Disperser effectiveness is the number of new plants resulting from the activity of one disperser relative to other dispersers or to nondispersed seeds. Effectiveness remains largely uninvestigated due to the complexity of its measurement. We measured the effectiveness of seed dispersers (Larus michahellis, Turdus merula, and Oryctolagus cuniculus) of the shrub Corema album (Empetraceae) using a simulation model of the recruitment process that was parameterized with field data of seed dispersal, predation, and seedling emergence and validated with independent data on seedling density. The model allows tracking the fate of seeds dispersed by each animal and estimating, for the first time, disperser effectiveness as seedlings per square meter contributed by each disperser. It also allows quantifying the relative importance of different recruitment processes in determining the quantity and spatial distribution of recruitment. Larus michahellis was the most effective disperser in two of the three habitats studied, contributing 3-125 times more than the other two species, whose lower effectiveness depended mostly on deposition patterns (T. merula) or deleterious effects on seedling emergence (O. cuniculus). The dependence of the plant on each disperser differed between habitats and was the greatest in sparse scrub, where recruitment depended almost exclusively on gulls (90%). Quantity and quality of dispersal were not correlated; quality was a better predictor of disperser effectiveness. Seedling emergence was the most crucial process in determining both the spatial pattern of recruitment among microhabitats (99.8% of variance explained) and the quantity of recruitment within microhabitats (43-83%). A sensitivity analysis showed that increasing seed dispersal improved the recruitment for all dispersers when there was no competition for fruits. However, with limited fruits, increased dispersal of lower quality dispersers reduced overall recruitment. Our results show important differences in effectiveness among dispersers and illustrate the different influences of the components of effectiveness, which varied depending not only on the disperser but also on the circumstances (e.g., type of habitat).
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We analyze the capabilities of CASI data for the discrimination of vine varieties in hyperspectral images. To analyze the discrimination capabilities of the CASI data, principal components analysis and linear discriminant analysis methods are used. We assess the performance of various classification techniques: Multi-layer perceptrons, radial basis function neural networks, and support vector machines. We also discuss the trade-off between spatial and spectral resolutions in the framework of precision viticulture. ⋆ This work has been cofinanced with FEDER funds through the Interreg IIIb "Atlantic Area" program within the PIMHAI project.
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