Abstract: Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.

Abstract: Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.

Abstract: Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.

Abstract: Closely related tree species that grow in sympatry are abundant in rainforests. However, little is known of the ecoevolutionary processes that govern their niches and local coexistence. We assessed genetic species delimitation in closely related sympatric species belonging to two Neotropical tree species complexes and investigated their genomic adaptation to a fine-scale topographic gradient with associated edaphic and hydrologic features. Combining LiDAR-derived topography, tree inventories, and single nucleotide polymorphisms (SNPs) from gene capture experiments, we explored genome-wide population genetic structure, covariation of environmental variables, and genotype-environment association to assess microgeographic adaptations to topography within the species complexes Symphonia (Clusiaceae), and Eschweilera (Lecythidaceae) with three species per complex and 385 and 257 individuals genotyped, respectively. Within species complexes, closely related tree species had different realized optima for topographic niches defined through the topographic wetness index or the relative elevation, and species displayed genetic signatures of adaptations to these niches. Symphonia species were genetically differentiated along water and nutrient distribution particularly in genes responding to water deprivation, whereas Eschweilera species were genetically differentiated according to soil chemistry. Our results suggest that varied topography represents a powerful driver of processes modulating tropical forest biodiversity with differential adaptations that stabilize local coexistence of closely related tree species.

Abstract: Questions Changes in the functional composition of tree communities along resource availability gradients have received attention, but it is unclear whether understorey and canopy guilds respond similarly to different light, biomechanical, and hydraulic constraints. Location An anthropically undisturbed, old-growth wet evergreen dipterocarp forest plot located in Karnataka State, India. Methods We measured leaf and wood traits of 89 tree species representing 99% of all individuals in a 10-ha permanent plot with varying topographic and canopy conditions inferred from Light Detection And Ranging (LiDAR) data. We assigned tree species to guilds of canopy and understorey species and assessed the variation of the guild-weighted means of functional-trait values with canopy height and topography. Results The functional-trait space did not differ between canopy and understorey tree species. However, environmental filtering led to significantly different functional composition of canopy and understorey guild assemblages. Furthermore, they responded differently along environmental gradients related to water, nutrients, light, and wind exposure. For example, the canopy guild responded to wind exposure while the understorey guild did not. Conclusions The pools of understorey and canopy species are functionally similar. However, fine-scale environmental heterogeneity impacts differently on these two guilds, generating striking differences in functional composition between understorey and canopy guild assemblages. Accounting for vertical guilds improves our understanding of forest communities' assembly processes.