Eliciting cognitive processes underlying patterns of human–wildlife interactions for agent-based modelling

► We model whale-watching excursions in the St. Lawrence Estuary using ABM. ► We use pattern-oriented modelling to select a valid model of captain decisions. ► Decision models’ performance show a ranking of whale species attractiveness. ► Information sharing between captains is a major driver of excursion dynamics. ► Heuristics of bounded rationality capture major aspects of captains’ decision making. 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