Identification of métiers of the Northern Spanish coastal bottom pair trawl fleet by using the partitioning method CLARA

Observing Trip Chain Characteristics of Round-Trip Carsharing Users in China: A Case Study Based on GPS Data in Hangzhou City

“…With the increase in the value of n, the number of subsets increases dramatically; for a fixed k, the rate of increase is in the order of the kth power of n. Another factor with the same effect is the storage requirement, which makes the number of memory locations less dependent on the number of objects, of which it is a quadratic function in the PAM algorithm. Several studies [29,30] obtained good clustering results with CLARA algorithm in a datasize around 30,000.…”

“…of memory locations less dependent on the number of objects, of which it is a quadratic function in the PAM algorithm. Several studies [29,30] obtained good clustering results with CLARA algorithm in a datasize around 30,000. In this paper, use of the CLARA algorithm is attempted to complete the analysis, using R program for statistical computation.…”

When behaviour reveals activity: Assigning fishing effort to métiers based on VMS data using artificial neural networks

“…Fleets therefore need to be disaggregated into homogeneous categories of fishing activities, defined according to target species or assemblages, fishing area and fishing season (Marchal, 2008;Salas and Gaertner, 2004;Tzanatos et al, 2006). This appears to be particularly important in multi-species, multifleet fisheries such as those in Mediterranean Sea (Moranta et al, 2008) and specifically the Italian fleet (Colloca et al, 2003), in which a variety of species are caught together given a complex scheme of technical interactions among fleets (Castro et al, 2010;). Thus, a major topic of fishery management is concerned with 0165-7836/$ -see front matter © 2011 Elsevier B.V. All rights reserved.…”

“…This issue has been historically addressed in three ways: (1) by the so-called "output-based" methods (Marchal, 2008), consisting of a posteriori quantitative analyses of catch or landings composition, with or without effort information (gear, season, location) to assign métier. Various multivariate procedures have been applied for this purpose: principal component analysis (PCA; Castro et al, 2010;Jabeur et al, 2000;Laurec et al, 1991), multiple correspondence analysis (Pelletier and Ferraris, 2000), and cluster analysis (Lewy and Vinther, 1994); (2) by the so-called "input-based" (Marchal, 2008) methods, based on a priori qualitative knowledge of the fisheries, mainly collected during face-to-face interviews, so that the allocation of each fishing trip to métier relies on a process of trial and error, by deriving discriminating thresholds based either on landings (weight or value), or mesh size (Biseau, 1998;Tétard et al, 1995;Ulrich and Andersen, 2004); or (3) by a combination of the input and output-based methods (Bastardie et al, 2010;Ulrich and Andersen, 2004). However, both types of method (as well as their combinations) have some limitations: they depend on available data, and landings data may be unreliable and may not contain enough information to infer fishing intention (Chang, 2011;Marchal, 2008).…”

Identification of métiers of the Spanish set-longline fleet operating in non-Spanish European waters

“…One valuable advantage of CLARA and PAM is that they provide a quality index which facilitates a more objective selection of the most appropriate number of clusters. Nevertheless, there have been few applications to date of CLARA and PAM methods in fisheries research (Duarte et al, 2009;Castro et al, 2010). Therefore, the main objective of the current work is to find an appropriate approach to identify métiers within the fishing activity of Spanish longliners operating in non-Spanish European waters, which can be routinely applied to allow disaggregation of effort and landings data by fishing units as is required for fleet-based management.…”