Science advice to support the components of a jeopardy assessment framework for permitting under the SARA
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,082 | 0,105 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,009 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,009 |
| Communication savante | 0,014 | 0,011 |
| Science ouverte | 0,011 | 0,009 |
| Intégrité de la recherche | 0,016 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».