White paper – On the use of LiDAR data at AmeriFlux sites

Our aim is to inform the AmeriFlux community on existing and upcoming LiDAR technologies (atmospheric Doppler or Raman LiDAR often deployed at flux sites are not considered here), how it is currently used at flux sites, and how we believe it could, in the future, further contribute to the AmeriFlux vision. Heterogeneity in vegetation and ground properties at various spatial scales is omnipresent at flux sites, and 3D mapping of canopy, understory, and ground surface can help move the science forward

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary withrespect to discrete return airborne equivalents due to their greater sensitivity to reflectance differencesbetween canopy and ground surfaces resulting from differences in footprint size, energy thresholding,noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopyportions of waveforms has successfully circumvented this issue, but not at large scales. This studydevelops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensedvegetation, terrain and environmental attributes are best suited to predicting scaling factors basedon an independent measure of importance. The most important attributes were identified as: soilphosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields productand terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS datafor all available data and an optimized subset, where the latter produced most encouraging results(R2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates ofGF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors.Large-scale active sensor estimates of GF can establish a baseline from which future monitoringinvestigations can be initiated via upcoming Earth Observation missions

Strategies designed to help healthcare professionals to recruit participants to research studies.

BACKGROUND: Identifying and approaching eligible participants for recruitment to research studies usually relies on healthcare professionals. This process is sometimes hampered by deliberate or inadvertent gatekeeping that can introduce bias into patient selection. OBJECTIVES: Our primary objective was to identify and assess the effect of strategies designed to help healthcare professionals to recruit participants to research studies. SEARCH METHODS: We performed searches on 5 January 2015 in the following electronic databases: Cochrane Methodology Register, CENTRAL, MEDLINE, EMBASE, CINAHL, British Nursing Index, PsycINFO, ASSIA and Web of Science (SSCI, SCI-EXPANDED) from 1985 onwards. We checked the reference lists of all included studies and relevant review articles and did citation tracking through Web of Science for all included studies. SELECTION CRITERIA: We selected all studies that evaluated a strategy to identify and recruit participants for research via healthcare professionals and provided pre-post comparison data on recruitment rates. DATA COLLECTION AND ANALYSIS: Two review authors independently screened search results for potential eligibility, read full papers, applied the selection criteria and extracted data. We calculated risk ratios for each study to indicate the effect of each strategy. MAIN RESULTS: Eleven studies met our eligibility criteria and all were at medium or high risk of bias. Only five studies gave the total number of participants (totalling 7372 participants). Three studies used a randomised design, with the others using pre-post comparisons. Several different strategies were investigated. Four studies examined the impact of additional visits or information for the study site, with no increases in recruitment demonstrated. Increased recruitment rates were reported in two studies that used a dedicated clinical recruiter, and five studies that introduced an automated alert system for identifying eligible participants. The studies were embedded into trials evaluating care in oncology mainly but also in emergency departments, diabetes and lower back pain. AUTHORS' CONCLUSIONS: There is no strong evidence for any single strategy to help healthcare professionals to recruit participants in research studies. Additional visits or information did not appear to increase recruitment by healthcare professionals. The most promising strategies appear to be those with a dedicated resource (e.g. a clinical recruiter or automated alert system) for identifying suitable participants that reduced the demand on healthcare professionals, but these were assessed in studies at high risk of bias.We would like to acknowledge the support of the Methodology theme of theCancer ExperiencesCollaborative (CECo), who have supported this review.This is the final published version. It first appeared at http://onlinelibrary.wiley.com/doi/10.1002/14651858.MR000036.pub2/abstract

Validity of Six Activity Monitors in Chronic Obstructive Pulmonary Disease: A Comparison with Indirect Calorimetry

<div><p>Reduced physical activity is an important feature of Chronic Obstructive Pulmonary Disease (COPD). Various activity monitors are available but their validity is poorly established. The aim was to evaluate the validity of six monitors in patients with COPD. We hypothesized triaxial monitors to be more valid compared to uniaxial monitors. Thirty-nine patients (age 68±7years, FEV₁ 54±18%predicted) performed a one-hour standardized activity protocol. Patients wore 6 monitors (Kenz Lifecorder (Kenz), Actiwatch, RT3, Actigraph GT3X (Actigraph), Dynaport MiniMod (MiniMod), and SenseWear Armband (SenseWear)) as well as a portable metabolic system (Oxycon Mobile). Validity was evaluated by correlation analysis between indirect calorimetry (VO₂) and the monitor outputs: Metabolic Equivalent of Task [METs] (SenseWear, MiniMod), activity counts (Actiwatch), vector magnitude units (Actigraph, RT3) and arbitrary units (Kenz) over the whole protocol and slow versus fast walking. Minute-by-minute correlations were highest for the MiniMod (r = 0.82), Actigraph (r = 0.79), SenseWear (r = 0.73) and RT3 (r = 0.73). Over the whole protocol, the mean correlations were best for the SenseWear (r = 0.76), Kenz (r = 0.52), Actigraph (r = 0.49) and MiniMod (r = 0.45). The MiniMod (r = 0.94) and Actigraph (r = 0.88) performed better in detecting different walking speeds. The Dynaport MiniMod, Actigraph GT3X and SenseWear Armband (all triaxial monitors) are the most valid monitors during standardized physical activities. The Dynaport MiniMod and Actigraph GT3X discriminate best between different walking speeds.</p> </div

Evolution and Functional Diversification of Fructose Bisphosphate Aldolase Genes in Photosynthetic Marine Diatoms

Diatoms and other chlorophyll-c containing, or chromalveolate, algae are among the most productive and diverse phytoplankton in the ocean. Evolutionarily, chlorophyll-c algae are linked through common, although not necessarily monophyletic, acquisition of plastid endosymbionts of red as well as most likely green algal origin. There is also strong evidence for a relatively high level of lineage-specific bacterial gene acquisition within chromalveolates. Therefore, analyses of gene content and derivation in chromalveolate taxa have indicated particularly diverse origins of their overall gene repertoire. As a single group of functionally related enzymes spanning two distinct gene families, fructose 1,6-bisphosphate aldolases (FBAs) illustrate the influence on core biochemical pathways of specific evolutionary associations among diatoms and other chromalveolates with various plastid-bearing and bacterial endosymbionts. Protein localization and activity, gene expression, and phylogenetic analyses indicate that the pennate diatom Phaeodactylum tricornutum contains five FBA genes with very little overall functional overlap. Three P. tricornutum FBAs, one class I and two class II, are plastid localized, and each appears to have a distinct evolutionary origin as well as function. Class I plastid FBA appears to have been acquired by chromalveolates from a red algal endosymbiont, whereas one copy of class II plastid FBA is likely to have originated from an ancient green algal endosymbiont. The other copy appears to be the result of a chromalveolate-specific gene duplication. Plastid FBA I and chromalveolate-specific class II plastid FBA are localized in the pyrenoid region of the chloroplast where they are associated with β-carbonic anhydrase, which is known to play a significant role in regulation of the diatom carbon concentrating mechanism. The two pyrenoid-associated FBAs are distinguished by contrasting gene expression profiles under nutrient limiting compared with optimal CO2 fixation conditions, suggestive of a distinct specialized function for each. Cytosolically localized FBAs in P. tricornutum likely play a role in glycolysis and cytoskeleton function and seem to have originated from the stramenopile host cell and from diatom-specific bacterial gene transfer, respectively

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available