Isotopic variation complicates analysis of trophic relations within the fish community of Plußsee: a small, deep, stratifying lake

Analysis of carbon and nitrogen stable isotopes has allowed freshwater ecologists to examine lake food webs in increasing detail. Many such studies have highlighted the existence of separate within-lake pelagic and benthic-littoral food webs but are typically conducted on large (> 10 km2) lakes, whereas the majority of lakes are actually relatively small. We used stable isotope analysis (δ13C & δ15N) to examine trophic interactions between fish and their prey in Plu�see, as an example of a small, stratifying lake, and to determine whether separate pelagic/benthic-littoral food webs could be distinguished in such systems. Our results indicate that the Plu�see food web was complicated, and due to extensive intra-annual isotopic variation in zooplankton (e.g. cladoceran δ13C annual range = 25.6�), it may be impossible to definitively assign consumers from small, eutrophic stratified lakes to pelagic or benthic-littoral food webs. We present evidence that some components of the Plu�see food web (large bream) may be subsidised by carbon of methanogenic origin.

A novel shrinkage technique based on the normal inverse Gaussian density model

This paper proposes a novel image denoising technique based on the normal inverse Gaussian (NIG) density model using an extended non-negative sparse coding (NNSC) algorithm proposed by us. This algorithm can converge to feature basis vectors, which behave in the locality and orientation in spatial and frequency domain. Here, we demonstrate that the NIG density provides a very good fitness to the non-negative sparse data. In the denoising process, by exploiting a NIG-based maximum a posteriori estimator (MAP) of an image corrupted by additive Gaussian noise, the noise can be reduced successfully. This shrinkage technique, also referred to as the NNSC shrinkage technique, is self-adaptive to the statistical properties of image data. This denoising method is evaluated by values of the normalized signal to noise rate (SNR). Experimental results show that the NNSC shrinkage approach is indeed efficient and effective in denoising. Otherwise, we also compare the effectiveness of the NNSC shrinkage method with methods of standard sparse coding shrinkage, wavelet-based shrinkage and the Wiener filter. The simulation results show that our method outperforms the three kinds of denoising approaches mentioned above.