Scale-Dependent Habitat Selection of Nesting Great Egrets and Snowy Egrets

Foraging habitat selection of nesting Great Egrets ( Ardea alba ) and Snowy Egrets ( Egretta thula ) was investigated within an estuary with extensive impounded salt marsh habitat. Using a geographic information system, available habitat was partitioned into concentric bands at five, ten, and 15 km radius from nesting colonies to assess the relative effects of habitat composition and distance on habitat selection. Snowy Egrets were more likely than Great Egrets to depart colonies and travel to foraging sites in groups, but both species usually arrived at sites that were occupied by other wading birds. Mean flight distances were 6.2 km (SE = 0.4, N = 28, range 1.8-10.7 km) for Great Egrets and 4.7 km (SE = 0.48, N = 31, range 0.7-12.5 km) for Snowy Egrets. At the broadest spatial scale both species used impounded (mostly salt marsh) and estuarine edge habitat more than expected based on availability while avoiding unimpounded (mostly fresh water wetland) habitat. At more local scales habitat use matched availability. Interpretation of habitat preference differed with the types of habitat that were included and the maximum distance that habitat was considered available. These results illustrate that caution is needed when interpreting the results of habitat preference studies when individuals are constrained in their choice of habitats, such as for central place foragers.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.