Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest

Drought shortens subtropical understory growing season by advancing leaf senescence

Subtropical forests, recognized for their intricate vertical canopy stratification, exhibit high resistance to extreme drought. However, the response of leaf phenology to drought in the species-rich understory remains poorly understood. In this study, we constructed a digital camera system, amassing over 360,000 images through a 70% throughfall exclusion experiment, to explore the drought response of understory leaf phenology. The results revealed a significant advancement in understory leaf senescence phenology under drought, with 11.75 and 15.76 days for the start and end of the leaf-falling event, respectively. Pre-season temperature primarily regulated leaf development phenology, whereas soil water dominated the variability in leaf senescence phenology. Under drought conditions, temperature sensitivities for the end of leaf emergence decreased from -13.72 to -11.06 days °C-1, with insignificance observed for the start of leaf emergence. Consequently, drought treatment shortened both the length of the growing season (15.69 days) and the peak growth season (9.80 days) for understory plants. Moreover, this study identified diverse responses among intraspecies and interspecies to drought, particularly during the leaf development phase. These findings underscore the pivotal role of water availability in shaping understory phenology patterns, especially in subtropical forests.

Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue

This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.

Asymmetric response of Amazon forest water and energy fluxes to wet and dry hydrological extremes reveals onset of a local drought-induced tipping point

Understanding the effects of intensification of Amazon basin hydrological cycling-manifest as increasingly frequent floods and droughts-on water and energy cycles of tropical forests is essential to meeting the challenge of predicting ecosystem responses to climate change, including forest "tipping points". Here, we investigated the impacts of hydrological extremes on forest function using 12+ years of observations (between 2001-2020) of water and energy fluxes from eddy covariance, along with associated ecological dynamics from biometry, at the Tapajós National Forest. Measurements encompass the strong 2015-2016 El Niño drought and La Niña 2008-2009 wet events. We found that the forest responded strongly to El Niño-Southern Oscillation (ENSO): Drought reduced water availability for evapotranspiration (ET) leading to large increases in sensible heat fluxes (H). Partitioning ET by an approach that assumes transpiration (T) is proportional to photosynthesis, we found that water stress-induced reductions in canopy conductance (Gs ) drove T declines partly compensated by higher evaporation (E). By contrast, the abnormally wet La Niña period gave higher T and lower E, with little change in seasonal ET. Both El Niño-Southern Oscillation (ENSO) events resulted in changes in forest structure, manifested as lower wet-season leaf area index. However, only during El Niño 2015-2016, we observed a breakdown in the strong meteorological control of transpiration fluxes (via energy availability and atmospheric demand) because of slowing vegetation functions (via shutdown of Gs and significant leaf shedding). Drought-reduced T and Gs , higher H and E, amplified by feedbacks with higher temperatures and vapor pressure deficits, signaled that forest function had crossed a threshold, from which it recovered slowly, with delay, post-drought. Identifying such tipping point onsets (beyond which future irreversible processes may occur) at local scale is crucial for predicting basin-scale threshold-crossing changes in forest energy and water cycling, leading to slow-down in forest function, potentially resulting in Amazon forests shifting into alternate degraded states.

StrucNet: a global network for automated vegetation structure monitoring

Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

Leaf morpho-physiological traits of Populus sibirica and Ulmus pumila in different irrigation regimes and fertilizer types

Background

The impacts of climate change, such as increased soil dryness and nutrient deficiency, highlight the need for environmentally sustainable restoration of forests and groundwater resources. However, it is important to consider that extensive afforestation efforts may lead to a depletion of groundwater supply due to higher evapotranspiration rates, exacerbating water scarcity issues. Consequently, we conducted a study to examine how the fast-growing tree species Populus sibirica (Horth ex Tausch) and Ulmus pumila (L.) respond morpho-physiologically to varying watering regimes and types of fertilizers, aiming to better understand their specific water and nutrient requirements.

Methods

We used two-year-old nursery-growth seedlings (N = 512) of P. sibirica and U. pumila with initial root collar diameter (RCD) and the height of 0.51 ± 0.02 mm and 68 ± 2.94 cm and 0.33 ± 0.01 mm and 51 ± 1.14 cm, respectively. The leaf area (LA), specific leaf area (SLA), chlorophyll concentration, stomatal conductance (gs), chlorophyll fluorescence, and predawn and midday leaf water potential were measured across treatments. Four different irrigation regimes and two different fertilizer types were applied: no irrigation (control, 0 L h-1), 2 L h-1 = 0.25 mm m-2, 4 L h-1 = 0.5 mm m-2, 8 L h-1 = 1.0 mm m-2 and 120 g and 500 g tree-1 of NPK and compost (COMP). Twelve plots (600 m2) were established in the study site for each species and treatments.

Results

During the first growing season (2021), the LA of P. sibirica was larger in the 4-8 L h-1 without fertilizer, but it was smaller in the 4 L h-1+ COMP during the second growing season (2022). The 2 L h-1 without fertilizer and 2 L h-1 + NPK had larger LA compared with the control (CONT) for the first and second growing seasons, respectively, for U. pumila. P. sibirica seedlings at 4 L h-1 without fertilizer had the highest SLA for 2021 and at 2 L h-1 + NPK for 2022, whereas CONT and 4 L h-1 had the highest SLA than the other treatments for 2021 and 2022 growing seasons, respectively, for U. pumila. The chlorophyll concentration of P. sibirica seedlings in the first year was generally higher in CONT, while the 2 L h-1 without any fertilizer yielded a significantly higher chlorophyll concentration of U. pumila. Chlorophyll fluorescence parameters (PIABS and Fm) were generally lower in CONT with/without NPK or COMP for both species. The CONT with NPK/COMP generally had a higher gs compared with the other treatments in both experimental periods for U. pumila, whereas CONT and 2 L h-1+ NPK-treated P. sibirica seedlings had a significantly greater gs during the first year and second year, respectively. The predawn and midday leaf water potentials of both species were generally the lowest in CONT, followed by 2 L h-1+ NPK/COMP during the first growing season, but a different pattern was observed during the second growing season. Overall, the morpho-physiological traits of the two species were affected by watering and fertilizer treatments, and the magnitude of the effects varied depending on growing season, amount of irrigation, and fertilizer type, and their interactions.

Patterns of physical, chemical, and metabolic characteristics of sugar maple leaves with depth in the crown and in response to nitrogen and phosphorus addition

Few previous studies have described patterns of leaf characteristics in response to nutrient availability and depth in the crown. Sugar maple has been studied for both sensitivity to light, as a shade-tolerant species, and sensitivity to soil nutrient availability, as a species in decline due to acid rain. To explore leaf characteristics from the top to bottom of the canopy, we collected leaves along a vertical gradient within mature sugar maple crowns in a full-factorial nitrogen by phosphorus addition experiment in three forest stands in central New Hampshire, USA. Thirty-two of the 44 leaf characteristics had significant relationships with depth in the crown, with the effect of depth in the crown strongest for leaf area, photosynthetic pigments, and polyamines. Nitrogen addition had a strong impact on the concentration of foliar N, chlorophyll, carotenoids, alanine, and glutamate. For several other elements and amino acids, N addition changed patterns with depth in the crown. Phosphorus addition increased foliar P and B; it also caused a steeper increase of P and B with depth in the crown. Since most of these leaf characteristics play a direct or indirect role in photosynthesis, metabolic regulation, or cell division, studies that ignore the vertical gradient may not accurately represent whole-canopy performance.

The other side of tropical forest drought: do shallow water table regions of Amazonia act as large-scale hydrological refugia from drought?

Tropical forest function is of global significance to climate change responses, and critically determined by water availability patterns. Groundwater is tightly related to soil water through the water table depth (WT), but historically neglected in ecological studies. Shallow WT forests (WT < 5 m) are underrepresented in forest research networks and absent in eddy flux measurements, although they represent c. 50% of the Amazon and are expected to respond differently to global-change-related droughts. We review WT patterns and consequences for plants, emerging results, and advance a conceptual model integrating environment and trait distributions to predict climate change effects. Shallow WT forests have a distinct species composition, with more resource-acquisitive and hydrologically vulnerable trees, shorter canopies and lower biomass than deep WT forests. During 'normal' climatic years, shallow WT forests have higher mortality and lower productivity than deep WT forests, but during moderate droughts mortality is buffered and productivity increases. However, during severe drought, shallow WT forests may be more sensitive due to shallow roots and drought-intolerant traits. Our evidence supports the hypothesis of neglected shallow WT forests being resilient to moderate drought, challenging the prevailing view of widespread negative effects of climate change on Amazonian forests that ignores WT gradients, but predicts they could collapse under very strong droughts.

Thermal sensitivity across forest vertical profiles: patterns, mechanisms, and ecological implications

Rising temperatures are influencing forests on many scales, with potentially strong variation vertically across forest strata. Using published research and new analyses, we evaluate how microclimate and leaf temperatures, traits, and gas exchange vary vertically in forests, shaping tree, and ecosystem ecology. In closed-canopy forests, upper canopy leaves are exposed to the highest solar radiation and evaporative demand, which can elevate leaf temperature (T_leaf ), particularly when transpirational cooling is curtailed by limited stomatal conductance. However, foliar traits also vary across height or light gradients, partially mitigating and protecting against the elevation of upper canopy T_leaf . Leaf metabolism generally increases with height across the vertical gradient, yet differences in thermal sensitivity across the gradient appear modest. Scaling from leaves to trees, canopy trees have higher absolute metabolic capacity and growth, yet are more vulnerable to drought and damaging T_leaf than their smaller counterparts, particularly under climate change. By contrast, understory trees experience fewer extreme high T_leaf 's but have fewer cooling mechanisms and thus may be strongly impacted by warming under some conditions, particularly when exposed to a harsher microenvironment through canopy disturbance. As the climate changes, integrating the patterns and mechanisms reviewed here into models will be critical to forecasting forest-climate feedback.

Effects of Ground Subsidence on Vegetation Chlorophyll Content in Semi-Arid Mining Area: From Leaf Scale to Canopy Scale

Ground subsidence is the main cause of vegetation degradation in mining areas. It is of great significance to study the effects of ground subsidence on vegetation. At present, few studies have analyzed the effects of ground subsidence on vegetation from different scales. However, the conclusions on different scales may differ. In this experiment, chlorophyll content was used as an indicator of vegetation degradation. We conducted a long-term field survey in the Lijiahao coalfield in China. Based on field survey data and remote sensing images, we analyzed the effects of ground subsidence on chlorophyll content from two scales (leaf scale and canopy scale) and summarized the similarities and differences. We found that, regardless of leaf scale or canopy scale, the effects of subsidence on chlorophyll content have the following three characteristics: (1) mining had the least effect on chlorophyll content in the neutral area, followed by the compression area, and the greatest effect on chlorophyll content in the extension area; (2) subsidence had a slight effect on chlorophyll content of Caragana korshins, but a serious effect on chlorophyll content of Stipa baicalensis; (3) chlorophyll content was not immediately affected when the ground sank. It was the cumulative subsidence that affects chlorophyll content. The difference between leaf scale and canopy scale was that the chlorophyll content at canopy scale is more affected by mining. This means that when assessing vegetation degradation, the results obtained by remote sensing were more severe than those measured in the field. We believe that this is because the canopy chlorophyll content obtained by remote sensing is also affected by the plant canopy structure. We recommend that mining and ecological restoration should be carried out concurrently, and that ground fissures should be taken as the focus of ecological restoration. In addition, Caragana korshins ought to be widely planted. Most importantly, managers should assess the effects of ground subsidence on vegetation on different scales. However, managers need to be aware of differences at different scales.

Plant sizes and shapes above and belowground and their interactions with climate

Although the above and belowground sizes and shapes of plants strongly influence plant competition, community structure, and plant-environment interactions, plant sizes and shapes remain poorly characterized across climate regimes. We investigated relationships among shoot and root system size and climate. We assembled and analyzed, to our knowledge, the largest global database describing the maximum rooting depth, lateral spread, and shoot size of terrestrial plants - more than doubling the Root Systems of Individual Plants database to 5647 observations. Water availability and growth form greatly influence shoot size, and rooting depth is primarily influenced by temperature seasonality. Shoot size is the strongest predictor of lateral spread, with root system diameter being two times wider than shoot width on average for woody plants. Shoot size covaries strongly with rooting system size; however, the geometries of plants differ considerably across climates, with woody plants in more arid climates having shorter shoots, but deeper, narrower root systems. Additionally, estimates of the depth and lateral spread of plant root systems are likely underestimated at the global scale.

Photosynthetic and hydraulic traits influence forest resistance and resilience to drought stress across different biomes

Drought events lead to depressions in gross primary productivity (GPP) of forest ecosystems. Photosynthetic and hydraulic traits are important factors governing GPP variation. However, how these functional traits affect GPP responses to drought has not been well understood. We quantified the capacity of GPP to withstand changes during droughts (GPP_resistance) and its post-drought responses (GPP_resilience) using eddy covariance data from the FLUXNET2015 dataset, and investigated how functional traits of dominant tree species that comprised >80% of the biomass (or composition) influenced GPP_resistance or GPP_resilience. Light-saturated photosynthetic rate of dominant tree species was negatively related to GPP_resistance, and was positively correlated with GPP_resilience. Forests dominated by species with higher hydraulic safety margins (HSM), smaller vessel diameter (V_dia) and lower sensitivity of canopy stomatal conductance per unit land area (G_s) to droughts had a higher GPP_resistance, while those dominated by species with lower HSM, larger V_dia and higher sensitivity of G_s to droughts exhibited a higher GPP_resilience. Differences in functional traits of forests located in diverse climate regions led to distinct GPP sensitivities to droughts. Forests located in humid regions had a higher GPP_resilience while those in arid regions exhibited a higher GPP_resistance. Forest GPP_resistance was negatively related to drought intensity, and GPP_resilience was negatively related to drought duration. Our findings highlight the significant role of functional traits in governing forest resistance and resilience to droughts. Overall, forests dominated by species with higher hydraulic safety were more resistant to droughts, while forests containing species with higher photosynthetic and hydraulic efficiency recovered better from drought stress.

Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model

In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m-RNN) for light environment optimization. By analyzing the structure of m-RNN and combining the current research frontiers of image processing and natural language processing, we find out the problem of the ineffectiveness of m-RNN for some image generation descriptions, starting from both the image feature extraction part and text sequence data processing. Unlike traditional image automatic processing algorithms, this algorithm does not need to add complex rules manually. Still, it evaluates and filters through the training image collection and finally generates image automatic processing models by m-RNN. An image semantic segmentation algorithm is proposed based on multimodal attention and adaptive feature fusion. The main idea of the algorithm is to combine adaptive and feature fusion and then introduce data enhancement for small-scale multimodal light environment datasets by extracting the importance between images through multimodal attention. The model proposed in this paper can span the semantic differences of different modalities and construct feature relationships between different modalities to achieve an inferable, interpretable, and scalable feature representation of multimodal data. The automatic processing of light environment images using multimodal neural networks based on traditional algorithms eliminates manual processing and greatly reduces the time and effort of image processing.

Regional and local determinants of drought resilience in tropical forests

The increase in severity of droughts associated with greater mortality and reduced vegetation growth is one of the main threats to tropical forests. Drought resilience of tropical forests is affected by multiple biotic and abiotic factors varying at different scales. Identifying those factors can help understanding the resilience to ongoing and future climate change. Altitude leads to high climate variation and to different forest formations, principally moist or dry tropical forests with contrasted vegetation structure. Each tropical forest can show distinct responses to droughts. Locally, topography is also a key factor controlling biotic and abiotic factors related to drought resilience in each forest type. Here, we show that topography has key roles controlling biotic and abiotic factors in each forest type. The most important abiotic factors are soil nutrients, water availability, and microclimate. The most important biotic factors are leaf economic and hydraulic plant traits, and vegetation structure. Both dry tropical forests and ridges (steeper and drier habitats) are more sensitive to droughts than moist tropical forest and valleys (flatter and wetter habitats). The higher mortality in ridges suggests that conservative traits are not sufficient to protect plants from drought in drier steeper habitats. Our synthesis highlights that altitude and topography gradients are essential to understand mechanisms of tropical forest's resilience to future drought events. We described important factors related to drought resilience, however, many important knowledge gaps remain. Filling those gaps will help improve future practices and studies about mitigation capacity, conservation, and restoration of tropical ecosystems.

Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits

The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits. We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography. When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy. Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.

Variations in leaf and stem traits across two elevations in subtropical forests

Understanding the variations in plant traits across elevations may provide valuable insights into the species structure and function of forests and their responses to climate change. To explore the patterns of trait variation across elevations, we analysed 14 leaf and stem traits associated with resource acquisition and stress tolerance in Schima superba Gardner & Champion, Castanopsis chinensis (Sprengel) Hance, and Pinus massoniana Lambert trees at two elevations in a subtropical forest in southern China. Wood density increased, whereas crown width, leaf water potential at 0700 hours (Ψ L-0700 ), and leaf δ 18 O decreased in high-elevation plants. Vessel diameter, daily maximum sap flux density, leaf δ 13 C, and leaf C and N concentrations per unit mass were comparable across elevations. We found species-specific variations in specific leaf area, midday leaf water potential, and leaf P concentration across elevations. Decreasing crown width with increasing elevation was associated with decreasing leaf δ 18 O and Ψ L-0700 , suggesting that higher stomatal conductance may moderate the loss of carbon assimilation. We elucidated the adaptive strategies of plants in response to environmental change, and showed that physiological traits varied in coordination with structural traits. Future studies incorporating multi-dimensional trait analyses can improve our understanding of the responses of forest ecosystems to climate change and global warming.

Forest fragmentation impacts the seasonality of Amazonian evergreen canopies

Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial. Here, we use terrestrial LiDAR surveys every two weeks spanning wet and dry seasons in Central Amazonia to show that plant phenology varies strongly across vertical strata in old-growth forests, but is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we find that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests lost plant material. In contrast, the understory greened up with increased light availability driven by the upper canopy loss, alongside increases in solar radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures in forest edges exacerbated the upper canopy losses of large trees throughout the dry season, whereas the understory in these light-rich environments was less dependent on the altered upper canopy structure. Our findings reveal a strong influence of edge effects on phenological controls in wet forests of Central Amazonia.

Even evergreen tropical forests can have seasonal dynamics, which may be sensitive to disturbance. Here, the authors combine high-resolution remote sensing observations and microclimate data to show that forest fragmentation impacts canopy phenology dynamics in the Amazon forest.

Physical structure and biological composition of canopies in tropical secondary and old-growth forests

The area of tropical secondary forests is increasing rapidly, but data on the physical and biological structure of the canopies of these forests are limited. To obtain such data and to measure the ontogeny of canopy structure during tropical rainforest succession, we studied patch-scale (5 m2) canopy structure in three areas of 18-36 year-old secondary forest in Costa Rica, and compared the results to data from old-growth forest at the same site. All stands were sampled with a stratified random design with complete harvest from ground level to the top of the canopy from a modular portable tower. All canopies were organized into distinct high- and low-leaf-density layers (strata), and multiple strata developed quickly with increasing patch height. The relation of total Leaf Area Index (LAI, leaf area per area of ground) to patch canopy height, the existence of distinct high and low leaf- density layers (strata and free air spaces), the depth and LAI of the canopy strata and free air spaces, and the relation of the number of strata to patch canopy height were remarkably constant across the entire successional gradient. Trees were the most important contributor to LAI at all stages, while contribution of palm LAI increased through succession. We hypothesize that canopy physical structure at the patch scale is driven by light competition and discuss how this hypothesis could be tested. That canopy physical structure was relatively independent of the identity of the species present suggests that canopy physical structure may be conserved even as canopy floristics shift due to changing climate.

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

Asymbiotic nitrogen fixation in the phyllosphere of the Amazon forest: Changing nitrogen cycle paradigms

Biological nitrogen fixation is a key process for the maintenance of natural ecosystems productivity. In tropical forests, the contribution of asymbiotic nitrogen fixation (ANF) to the nitrogen (N) input has been underestimated, even though few studies have shown that ANF may be as important as symbiotic nitrogen fixation in such environments. The inputs and abiotic modulators of ANF in the Amazon forest are not completely understood. Here, we determined ANF rates and estimated the N inputs from ANF in the phyllosphere, litter and rhizospheric soil of nine tree species in the Amazon forest over time, including an extreme drought period induced by the El Niño-Southern Oscillation. Our data showed that ANF rates in the phyllosphere were 2.8- and 17.6-fold higher than in the litter and rhizospheric soil, respectively, and was highly dependent on tree taxon. Sampling time was the major factor modulating ANF in all forest compartments. At the driest period, ANF rates were approximately 1.8-fold and 13.1-fold higher than at periods with higher rainfall, before and after the extreme drought period, respectively. Tree species was a key modulator of ANF in the phyllosphere, as well as N and Vanadium concentrations. Carbon, molybdenum and vanadium concentrations were significant modulators of ANF in the litter. Based on ANF rates at the three sampling times, we estimated that the N input in the Amazon forest through ANF in the phyllosphere, litter and rhizospheric soil, was between 0.459 and 0.714 kg N ha^-1 yr^-1. Our results highlight the importance of ANF in the phyllosphere for the N input in the Amazon forest, and suggest that changes in the patterns of ANF driven by large scale climatic events may impact total N inputs and likely alter forest productivity.

Drought-driven wildfire impacts on structure and dynamics in a wet Central Amazonian forest

While the climate and human-induced forest degradation is increasing in the Amazon, fire impacts on forest dynamics remain understudied in the wetter regions of the basin, which are susceptible to large wildfires only during extreme droughts. To address this gap, we installed burned and unburned plots immediately after a wildfire in the northern Purus-Madeira (Central Amazon) during the 2015 El-Niño. We measured all individuals with diameter of 10 cm or more at breast height and conducted recensuses to track the demographic drivers of biomass change over 3 years. We also assessed how stem-level growth and mortality were influenced by fire intensity (proxied by char height) and tree morphological traits (size and wood density). Overall, the burned forest lost 27.3% of stem density and 12.8% of biomass, concentrated in small and medium trees. Mortality drove these losses in the first 2 years and recruitment decreased in the third year. The fire increased growth in lower wood density and larger sized trees, while char height had transitory strong effects increasing tree mortality. Our findings suggest that fire impacts are weaker in the wetter Amazon. Here, trees of greater sizes and higher wood densities may confer a margin of fire resistance; however, this may not extend to higher intensity fires arising from climate change.

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top of canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the three -dimensional spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top-of-canopy percent N and total canopy N (i.e., the total amount of N [g/m² ] within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Our resulting maps suggest that, in contrast to top of canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

Does economic optimisation explain LAI and leaf trait distributions across an Amazon soil moisture gradient?

Leaf area index (LAI) underpins terrestrial ecosystem functioning, yet our ability to predict LAI remains limited. Across Amazon forests, mean LAI, LAI seasonal dynamics and leaf traits vary with soil moisture stress. We hypothesise that LAI variation can be predicted via an optimality-based approach, using net canopy C export (NCE, photosynthesis minus the C cost of leaf growth and maintenance) as a fitness proxy. We applied a process-based terrestrial ecosystem model to seven plots across a moisture stress gradient with detailed in situ measurements, to determine nominal plant C budgets. For each plot, we then compared observations and simulations of the nominal (i.e. observed) C budget to simulations of alternative, experimental budgets. Experimental budgets were generated by forcing the model with synthetic LAI timeseries (across a range of mean LAI and LAI seasonality) and different leaf trait combinations (leaf mass per unit area, lifespan, photosynthetic capacity and respiration rate) operating along the leaf economic spectrum. Observed mean LAI and LAI seasonality across the soil moisture stress gradient maximised NCE, and were therefore consistent with optimality-based predictions. Yet, the predictive power of an optimality-based approach was limited due to the asymptotic response of simulated NCE to mean LAI and LAI seasonality. Leaf traits fundamentally shaped the C budget, determining simulated optimal LAI and total NCE. Long-lived leaves with lower maximum photosynthetic capacity maximised simulated NCE under aseasonal high mean LAI, with the reverse found for short-lived leaves and higher maximum photosynthetic capacity. The simulated leaf trait LAI trade-offs were consistent with observed distributions. We suggest that a range of LAI strategies could be equally economically viable at local level, though we note several ecological limitations to this interpretation (e.g. between-plant competition). In addition, we show how leaf trait trade-offs enable divergence in canopy strategies. Our results also allow an assessment of the usefulness of optimality-based approaches in simulating primary tropical forest functioning, evaluated against in situ data.

New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10-15 min. Our analysis of NDVI data collected over the Amazon during 2018-19 shows that ABI provides 21-35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes.

Hygric Niches for Tropical Endotherms

Biotic selective pressures dominate explanations for the evolutionary ecology of tropical endotherms. Yet, abiotic factors, principally precipitation regimes, shape biogeographical and phenological patterns in tropical regions. Despite its importance, we lack a framework for understanding when, why, and how rain affects endotherms. Here, we review how tropical birds and mammals respond to rain at individual, population, and community levels, and propose a conceptual framework to interpret divergent responses. Diverse direct and indirect mechanisms underlie responses to rainfall, including physiological, top-down, and food-related drivers. Our framework constitutes a roadmap for the empirical studies required to understand the consequences of rainfall variability. Identifying the patterns and mechanisms underpinning responses to temporal variation in precipitation is crucial to anticipate consequences of anthropogenic climate change.

Standardizing Ecosystem Morphological Traits from 3D Information Sources

3D-imaging technologies provide measurements of terrestrial and aquatic ecosystems' structure, key for biodiversity studies. However, the practical use of these observations globally faces practical challenges. First, available 3D data are geographically biased, with significant gaps in the tropics. Second, no data source provides, by itself, global coverage at a suitable temporal recurrence. Thus, global monitoring initiatives, such as assessment of essential biodiversity variables (EBVs), will necessarily have to involve the combination of disparate data sets. We propose a standardized framework of ecosystem morphological traits - height, cover, and structural complexity - that could enable monitoring of globally consistent EBVs at regional scales, by flexibly integrating different information sources - satellites, aircrafts, drones, or ground data - allowing global biodiversity targets relating to ecosystem structure to be monitored and regularly reported.

Understanding spatiotemporal patterns of global forest NPP using a data-driven method based on GEE

Spatiotemporal patterns of global forest net primary productivity (NPP) are pivotal for us to understand the interaction between the climate and the terrestrial carbon cycle. In this study, we use Google Earth Engine (GEE), which is a powerful cloud platform, to study the dynamics of the global forest NPP with remote sensing and climate datasets. In contrast with traditional analyses that divide forest areas according to geographical location or climate types to retrieve general conclusions, we categorize forest regions based on their NPP levels. Nine categories of forests are obtained with the self-organizing map (SOM) method, and eight relative factors are considered in the analysis. We found that although forests can achieve higher NPP with taller, denser and more broad-leaved trees, the influence of the climate is stronger on the NPP; for the high-NPP categories, precipitation shows a weak or negative correlation with vegetation greenness, while lacking water may correspond to decrease in productivity for low-NPP categories. The low-NPP categories responded mainly to the La Niña event with an increase in the NPP, while the NPP of the high-NPP categories increased at the onset of the El Niño event and decreased soon afterwards when the warm phase of the El Niño-Southern Oscillation (ENSO) wore off. The influence of the ENSO changes correspondingly with different NPP levels, which infers that the pattern of climate oscillation and forest growth conditions have some degree of synchronization. These findings may facilitate the understanding of global forest NPP variation from a different perspective.

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

Developing accurate methods to map vegetation structure in tropical forests is essential to protect their biodiversity and improve their carbon stock estimation. We integrated LIDAR (Light Detection and Ranging), multispectral and SAR (Synthetic Aperture Radar) data to improve the prediction and mapping of canopy height (CH) at high spatial resolution (30 m) in tropical forests in South America. We modeled and mapped CH estimated from aircraft LiDAR surveys as a ground reference, using annual metrics derived from multispectral and SAR satellite imagery in a dry forest, a moist forest, and a rainforest of tropical South America. We examined the effect of the three forest types, five regression algorithms, and three predictor groups on the modelling and mapping of CH. Our CH models reached errors ranging from 1.2–3.4 m in the dry forest and 5.1–7.4 m in the rainforest and explained variances from 94–60% in the dry forest and 58–12% in the rainforest. Our best models show higher accuracies than previous works in tropical forests. The average accuracy of the five regression algorithms decreased from dry forests (2.6 m +/− 0.7) to moist (5.7 m +/− 0.4) and rainforests (6.6 m +/− 0.7). Random Forest regressions produced the most accurate models in the three forest types (1.2 m +/− 0.05 in the dry, 4.9 m +/− 0.14 in the moist, and 5.5 m +/− 0.3 the rainforest). Model performance varied considerably across the three predictor groups. Our results are useful for CH spatial prediction when GEDI (Global Ecosystem Dynamics Investigation lidar) data become available.

Cryptic phenology in plants: Case studies, implications, and recommendations

Plant phenology-the timing of cyclic or recurrent biological events in plants-offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"-that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

Functional composition of epiphyte communities in the Colombian Andes

We identify changes in the functional composition of vascular epiphytes along a tropical elevational gradient with the aim of quantifying the role of climate in determining the assembly of epiphyte communities. We measured seven leaf functional traits (leaf area, specific leaf area, leaf dry-matter content, leaf thickness, force to punch, stomatal density, and potential conductance index) in the 163 most abundant epiphyte species recorded across 10 sites located along an elevational gradient between 60 and 2,900 m above sea level in the Colombian Andes. We grouped the epiphyte species into seven hierarchical functional groups according to their most characteristic leaf traits. Along the elevational gradient, the two main independent leaf trait dimensions that distinguished community assemblages were defined primarily by leaf area-photosynthetic (LAPS) and mass-carbon (LMCS) gradients. Mean annual temperature was the main determinant of species position along LAPS. In contrast, local changes in specific leaf area due to variation in the epiphytes' relative height of attachment was the main determinant of their position along the LMCS. Our findings indicate that epiphytic plant leaves have evolved to optimize and enhance photosynthesis through a leaf area-based strategy and carbon acquisition through investments in construction costs of leaf area per unit of biomass that aim to regulate light capture and tissue development. Given that most studies of plant functional traits neglect vascular epiphytes, our quantification of the multiple dimensions of epiphyte leaf traits greatly augments our understanding of vascular plant function and adaptation to changing environments.

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.