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

AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require datamanagement plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices

Decoding the massive genome of loblolly pine using haploid DNA and novel assembly strategies

BACKGROUND: The size and complexity of conifer genomes has, until now, prevented full genome sequencing and assembly. The large research community and economic importance of loblolly pine, Pinus taeda L., made it an early candidate for reference sequence determination. RESULTS: We develop a novel strategy to sequence the genome of loblolly pine that combines unique aspects of pine reproductive biology and genome assembly methodology. We use a whole genome shotgun approach relying primarily on next generation sequence generated from a single haploid seed megagametophyte from a loblolly pine tree, 20-1010, that has been used in industrial forest tree breeding. The resulting sequence and assembly was used to generate a draft genome spanning 23.2 Gbp and containing 20.1 Gbp with an N50 scaffold size of 66.9 kbp, making it a significant improvement over available conifer genomes. The long scaffold lengths allow the annotation of 50,172 gene models with intron lengths averaging over 2.7 kbp and sometimes exceeding 100 kbp in length. Analysis of orthologous gene sets identifies gene families that may be unique to conifers. We further characterize and expand the existing repeat library based on the de novo analysis of the repetitive content, estimated to encompass 82% of the genome. CONCLUSIONS: In addition to its value as a resource for researchers and breeders, the loblolly pine genome sequence and assembly reported here demonstrates a novel approach to sequencing the large and complex genomes of this important group of plants that can now be widely applied

Field-level characteristics influence wild bee functional guilds on public lands managed for conservation

Throughout the Midwestern US, many public lands set aside for conservation engage in management activities (e.g., agriculture) that may act as stressors on wild bee populations. Several studies have investigated how wild bees respond to large-scale agriculture production; however, there has been limited assessment of how wild bees may be impacted by agricultural activity on public lands or how local variables may influence bee communities in these same areas. In this study, we assessed the abundance and richness of wild bee floral and nesting guilds at 30 agricultural field margins located on five Conservation Areas in Missouri. Generally, regardless of guild, bee abundance and richness was greater in field margins with more floral diversity and taller vegetation. Bee guilds responded negatively to agricultural production in Conservation Areas with fewer soil- and cavity-nesting bees collected in margins adjacent to annually cropped fields. Although fewer diet specialists were collected, specialist bee abundance and richness was greater in margins near fields that were uncropped (i.e., vegetated, but not row-cropped) during the previous year. Overall, the percentage of trees and shrubs within 800 m of study fields (i.e., “woodland”) was negatively associated with abundance and richness of bees, but specifically, reduced richness of soil-nesters and diet specialists. Our findings indicate agricultural management activities on public lands may lead to decreased abundance and richness of wild bee guilds. If public lands are to be managed for species diversity, including wild bees, maintaining diverse plant communities with taller vegetation (>100 cm) near cultivated fields and/or modifying agricultural production practices on public lands may greatly improve the conservation of local bee communities. Keywords: Bee guilds, Abundance, Richness, Conservation areas, Field margins, Agroecosystem

Neonicotinoid insecticides negatively affect performance measures of non-target terrestrial arthropods: a meta-analysis. Ecological Applications. EAP17-0530.R1

Data presented here was used in Main et al. 2018 to calculate all meta-analytical statistics including standard mean difference, random effects models, and meta-regression models. Data include source of information (e.g., table, figures, in-text), authors, publication year, reported means, SE, sample size, and specific study variables (e.g., study type, active ingredient, organism evaluated, performance measure). See the corresponding metadata for description of variables.<br><br>The metadata provide a detailed description of all variables listed in the master data file. Note: data files for subset analyses (see attached R code) were created (as necessary) from the master data file; however, all information is the same.<br

Tripal: a construction toolkit for online genome databases

As the availability, affordability and magnitude of genomics and genetics research increases so does the need to provide online access to resulting data and analyses. Availability of a tailored online database is the desire for many investigators or research communities; however, managing the Information Technology infrastructure needed to create such a database can be an undesired distraction from primary research or potentially cost prohibitive. Tripal provides simplified site development by merging the power of Drupal, a popular web Content Management System with that of Chado, a community-derived database schema for storage of genomic, genetic and other related biological data. Tripal provides an interface that extends the content management features of Drupal to the data housed in Chado. Furthermore, Tripal provides a web-based Chado installer, genomic data loaders, web-based editing of data for organisms, genomic features, biological libraries, controlled vocabularies and stock collections. Also available are Tripal extensions that support loading and visualizations of NCBI BLAST, InterPro, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses, as well as an extension that provides integration of Tripal with GBrowse, a popular GMOD tool. An Application Programming Interface is available to allow creation of custom extensions by site developers, and the look-and-feel of the site is completely customizable through Drupal-based PHP template files. Addition of non-biological content and user-management is afforded through Drupal. Tripal is an open source and freely available software package found at http://tripal.sourceforge.net