Small MPAs do not protect cuttlefish (Sepia officinalis)

Marine reserves have been widely implemented as tools for biodiversity conservation and fisheries management, amongst other goals. A large number of empirical studies have focused on their effects on reef fish populations. Yet, few studies have looked at their effectiveness on semelparous species such as cephalopods, in spite of their commercial importance in many coastal regions across the globe. In this study we combine behavioural (biotelemetry) and demographic (experimental fishing) data to understand the effects of the Luiz Saldanha Marine Park (LSMP) on local populations of cuttlefish, Sepia officinalis. We used a beyond-BACI design to analyze the possible effect of the implementation of a no-take area on the abundance and biomass of this species and acoustic telemetry data to assess its site fidelity and movements within the study area. Results indicate that there was no detectable effect of the implementation of the no-take area on the abundance or biomass of cuttlefish. We found evidence that acoustically tagged adult cuttlefish leave the reserve a few days or weeks after tagging. The fact that cuttlefish have low site fidelity inside the reserve and large movements across and beyond the study area explains why there is no increase in the population inside the MPA. These results suggest that small coastal marine reserves such as the LSMP are not effective in providing long term protection to cuttlefish populations and, probably, those of other short-lived, highly mobile cephalopods. (C) 2013 Elsevier B.V. All rights reserved.

Movements of Diplodus sargus (Sparidae) within a Portuguese coastal Marine Protected Area: Are they really protected?

Mark-recapture tagging and acoustic telemetry were used to study the movements of Diplodus sargus within the Pessegueiro Island no-take Marine Protected Area (MPA), (Portugal) and assess its size adequacy for this species' protection against fishing activities. Therefore, 894 Diplodus sargus were captured and marked with conventional plastic t-bar tags. At the same time, 19 D. sargus were tagged with acoustic transmitters and monitored by 20 automatic acoustic receivers inside the no-take MPA for 60 days. Recapture rate of conventionally tagged specimens was 3.47%, most occurring during subsequent marking campaigns. One individual however was recaptured by recreational fishermen near Faro (ca. 250 km from the tagging location) 6 months after release. Furthermore, three specimens were recaptured in October 2013 near releasing site, one year after being tagged. Regarding acoustic telemetry, 18 specimens were detected by the receivers during most of the study period. To analyse no-take MPA use, the study site was divided into five areas reflecting habitat characteristics, three of which were frequently used by the tagged fish: Exterior, Interior Protected and Interior Exposed areas. Information on no-take protected area use was also analysed according to diel and tidal patterns. Preferred passageways and permanence areas were identified and high site fidelity was confirmed. The interaction between tide and time of day influenced space use patterns, with higher and more variable movements during daytime and neap tides. This no-take MPA proved to be an important refuge and feeding area for this species, encompassing most of the home ranges of tagged specimens. Therefore, it is likely that this no-take MPA is of adequate size to protect D. sargus against fishing activities, thus contributing to its sustainable management in the region.

Using syntactic and semantic features for classifying mo- dal values in the portuguese language

This paper presents a study made in a field poorly explored in the Portuguese language – modality and its automatic tagging. Our main goal was to find a set of attributes for the creation of automatic tag- gers with improved performance over the bag-of-words (bow) approach. The performance was measured using precision, recall and F1. Because it is a relatively unexplored field, the study covers the creation of the corpus (composed by eleven verbs), the use of a parser to extract syntac- tic and semantic information from the sentences and a machine learning approach to identify modality values. Based on three different sets of attributes – from trigger itself and the trigger’s path (from the parse tree) and context – the system creates a tagger for each verb achiev- ing (in almost every verb) an improvement in F1 when compared to the traditional bow approach.