Notice bibliographique
Résumé
Cumulative impact mapping is a spatially-explicit, semi-quantitative method useful for illustrating the relative cumulative effects of human activities on marine habitats over broad spatial scales. Cumulative impact mapping uses a relatively simple model that identifies areas where activities and habitats intersect in space, then applies a vulnerability weight to determine an impact score for each activity-habitat intersection. The cumulative impact score is the sum of all habitat-activity intersections within a unit area. The method requires three data sources: 1) spatial distribution of marine habitat classes (e.g., beach intertidal, shallow pelagic, and seagrass beds), 2) spatial distribution and relative intensity of human activities (e.g., fishing, shipping, and industrial sites) and their associated stressors (e.g., sedimentation and noise), and 3) a matrix of vulnerability scores to quantify the relative impact of each stressor on each habitat class. Habitat classes encompass intertidal, subtidal, shelf, and deep ecosystems, and include inorganic substrates and biogenic features in each. Pelagic ecosystems are also included, separated into shallow and deep classes. Between Pacific and Atlantic coasts, the division of habitat classes is similar, although the depth ranges vary slightly. Human activities/stressors are separated into four data types: land-based, coastal, marine, and fishing. Human activities/stressors are represented as a relative intensity value, which depends on the nature of the activity or stressor, the way they may interact with the habitat, and data availability. Vulnerability matrices for use in the Canadian Pacific and Atlantic regions were adapted from existing vulnerability matrices developed previously for the California Current (Pacific) and Massachusetts coast (Atlantic). Regional ecosystem experts were surveyed for their expert opinion on vulnerability scores and rankings for all habitat-stressor combinations relevant to each coast. Previously generated vulnerability matrix scores were updated in the associated Research Document (Clarke Murray et al 2023) based on expert review, and recommendations were provided for activity/stressor rankings per habitat class. In Pacific region, 120 (12%) habitat stressor scores were increased and 26 (3%) were reduced based on expert feedback. For the Atlantic matrix, 105 (11%) and 90 (9%) habitat-stressor scores were increased or decreased, respectively. New activities/stressors were also recommended for consideration but not reviewed and could be updated in the future. Efforts to update vulnerability scores through elicitation of regional habitat experts was supported as an improvement to previous applications of the Halpern method. Future enhancements could include reviewing vulnerability scores across habitat types with stressor experts (e.g., experts on the impacts of fishing). Cumulative impact mapping has a number of potential uses: to inform and assess future environmental change or marine spatial planning scenarios (e.g., to assess planned development and climate change), to inform strategic or regional environmental assessment, to identify areas for field research and investigation, or to prioritize activities or stressors for management or mitigation action. Its specific use within the Canadian marine spatial planning context is still to be determined. In addition to the resulting maps, these high-quality spatially-explicit marine data and knowledge products generated as individual components during the cumulative impact mapping process can benefit planners, stakeholders, and other scientists engaged in Marine Spatial Planning and ecosystem-based management, including conservation planning. Given this is a data-driven approach, with outputs that represents a snap-shot in time, the quality and age of the data inputs will impact its usefulness for planning. Sensitivity analyses, e.g., further investigating the impact of changing vulnerability scores on overall results, may help to improve confidence in the results.
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 | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,014 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,071 | 0,011 |
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 ».