A behavioral ecology of shermen: hidden stories from trajectory data in the Northern Humboldt Current System
This work proposes an original contribution to the understanding of shermen spatial behavior, based on the behavioral ecology and movement ecology paradigms. Through the analysis of Vessel Monitoring System (VMS) data, we characterized the spatial behavior of Peruvian anchovy shermen at di erent scales: (1) the behavioral modes within shing trips (i.e., searching, shing and cruising); (2) the behavioral patterns among shing
trips; (3) the behavioral patterns by shing season conditioned by ecosystem scenarios;
and (4) the computation of maps of anchovy presence proxy from the spatial patterns of
behavioral mode positions. At the rst scale considered, we compared several Markovian
(hidden Markov and semi-Markov models) and discriminative models (random forests,
support vector machines and arti cial neural networks) for inferring the behavioral modes
associated with VMS tracks. The models were trained under a supervised setting and
validated using tracks for which behavioral modes were known (from on-board observers
records). Hidden semi-Markov models performed better, and were retained for inferring
the behavioral modes on the entire VMS dataset. At the second scale considered, each
shing trip was characterized by several features, including the time spent within each
behavioral mode. Using a clustering analysis, shing trip patterns were classi ed into
groups associated to management zones,
eet segments and skippers' personalities. At the third scale considered, we analyzed how ecological conditions shaped shermen behavior.
By means of co-inertia analyses, we found signi cant associations between shermen,
anchovy and environmental spatial dynamics, and shermen behavioral responses were
characterized according to contrasted environmental scenarios. At the fourth scale considered, we investigated whether the spatial behavior of shermen re ected to some extent the spatial distribution of anchovy. Finally, this work provides a wider view of shermen behavior: shermen are not only economic agents, but they are also foragers, constrained by ecosystem variability. To conclude, we discuss how these ndings may be of importance for sheries management, collective behavior analyses and end-to-end models.
trips; (3) the behavioral patterns by shing season conditioned by ecosystem scenarios;
and (4) the computation of maps of anchovy presence proxy from the spatial patterns of
behavioral mode positions. At the rst scale considered, we compared several Markovian
(hidden Markov and semi-Markov models) and discriminative models (random forests,
support vector machines and arti cial neural networks) for inferring the behavioral modes
associated with VMS tracks. The models were trained under a supervised setting and
validated using tracks for which behavioral modes were known (from on-board observers
records). Hidden semi-Markov models performed better, and were retained for inferring
the behavioral modes on the entire VMS dataset. At the second scale considered, each
shing trip was characterized by several features, including the time spent within each
behavioral mode. Using a clustering analysis, shing trip patterns were classi ed into
groups associated to management zones,
eet segments and skippers' personalities. At the third scale considered, we analyzed how ecological conditions shaped shermen behavior.
By means of co-inertia analyses, we found signi cant associations between shermen,
anchovy and environmental spatial dynamics, and shermen behavioral responses were
characterized according to contrasted environmental scenarios. At the fourth scale considered, we investigated whether the spatial behavior of shermen re ected to some extent the spatial distribution of anchovy. Finally, this work provides a wider view of shermen behavior: shermen are not only economic agents, but they are also foragers, constrained by ecosystem variability. To conclude, we discuss how these ndings may be of importance for sheries management, collective behavior analyses and end-to-end models.
Tesis (Doctorat). -- Universite de Montpellier II
IRD / IMARPE