Forecasting Trends
Predictive skill varied among the three models across the forecasting period. The majority of the data (~97%) during this period consisted of logbook records (Figure 3), reflecting the seasonality of the albacore fishery in the California Current, which operates primarily between May – November (Xu et al. 2017). Among the models, predictive skill varied primarily based on spatial dependence treatments (i.e., spatially implicit or explicit). GF and iSDM exhibited similar trends, performing best on average in 2014 and 2019, with their poorest performance in 2016 (Figure 3a,b). HE followed similar patterns, but consistently showed lower performance, with its poorest performance in the beginning of the forecasting period (Figure 3a,b). Furthermore, the degree of novelty for each variable was correlated with the patterns of AUC (r = -0.62 and -0.58 for MLD and SST, respectively) and MAE (r = 0.7 and 0.55 for MLD and SST, respectively), equating to poorer predictive skill as conditions became increasingly novel relative to the training period (Figure 3c). For example, months experiencing the highest degree of novelty in MLD and/or SST (e.g., May 2018, April 2016, and January 2016) exhibited the poorest model performance, particularly for the HE model (Figure 3). Trends in novelty for MLD and SST were similar across all years, with small differences in Hellinger Distance between MLD and SST (Figure 3c), suggesting both variables exhibited comparable levels of novelty and variability during the forecasting period relative to the training period.