural regeneration and succession towards old growth forests, but extreme drought events may challenge this process. While old growth forests possess a certain level of resilience, we lack understanding as to how logging may affect forest responses to drought. This study examines the drought-logging interaction in seedling dynamics within a landscape of logged and unlogged forests in Sabah Malaysia, based on 73 plots monitored before and after the 2015-16 El Niño drought. Drought increased seedling mortality in all forests, but the magnitude of this impact was modulated by logging intensity, with forests with lower canopy leaf area index and above-ground biomass experiencing greater drought induced mortality. Moreover, community traits in more heavily logged forests shifted towards being more ruderal after drought, suggesting that the trajectory of forest succession had been reversed. These results indicate that with reoccurring strong droughts under a changing climate, logged forests that have had over half of their biomass removed may suffer permanently arrested succession. Targeted management interventions may therefore be necessary to lift the vulnerable forests above the biomass threshold.

Sabah contributes 4.2 million hectares to the total Heart of Borneo (HoB) areas. Some of the forest reserves in the HoB are newly gazetted as Totally Protected Forest. Hence, their mammal diversity has to be comprehensively documented. This study aims to record the presence of terrestrial mammal species, and assess the prevalence of poaching in selected forest reserves within the Sabah HoB area. A total of 15 forest reserves were surveyed within a 5-year timeframe which recorded 60 terrestrial mammal species, including 21 Bornean endemics. The variation in total enumerated mammal species in the study sites may be derived from unequal sampling efforts, geographical factors and anthropogenic influences. The intensity of poaching within the study sites is high. Though this study is a rapid assessment, it created baseline information for mammal diversity in some of the least studied forest reserves in Sabah, important for conservation of its terrestrial mammals.

A life‐history trade‐off between low mortality in the dark and rapid growth in the light is one of the most widely accepted mechanisms underlying plant ecological strategies in tropical forests. Differences in plant functional traits are thought to underlie these distinct ecological strategies; however, very few studies have shown relationships between functional traits and demographic rates within a functional group. We present 8 years of growth and mortality data from saplings of 15 species of Dipterocarpaceae planted into logged‐over forest in Malaysian Borneo, and the relationships between these demographic rates and four key functional traits: wood density, specific leaf area (SLA), seed mass, and leaf C:N ratio. Species‐specific differences in growth rates were separated from seedling size effects by fitting nonlinear mixed‐effects models, to repeated measurements taken on individuals at multiple time points. Mortality data were analyzed using binary logistic regressions in a mixed‐effects models framework. Growth increased and mortality decreased with increasing light availability. Species differed in both their growth and mortality rates, yet there was little evidence for a statistical interaction between species and light for either response. There was a positive relationship between growth rate and the predicted probability of mortality regardless of light environment, suggesting that this relationship may be driven by a general trade‐off between traits that maximize growth and traits that minimize mortality, rather than through differential species responses to light. Our results indicate that wood density is an important trait that indicates both the ability of species to grow and resistance to mortality, but no other trait was correlated with either growth or mortality. Therefore, the growth mortality trade‐off among species of dipterocarp appears to be general in being independent of species crossovers in performance in different light environments. We analysed long term growth and mortality of tropical tree seedlings using non‐linear mixed effects models. We show a strong trade‐off between growth and mortality independent of an interaction between species and light. This trade‐off appears to be associated with wood density such that trees that have denser wood have lower diameter growth rates and lower mortality.

The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape‐level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object‐based classification was more effective at predicting liana infestation when compared to a pixel‐based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel‐based approach (RMSD = 27.0% ± 0.80) in comparison to an object‐based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object‐ versus pixel‐based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object‐based approaches which require refinement in order to accurately segment imagery across contiguous closed‐canopy forests. We conclude that the decision on whether to use a pixel‐ or object‐based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management. The ability to accurately assess liana infestation at the landscape‐level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape‐level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object‐based classification was more effective at predicting liana infestation when compared to a pixel‐based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel‐based approach (RMSD = 27.0% ± 0.80) in comparison to an object‐based approach (RMSD = 32.6% ±4.84). We demonstrate that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest which can provide a valuable tool to inform effective conservation and forest management.