Environmental <scp>DNA</scp> and metagenomics of terrestrial mammals as keystone taxa of recent and past ecosystems
Bibliographic record
Abstract
ABSTRACT Terrestrial mammals shape their ecosystems, and mammalian community assemblages can be important indicators of ecosystem functioning and ecosystem changes over time. Numerous taxa of terrestrial mammals are currently threatened by habitat loss and face displacement to new geographical areas or systems to which they are less suited and where they may affect the original communities. Understanding past ecosystem changes is important for predicting future responses of species assemblages to changes in their environments. Thus, ecological and evolutionary history, as well as adaptive capacity, are important predictors of future population viability. Genomic and metagenomic approaches using environmental or ancient DNA offer a wealth of information regarding genome‐wide variation of changing communities or of taxonomic groups over time, which may help explain past changes and predict future responses of communities to changes in their environment; however, to date, such studies are relatively scarce. We review studies on environmental DNA and environmental genomics of terrestrial mammals to assess the potential of such approaches regarding past, contemporary, and future terrestrial ecosystems, identify inherent challenges, and discuss potential applications. We elaborate on lessons to be learned from mammal genomics of past ecosystems and compare metabarcoding with general metagenetic and metagenomic techniques. We provide a comprehensive overview of current applications, challenges, and future potential of environmental DNA with regards to terrestrial mammals. As current major challenges regarding mammalian eDNA we identify its scarcity and patchy distribution, along with the persistent necessity of genomic reference data. While the latter are steadily increasing, the former can only be tackled by explicitly mapping the environment to gain understanding of spatial eDNA distribution. Such understanding may facilitate informed choices of sample sites and substrates and, together with new sequencing techniques, this can allow mammalian eDNA to be maximally exploited as a source of biodiversity data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".