Figure 1.
Fishing trip with VMS records and their corresponding behavioural modes.
Figure 2.
Schematic representation of a HSMM.
At each step, an observed feature is related to a state, which encodes a behavioural mode (C: cruising, F: fishing, S: searching). The state process is modelled at the segment scale and it is characterized by durations and transitions as shown above.
Table 1.
Indicators of model performance.
Table 2.
Distributions for each observed variable and duration conditioned on states.
Table 3.
Performance of all models for their corresponding best subsets of observed variables.
Figure 3.
Distribution of the duration of each behavioural mode.
For each model, an empirical distribution of the duration of each mode is estimated based on the duration of all inferred segments encoding the mode. RF: random forest. SVM: support vector machine. ANN: artificial neural network. HMM: hidden Markov model. HSMM: hidden semi-Markov model. Real: known behavioural modes.
Figure 4.
Left upper panel: track with real behavioural modes. Right upper panel: track with inferred modes using the HSMM. Lower panel: temporal representation of the behavioural mode sequences, real and inferred, where 0 in the x-axis represents the beginning of the trip.
Figure 5.
Mean accuracy for simulated sequences for different sampling rates using HSMM and HMM.