Science advice to support the components of a jeopardy assessment framework for permitting under the SARA
Bibliographic record
Abstract
A proposed science-based framework that builds on past science advice was reviewed concerning the concept of allowable harm to assess whether works/undertakings/activities (w/u/a) will result in direct or indirect harm to jeopardize the survival or recovery of a SARA listed species for the purposes of satisfying requirements of SARA subsection 73(3)(c). The proposed conceptual framework has three main components to evaluate how a project may affect survival and recovery of the species: a) the relationship between the w/u/a (including offsets, if pursued) and changes in habitat condition; b) the relationship between habitat change and species vital rates (mortality, fertility, growth); and, c) the relationship between changes in vital rates and a population’s response. Two example analyses were presented that fit into the three main components of the framework: 1) a modelling exercise to determine impacts of changes to vital rates for populations of aquatic species at risk with five different population growth trajectories; and, 2) a meta-analysis of vital rate responses to changes in habitat for freshwater mussels and freshwater fishes. The purpose of the modelling exercise was to predict how population growth or decline may respond to life-stage specific impacts. Several limitations may have influenced the results of this analysis, including lack of data for species-specific life-history characteristics and underlying assumptions. This modelling exercise is not intended to replace more robust population models or frameworks such as those for marine mammals, but is meant to provide a potential standardized approach for data-limited species. Results of the meta-analysis of vital rate responses to changes in habitat for freshwater mussels and freshwater fishes indicate that non-linear responses appear to be more common than linear. Additional work is needed on how to integrate multiple stressors for both linear and non-linear responses. The DFO Pathways of Effects models are a way of linking effects of w/u/a to changes in habitat condition, which can be used to understand the impact on species’ vital rates. Although habitat offsetting is a commonly used tool, the literature review found few examples where policies specifically addressed additional requirements needed to implement habitat offsetting for species at risk. Where the policies were specific, the magnitude of habitat offsetting was required to be much higher than the level of the habitat impact. Defensible criteria for evaluating population responses to habitat offsetting were not found. There is uncertainty around understanding how vital rates respond to offsetting measures in the species at risk context. There are uncertainties in the components of the framework. As the framework is further developed, strategies for managing risk associated with uncertainties, knowledge gaps, and assumptions need to be built in to account for these uncertainties. The framework as presented is largely conceptual; approaches and next steps to operationalize the framework were discussed and presented.
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 | 0.082 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".