Revolution in food web analysis and trophic ecology: diet analysis by DNA and stable isotope analysis
Bibliographic record
Abstract
Characterization of energy flow in ecosystems is one of the primary goals of ecology, and the analysis of trophic interactions and food web dynamics is key to quantifying energy flow. Predator-prey interactions define the majority of trophic interactions and food web dynamics, and visual analysis of stomach, gut or fecal content composition is the technique traditionally used to quantify predator-prey interactions. Unfortunately such techniques may be biased and inaccurate due to variation in digestion rates (Sheppard & Hardwood 2005); however, those limitations can be largely overcome with new technology. In the last 20 years, the use of molecular genetic techniques in ecology has exploded (King et al. 2008). The growing availability of molecular genetic methods and data has fostered the use of PCR-based techniques to accurately distinguish and identify prey items in stomach, gut and fecal samples. In this month’s issue of Molecular Ecology Resources, Corse et al. (2010) describe and apply a new approach to quantifying predator-prey relationships using an ecosystem-level genetic characterization of available and consumed prey in European freshwater habitats (Fig. 1a). In this issue of Molecular Ecology, Hardy et al. (2010) marry the molecular genetic analysis of prey with a stable isotope (SI) analysis of trophic interactions in an Australian reservoir community (Fig. 1b). Both papers demonstrate novel and innovative approaches to an old problem – how do we effectively explore food webs and energy movement in ecosystems? Figure 1Open in figure viewerPowerPoint The aquatic habitats used for two studies of diet and trophic interactions that employed molecular genetic and stable isotope analyses. Panel a: Example of Rhone basin habitat (France) where fish diet was determined using PCR to classify prey to a series of ecological clades (photo by Emmanuel Corse). Panel b: A weir pool on the lower Murray River (Australia) where food web and prey use was evaluated using a combination of advanced molecular genetic and stable isotope analyses (photo credit: CSIRO).
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".