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Parrott L.,University of Montréal | Chion C.,University of Montréal | Chion C.,École de Technologie Supérieure of Montreal | Martins C.C.A.,University of Montréal | And 9 more authors.
Environmental Modelling and Software | Year: 2011

We describe a decision support system that has been developed to inform management and planning in a portion of the St. Lawrence Estuary in Canada (covering the Saguenay-St. Lawrence Marine Park and the proposed St. Lawrence Estuary Marine Protected Area). The system is composed of a spatiotemporal, georeferenced database, a simulator (3MTSim) that reproduces the spatiotemporal movement of marine mammals and maritime traffic in the estuary, and data post-processing tools that can be used to analyse the output of 3MTSim. 3MTSim allows users to test different management scenarios for maritime traffic (e.g., area closures, speed limits, regulations concerning the observation of marine mammals) in order to assess their effects on navigational patterns which may influence marine mammal exposure to vessels. 3MTSim includes an individual-based model of marine mammal movement patterns that has been elaborated based on existing telemetry data on fin, blue, and beluga whales as well as on land-based theodolite tracking of humpback and minke whales. Observations recorded aboard research and whale-watching vessels have provided the spatial data necessary to estimate species' abundances and distribution maps that are used to initialise the whale model. Different types of vessels, including cargo ships and commercial whale-watching boats are also modelled individually, using an agent-based approach. The boat model represents the decision-making process of boat captains as a function of environmental conditions, the contextual setting, and their respective goals. An extensive database of real-time tracking data available for the different types of vessels, coupled with observations and interviews, has served in the elaboration of the boat model. In this paper, an overview of the entire system is presented and its effectiveness as a decision support tool is demonstrated via the results from a sample of scenario-based simulations. © 2011 Elsevier Ltd.


Chion C.,École de Technologie Supérieure of Montreal | Chion C.,University of Montréal | Lamontagne P.,École de Technologie Supérieure of Montreal | Turgeon S.,University of Montréal | And 8 more authors.
Ecological Modelling | Year: 2011

Integrating humans in our perception of ecosystems is of critical importance to adequately protect natural resources. This poses the challenge of understanding human decision making in the context of decisions potentially threatening nature's integrity. We developed a spatially explicit agent-based model that simulates commercial whale-watching vessel movements based on a representation of the captains' decision making process when observing marine mammals in and around the Saguenay-St. Lawrence Marine Park in Québec, Canada. We focus here on the human part of the global model, the submodel of whale movements having been developed and validated independently (Lamontagne, 2009). The objective of this study is to select and validate a model of whale-watching captains' decision making using the pattern-oriented modelling approach (POM): three models of cognitive heuristics (satisficing, tallying and Take The Best) along with a null model (random choice) were tested. These concurrent decision making models were built upon knowledge extracted from data collected during field investigations, including interviews with whale-watching captains and park wardens, onboard and shore-based observations, and analyses of a multi-year dataset of sampled whale-watching excursions. Model selection is performed by statistically comparing simulated and real patterns of boat trajectories (excursion length), spatial hotspots (kernel home range 50%), and excursion content (species observed, time allocated to different activities). The selection process revealed that the Take The Best heuristic was the best performing model. We used the distribution of the number of whale-watching boats in the vicinity (2000. m) of each vessel as a secondary pattern to validate the ability of each decision making model to reproduce real observations. Given the prevalence of the species attribute in the choice of which whale to observe, the Take The Best heuristic's ability to deal with non-compensatory information partly explains its overall best performance. Moreover, implementation of communication abilities between modelled captains led to the emergence of persistent observation sites in the park, which is a well-known collective spatiotemporal characteristic of the whale-watching industry; thus validating the fundamental assumption that cooperation is an important mechanism behind the pattern of whale-watching boat dynamics. The relatively good performance of the satisficing and tallying heuristics supports both field evidence and literature on bounded rationality in that humans likely use collections of heuristics (adaptive toolbox) to solve decision problems in different contexts. The POM strategy appears suitable to build up an informative ABM regarding the management of human activities in a natural environment so that further developments will be assessed following the same approach. © 2011 Elsevier B.V.

Loading Group for Research and Education on Marine Mammals GREMM collaborators
Loading Group for Research and Education on Marine Mammals GREMM collaborators