A histological evaluation of pansteatitis-affected Mozambique tilapia,Oreochromis mossambicus (Peters 1852), from different geographical locations in South Africa

Pansteatitis is the leading cause for the decline in Nile crocodile populations and the sporadic mortality of fish in the Olifants River System, South Africa. To determine the prevalence of this disease in lentic systems, Mozambique tilapia, Oreochromis mossambicus, were collected from Lake Loskop, Lake Flag Boshielo, Phalaborwa Barrage and Lake Luphephe-Nwanedi. The former three impoundments are located within the main stem of the Olifants River, while the latter, which is geographically isolated and situated in the Limpopo River System, served as a reference site. Mesenteric adipose, liver, serosa of the swim bladder, gill and the skeletal muscle of fish sampled were examined for gross and microscopic evidence of pansteatitis. Microscopically observed changes were used to statistically compare pansteatitis prevalence between samples and sites. Based on histopathological evaluation, the adipose tissue in the liver, swim bladder serosa and coelom from severely debilitated individuals showed the most significant pathological changes. Lesions indicative of steatitis were observed in fish collected from Lake Loskop (75%), Lake Flag Boshielo (22%) and Lake Luphephe-Nwanedi (15%). Further investigation is warranted to understand the pervasiveness and mechanisms driving pathological changes of pansteatitis at Lake Flag Boshielo, Phalaborwa Barrage and Lake Luphephe-Nwanedi.     

The ecosystem services concept: a new Esperanto to facilitate participatory planning processes?

Landscape Ecology - Several case studies investigated the role of ecosystem services in participatory planning processes. However, no systematic study exists that cuts across a large number of...     

Prof. Dipl. Ing. Dr. Dr. h. c. Leo Tschermak zum Gedenken

Ohne Zusammenfassung     

Methods to interpolate soil categorical variables from profile observations: Lessons from Iran

The paper compares semi-automated interpolation methods to produce soil-class maps from profile observations and by using multiple auxiliary predictors such as terrain parameters, remote sensing indices and similar. The Soil Profile Database of Iran, consisting of 4250 profiles, was used to test different soil-class interpolators. The target variables were soil texture classes and World Reference Base soil groups. The predictors were 6 terrain parameters, 11 MODIS EVI images and 17 physiographic regions (polygon map) of Iran. Four techniques were considered: (a) supervised classification using maximum likelihoods; (b) multinominal logistic regression; (c) regression-kriging on memberships; and (d) classification of taxonomic distances. The predictive capabilities were assessed using a control subset of 30% profiles and the kappa statistics as criterion. Supervised classification and multinominal logistic regression can lead to poor results if soil-classes overlap in the feature space, or if the correlation between the soil-classes and predictors is low. The two other methods have better predictive capabilities, although both are computationally more demanding. For both mapping of texture classes and soil types, the best prediction was achieved using regression-kriging of indicators/memberships (κ = 45%, κ = 54%). In all cases kappa was smaller than 60%, which can be explained by the preferential sampling plan, the poor definition of soil-classes and the high variability of soils. Steps to improve interpolation of soil-class data, by taking into account the fuzziness of classes directly on the field are further discussed.