An agent-based model for the sustainable management of navigation activities in the Saint Lawrence Estuary
Notice bibliographique
Résumé
Natural resource managers of protected areas are concerned with the management of human activities potentially harmful to ecosystems’ health and/or integrity. These systems where human interact with natural resources are called social-ecological systems (SES) and possess the characteristics of complex adaptive systems (e.g. co-evolution). The SES of navigation activities and whales interacting within the Saguenay–St. Lawrence Marine Park (SSLMP) and the projected St. Lawrence Estuary Marine Protected Area in Quebec, Canada, has been investigated and modelled using the agent-based modelling (ABM) technology: The resulting Marine Mammal and Maritime Traffic Simulator (3MTSim) is designed to support marine protected area managers in their effort to reduce the frequency and intensity of boat-whale co-occurrences within the St Lawrence Estuary and mitigate the risks of vessel strikes. This dissertation presents the building process of the 3MTSim’s boat ABM. \n \nThe knowledge extracted from analyses of gathered and collected data relative to all forms of sailing and motorized navigation supported the decision to first focus on the modelling of commercial excursions (including whale-watching trips), cargo ships, and cruise liners. Data analyses allowed, for the first time, to draw a comprehensive portrait of navigation activities throughout the region where whales congregate in great numbers during the summer season. Among others, a quantitative analysis led to an accurate estimate of the total navigation time within each separate ecosystem of the region. This study identified areas intensively used by maritime traffic such as the mouth of the Saguenay River and offshore Les Escoumins. \n \nSeveral field campaigns carried out in the context of this project allowed to link some undesirable collective patterns of whale-watching excursions (regarding both whale conservation and SSLMP visitors’ experience) with contextual factors including whale species’ abundance and distribution, management gaps, and companies and captains’ decisions. The bounded rationality framework was chosen to investigate captains’ decision making and more generally the dynamics of the whole whale-watching SES. A portrait of the decision strategies followed by whale-watching captains has been drawn. The results will lead to a set of recommendations regarding the sustainable management of whale-watching excursions in and around the SSLMP. \n \nResults from field investigations and data analyses have fed the model building process, including an explicit representation of the whale-watching captains’ decision making. Data analyses revealed that cargo ships and ocean liners tend to follow predictable routes with low variability. Consequently, a complex behavioural modelling approach was deemed unnecessary in favour of a statistical approach, justified by the large volume of high-quality historical data available for both components. \n \nThe pattern-oriented modelling approach proved appropriate for selecting a valid model of whale-watching excursions. Model simulations confirmed that whale-watching captains do favour the observation of a few rare rorqual species (e.g. humpback whales), leaving aside the most abundant one, namely the minke whales. Therefore, 3MTSim was run to quantify the impact that whale-watching captains changing their decision strategy could have on both whale exposure to boats (conservation concern) and excursion content (commercial concern). It was found that captains willing to avoid crowded observation sites and/or seeking to increase the diversity of species observed could have statistically significant benefits regarding conservation issues without affecting important features of their excursions. Finally, the convincing performance of the 3MTSim’s boat ABM ensures its safe use as a decision-support tool for management insofar as model limitations are understood and accounted for in the results and discussion.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,004 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».