Ecobiomics: Environmental metagenomic biomonitoring
Bibliographic record
Abstract
Land use disturbances are having enormous adverse impacts on the biodiversity and integrity of natural and managed ecosystems around the world. Adverse impacts on biodiversity are compromising ecosystem services and processes, reducing ecosystem resilience, and leading to unpredictable ecosystem responses to environmental change. The Metagenomics-Based Ecosystem Biomonitoring Project (EcoBiomics) focuses on the urgent need to better understand the extent and significance of ongoing changes to biodiversity in the soil and aquatic ecosystems that sustain essential ecosystem services upon which Canadians and the Canadian economy depend. This project uniquely recognizes that a breadth of scientific expertise from within the Canadian government is required to undertake this research, which is spread across relevant departments and agencies with Biodiversity portfolios. It involves over 50 participants (researchers, technicians, bioinformaticians, software developers, managers and students, etc.) contributing to many smaller projects in several locations across Canada organized by themes. The objectives of this project include: Develop standard soil and water methods and a federal Bioinformatics Platform to harmonize analyses of metabarcoding, metagenomics and metatranscriptomics data across federal departments/agencies; Establish genomic observatories and comprehensive biodiversity baselines for assessing future changes to water and soil biodiversity at long-term environmental monitoring sites in Canada; Develop new knowledge to improve water quality and soil health by comprehensively characterizing aquatic microbiomes, soil microbiomes, and invertebrate zoobiomes, and testing hypotheses to enhance environmental assessment, monitoring, and remediation activities. Develop standard soil and water methods and a federal Bioinformatics Platform to harmonize analyses of metabarcoding, metagenomics and metatranscriptomics data across federal departments/agencies; Establish genomic observatories and comprehensive biodiversity baselines for assessing future changes to water and soil biodiversity at long-term environmental monitoring sites in Canada; Develop new knowledge to improve water quality and soil health by comprehensively characterizing aquatic microbiomes, soil microbiomes, and invertebrate zoobiomes, and testing hypotheses to enhance environmental assessment, monitoring, and remediation activities. Our poster will focus primarily on the challenges associated with the first objective. Genomic technologies are revolutionizing biodiversity assessment in soil and aquatic ecosystems, and they now offer the only practical way to comprehensively characterize this enormous biodiversity. These technologies and associated tools allow us to obtain comprehensive baseline biodiversity data that are essential to support evidence-based decision-making. However, a strong focus of this project is to enable environmental assessment, monitoring, and remediation activities by a multitude of potential end users and thus standardized protocols and processes must be determined and shared. For the data generated, several procedures were defined. A minimum metadata profile related to the sampling event is required for all projects, which follows existing standards including the DarwinCore (https://dwc.tdwg.org). Sample preparation was also standardized based primarily on protocols developed in earlier projects that were validated for use in Ecobiomics, for example the Earth Microbiome Project (http://www.earthmicrobiome.org). The procedures for DNA extraction through sequencing largely followed the Minimum Information about “X” Sequence (MIxS) standard (gensc.org/mixs). This data is all input into an in-house custom-built software package, Sequence Database (SeqDB) used by all participants across the entire project, which is made available centrally via a federal high-performance computing centre. SeqDB not only stores the metadata and data generated but also maintains provenance based on defined workflows for metabarcoding, shotgun metagenomics, and other “omics” pipelines. It also supports project management through various metrics and visualizations. We will document some of the challenges to standardizing data and workflows in large multi-domain and multi-department project like Ecobiomics and the need for further standard development to truly support data sharing and integration across a highly diverse ecosystem of genomic observatories globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".