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Título

Dynamic self-thinning model for sessile animal populations with multilayered distribution

AutorFuentes-Santos, I. ; Cubillo, Alhambra ; Fernández-Reiriz, María José ; Labarta, Uxío
Palabras claveDynamic approach
Intraspecific competition
Multilayered population
Population density
Population dynamics
Fecha de publicación2014
EditorWiley-Blackwell
CitaciónReviews in Aquaculture 6(2): 115-127 (2014)
ResumenThe main drawback of the traditional self-thinning model is how time is handled. Self-thinning (ST) has been formally recognized as a dynamic process, while the current ST models have not included the temporal effect. This restricts the analysis to the average competitive behaviour of the population and produces a biased estimation of the self-thinning parameters. In this study, we extend the dynamic ST model introduced by Roderick and Barnes (2004) to the analysis of multilayered sessile animal populations. For this purpose, we incorporate the number of layers and the density per layer into the dynamical approach. The performance of the dynamic model was checked and compared with the classical ST model through the analysis of mussel populations grown at different density treatments. Unlike the traditional model, the dynamical approach detected the effect of culture density on the competitive behaviour of individuals and allowed to analyse the temporal evolution of intraspecific competition by estimating the ST exponent trajectory. Moreover, this approach provided an ecological interpretation of any possible value of the ST exponent. Thus, our results support the use of the dynamic model in the analysis of self-thinning in sessile animal multilayered populations. The estimation of the ST exponent trajectory reflects the dynamic nature of the ST process, providing a more realistic description of population dynamics than the traditional model.
Descripción12 páginas, 8 figuras, 3 tablas, 1 apéndice
Versión del editorhttp://dx.doi.org/10.1111/raq.12032
URIhttp://hdl.handle.net/10261/99190
DOI10.1111/raq.12032
ISSN1753-5123
E-ISSN1753-5123
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