Using terrestrial laser scanning to evaluate non-destructive aboveground biomass allometries in diverse Northern California forests

A crucial part of carbon accounting is quantifying a tree’s aboveground biomass (AGB) using allometric equations, but species-specific equations are limited because data to inform these equations requires destructive harvesting of many trees which is difficult and time-consuming. Here, we used terrestrial laser scanning (TLS) to non-destructively estimate AGB for 282 trees from 5 species at 3 locations in Northern California using stem and branch volume estimates from quantitative structure models (QSMs) and wood density from the literature. We then compared TLS QSM estimates of AGB with published allometric equations and used TLS-based AGB, diameter at breast height (DBH), and height to derive new species-specific allometric AGB equations for our study species. To validate the use of TLS, we used traditional forestry approaches to collect DBH (n = 550) and height (n = 291) data on individual trees. TLS-based DBH and height were not significantly different from field inventory data (R2 = 0.98 for DBH, R2 = 0.95 for height). Across all species, AGB calculated from TLS QSM volumes were approximately 30% greater than AGB estimates using published Forest Service’s Forest Inventory and Analysis Program equations, and TLS QSM AGB estimates were 10% greater than AGB calculated with existing equations, although this variation was species-dependent. In particular, TLS AGB estimates for Quercus agrifolia and Sequoia sempervirens differed the most from AGB estimates calculated using published equations. New allometric equations created using TLS data with DBH and height performed better than equations that only included DBH and matched most closely with AGB estimates generated from QSMs. Our results support the use of TLS as a method to rapidly estimate height, DBH, and AGB of multiple trees at a plot-level when species are identified and wood density is known. In addition, the creation of new TLS-based non-destructive allometric equations for our 5 study species may have important applications and implications for carbon quantification over larger spatial scales, especially since our equations estimated greater AGB than previous approaches

Individual-Based Modeling of Amazon Forests Suggests That Climate Controls Productivity While Traits Control Demography

Climate, species composition, and soils are thought to control carbon cycling and forest structure in Amazonian forests. Here, we add a demographics scheme (tree recruitment, growth, and mortality) to a recently developed non-demographic model—the Trait-based Forest Simulator (TFS)—to explore the roles of climate and plant traits in controlling forest productivity and structure. We compared two sites with differing climates (seasonal vs. aseasonal precipitation) and plant traits. Through an initial validation simulation, we assessed whether the model converges on observed forest properties (productivity, demographic and structural variables) using datasets of functional traits, structure, and climate to model the carbon cycle at the two sites. In a second set of simulations, we tested the relative importance of climate and plant traits for forest properties within the TFS framework using the climate from the two sites with hypothetical trait distributions representing two axes of functional variation (“fast” vs. “slow” leaf traits, and high vs. low wood density). The adapted model with demographics reproduced observed variation in gross (GPP) and net (NPP) primary production, and respiration. However, NPP and respiration at the level of plant organs (leaf, stem, and root) were poorly simulated. Mortality and recruitment rates were underestimated. The equilibrium forest structure differed from observations of stem numbers suggesting either that the forests are not currently at equilibrium or that mechanisms are missing from the model. Findings from the second set of simulations demonstrated that differences in productivity were driven by climate, rather than plant traits. Contrary to expectation, varying leaf traits had no influence on GPP. Drivers of simulated forest structure were complex, with a key role for wood density mediated by its link to tree mortality. Modeled mortality and recruitment rates were linked to plant traits alone, drought-related mortality was not accounted for. In future, model development should focus on improving allocation, mortality, organ respiration, simulation of understory trees and adding hydraulic traits. This type of model that incorporates diverse tree strategies, detailed forest structure and realistic physiology is necessary if we are to be able to simulate tropical forest responses to global change scenarios

Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient

Spatially continuous data on functional diversity will improve our ability to predict global change impacts on ecosystem properties. We applied methods that combine imaging spectroscopy and foliar traits to estimate remotelysensed functional diversity in tropical forests across an Amazon-to-Andes elevation gradient (215 to 3537 m). We evaluated the scale dependency of community assembly processes and examined whether tropical forest productivitycould be predicted by remotely sensed functional diversity. Functional richness of the community decreased withincreasing elevation. Scale-dependent signals of trait convergence, consistent with environmental filtering, play animportant role in explaining the range of trait variation within each site and along elevation. Single- and multitraitremotely sensed measures of functional diversity were important predictors of variation in rates of net and grossprimary productivity. Our findings highlight the potential of remotely sensed functional diversity to inform trait-based ecology and trait diversity-ecosystem function linkages in hyperdiverse tropical forests.Fil: Durán, Sandra M.. University of Arizona; Estados UnidosFil: Martin, Roberta E.. Arizona State University; Estados UnidosFil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Maitner, Brian S.. Arizona State University; Estados UnidosFil: Malhi, Yadvinder. University of Oxford; Reino UnidoFil: Salinas, Norma. University of Oxford; Reino Unido. Pontificia Universidad Católica de Perú; PerúFil: Shenkin, Alexander. University of Oxford; Reino UnidoFil: Silman, Miles R.. Wake Forest University; Estados UnidosFil: Wieczynski, Daniel J.. University of Oxford; Reino UnidoFil: Asner, Gregory P.. Arizona State University; Estados UnidosFil: Bentley, Lisa Patrick. Sonoma State University; Estados UnidosFil: Savage, Van M.. University of California; Estados UnidosFil: Enquist, Brian J.. Arizona State University; Estados Unido

Temperature response surfaces for mortality risk of tree species with future drought

Widespread, high levels of tree mortality, termed forest die-off, associated with drought and rising temperatures, are disrupting forests worldwide. Drought will likely become more frequent with climate change, but even without more frequent drought, higher temperatures can exacerbate tree water stress. The temperature sensitivity of drought-induced mortality of tree species has been evaluated experimentally for only single-step changes in temperature (ambient compared to ambient + increase) rather than as a response surface (multiple levels of temperature increase), which constrains our ability to relate changes in the driver with the biological response. Here we show that time-to-mortality during drought for seedlings of two western United States tree species, Pinus edulis (Engelm.) and Pinus ponderosa (Douglas ex C. Lawson), declined in continuous proportion with increasing temperature spanning a 7.7 °C increase. Although P. edulis outlived P. ponderosa at all temperatures, both species had similar relative declines in time-to-mortality as temperature increased (5.2% per °C for P. edulis; 5.8% per °C for P. ponderosa). When combined with the non-linear frequency distribution of drought duration—many more short droughts than long droughts—these findings point to a progressive increase in mortality events with global change due to warming alone and independent of additional changes in future drought frequency distributions. As such, dire future forest recruitment patterns are projected assuming the calculated 7–9 seedling mortality events per species by 2100 under business-as-usual warming occur, congruent with additional vulnerability predicted for adult trees from stressors like pathogens and pests. Our progressive projection for increased mortality events was driven primarily by the non-linear shape of the drought duration frequency distribution, a common climate feature of drought-affected regions. These results illustrate profound benefits for reducing emissions of carbon to the atmosphere from anthropogenic sources and slowing warming as rapidly as possible to maximize forest persistence.Peer reviewedPlant Biology, Ecology and Evolutio

Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

Funding Information: This work is a product of the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk). J.A.G. was funded by the Natural Environment Research Council (NERC; NE/T011084/1 and NE/S011811/1) and the Netherlands Organisation for Scientific Research (NWO) under the Rubicon programme with project number 019.162LW.010. The traits field campaign was funded by a grant to Y.M. from the European Research Council (Advanced Grant GEM-TRAIT: 321131) under the European Union‘s Seventh Framework Programme (FP7/2007-2013), with additional support from NERC Grant NE/D014174/1 and NE/J022616/1 for traits work in Peru, NERC Grant ECOFOR (NE/K016385/1) for traits work in Santarem, NERC Grant BALI (NE/K016369/1) for plot and traits work in Malaysia and ERC Advanced Grant T-FORCES (291585) to Phillips for traits work in Australia. Plot setup in Ghana and Gabon were funded by a NERC Grant NE/I014705/1 and by the Royal Society-Leverhulme Africa Capacity Building Programme. The Malaysia campaign was also funded by NERC GrantNE/K016253/1. Plot inventories in Peru were supported by funding from the US National Science Foundation Long-Term Research in Environmental Biology program (LTREB; DEB 1754647) and the Gordon and Betty Moore Foundation Andes-Amazon Program. Plots inventories in Nova Xavantina (Brazil) were supported by the National Council for Scientific and Technological Development (CNPq), Long Term Ecological Research Program (PELD), Proc. 441244/2016-5, and the Foundation of Research Support of Mato Grosso (FAPEMAT), Project ReFlor, Proc. 589267/2016. During data collection, I.O. was supported by a Marie Curie Fellowship (FP7-PEOPLE-2012-IEF-327990). GEM trait data in Gabon was collected under authorisation to Y.M. and supported by the Gabon National Parks Agency. D.B. was funded by the Fondation Wiener-Anspach. W.D.K. acknowledges support from the Faculty Research Cluster ‘Global Ecology’ of the University of Amsterdam. M.S. was funded by a grant from the Ministry of Education, Youth and Sports of the Czech Republic (INTER-TRANSFER LTT19018). Y.M. is supported by the Jackson Foundation. We thank the two anonymous reviewers and Associate Editor G. Henebry for their insightful comments that helped improved this manuscript.Peer reviewedPostprin

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles

Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loc

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead