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Abstract Background and Aims Despite the critical role of woody tissues in determining net carbon exchange of terrestrial ecosystems, relatively little is known regarding the drivers of sapwood and bark respiration. Methods Using one of the most comprehensive wood respiration datasets to date (82 species from Australian rainforest, savanna and temperate forest), we quantified relationships between tissue respiration rates (Rd) measured in vitro (i.e. ‘respiration potential’) and physical properties of bark and sapwood, and nitrogen concentration (Nmass) of leaves, sapwood and bark. Key Results Across all sites, tissue density and thickness explained similar, and in some cases more, variation in bark and sapwood Rd than did Nmass. Higher density bark and sapwood tissues had lower Rd for a given Nmass than lower density tissues. Rd–Nmass slopes were less steep in thicker compared with thinner-barked species and less steep in sapwood than in bark. Including the interactive effects of Nmass, density and thickness significantly increased the explanatory power for bark and sapwood respiration in branches. Among these models, Nmass contributed more to explanatory power in trunks than in branches, and in sapwood than in bark. Our findings were largely consistent across sites, which varied in their climate, soils and dominant vegetation type, suggesting generality in the observed trait relationships. Compared with a global compilation of leaf, stem and root data, Australian species showed generally lower Rd and Nmass, and less steep Rd–Nmass relationships. Conclusions To the best of our knowledge, this is the first study to report control of respiration–nitrogen relationships by physical properties of tissues, and one of few to report respiration–nitrogen relationships in bark and sapwood. Together, our findings indicate a potential path towards improving current estimates of autotrophic respiration by integrating variation across distinct plant tissues. 

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP. 

Abstract. Evaporation (E) and transpiration (T) respond differentlyto ongoing changes in climate, atmospheric composition, and land use. It isdifficult to partition ecosystem-scale evapotranspiration (ET) measurementsinto E and T, which makes it difficult to validate satellite data and landsurface models. Here, we review current progress in partitioning E and T andprovide a prospectus for how to improve theory and observations goingforward. Recent advancements in analytical techniques create newopportunities for partitioning E and T at the ecosystem scale, but theirassumptions have yet to be fully tested. For example, many approaches topartition E and T rely on the notion that plant canopy conductance andecosystem water use efficiency exhibit optimal responses to atmosphericvapor pressure deficit (D). We use observations from 240 eddy covariance fluxtowers to demonstrate that optimal ecosystem response to D is a reasonableassumption, in agreement with recent studies, but more analysis is necessaryto determine the conditions for which this assumption holds. Anothercritical assumption for many partitioning approaches is that ET can beapproximated as T during ideal transpiring conditions, which has beenchallenged by observational studies. We demonstrate that T can exceed 95 %of ET from certain ecosystems, but other ecosystems do not appear to reachthis value, which suggests that this assumption is ecosystem-dependent withimplications for partitioning. It is important to further improve approachesfor partitioning E and T, yet few multi-method comparisons have beenundertaken to date. Advances in our understanding of carbon–water couplingat the stomatal, leaf, and canopy level open new perspectives on how toquantify T via its strong coupling with photosynthesis. Photosynthesis can beconstrained at the ecosystem and global scales with emerging data sourcesincluding solar-induced fluorescence, carbonyl sulfide flux measurements,thermography, and more. Such comparisons would improve our mechanisticunderstanding of ecosystem water fluxes and provide the observationsnecessary to validate remote sensing algorithms and land surface models tounderstand the changing global water cycle. 

Abstract The leaf economics spectrum 1,2 and the global spectrum of plant forms and functions 3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species 2 . Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities 4 . However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability 4,5 . Here we derive a set of ecosystem functions 6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems 7,8 . 

Abstract. Plant transpiration links physiological responses ofvegetation to water supply and demand with hydrological, energy, and carbonbudgets at the land–atmosphere interface. However, despite being the mainland evaporative flux at the global scale, transpiration and its response toenvironmental drivers are currently not well constrained by observations.Here we introduce the first global compilation of whole-plant transpirationdata from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021).We harmonized and quality-controlled individual datasets supplied bycontributors worldwide in a semi-automatic data workflow implemented in theR programming language. Datasets include sub-daily time series of sap flowand hydrometeorological drivers for one or more growing seasons, as well asmetadata on the stand characteristics, plant attributes, and technicaldetails of the measurements. SAPFLUXNET contains 202 globally distributeddatasets with sap flow time series for 2714 plants, mostly trees, of 174species. SAPFLUXNET has a broad bioclimatic coverage, withwoodland/shrubland and temperate forest biomes especially well represented(80 % of the datasets). The measurements cover a wide variety of standstructural characteristics and plant sizes. The datasets encompass theperiod between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data areavailable for most of the datasets, while on-site soil water content isavailable for 56 % of the datasets. Many datasets contain data for speciesthat make up 90 % or more of the total stand basal area, allowing theestimation of stand transpiration in diverse ecological settings. SAPFLUXNETadds to existing plant trait datasets, ecosystem flux networks, and remotesensing products to help increase our understanding of plant water use,plant responses to drought, and ecohydrological processes. SAPFLUXNET version0.1.5 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 processSAPFLUXNET data – is available from CRAN.