Adapting a multi‐species tool for single‐species impact assessments: Managing fishes at risk in Canada
Bibliographic record
Abstract
Abstract To assess the impacts of human activity on fishes and fish habitat, impact assessment tools use single‐ and multi‐species approaches depending on the ecological and socio‐economic objectives. In Canadian aquatic ecosystems, single‐ and multi‐species impact assessments are guided by the Species at Risk Act and Fisheries Act, respectively. Yet, for species protected under the Species at Risk Act, the sparse data often require alternative approaches to risk assessment. The goal of this study was to evaluate whether a database‐derived multi‐species tool – the Habitat Ecosystem Assessment Tool (HEAT) – can be used for single‐species impact assessments. Using an empirical example of proposed drain maintenance in a tributary of Lake St. Clair, the net loss of suitable habitat was evaluated across six conservation targets, ranging from single species, such as the pugnose shiner (Notropis anogenus) and the yellow perch (Perca flavescens), to the entire fish assemblage. Model outcomes were compared across various habitat suitability indices, spatial resolutions, and environmental habitat layers. The net loss of suitable habitat varied widely across conservation targets and was greatest for the rare specialist species (pugnose shiner). Single‐species conservation targets were more sensitive to variation in spatial resolution and uncertainty in model input parameters. The results of this study emphasize that single‐ and multi‐species conservation targets should not be considered equal, especially when species differ in abundance and niche breadth. This study demonstrates the flexibility of HEAT for evaluating potential impacts of human disturbance on fishes and their habitat. Future development of this tool should expand beyond physical habitat, to include other factors relevant to species distribution and survival (e.g. biotic interactions).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".