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Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

1. Plant–soil feedback (PSF), plant trait and functional group concepts advanced our understanding of plant community dynamics, but how they are interlinked is poorly known. 2. To test how plant functional groups (FGs: graminoids, small herbs, tall herbs, legumes) and plant traits relate to PSF, we grew 48 grassland species in sterilized soil, sterilized soil with own species soil inoculum and sterilized soil with soil inoculum from all species, and quantified relative growth rate (RGR), specific leaf area (SLA), specific root length (SRL) and per cent arbuscular mycorrhizal fungi colonization (%AMF). 3. Plant growth response to the plant species' own soil biota relative to sterilized soil (PSFsterilized) reflects net effects of all (generalist + specialized) soil biota. Growth response to the plant species' own soil biota relative to soil biota of all plant species (PSFaway) reveals effects of more specialized soil organisms. 4. PSFsterilized showed that graminoids and small herbs have a negative and tall herbs a positive response to their own soil biota, whereas legumes responded neutrally. However, PSFaway showed that on average, all plant FGs benefitted from growing with other species' soil biota, suggesting that pathogens are more specialized than plant growth-promoting soil biota. Feedback to plant growth from all soil biota (PSFsterilized) was stronger than from more specialized soil biota (PSFaway) and could be predicted by SRL and especially by %AMF colonization. Species with high SRL and low %AMF colonization when grown in away soil experienced most negative soil feedback. 5. Synthesis. Plant species from all plant FGs grow better in soil from other species because of less net negative effects of soil biota (in graminoids), or because of more net positive soil biota effects (in tall herbs). Explorative plant species (high SRL, low %AMF colonization) suffer most from negative feedback of all soil biota, whereas more resource conservative species (low SRL, high %AMF colonization) benefit from soil feedback of all soil biota. These findings help to understand replacement of explorative species during succession. Moreover, we suggest a potentially larger role for species with positive feedback than for species with negative feedback to contribute to maintain plant community productivity of diverse communities over time.

Topography affects abiotic conditions which can influence the structure, function, and dynamics of ecological communities. An increasing number of studies have demonstrated biological consequences of fine-scale topographic heterogeneity but we have a limited understanding of how We merged high-resolution (1 sq. meter) data on topography and canopy height derived from airborne lidar with ground-based data from 15 forest plots in Puerto Rico distributed along a precipitation gradient spanning ca. 800 to 3,500 mm yr(exp -1). Ground-based data included species composition, estimated above-ground biomass (AGB), and two key functional traits (wood density and leaf mass per area, LMA) that reflect resource-use strategies and a trade-off between hydraulic safety and hydraulic efficiency. We used hierarchical Bayesian models to evaluate how the interaction between topography climate is related to metrics of forest structure (i.e., canopy height and AGB), as well as taxonomic and functional alpha- and beta-diversity. Fine-scale topography (characterized with the topographic wetness index, TWI) significantly affected forest structure and the strength (and in some cases direction) of these effects varied across the precipitation gradient. In all plots, canopy height increased with topographic wetness but the effect was much stronger in dry compared to wet forest plots. In dry forest plots, topographically wetter microsites also had higher levels of AGB but in wet forest plots, topographically drier microsites had higher AGB. Fine-scale topography influenced functional composition but had only weak or non-significant effects on taxonomic and functional alpha- and beta-diversity. For instance, community-weighted wood density followed a similar pattern to AGB across plots. We also found a marginally significant association between variation of wood density and topographic heterogeneity that depended on climate context. Synthesis: The effects of fine-scale topographic heterogeneity on tropical forest structure and composition depend on the climate context. Our study demonstrates how a stronger integration of topographic heterogeneity across precipitation gradients could improve estimates of forest structure and biomass, and may provide insight to the ways that topography might mediate species responses to drought and climate change.

QUESTIONS : Invasive species establish either by possessing traits, or trait trade‐offs similar to native species, suggesting pre‐adaptation to local conditions; or by having a different suite of traits and trait trade‐offs, which allow them to occupy unfilled niches. The trait differences between invasives and non‐invasives can inform on which traits confer invasibility. Here, we ask: (a) are invasive species functionally different or similar to native species? (b) which traits of invasives differ from traits of non‐invasive aliens and thus confer invasibility? and (c) do results from the sub‐Antarctic region, where this study was conducted, differ from findings from other regions? LOCATION : Sub‐Antarctic Marion Island. METHODS : We measured 13 traits of all terrestrial native, invasive and non‐invasive alien plant species. Using principal components analysis and phylogenetic generalized least‐squares models, we tested for differences in traits between invasive (widespread alien species) and native species. Bivariate trait relationships between invasive and native species were compared using standardized major axis regressions to test for differences in trait trade‐offs between the two groups. Second, using the same methods, we compared the traits of invasive species to non‐invasive aliens (alien species that have not spread). RESULTS : Between invasive and native species, most traits differed, suggesting that the success of invasive species is mediated by being functionally different to native species. Additionally, most bivariate trait relationships differed either in terms of their y‐intercept or their position on the axes, highlighting that plants are positioned differently along a spectrum of shared trait trade‐offs. Compared to non‐invasive aliens, invasive species had lower plant height, smaller leaf area, lower frost tolerance, and higher specific leaf area, suggesting that these traits are associated with invasiveness. The findings for the sub‐Antarctic corresponded to those of other regions, except lower plant height which provides a competitive advantage to invaders in the windy sub‐Antarctic context. CONCLUSION : Our findings support the expectation that trait complexes of invasive species are predominantly different to those of coexisting native species, and that high resource acquisition and low defence investment are characteristic of invasive plant species.

Above‐ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot‐wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open‐source software.

Summary Despite growing evidence that, on average, diverse forests tend to be more productive than species‐poor ones, individual studies often report strongly contrasting relationships between tree species richness and above‐ground wood production ( AWP ). In the attempt to reconcile these apparently inconsistent results, we explored whether the strength and shape of AWP –diversity relationships shifts along spatial and temporal environmental gradients in forests across Europe. We used tree ring data from a network of permanent forest plots distributed at six sites across Europe to estimate annual AWP over a 15‐year period (1997–2011). We then tested whether the relationship between tree species richness and AWP changes (i) across sites as a function of large‐scale gradients in climatic productivity and tree packing density and (ii) among years within each sites as a result of fluctuating climatic conditions. AWP –species richness relationships varied markedly among sites. As predicted by theory, the relationship shifted from strongly positive at sites where climate imposed a strong limitation on wood production and tree packing densities were low, to weakly negative at sites where climatic conditions for growth were most suitable. In contrast, we found no consistent effect of interannual fluctuations in climate on the strength of AWP –species richness relationships within sites. Synthesis . Our results indicate that the shape and strength of the relationship between tree diversity and forest productivity depends critically on environmental context. Across Europe, tree diversity shows the greatest potential to positively influence forest productivity at either end of the latitudinal gradient, where adverse climatic conditions limit productivity and lead to the development of less densely packed stands.

Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset.Location Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org.bases-doc.univ-lorraine.fr/10.5521/FORESTPLOTS.NET/2014_1Methods Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons.Results The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over-or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%.Main conclusions Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

1.The use of plant traits to predict ecosystem functions has been gaining growing attention. Above-ground plant traits, such as leaf nitrogen (N) content and specific leaf area (SLA), have been shown to strongly relate to ecosystem productivity, respiration and nutrient cycling. Furthermore, increasing plant functional trait diversity has been suggested as a possible mechanism to increase ecosystem carbon (C) storage. However, it is uncertain whether below-ground plant traits can be predicted by above-ground traits, and if both above- and below-ground traits can be used to predict soil properties and ecosystem-level functions. 2. Here, we used two adjacent field experiments in temperate grassland to investigate if above- and below-ground plant traits are related, and whether relationships between plant traits, soil properties and ecosystem be detected in mixed field communities. 3. We found that certain shoot traits (e.g. shoot N and C, and leaf dry matter content) were related to root traits (e.g. root N, root C:N and root dry matter content) in monocultures, but such relationships were either weak or not detected in mixed communities. Some relationships between plant traits (i.e. shoot N, root N and/or shoot C:N) and soil properties (i.e. inorganic N availability and microbial community structure) were similar in monocultures and mixed communities, but they were more strongly linked to shoot traits in monocultures and root traits in mixed communities. Structural equation modelling showed that aboveand fluxes (i.e. ecosystem respiration and net ecosystem exchange) measured in potted monocultures couldbelow-ground traits and soil properties improved predictions of ecosystem C fluxes in monocultures,but not in mixed communities on the basis of community-weighted mean traits. 4. Synthesis. Our results from a single grassland habitat detected relationships in monocultures between above- and below-ground plant traits, and between plant traits, soil properties and ecosystem C fluxes. However, these relationships were generally weaker or different in mixed communities. Our results demonstrate that while plant traits can be used to predict certain soil properties and ecosystem functions in monocultures, they are less effective for predicting how changes in plant species composition influence ecosystem functions in mixed communities.

Nutrient additions typically increase terrestrial ecosystem productivity, reduce plant diversity and alter plant community composition; however, the effects of P additions and interactions between N and P are understudied. We added both N (10 g m⁻²) and three levels of P (2.5, 5 and 10 g m⁻²) to a native, ungrazed tallgrass prairie burned biennially in northeastern Kansas, USA, to determine the independent and interactive effects of N and P on plant community composition and above‐ground net primary productivity (ANPP). After a decade of nutrient additions, we found few effects of P alone on plant community composition, N alone had stronger effects, and N and P additions combined resulted in much larger effects than either alone. The changes in the plant community were driven by decreased abundance of C₄ grasses, perhaps in response to altered interactions with mycorrhizal fungi, concurrent with increased abundance of non‐N‐fixing perennial and annual forbs. Surprisingly, this large shift in plant community composition had little effect on plant community richness, evenness and diversity. The shift in plant composition with N and P combined had large but variable effects on ANPP over time. Initially, N and N and P combined increased above‐ground productivity of C₄ grasses, but after 4 years, productivity returned to ambient levels as grasses declined in abundance and the community shifted to dominance by non‐N‐fixing and annual forbs. Once these forbs increased in abundance and became dominant, ANPP was more variable, with pulses in forb production only in years when the site was burned. Synthesis. We found that a decade of N and P additions interacted to drive changes in plant community composition, which had large effects on ecosystem productivity but minimal effects on plant community diversity. The large shift in species composition increased variability in ANPP over time as a consequence of the effects of burning. Thus, increased inputs of N and P to terrestrial ecosystems have the potential to alter stability of ecosystem function over time, particularly within the context of natural disturbance regimes.

Accurate assessment of above-ground biomass (AGB) is important for the sustainable management of forests, especially buffer zone (areas within the protected area, where restrictions are placed upon resource use and special measure are undertaken to intensify the conservation value of protected area) areas with a high dependence on forest products. This study presents a new AGB estimation method and demonstrates the potential of medium-resolution Sentinel-2 Multi-Spectral Instrument (MSI) data application as an alternative to hyperspectral data in inaccessible regions. Sentinel-2 performance was evaluated for a buffer zone community forest in Parsa National Park, Nepal, using field-based AGB as a dependent variable, as well as spectral band values and spectral-derived vegetation indices as independent variables in the Random Forest (RF) algorithm. The 10-fold cross-validation was used to evaluate model effectiveness. The effect of the input variable number on AGB prediction was also investigated. The model using all extracted spectral information plus all derived spectral vegetation indices provided better AGB estimates (R2 = 0.81 and RMSE = 25.57 t ha-1). Incorporating the optimal subset of key variables did not improve model variance but reduced the error slightly. This result is explained by the technically-advanced nature of Sentinel-2, which includes fine spatial resolution (10, 20 m) and strategically-positioned bands (red-edge), conducted in flat topography with an advanced machine learning algorithm. However, assessing its transferability to other forest types with varying altitude would enable future performance and interpretability assessments of Sentinel-2.