Traits-based approaches in bioassessment and ecological risk assessment: Strengths, weaknesses, opportunities and threats
Bibliographic record
Abstract
We discuss the application of traits-based bioassessment approaches in retrospective bioassessment as well as in prospective ecological risk assessments in regulatory frameworks. Both approaches address the interaction between species and stressors and their consequences at different levels of biological organization, but the fact that a specific species may be less abundant in a potentially impacted site compared with a reference site is, regrettably, insufficient to provide diagnostic information. Species traits may, however, overcome the problems associated with taxonomy-based bioassessment. Trait-based approaches could provide signals regarding what environmental factors may be responsible for the impairment and, thereby, provide causal insight into the interaction between species and stressors. For development of traits-based (TBA), traits should correspond to specific types of stressors or suites of stressors. In this paper, a strengths, weaknesses, opportunities, and threats (SWOT) analysis of TBA in both applications was used to identify challenges and potentials. This paper is part of a series describing the output of the TERA (Traits-based ecological risk assessment: Realising the potential of ecoinformatics approaches in ecotoxicology) Workshop held between 7 and 11 September, 2009, in Burlington, Ontario, Canada. The recognized strengths were that traits are transferrable across geographies, add mechanistic and diagnostic knowledge, require no new sampling methodology, have an old tradition, and can supplement taxonomic analysis. Weaknesses include autocorrelation, redundancy, and inability to protect biodiversity directly. Automated image analysis, combined with genetic and biotechnology tools and improved data analysis to solve autocorrelation problems were identified as opportunities, whereas low availability of trait data, their transferability, their quantitative interpretation, the risk of developing nonrelevant traits, low quality of historic databases, and their standardization were listed as threats.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".