Reconstruction of the aquifer history of Gran Canaria

[EN]A study on the recent history and current state of the aquifer in the Island of Gran Canaria (Canary Is., 28oN, 15oW) is performed. Though rainfall is scarce on the island, traditional agricultural practices and small population were able to keep the aquifer in a constant state for centuries. Nevertheless, at the beginning of the 20th Century, culture of several water-consuming species was introduced on a commercial basis due to the relative proximity of the Canaries to continental Europe and to the possibility of more than one yearly harvest. This led to generalised well digging (more than 300m deep in many cases) and to the appearance of a chronic hydraulic deficit, as well as to spoiling vastcoastal areas of the aquifer through intrusion of brackish water. In the mid 1960’s, coincident with the apex of agricultural exploitation, massive tourism appeared in the scene. This new activity soon became a susbstitute for Agriculture, but it attracted more new labour force to the island, and a fast growth of population was the main result. Moreover, new water use practices entered the scene. As a consequence, the main causes for the aquifer decline are population growth and extensive Agriculture practices in use during the last half of the 20th Century. Some remarks on sustainability issues in order to cope with Climate Change are also offered.

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.