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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 (~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.

Streptococcus pneumoniae causes both invasive and nonin vasi ve disease. Since the introduction of 7-valent and 13-valent protein-polysaccharide conjugated pneumococcal vaccines (PCV7 and PCV13, respectively) for children, vaccine serotype disease [...]

Drone lidar has the potential to provide detailed measurements of vertical forest structure throughout large areas, but a systematic evaluation of unsampled forest structure in comparison to independent reference data has not been performed. Here, we used ray tracing on a high-resolution voxel grid to quantify sampling variation in a temperate mountain forest in the southwest Czech Republic. We decoupled the impact of pulse density and scan-angle range on the likelihood of generating a return using spatially and temporally coincident TLS data. We show three ways that a return can fail to be generated in the presence of vegetation: first, voxels could be searched without producing a return, even when vegetation is present; second, voxels could be shadowed (occluded) by other material in the beam path, preventing a pulse from searching a given voxel; and third, some voxels were unsearched because no pulse was fired in that direction. We found that all three types existed, and that the proportion of each of them varied with pulse density and scan-angle range throughout the canopy height profile. Across the entire data set, 98.1% of voxels known to contain vegetation from a combination of coincident drone lidar and TLS data were searched by high-density drone lidar, and 81.8% of voxels that were occupied by vegetation generated at least one return. By decoupling the impacts of pulse density and scan angle range, we found that sampling completeness was more sensitive to pulse density than to scan-angle range. There are important differences in the causes of sampling variation that change with pulse density, scan-angle range, and canopy height. Our findings demonstrate the value of ray tracing to quantifying sampling completeness in drone lidar. [ABSTRACT FROM AUTHOR]

Background: There are few reports of longitudinal serologic responses in children following Sars-CoV-2 infection and vaccination. This study describes longitudinal SARS-CoV-2 antibody responses following infection, vaccination, or both (hybrid immunity) in a cohort of Canadian children. The objectives of our study were to compare antibody levels following SARS-CoV-2 infection, vaccination, and hybrid immunity and to examine antibody decline after final antigen exposure. Methods: The Alberta Childhood COVID-19 Cohort (AB3C) study was a prospective longitudinal cohort study conducted from July 2020 to September 2022 with repeat sampling across 5 visits. Children under 18 years of age were enrolled for serial measurement of antibody responses to SARS-CoV-2 virus vaccine and infection. Results: The final sample size was 919; participants were 50.5% female, 48.2% were > 12 years and 88.5% were white ethnicity. The median peak spike IgG level of those with only infection was not different from those with no vaccination or infection (233 AU/mL (IQR: 99–944 AU/mL) vs. 3 AU/mL (IQR: 1–5 AU/mL; P = 0.1765). Participants with infections after vaccination had higher IgG levels than those where infection preceded vaccination (median: 36,660 (IQR: 22,084 − 40,000 AU/mL) vs. 17,461 AU/mL (IQR: 10,617 − 33,212 AU/mL); P < 0.0001). In a linear mixed methods model, children with infection-only had low levels of antibody that stayed stable over the study duration without further antigen exposures. Those with infection after vaccination had the slowest rate of antibody decline over time at 4% (95%CI: 2-5%) per week, compared with children where infection preceded vaccine 7% (95%CI: 6-8%) per week. Conclusions: Children with hybrid immunity conferred through vaccination (2 + doses) followed by a SARS-CoV-2 infection had the highest and longest lasting antibody levels, compared to children who had an infection followed by vaccination, vaccination-only, or infection-only. The longer-term clinical importance of these findings, related to prevention of repeated infections and severe outcomes and need for further vaccine doses, is not yet known. [ABSTRACT FROM AUTHOR]

Increases in organic carbon within agricultural soils are widely recognized as a "negative emission" that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS). [ABSTRACT FROM AUTHOR]

Objectives Beginning early in the pandemic, there was a worldwide effort to develop effective vaccines against the SARS-CoV-2 virus. Before and after the approval and implementation of vaccines, there were concerns about their need as well as their safety and rapid development. We explored child demographic characteristics and parental concerns to identify factors associated with the decision to vaccinate. Methods A cohort of 1035 children from Calgary was assembled in 2020 to participate in 5 visits every 6 months for survey completion and blood sampling for SARS-CoV-2 antibodies. Visits 1 to 2 occurred before approval of vaccines for children; Visits 3 to 5 occurred after vaccine approval for different age groups. We described vaccine concerns and utilized logistic regression to examine factors associated with the decision to vaccinate in children ≥5 years of age. Results Children ≥12 years of age, of non-white or non-black ethnicity, and who had received previous influenza vaccines had higher odds of being vaccinated against SARS-CoV-2. Children with previous SARS-CoV-2 infection had lower odds of being vaccinated. The most common concerns in early 2021 were about vaccine safety. By summer 2022, the most common concern was a belief that vaccines were not necessary. Through the study 88% of children were vaccinated. Conclusions Age, ethnicity, previous infections, and vaccine attitudes were associated with parental decision to vaccinate against SARS-CoV-2. For children who remained unvaccinated, parents continued to have safety concerns and questioned the necessity of the vaccine. Complacency about the need for vaccination may be more challenging to address and overcome than concerns about safety alone. [ABSTRACT FROM AUTHOR]

Predictions of climate-related shifts in species ranges have largely been based on correlative models. Due to limitations of these models, there is a need for more integration of experimental approaches when studying impacts of climate change on species distributions. Here, we used controlled experiments to identify physiological thresholds that control poleward range limits of three species of mangroves found in North America. We found that all three species exhibited a threshold response to extreme cold, but freeze tolerance thresholds varied among species. From these experiments, we developed a climate metric, freeze degree days (FDD), which incorporates both the intensity and the frequency of freezes. When included in distribution models, FDD accurately predicted mangrove presence/absence. Using 28 years of satellite imagery, we linked FDD to observed changes in mangrove abundance in Florida, further exemplifying the importance of extreme cold. We then used downscaled climate projections of FDD to project that these range limits will move northward by 2.2-3.2 km yr(-1) over the next 50 years.

We used measurements from airborne imaging spectroscopy and LiDAR to quantify the biophysical structure and composition of vegetation on a dryland substrate age gradient in Hawaii. Both vertical stature and species composition changed during primary succession, and reveal a progressive increase in vertical stature on younger substrates followed by a collapse on Pleistocene-aged flows. Tall-stature Metrosideros polymorpha woodlands dominated on the youngest substrates (hundreds of years), and were replaced by the tall-stature endemic tree species Myoporum sandwicense and Sophora chrysophylla on intermediate-aged flows (thousands of years). The oldest substrates (tens of thousands of years) were dominated by the short-stature native shrub Dodonaea viscosa and endemic grass Eragrostis atropioides. We excavated 18 macroscopic charcoal fragments from Pleistocene-aged substrates. Mean radiocarbon age was 2,002 years and ranged from < 200 to 7,730. Genus identities from four fragments indicate that Osteomeles spp. or M. polymorpha once occupied the Pleistocene-aged substrates, but neither of these species is found there today. These findings indicate the existence of fires before humans are known to have occupied the Hawaiian archipelago, and demonstrate that a collapse in vertical stature is prevalent on the oldest substrates. This work contributes to our understanding of prehistoric fires in shaping the trajectory of primary succession in Hawaiian drylands.

Question: How do canopy disturbance and soil properties structure vascular plant community species composition and resilience to encroachment by exotic species in a tropical montane wet forest? Location: Hawai'i Experimental Tropical Forest (HETF), a tropical montane wet forest, on Mauna Kea, Hawai'i Island, Hawai'i, USA. Methods: Previous studies employing airborne LiDAR were used to define three zones across an elevation gradient from 900 to 1500 m. Within each zone, a ~1000-m block transect was selected to cross two different volcanic substrates: one derived from surface lava and one derived from thick ash deposits. Non-metric multidimensional scaling (NMS) scores of vegetation data were related to independently-derived environmental NMS scores and spatial location with generalized additive models (GAM). Results: Vascular plant species composition in all elevation zones consists of three NMS axes, which are best modelled by one of three possible environmental NMS axes or by location. The first NMS axis of species composition in the lowest elevation zone (40% variance explained (VE)) is a function of location on volcanic substrates (61% deviance explained (DE)). The second lowest elevation axis (27% VE) is a function of unexplained spatial heterogeneity (31% DE). The third lowest elevation NMS axis (24% VE) is a function of the spatial mosaic of canopy disturbance (16% DE). In the middle elevation zone, species composition most strongly relates to the interaction between volcanic substrate and the condition of the soil surface for all three NMS axes (41%, 27%, 24% VE; 70%, 16%, 24% DE). The primary axis of species composition in the highest elevation zone (41% VE) corresponds with substrate and soil condition (55% DE) but the second and third axes of species composition (27% and 25% VE) relate to canopy dieback disturbance (36%, 14% DE). Counts of exotic species and 0-2 m height class native tree species respond to the type of volcanic substrate and soil surface condition in all three elevation zones. Lava-derived substrates have a higher incidence of exotic species and less native tree regeneration; whereas ash-derived substrates have higher numbers of native tree species regenerating and many fewer exotic species. Discussion: The tropical montane forests on Mauna Kea reflect a native-dominated plant community response to disturbance on both lava- and ash-derived volcanic substrates, and a higher propensity for exotic species to occur on the lava-derived substrate. Native plant communities on ash-derived soils may have higher resilience to exotic invasion than communities on lava-derived substrates. Our results indicate resource managers should explicitly account for variation in soils and substrate type when prioritizing, implementing and monitoring management interventions to foster native plant assemblages and control the spread of exotic and invasive species. © 2013 International Association for Vegetation Science.