Monitoring and conservation of the largest loggerhead turtle (Caretta caretta) nesting population in Africa: successes and challenges.

[EN] The Republic of Cape Verde is situated about 500 km off the coast of Senegal, West Africa. The islands of Sal, Boa Vista and Maio constitute the Eastern group and harbour the most important nesting beaches for loggerhead turtles in the Archipelago. During 1998-2004 nesting seasons, fieldwork has been focused on the eastern beaches of Boa Vista and, occasionally, in other beaches of Boa Vista and in the islands of Maio, Sal and Santa Luzia. The main study area includes a total extension of 3.1 km of suitable nesting beaches. All through these years, a tagging program has been carried out, alongside recording of biological data and monitoring of nests.

Efforts to increase the participation of local communities in the conservation of the endangered loggerhead population of Cape Verde

[EN] The loggerhead population of Cape Verde is one of the most important in the world. Several islands from this archipelago capture nesting females for human consumption. This a widespread practice in the local population that can be killing more than 25% of nesting females every year. This activity is not relevant for the general economy of the country but can be important for some families.

Reconstruction of spatiotemporal capture data by means of orthogonal functions: the case of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic

[EN] The information provided by the International Commission for the Conservation of Atlantic Tunas (ICCAT) on captures of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic has a number of limitations, such as gaps in the statistics for certain fleets and the level of spatiotemporal detail at which catches are reported. As a result, the quality of these data and their effectiveness for providing management advice is limited. In order to reconstruct missing spatiotemporal data of catches, the present study uses Data INterpolating Empirical Orthogonal Functions (DINEOF), a technique for missing data reconstruction, applied here for the first time to fisheries data. DINEOF is based on an Empirical Orthogonal Functions decomposition performed with a Lanczos method. DINEOF was tested with different amounts of missing data, intentionally removing values from 3.4% to 95.2% of data loss, and then compared with the same data set with no missing data. These validation analyses show that DINEOF is a reliable methodological approach of data reconstruction for the purposes of fishery management advice, even when the amount of missing data is very high.