## 4 Discussion  
Understanding the environmental processes influencing fish species abundance is important in order to manage the fishing grounds and offer advice on the exploitation of this important resource. It is evident that out of the 6830 features that our feature engineering process generated, about 136 (or 2%) of these are the most important and are advised to be used to reach the lowest possible prediction error. Most of the other species distribution studies, use about 5 to 20 features for their models, which are an order fewer than the 136 used in the present model. This happens because (a) very few studies (if any of them) use time series features, (b) they do not generate such a huge number of features to choose from, (c) they empirically pick the features depending on their domain knowledge and data availability. The present approach is different from the existing studies in this respect. It is not limited to basic features like maximum sea surface temperature or mean salinity, but it expands upon very detailed time series features. It suggests that about 2% of such features are appropriate  
Figure 6: High resolution produced habitat maps depicting the probability of occurrence in the whole Mediterranean Sea for the eight commercial fish species. Each unknown point was predicted using the final trained model of the proposed work. for all our datasets, and if less predictors are used, then performance worsens. For overfitting and generalization purposes the exact number of features that minimizes their RMSE for each species is not used, but rather a better bias-variance trade-off feature cutoff is preferred. Having very few features, the model's reliability diminishes, since these features are very specialized, and case only the very top-ranked ones are kept, whole predictors like chlorophyll or nitrate cease to exist in the model. Thus, having only variations of temperature is not enough. The whole feature selection process not only boosts execution time and performance, but also gives valuable insights into which variables are most important for each species. These insights are depicted in Figure 4, where the importance of feature categories generated by the feature engineering process is visible. The figure clearly shows which feature categories are worth investing time in feature engineering and which are not. The neighbor-based features seem to be the strongest ones. This is the only hand-crafted feature and it computes the minimum, mean, maximum and standard deviation of the eight cells that surround the given one on all predictors. For example, the feature 'temperatureSurface_neighbor_mean' calculates for every observation the mean surface temperature of its eight neighboring observations. Surrounding cells have been mentioned before [28] that might influence the distribution of species. It is evident that the probability of species presence is greatly affected by the conditions of the neighboring environmental parameters. This means that the species live in large areas next to each other and not in small isolated ones with different conditions. The importance of neighboring features comes in agreement with the fact that longitude and latitude predictors are also vital as it will be demonstrated later on. In the same manner, features with extreme values depict strong importance. Minima, maxima and quantiles are much stronger in terms of importance than means and medians. These results are in line with the bibliography, as the authors of Reference [57] state that quantiles near the maximum create good features when other variables are not limiting, and Reference [58] claims that low quantiles are relevant to estimate the lowest recruitment level for a species. Another feature of descriptive statistics, which is of major importance is skewness. It is very common for researchers to use mean-monthly data [16; 59] and only rarely mean-annual data. In this study, it was demonstrated that both feature categories are strong, with the monthly features being slightly better.