Adapting a multi‐species tool for single‐species impact assessments: Managing fishes at risk in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it