Logging and soil nutrients independently explain plant trait expression in tropical forests

Application of hyperspectral and deep learning in farmland soil microplastic detection

The ecological environment is gravely threatened by the buildup of microplastics (MPs) in soil. Currently, there are no established techniques for detecting MPs in soil. Some of the standard chemical detection methods now in use are time-consuming and cumbersome. This research suggested a method for identifying soil microplastic polymers (MPPs) based on convolutional neural networks (CNN) and hyperspectral imaging (HSI) technologies to address this issue. The categorization model for MPPs on the soil surface was first established by simulating the natural soil environment in the lab. While decision tree (DT) and support vector machine (SVM) models' classification accuracy was 87.9 % and 85.6 %, respectively, that of CNN was 92.6 %. The HIS and CNN model combination produced the best classification results out of all of these models. Secondly, farmland in Guangzhou's Tianhe, Panyu, and Zengcheng districts was sampled for surface soil samples measuring 0-20 cm in order to confirm the model's accuracy in the actual environment. Before data analysis, the physicochemical properties of soil samples were determined by a standardization scheme. MPs in soil samples were extracted by traditional chemical detection method and their chemical properties were obtained as the results of the control group. Then, CNN was applied to hyperspectral data from soil samples collected for MPs detection. Finally, it was demonstrated that the physical and chemical properties of the soil have an impact on the accuracy of the model through the investigation of the physical and chemical characteristics of soil samples from three distinct areas. On the other hand, the results indicated that the suggested technique offers quick and non-destructive results for MPPs detection when comparing the detection results of hyperspectral and conventional chemical methods.

Divergent coupling mechanism of precipitation on plant community multifunction across alpine grassland on the Tibetan Plateau

INTRODUCTION

It is essential to understand plant adaptive strategies on plant stoichiometric traits at the species level rather than at the community level under various environmental conditions across the Tibetan Plateau (TP).

METHODS

Here, plant community function and edaphic and meteorological factors were collected at 111 sites along an extensive water-heat gradient during the peak growing season in 2015. Community-weighted mean trait (CWM) was introduced to illuminating dynamics of the functional trait at the community level.

RESULTS

Our results indicated that plant functional traits, including CWM-leaf total carbon (CWM_LTC), CWM-leaf total nitrogen (CWM_LTN), and CWM-leaf total phosphorus (CWM_LTP), showed similar and comparatively marked increases from alpine meadow (AM) to alpine steppe (AS). Moreover, since the tightly coordinated variation among each plant functional trait of AM was higher than that of AS, a more stable coupling mechanism of these plant functional traits could be observed in AM under a long-term evolutionary habit. Specifically, there was higher annual mean precipitation (AMP) in AM than that in AS significantly (P < 0.01), and AMP was significantly correlated with soil moisture and soil total phosphorus in AM. Generally, our findings suggest that precipitation determines divergent coupling plant community function in both AS and AM.

Logged tropical forests have amplified and diverse ecosystem energetics

Old-growth tropical forests are widely recognized as being immensely important for their biodiversity and high biomass1. Conversely, logged tropical forests are usually characterized as degraded ecosystems2. However, whether logging results in a degradation in ecosystem functions is less clear: shifts in the strength and resilience of key ecosystem processes in large suites of species have rarely been assessed in an ecologically integrated and quantitative framework. Here we adopt an ecosystem energetics lens to gain new insight into the impacts of tropical forest disturbance on a key integrative aspect of ecological function: food pathways and community structure of birds and mammals. We focus on a gradient spanning old-growth and logged forests and oil palm plantations in Borneo. In logged forest there is a 2.5-fold increase in total resource consumption by both birds and mammals compared to that in old-growth forests, probably driven by greater resource accessibility and vegetation palatability. Most principal energetic pathways maintain high species diversity and redundancy, implying maintained resilience. Conversion of logged forest into oil palm plantation results in the collapse of most energetic pathways. Far from being degraded ecosystems, even heavily logged forests can be vibrant and diverse ecosystems with enhanced levels of ecological function.

Shrubs Should Be Valued: The Functional Traits of Lonicera fragrantissima var. lancifolia in a Qinling Huangguan Forest Dynamics Plot, China

Previous studies have focused on the functional traits of trees, while undergrowth shrubs have not received the same attention. We collected 97 shrubs from 6 habitats in 3 diameter classes to measure the functional traits of Lonicera fragrantissima var. lancifolia, which is one of the dominant species in the shrub layer of the Qinling Huangguan plot. We found that leaf thickness (LT) decreased with an increase in diameter classes. Other functional traits did not change significantly with the diameter classes. Most of the functional traits changed with the habitats, which may be influenced by topography and soil. On the whole, Lonicera fragrantissima var. lancifolia showed low variation, which indicates that its growth was stable and good. The relationships between functional traits within species was in accordance with the leaf economic spectrum. The positive correlation between soil total nitrogen (STN) and C:N verified the “nutrition luxury hypothesis”.

Evolution and Climate Drivers of NDVI of Natural Vegetation during the Growing Season in the Arid Region of Northwest China

Vegetation plays an important role in linking water, atmosphere, and soil. The dynamic change in vegetation is an important indicator for the regulation of the terrestrial carbon balance and climate change. This study applied trend analysis, detrended correlation analysis, and the Hierarchical Partitioning Algorithm (HPA) to GIMMS NDVI3g data, meteorological data, and natural vegetation types for the period 1983 to 2015 to analyze the temporal and spatial changes in NDVI during the growing season and its driving factors in the arid region of northwestern China. The results showed that: (1) the growing season length (GSL) was delayed, with a regional trend of 8 d/33 a, due to a significant advancement in the start of the growing season (SOS, −7 d/33 a) and an insignificant delay to the end of growing season (EOS, 2 d/33 a). (2) The regional change in NDVI was mainly driven by temperature and precipitation, contributing to variations in NDVI of forest of 36% and 15%, respectively, and in the NDVI of grassland, of 35% and 21%, respectively. In particular, changes to forested land and medium-coverage grassland (Mgra) were closely related to temperature and precipitation, respectively. (3) The spatial distribution of the mean NDVI of forest was closely related with precipitation, temperature, and solar radiation, with these meteorological variables explaining 20%, 15%, and 10% of the variation in NDVI, respectively. Precipitation and solar radiation explained 29% and 17% of the variation in the NDVI of grassland, respectively. The study reveals the spatial–temporal evolution and driving mechanism of the NDVI of natural vegetation in the arid region of Northwest China, which can provide theoretical and data support for regional vegetation restoration and conservation.

Impact of Selected Environmental Factors on Variation in Leaf and Branch Traits on Endangered Karst Woody Plants of Southwest China

We explored the adaptability of endangered plants in degraded karst habitats through functional trait variation, using three endangered woody plants (E. cavaleriei, H. bodinieri and K. septentrionalis) in karst peak-cluster depression. We investigated the variation decomposition and correlation analysis of 13 branch and leaf functional traits using a mixed linear model, variance decomposition, Pearson’s correlation analysis, random forest regression, and generalized linear regression. The degree of variation in phosphorus concentration in the branches was the highest, while that in the carbon concentration in the leaves was the smallest. The variation in the carbon concentration in the branches and leaves, and the dry matter concentration in the leaves was mainly within species, while the variation in other functional traits was mainly between species. We found significant correlations among leaf traits, branch traits, and leaf–branch traits to different degrees; however, there were no significant correlations among branch traits in H. bodinieri. The significant correlations were higher in E. cavaleriei and K. septentrionalis than in H. bodinieri. Plant functional traits were influenced by soil and topographic factors, and the relationship between them varied by species. Our findings will enhance our understanding of the variation in leaf and branch traits in karst endangered plants and the adaptative strategies of endangered plants in degraded habitat, and will provide a scientific basis for vegetation conservation in the karst region of southwest China.

Functional susceptibility of tropical forests to climate change

Tropical forests are some of the most biodiverse ecosystems in the world, yet their functioning is threatened by anthropogenic disturbances and climate change. Global actions to conserve tropical forests could be enhanced by having local knowledge on the forests' functional diversity and functional redundancy as proxies for their capacity to respond to global environmental change. Here we create estimates of plant functional diversity and redundancy across the tropics by combining a dataset of 16 morphological, chemical and photosynthetic plant traits sampled from 2,461 individual trees from 74 sites distributed across four continents together with local climate data for the past half century. Our findings suggest a strong link between climate and functional diversity and redundancy with the three trait groups responding similarly across the tropics and climate gradient. We show that drier tropical forests are overall less functionally diverse than wetter forests and that functional redundancy declines with increasing soil water and vapour pressure deficits. Areas with high functional diversity and high functional redundancy tend to better maintain ecosystem functioning, such as aboveground biomass, after extreme weather events. Our predictions suggest that the lower functional diversity and lower functional redundancy of drier tropical forests, in comparison with wetter forests, may leave them more at risk of shifting towards alternative states in face of further declines in water availability across tropical regions.

Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging

Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH₄-N), sodium nitrate (nitrate nitrogen, NO₃-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH₄-N, R²_p = 0.92, RMSE_P = 0.77% and RPD = 3.63; for NO₃-N, R²_p = 0.92, RMSE_P = 0.74% and RPD = 4.17; for urea-N, R²_p = 0.96, RMSE_P = 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.

30 Years of postdisturbance recruitment in a Neotropical forest

QUESTIONS

Long-term community response to disturbance can follow manifold successional pathways depending on the interplay between various recruitment processes. Analyzing the succession of recruited communities provides a long-term perspective on forest response to disturbance. Specifically, postdisturbance recruitment trajectories assess (a) the successive phases of postdisturbance response and the role of deterministic recruitment processes, and (b) the return to predisturbance state of recruits taxonomic/functional diversity/composition.

LOCATION

Amazonian rainforest, Paracou station, French Guiana.

METHODS

We analyzed trajectories of recruited tree communities, from twelve forest plots of 6.25 ha each, during 30 years following a disturbance gradient that ranged from 10% to 60% of aboveground biomass removed. We measured recruited community taxonomic composition turnover, compared to whole predisturbance community, and assessed their functional composition by measuring the community weighted means for seven leaf, stem, and life-history functional traits. We also measured recruited community taxonomic richness, taxonomic evenness, and functional diversity and compared them to the diversity values from a random recruitment process.

RESULTS

While control plots trajectories resembled random recruitment trajectories, postdisturbance trajectories diverged significantly. This divergence corresponded to an enhanced recruitment of light-demanding species that became dominant above a disturbance intensity threshold. After breakpoints in time, though, recruitment trajectories returned to diversity values and composition similar to those of predisturbance and control plots community.

CONCLUSIONS

Following disturbance, recruitment processes specific to undisturbed community were first replaced by the emergence of more restricted, deterministic recruitment processes favoring species with efficient light use and acquisition. Then, a second phase corresponded to a decades-long recovery of recruits predisturbance taxonomic and functional diversity and composition that remained unachieved after 30 years.

Soil properties mediate ecosystem intrinsic water use efficiency and stomatal conductance via taxonomic diversity and leaf economic spectrum

The interactions between plants and soils lead to complex feedbacks that regulate intrinsic water use efficiency (iWUE) and stomatal conductance (gs) at ecosystem level and reflect water constraints on plant productivity. However, the relationships among soil properties, biodiversity, and leaf functional traits contributing to the variability in ecosystem iWUE and gs remain largely unknown. To elucidate these relationships, we used principal component analysis to reduce soil properties to a fertility spectrum and a limiting-resource spectrum across grassland, and early-, mid- and late-successional forests in a karst catchment. Leaf functional traits at community level were calculated based on leaf biomass, and were reduced to an economic spectrum and a limiting-resource spectrum. Leaf carbon (δ¹³C) and oxygen (δ¹⁸O) stable isotopes at community levels were used as proxies for ecosystem iWUE and g_s. The effects of soil properties, biodiversity (taxonomic, functional and phylogenetic diversity) and leaf traits on δ¹³C and δ¹⁸O were evaluated using structural equation models. Our results showed that variability in ecosystem iWUE and gs was determined overwhelmingly by indirect effects of soil properties via two different pathways: the soil fertility spectrum, determining the number of coexisting species (taxonomic diversity) and turnover of species (leaf economic spectrum), and the soil limiting-resource spectrum, shaping the specific phylogenetic lineages (phylogenic diversity). In addition, δ¹³C and δ¹⁸O were constrained by the interactive effects of leaf economic spectrum, and taxonomic and phylogenic diversity; total effects of biodiversity on δ¹³C and δ¹⁸O were larger than those of leaf economic spectrum. Our study highlighted the critical role of the evaluating interaction relationships between leaf functional traits, biodiversity metrics and soil properties in understanding the mechanisms of ecosystem function responding to environmental change.

An effective method for the rapid detection of microplastics in soil

Microplastic accumulation in the soil may have a negative impact on environmental security and human health. The lack of standardized method to identify and quantify microplastics in the soil is an obstacle to research. Existing techniques are time-consuming and cumbersome. We took the mixture of soil and low-density polyethylene (LDPE)∖polyvinyl chloride (PVC) as the research object to explore its spectral characteristics in the 0.6-1.8 THz (THz) band. We have proposed the new method to establish the Least Squares Support Vector Machine (LS-SVM) model using THz spectral data to quickly detect the microplastic pollution level in the soil from different regions. The local model is based on local soil training data set to predict local microplastic pollution, for LDPE, the average correlation coefficient (R) is 0.9833, and the average root mean square error (RMSE) is 0.0050, whereas for PVC, the average R is 0.9686, and the average RMSE is 0.0071. However, it seems to be useful only for local regions. The multisource model is that nine training sets are combined into one training set to simultaneously predict the degree of microplastic pollution in each area, for LDPE, the average R is 0.9895, and the average RMSE is 0.0007, for PVC, the average R is 0.9831, and the average RMSE is 0.0009. The results indicated that terahertz combined with LS-SVM model have a good effect on predicting the degree of soil microplastic pollution.

Major and persistent shifts in below-ground carbon dynamics and soil respiration following logging in tropical forests

Soil respiration is the largest carbon efflux from the terrestrial ecosystem to the atmosphere, and selective logging influences soil respiration via changes in abiotic (temperature, moisture) and biotic (biomass, productivity, quantity and quality of necromass inputs) drivers. Logged forests are a predominant feature of the tropical forest landscape, their area exceeding that of intact forest. We quantified both total and component (root, mycorrhiza, litter, and soil organic matter, SOM) soil respiration in logged (n = 5) and old-growth (n = 6) forest plots in Malaysian Borneo, a region which is a global hotspot for emission from forest degradation. We constructed a detailed below-ground carbon budget including organic carbon inputs into the system via litterfall and root turnover. Total soil respiration was significantly higher in logged forests than in old-growth forests (14.3 ± 0.23 and 12.7 ± 0.60 Mg C ha^-1  year^-1 , respectively, p = 0.037). This was mainly due to the higher SOM respiration in logged forests (55 ± 3.1% of the total respiration in logged forests vs. 50 ± 3.0% in old-growth forests). In old-growth forests, annual SOM respiration was equal to the organic carbon inputs into the soil (difference between SOM respiration and inputs 0.18 Mg C ha^-1  year^-1 , with 90% confidence intervals of -0.41 and 0.74 Mg C ha^-1  year^-1 ), indicating that the system is in equilibrium, while in logged forests SOM respiration exceeded the inputs by 4.2 Mg C ha^-1  year^-1 (90% CI of 3.6 and 4.9 Mg C ha^-1  year^-1 ), indicating that the soil is losing carbon. These results contribute towards understanding the impact of logging on below-ground carbon dynamics, which is one of the key uncertainties in estimating emissions from forest degradation. This study demonstrates how significant perturbation of the below-ground carbon balance, and consequent net soil carbon emissions, can persist for decades after a logging event in tropical forests.

Recovery of logged forest fragments in a human-modified tropical landscape during the 2015-16 El Niño

The past 40 years in Southeast Asia have seen about 50% of lowland rainforests converted to oil palm and other plantations, and much of the remaining forest heavily logged. Little is known about how fragmentation influences recovery and whether climate change will hamper restoration. Here, we use repeat airborne LiDAR surveys spanning the hot and dry 2015-16 El Niño Southern Oscillation event to measure canopy height growth across 3,300 ha of regenerating tropical forests spanning a logging intensity gradient in Malaysian Borneo. We show that the drought led to increased leaf shedding and branch fall. Short forest, regenerating after heavy logging, continued to grow despite higher evaporative demand, except when it was located close to oil palm plantations. Edge effects from the plantations extended over 300 metres into the forests. Forest growth on hilltops and slopes was particularly impacted by the combination of fragmentation and drought, but even riparian forests located within 40 m of oil palm plantations lost canopy height during the drought. Our results suggest that small patches of logged forest within plantation landscapes will be slow to recover, particularly as ENSO events are becoming more frequent.

It is unclear whether tropical forest fragments within plantation landscapes are resilient to drought. Here the authors analyse LiDAR and ground-based data from the 2015-16 El Niño event across a logging intensity gradient in Borneo. Although regenerating forests continued to grow, canopy height near oil palm plantations decreased, and a strong edge effect extended up to at least 300 m away.

Automated and accurate segmentation of leaf venation networks via deep learning

Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales due to the difficulties both in segmenting networks from images and in extracting multiscale statistics from subsequent network graph representations. Here we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets of manually defined ground-truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of six independently trained CNNs were used to segment networks from larger leaf regions (c. 100 mm² ). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision-recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles and connectivity of veins. Multiscale statistics then enabled the identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multiscale quantification of leaf vein networks, facilitating the comparison across species and the exploration of the functional significance of different leaf vein architectures.

Linking functional traits to multiscale statistics of leaf venation networks

Leaf venation networks evolved along several functional axes, including resource transport, damage resistance, mechanical strength, and construction cost. Because functions may depend on architectural features at different scales, network architecture may vary across spatial scales to satisfy functional tradeoffs. We develop a framework for quantifying network architecture with multiscale statistics describing elongation ratios, circularity ratios, vein density, and minimum spanning tree ratios. We quantify vein networks for leaves of 260 southeast Asian tree species in samples of up to 2 cm² , pairing multiscale statistics with traits representing axes of resource transport, damage resistance, mechanical strength, and cost. We show that these multiscale statistics clearly differentiate species' architecture and delineate a phenotype space that shifts at larger scales; functional linkages vary with scale and are weak, with vein density, minimum spanning tree ratio, and circularity ratio linked to mechanical strength (measured by force to punch) and elongation ratio and circularity ratio linked to damage resistance (measured by tannins); and phylogenetic conservatism of network architecture is low but scale-dependent. This work provides tools to quantify the function and evolution of venation networks. Future studies including primary and secondary veins may uncover additional insights.

Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests

Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high-resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED-2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the eastern Amazon as initial conditions to ED-2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥66%) experienced water stress with declines in ET (up to 34%) and GPP (up to 35%) and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multiyear droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are driven by not only climate and deforestation but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.

Litter and soil biogeochemical parameters as indicators of sustainable logging in Central Amazonia

One-fourth of Brazilian Amazonia is managed for timber production, but only a small portion of active logging sites follow sustainable forest management plans (SFMPs). Amazon forests without SFMPs are susceptible to deforestation because such plans integrate the use of forest products and conservation goals by allowing selective wood extraction following regulations aimed at reducing the long-term impact of logging. However, it remains uncertain whether reduced-impact selective logging typical of SFMPs (17-20 m³ ha^-1 yr^-1 of 38-70 species) changes forest regeneration, carbon (C) stocks, and nutrient cycling. Here, we tested the hypothesis that litter and soil biogeochemical parameters serve as indicators of sustainable logging as forest regeneration, C stocks, and C-to-nutrient ratios in soil and litter become progressively similar to those of primary forests as time elapses after logging. We used a chronosequence spanning nine years since logging to relate litter and soil (at 0-10, 10-30, 30-50 cm depth) C stocks and 12 and 15 biogeochemical parameters, respectively, as well as canopy cover and tree seedling density (10-150 cm tall) in upland evergreen Amazon forests. In one unlogged and four logged stands sampled three, five, seven, and nine years after logging, we compared 15 permanent plots (three replicated 0.5 ha plots per time-since-logging category). We found that five parameters explained >80% of the variation in soil and litter properties among logged and unlogged stands. Litter parameters were more sensitive to logging than soil parameters, as litter C stocks and C-to-nutrient ratios increased systematically after logging. Canopy cover decreased over time and was ~14% lower nine years after logging. Total seedling density did not change consistently over time but was ~54% higher seven years after logging. Our data suggest that the SFMP guidelines have served the purpose of maintaining soil quality and forest regeneration. Litter and soil parameters can be useful indicators of sustainable forest management in upland evergreen forests in Central Amazonia.

Imaging spectroscopy reveals the effects of topography and logging on the leaf chemistry of tropical forest canopy trees

Logging, pervasive across the lowland tropics, affects millions of hectares of forest, yet its influence on nutrient cycling remains poorly understood. One hypothesis is that logging influences phosphorus (P) cycling, because this scarce nutrient is removed in extracted timber and eroded soil, leading to shifts in ecosystem functioning and community composition. However, testing this is challenging because P varies within landscapes as a function of geology, topography and climate. Superimposed upon these trends are compositional changes in logged forests, with species with more acquisitive traits, characterized by higher foliar P concentrations, more dominant. It is difficult to resolve these patterns using traditional field approaches alone. Here, we use airborne light detection and ranging-guided hyperspectral imagery to map foliar nutrient (i.e. P, nitrogen [N]) concentrations, calibrated using field measured traits, over 400 km² of northeastern Borneo, including a landscape-level disturbance gradient spanning old-growth to repeatedly logged forests. The maps reveal that canopy foliar P and N concentrations decrease with elevation. These relationships were not identified using traditional field measurements of leaf and soil nutrients. After controlling for topography, canopy foliar nutrient concentrations were lower in logged forest than in old-growth areas, reflecting decreased nutrient availability. However, foliar nutrient concentrations and specific leaf area were greatest in relatively short patches in logged areas, reflecting a shift in composition to pioneer species with acquisitive traits. N:P ratio increased in logged forest, suggesting reduced soil P availability through disturbance. Through the first landscape scale assessment of how functional leaf traits change in response to logging, we find that differences from old-growth forest become more pronounced as logged forests increase in stature over time, suggesting exacerbated phosphorus limitation as forests recover.

Disturbance history mediates climate change effects on subtropical forest biomass and dynamics

The responses of forest communities to interacting anthropogenic disturbances like climate change and logging are poorly known. Subtropical forests have been heavily modified by humans and their response to climate change is poorly understood. We investigated the 9-year change observed in a mixed conifer-hardwood Atlantic forest mosaic that included both mature and selectively logged forest patches in subtropical South America. We used demographic monitoring data within 10 1 ha plots that were subjected to distinct management histories (plots logged until 1955, until 1987, and unlogged) to test the hypothesis that climate change affected forest structure and dynamics differentially depending on past disturbances. We determined the functional group of all species based on life-history affinities as well as many functional traits like leaf size, specific leaf area, wood density, total height, stem slenderness, and seed size data for the 66 most abundant species. Analysis of climate data revealed that minimum temperatures and rainfall have been increasing in the last few decades of the 20th century. Floristic composition differed mainly with logging history categories, with only minor change over the nine annual census intervals. Aboveground biomass increased in all plots, but increases were higher in mature unlogged forests, which showed signs of forest growth associated with increased CO2, temperature, and rainfall/treefall gap disturbance at the same time. Logged forests showed arrested succession as indicated by reduced abundances of Pioneers and biomass-accumulators like Large Seeded Pioneers and Araucaria, as well as reduced functional diversity. Management actions aimed at creating regeneration opportunities for long-lived pioneers are needed to restore community functional diversity, and ecosystem services such as increased aboveground biomass accumulation. We conclude that the effects of climate drivers on the dynamics of Brazilian mixed Atlantic forests vary with land-use legacies, and can differ importantly from the ones prevalent in better known tropical forests.

Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.