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Herbivory on macro-algae affects colonization of beach-cast algal wrack by detritivores but not its decomposition

Eereveld P., Hübner L., Schaefer G., Zimmer M.

Oceanologia

2013

No. 55 (2)

339-358

Spatial subsidies have increasingly been considered significant sources of matter and energy to unproductive ecosystems. However, subsidy quality may both differ between subsidizing sources and vary over time. In our studies, sub-littoral herbivory by snails or isopods on red or brown macro-algae induced changes in algal tissues that affected colonization of beach-cast algal wrack by supra-littoral detritivores (amphipods). However, microbial decay and decomposition through the joint action of detritivores and microbes of algal wrack in the supra-littoral remained unaffected by whether or not red or brown algae had been fed upon by snails or isopods. Thus, herbivory on marine macro-algae affects the cross-system connection of sub-littoral and supra-littoral food webs transiently, but these effects diminish upon ageing of macro-algal wrack in the supra-littoral zone.

Rough C-means and Fuzzy Rough C-means for Colour Quantisation

Schaefer G., Hu Q., Zhou H., Peters J.F., Hassanien A. E.

Fundamenta Informaticae

2012

Vol. 119, nr 1

113-120

Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly deter- mines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. In this paper we propose rough c-means and fuzzy rough c-means clustering algorithms for colour quantisation of images. Both approaches utilise the concept of lower and upper approximations of clusters to define palette colours. While in the rough c-means approach cluster centroids are refined iteratively through a linear combination of elements of the lower and upper approximations, the fuzzy rough c-means technique assigns variable membership values to the elements in the boundary region which in turn are incorporated into the calculation of cluster centres. Experimental results on a standard set of images show that these approaches performs significantly better than other, purpose built colour quantisation algorithms.