Atmospheric phosphorus deposition amplifies carbon sinks in simulations of a tropical forest in Central Africa.

Spatial redistribution of nutrients by atmospheric transport and deposition could theoretically act as a continental-scale mechanism which counteracts declines in soil fertility caused by nutrient lock-up in accumulating biomass in tropical forests in Central Africa. However, to what extent it affects carbon sinks in forests remains elusive. Here we use a terrestrial biosphere model to quantify the impact of changes in atmospheric nitrogen and phosphorus deposition on plant nutrition and biomass carbon sink at a typical lowland forest site in Central Africa. We find that the increase in nutrient deposition since the 1980s could have contributed to the carbon sink over the past four decades up to an extent which is similar to that from the combined effects of increasing atmospheric carbon dioxide and climate change. Furthermore, we find that the modelled carbon sink responds to changes in phosphorus deposition, but less so to nitrogen deposition. The pronounced response of ecosystem productivity to changes in nutrient deposition illustrates a potential mechanism that could control carbon sinks in Central Africa. Monitoring the quantity and quality of nutrient deposition is needed in this region, given the changes in nutrient deposition due to human land use.

Damage to living trees contributes to almost half of the biomass losses in tropical forests.

Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha-1 year-1 ; 95% confidence interval [CI] 2.36-5.25) of total AGB loss (8.72 Mg ha-1 year-1 ; CI 5.57-12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage-related AGB losses rather than by mortality-related AGB losses. We show that conventional forest inventories overestimate stand-level AGB stocks by 4% (1%-17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%-57% range across forests) due to overlooked damage-related AGB losses, and overestimate AGB loss via mortality by 22% (7%-80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity.

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (-30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (-19%) and ecosystem (-7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

High photosynthetic capacity of Sahelian C3 and C4 plants.

The semi-arid ecosystems of the African Sahel play an important role in the global carbon cycle and are among the most sensitive ecosystems to future environmental pressures. Still, basic data of photosynthetic characteristics of Sahelian vegetation are very limited, preventing us to properly understand these ecosystems and to project their response to future global changes. Here, we aim to study and quantify key leaf traits, including photosynthetic parameters and leaf nutrients (Nleaf and Pleaf), of common C3 and C4 Sahelian plants (trees, lianas, and grasses) at the Dahra field site (Senegal). Dahra is a reference site for grazed semi-arid Sahelian savannah ecosystems in carbon cycle studies. Within the studied species, we found that photosynthetic parameters varied considerably between functional types. We also found significant relationships between and within photosynthetic parameters and leaf traits which mostly differed in their slopes from C3 to C4 plants. In agreement with the leaf economic spectrum, strong relationships (R2 = 0.71) were found between SLA and Nleaf whereby C3 and C4 plants showed very similar relationships. By comparing our data to a global dataset of plant traits, we show that measured Sahelian plants exhibit higher photosynthetic capacity (Asat) compared to the non-Sahelian vegetation, with values that are on average a fourfold of the global average. Moreover, Sahelian C3 plants showed photosynthetic nutrient use efficiencies that were on average roughly twice as high as global averages. We interpreted these results as the potential adaptation of Sahelian plants to short growing season lengths via an efficient nutrient allocation to optimize photosynthesis during this period. Our study provides robust estimates of key functional traits, but also traits relationships that will help to calibrate and validate vegetation models over this data-poor region.

Forest above-ground volume assessments with terrestrial laser scanning: a ground-truth validation experiment in temperate, managed forests.

BACKGROUND AND AIMS: Quantifying the Earth's forest above-ground biomass (AGB) is indispensable for effective climate action and developing forest policy. Yet, current allometric scaling models (ASMs) to estimate AGB suffer several drawbacks related to model selection and uncertainties about calibration data traceability. Terrestrial laser scanning (TLS) offers a promising non-destructive alternative. Tree volume is reconstructed from TLS point clouds with quantitative structure models (QSMs) and converted to AGB with wood basic density. Earlier studies have found overall TLS-derived forest volume estimates to be accurate, but highlighted problems for reconstructing finer branches. Our objective was to evaluate TLS for estimating tree volumes by comparison with reference volumes and volumes from ASMs. METHODS: We quantified the woody volume of 65 trees in Belgium (from 77 to 2800 L; Pinus sylvestris, Fagus sylvatica, Larix decidua, and Fraxinus excelsior) with QSMs and destructive reference measurements. We tested a volume expansion factor (VEF) approach by multiplying the solid and merchantable volume from QSMs by literature VEF values. KEY RESULTS: Stem volume was reliably estimated with TLS. Total volume was overestimated by +21 % using original QSMs, by +9 % and -12 % using two sets of VEF-augmented QSMs, and by -7.3 % using best-available ASMs. The most accurate method differed per site, and the prediction errors for each method varied considerably between sites. CONCLUSIONS: VEF-augmented QSMs were only slightly better than original QSMs for estimating tree volume for common species in temperate forests. Despite satisfying estimates with ASMs, the model choice was a large source of uncertainty, and species-specific models did not always exist. Therefore, we advocate for further improving tree volume reconstructions with QSMs, especially for fine branches, instead of collecting more ground-truth data to calibrate VEF and allometric models. Promising developments such as improved co-registration and smarter filtering approaches are ongoing to further constrain volumetric errors in TLS-derived estimates.

High fire-derived nitrogen deposition on central African forests.

Atmospheric nitrogen (N) deposition is an important determinant of N availability for natural ecosystems worldwide. Increased anthropogenic N deposition shifts the stoichiometric equilibrium of ecosystems, with direct and indirect impacts on ecosystem functioning and biogeochemical cycles. Current simulation data suggest that remote tropical forests still receive low atmospheric N deposition due to a lack of proximate industry, low rates of fossil fuel combustion, and absence of intensive agriculture. We present field-based N deposition data for forests of the central Congo Basin, and use ultrahigh-resolution mass spectrometry to characterize the organic N fraction. Additionally, we use satellite data and modeling for atmospheric N source apportionment. Our results indicate that these forests receive 18.2 kg N hectare-1 years-1 as wet deposition, with dry deposition via canopy interception adding considerably to this flux. We also show that roughly half of the N deposition is organic, which is often ignored in N deposition measurements and simulations. The source of atmospheric N is predominantly derived from intensive seasonal burning of biomass on the continent. This high N deposition has important implications for the ecology of the Congo Basin and for global biogeochemical cycles more broadly.

Century-long apparent decrease in intrinsic water-use efficiency with no evidence of progressive nutrient limitation in African tropical forests.

Forests exhibit leaf- and ecosystem-level responses to environmental changes. Specifically, rising carbon dioxide (CO2 ) levels over the past century are expected to have increased the intrinsic water-use efficiency (iWUE) of tropical trees while the ecosystem is gradually pushed into progressive nutrient limitation. Due to the long-term character of these changes, however, observational datasets to validate both paradigms are limited in space and time. In this study, we used a unique herbarium record to go back nearly a century and show that despite the rise in CO2 concentrations, iWUE has decreased in central African tropical trees in the Congo Basin. Although we find evidence that points to leaf-level adaptation to increasing CO2 -that is, increasing photosynthesis-related nutrients and decreasing maximum stomatal conductance, a decrease in leaf δ13 C clearly indicates a decreasing iWUE over time. Additionally, the stoichiometric carbon to nitrogen and nitrogen to phosphorus ratios in the leaves show no sign of progressive nutrient limitation as they have remained constant since 1938, which suggests that nutrients have not increasingly limited productivity in this biome. Altogether, the data suggest that other environmental factors, such as increasing temperature, might have negatively affected net photosynthesis and consequently downregulated the iWUE. Results from this study reveal that the second largest tropical forest on Earth has responded differently to recent environmental changes than expected, highlighting the need for further on-ground monitoring in the Congo Basin.

Lianas in silico, ecological insights from a model of structural parasitism.

Tropical forests are a critical component of the Earth system, storing half of the global forest carbon stocks and accounting for a third of terrestrial photosynthesis. Lianas are structural parasites that can substantially reduce the carbon sequestration capacity of these forests. Simulations of this peculiar growth form have only recently started and a single vegetation model included lianas so far. In this work we present a new liana implementation within the individual based model Formind. Initial tests indicate high structural realism both horizontal and vertical. In particular, we benchmarked the model against empirical observations of size distribution, mean liana cluster size and vertical leaf distribution for the Paracou site in French Guiana. Our model predicted a reduction of above-ground biomass between 10% for mature stands to 45% for secondary plots upon inclusion of lianas in the simulations. The reduced biomass was the result of a lower productivity due to a combination of lower tree photosynthesis and high liana respiration. We evaluated structural metrics (LAI, basal area, mean tree-height) and carbon fluxes (GPP, respiration) by comparing simulations with and without lianas. At the equilibrium, liana productivity was 1.9t C ha - 1 y - 1 , or 23% of the total GPP and the forest carbon stocks were between 5% and 11% lower in simulations with lianas. We also highlight the main strengths and limitations of this new approach and propose new field measurements to further the understanding of liana ecology in a modelling framework.

Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests.

There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana-specific plant functional type and mechanisms representing liana-tree interactions (such as light competition, liana-specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta-analysis. We tested the model for an old-growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner-derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 tC  ha-1  year-1 , which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above-ground biomass after 70 years of regrowth by up to 20 tC /ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old-growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana-rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.

Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests.

Lianas are key structural elements of tropical forests having a large impact on the global carbon cycle by reducing tree growth and increasing tree mortality. Despite the reported increasing abundance of lianas across neotropics, very few studies have attempted to quantify the impact of lianas on tree and forest structure. Recent advances in high resolution terrestrial laser scanning (TLS) systems have enabled us to quantify the forest structure, in an unprecedented detail. However, the uptake of TLS technology to study lianas has not kept up with the same pace as it has for trees. The slower technological adoption of TLS to study lianas is due to the lack of methods to study these complex growth forms. In this study, we present a semi-automatic method to extract liana woody components from plot-level TLS data of a tropical rainforest. We tested the method in eight plots from two different tropical rainforest sites (two in Gigante Peninsula, Panama and six in Nouragues, French Guiana) along an increasing gradient of liana infestation (from plots with low liana density to plots with very high liana density). Our method uses a machine learning model based on the Random Forest (RF) algorithm. The RF algorithm is trained on the eigen features extracted from the points in 3D at multiple spatial scales. The RF based liana stem extraction method successfully extracts on average 58% of liana woody points in our dataset with a high precision of 88%. We also present simple post-processing steps that increase the percentage of extracted liana stems from 54% to 90% in Nouragues and 65% to 70% in Gigante Peninsula without compromising on the precision. We provide the entire processing pipeline as an open source python package. Our method will facilitate new research to study lianas as it enables the monitoring of liana abundance, growth and biomass in forest plots. In addition, the method facilitates the easier processing of 3D data to study tree structure from a liana-infested forest.

Vegetation demographics in Earth System Models: A review of progress and priorities.

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.

An integrated pan-tropical biomass map using multiple reference datasets.

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models.

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

Environmental impact assessment and monetary ecosystem service valuation of an ecosystem under different future environmental change and management scenarios; a case study of a Scots pine forest.

For a sustainable future, we must sustainably manage not only the human/industrial system but also ecosystems. To achieve the latter goal, we need to predict the responses of ecosystems and their provided services to management practices under changing environmental conditions via ecosystem models and use tools to compare the estimated provided services between the different scenarios. However, scientific articles have covered a limited amount of estimated ecosystem services and have used tools to aggregate services that contain a significant amount of subjective aspects and that represent the final result in a non-tangible unit such as 'points'. To resolve these matters, this study quantifies the environmental impact (on human health, natural systems and natural resources) in physical units and uses an ecosystem service valuation based on monetary values (including ecosystem disservices with associated negative monetary values). More specifically, the paper also focuses on the assessment of ecosystem services related to pollutant removal/generation flows, accounting for the inflow of eutrophying nitrogen (N) when assessing the effect of N leached to groundwater. Regarding water use/provisioning, evapotranspiration is alternatively considered a disservice because it implies a loss of (potential) groundwater. These approaches and improvements, relevant to all ecosystems, are demonstrated using a Scots pine stand from 2010 to 2089 for a combination of three environmental change and three management scenarios. The environmental change scenarios considered interannual climate variability trends and included alterations in temperature, precipitation, nitrogen deposition, wind speed, Particulate matter (PM) concentration and CO2 concentration. The addressed flows/ecosystem services, including disservices, are as follows: particulate matter removal, freshwater loss, CO2 sequestration, wood production, NOx emissions, NH3 uptake and nitrogen pollution/removal. The monetary ecosystem service valuation yields a total average estimate of 361-1242 euro ha(-1) yr(-1). PM2.5 (<2.5 µm) removal is the key service, with a projected value of 622-1172 euro ha(-1) yr(-1). Concerning environmental impact assessment, with net CO2 uptake being the most relevant contributing flow, a loss prevention of 0.014-0.029 healthy life years ha(-1) yr(-1) is calculated for the respective flows. Both assessment methods favor the use of the least intensive management scenario due to its resulting higher CO2 sequestration and PM removal, which are the most important services of the considered ones.

Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest.

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937-1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate-phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals.

Trade-offs in biodiversity and ecosystem services between edges and interiors in European forests.

Forest biodiversity and ecosystem services are hitherto predominantly quantified in forest interiors, well away from edges. However, these edges also represent a substantial proportion of the global forest cover. Here we quantified plant biodiversity and ecosystem service indicators in 225 plots along forest edge-to-interior transects across Europe. We found strong trade-offs: phylogenetic diversity (evolutionary measure of biodiversity), proportion of forest specialists, decomposition and heatwave buffering increased towards the interior, whereas species richness, nectar production potential, stemwood biomass and tree regeneration decreased. These trade-offs were mainly driven by edge-to-interior structural differences. As fragmentation continues, recognizing the role of forest edges is crucial for integrating biodiversity and ecosystem service considerations into sustainable forest management and policy.

Accurate phenology analyses require bud traits and energy budgets.

Spring phenology is mainly driven by temperature in extratropical ecosystems. Recent evidence highlighted the key role of micrometeorology and bud temperature on delaying or advancing leaf unfolding. Yet, phenology studies, either using ground-based or remote sensing observations, always substitute plant tissue temperature by air temperature. In fact, temperatures differ substantially between plant tissues and the air because plants absorb and lose energy. Here, we build on recent observations and well-established energy balance theories to discuss how solar radiation, wind and bud traits might affect our interpretation of spring phenology sensitivity to warming. We show that air temperature might be an imprecise and biased predictor of bud temperature. Better characterizing the plants' phenological response to warming will require new observations of bud traits and temperature for accurately quantifying their energy budget. As consistent micrometeorology datasets are still scarce, new approaches coupling energy budget modelling and plant traits could help to improve phenology analyses across scales.

FunAndes - A functional trait database of Andean plants.

We introduce the FunAndes database, a compilation of functional trait data for the Andean flora spanning six countries. FunAndes contains data on 24 traits across 2,694 taxa, for a total of 105,466 entries. The database features plant-morphological attributes including growth form, and leaf, stem, and wood traits measured at the species or individual level, together with geographic metadata (i.e., coordinates and elevation). FunAndes follows the field names, trait descriptions and units of measurement of the TRY database. It is currently available in open access in the FIGSHARE data repository, and will be part of TRY's next release. Open access trait data from Andean plants will contribute to ecological research in the region, the most species rich terrestrial biodiversity hotspot.