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Tree canopy sampling is critical in many forestry-related applications, including ecophysiology, foliar nutrient diagnostics, remote sensing model development, genetic analysis, and biodiversity monitoring and conservation. Many of these applications require foliage samples that have been exposed to full sunlight. Unfortunately, current sampling techniques are severely limited in cases where site topography (e.g., rivers, cliffs, canyons) or tree height (i.e., branches located above 10 m) make it time-consuming, expensive, and possibly hazardous to collect samples. This paper reviews the recent developments related to unmanned aerial vehicle (UAV) based tree sampling and presents the DeLeaves tool, a new device that can be installed under a small UAV to efficiently sample small branches in the uppermost canopy (i.e., <25 mm stem diameter, <500 g total weight, any orientation). Four different sampling campaigns using the DeLeaves tool are presented to illustrate its real-life use in various environments. So far, the DeLeaves tool has been able to collect more than 250 samples from over 20 different species with an average sampling time of 6 min. These results demonstrate the potential of UAV-based tree sampling to greatly enhance key tasks in forestry, botany, and ecology. 

To understand controls on soil organic matter chemical composition across North America, we collected 13C NMR spectra and conducted and synthesized additional biogeochemical measurements from NEON Megapit soil samples as well as additional samples (total n = 42). This dataset supports the findings described in the associated manuscript by Hall, Ye et al. (2020). 

Abstract Previous studies found conflicting results on the importance of temperature and precipitation versus geochemical variables for predicting soil organic carbon (SOC) concentrations and trends with depth, and most utilized linear statistical models. To reconcile the controversy, we used data from 2574 mineral horizons from 675 pits from National Ecological Observatory Network sites across North America, typically collected to 1 m depth. Climate was a fundamental predictor of SOC and played similarly important roles as some geochemical predictors. Yet, this only emerged in the generalized additive mixed model and random forest model and was obscured in the linear mixed model. Relationships between water availability and SOC were strongest in very dry ecosystems and SOC increased most strongly at mean annual temperature < 0°C. In all models, depth, oxalate‐extractable Al (Al_ox), pH, and exchangeable calcium plus exchangeable magnesium were important while silt + clay, oxalate‐extractable Fe (Fe_ox), and vegetation type were weaker predictors. Climate and pH were independently related to SOC and also interacted with geochemical composition: Fe_oxand Al_oxrelated more strongly to SOC in wet or cold climates. Most predictors had nonlinear threshold relationships with SOC, and a saturating response to increasing reactive metals indicates soils where SOC might be limited by C inputs. We observed a mostly constant relative importance of geochemical and climate predictors of SOC with increasing depth, challenging previous statements. Overall, our findings challenge the notion that climate is redundant after accounting for geochemistry and demonstrate that considering their nonlinearities and interactions improves spatial predictions of SOC. 

Abstract. Data collected from research networks presentopportunities to test theories and develop models about factors responsiblefor the long-term persistence and vulnerability of soil organic matter(SOM). Synthesizing datasets collected by different research networkspresents opportunities to expand the ecological gradients and scientificbreadth of information available for inquiry. Synthesizing these data ischallenging, especially considering the legacy of soil data that havealready been collected and an expansion of new network science initiatives.To facilitate this effort, here we present the SOils DAta Harmonizationdatabase (SoDaH; https://lter.github.io/som-website, last access: 22 December 2020), a flexible database designed to harmonize diverse SOM datasets frommultiple research networks. SoDaH is built on several network scienceefforts in the United States, but the tools built for SoDaH aim to providean open-access resource to facilitate synthesis of soil carbon data.Moreover, SoDaH allows for individual locations to contribute results fromexperimental manipulations, repeated measurements from long-term studies,and local- to regional-scale gradients across ecosystems or landscapes.Finally, we also provide data visualization and analysis tools that can beused to query and analyze the aggregated database. The SoDaH v1.0 dataset isarchived and availableat https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020). 

This SOils DAta Harmonization (SoDaH) database is designed to bring together soil carbon data from diverse research networks into a harmonized dataset that can be used for synthesis activities and model development. The research network sources for SoDaH span different biomes and climates, encompass multiple ecosystem types, and have collected data across a range of spatial, temporal, and depth gradients. The rich data sets assembled in SoDaH consist of observations from monitoring efforts and long-term ecological experiments. The SoDaH database also incorporates related environmental covariate data pertaining to climate, vegetation, soil chemistry, and soil physical properties. The data are harmonized and aggregated using open-source code that enables a scripted, repeatable approach for soil data synthesis. 

Abstract. Advancing our understanding of Earth system dynamics (ESD) depends on thedevelopment of models and other analytical tools that apply physical,biological, and chemical data. This ambition to increase understanding anddevelop models of ESD based on site observations was the stimulus forcreating the networks of Long-Term Ecological Research (LTER), Critical ZoneObservatories (CZOs), and others. We organized a survey, the results of whichidentified pressing gaps in data availability from these networks, inparticular for the future development and evaluation of models that representESD processes, and provide insights for improvement in both data collectionand model integration. From this survey overview of data applications in the context of LTER andCZO research, we identified three challenges: (1) widen application ofterrestrial observation network data in Earth system modelling,(2) develop integrated Earth system models that incorporate processrepresentation and data of multiple disciplines, and (3) identifycomplementarity in measured variables and spatial extent, and promotingsynergies in the existing observational networks. These challenges lead toperspectives and recommendations for an improved dialogue between theobservation networks and the ESD modelling community, including co-locationof sites in the existing networks and further formalizing theserecommendations among these communities. Developing these synergies willenable cross-site and cross-network comparison and synthesis studies, whichwill help produce insights around organizing principles, classifications,and general rules of coupling processes with environmental conditions.