Rwenzori Score (RS): A Benthic Macroinvertebrate Index for Biomonitoring Rivers and Streams in the Rwenzori Region, Uganda

The Rwenzori region in Uganda, a global biodiversity hotspot, is currently undergoing exponential economic and population growth, which puts continuous stress on its freshwater ecosystems. In Sub-Saharan Africa, biomonitoring campaigns using region-specific biotic indices is limited, particularly in Uganda. In this research, we present the Rwenzori Score (RS), a new macroinvertebrate-based biotic index developed to specifically assess the aquatic health of Rwenzori streams and rivers. We collected and measured both biological and physicochemical variables and identified 34,202 macroinvertebrates, belonging to 64 different taxa. The RS was developed in two steps. First, using canonical ordination, we identified chemical variables that correlated significantly with gradients in macroinvertebrate assemblage distribution and diversity. Second, based on selected variables and weighted averages, we determined specific family indicator values and assigned pollution tolerance values (varying from 1: tolerant; to 10: sensitive) to a family. Finally, we established four water quality classes: poor, fair, good, and excellent. The RS is highly correlated with the Average Score Per Taxon System (p < 0.05), a well-known and widely used biotic index. The RS has 5 unique taxa that are not included in other regional indices. In this regard, the development of the RS is a beneficial tool for tailor-made biomonitoring that can contribute to the sustainable development of the Rwenzori stream and river basins.

Examining residual spatial correlation in variation partitioning of beta diversity in a subtropical forest

Aims The relative roles of ecological processes in structuring beta diversity are usually quantified by variation partitioning of beta diversity with respect to environmental and spatial variables or gamma diversity. However, if important environmental or spatial factors are omitted, or a scale mismatch occurs in the analysis, unaccounted spatial correlation will appear in the residual errors and lead to residual spatial correlation and problematic inferences. Methods Multi-scale ordination (MSO) partitions the canonical ordination results by distance into a set of empirical variograms which characterize the spatial structures of explanatory, conditional and residual variance against distance. Then these variance components can be used to diagnose residual spatial correlation by checking assumptions related to geostatistics or regression analysis. In this paper, we first illustrate the performance of MSO using a simulated data set with known properties, thus making statistical issues explicit. We then test for significant residual spatial correlation in beta diversity analyses of the Gutianshan (GTS) 24-ha subtropical forest plot in eastern China. Important Findings Even though we used up to 24 topographic and edaphic variables mapped at high resolution and spatial variables representing spatial structures at all scales, we still found significant residual spatial correlation at the 10 m × 10 m quadrat scale. This invalidated the analysis and inferences at this scale. We also show that MSO provides a complementary tool to test for significant residual spatial correlation in beta diversity analyses. Our results provided a strong argument supporting the need to test for significant residual spatial correlation before interpreting the results of beta diversity analyses.

Statistical methods for temporal and space–time analysis of community composition data

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran's eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.

Drivers of macrophyte development in rivers in an agricultural area: indicative species reactions

The ecological drivers of macrophyte development in a lowland agricultural area were tested based on a 2008 survey on the Wkra River catchment. Our survey was carried out in the rivers of an agricultural area with relatively high concentrations of both nitrates and phosphates in the water. By using the Polish macrophyte method, we were able to calculate several botanical metrics. Canonical ordination analyses used to relate biological data to environmental variables such as physical and chemical parameters of water, surface water velocity or river width, were carried out using CANOCO for Windows. Redundancy analysis (RDA) showed that pH and alkalinity were the parameters best correlated with the distribution of macrophytes and values of macrophyte indices. The recorded values of the Macrophyte Index for River in the Wkra River and its tributaries reflected their good and moderate ecological status (the Water Framework Directive scale). Despite the fact that nutrient concentrations in the water were relatively high and that most of the sites represented eutrophic conditions, the results of this survey showed that non-nutrient parameters may play an important role in explaining aquatic plant occurrence in rivers that have been subjected to eutrophication.

Effect of Carbohydrate Composition in Barley and Oat Cultivars on Microbial Ecophysiology and Proliferation of Salmonella enterica in an In Vitro Model of the Porcine Gastrointestinal Tract

ABSTRACT The influence of the carbohydrate (CHO) composition of cereal cultivars on microbial ecophysiology was studied using an in vitro model of the porcine gastrointestinal tract. Ten hull-less barley cultivars, six barley cultivars with hulls, six oat cultivars, and six oat groats that differed in β-glucan, nonstarch polysaccharide (NSP), and starch contents and starch type were hydrolyzed enzymatically and incubated for 72 h with pig feces. Fermentation kinetics were modeled, and microbial compositions and short-chain fatty acid (SCFA) profiles were analyzed using terminal restriction fragment length polymorphism and gas chromatography. Cluster analysis and canonical ordination revealed different effects on fermentation and microbial ecology depending on the type of CHO and cultivar. First, in cultivars of barley with hulls and oats, the cellulose and insoluble NSP contents (i) increased Ruminococcus flavefaciens-like and Clostridium xylanolyticum-like phylotypes, (ii) increased acetate production, and (iii) decreased fermentation activity. Second, in hull-less barley cultivars the β-glucan, amylose, amylopectin, crude protein, and soluble NSP contents determined the microbial community composition and activity as follows: (i) the amylose contents of the hull-less barley varieties increased the butyrate production and the abundance of Clostridium butyricum-like phylotypes, (ii) the β-glucan content determined the total amounts of SCFA, and (iii) the amylopectin and starch contents affected the abundance of Clostridium ramosum-like phylotypes, members of Clostridium cluster XIVa, and Bacteroides-like bacteria. Finally, the effect of CHO on proliferation of Salmonella enterica in the model was determined. Salmonella cell counts were not affected, but the relative proportion of Salmonella decreased with hull-less barley cultivars and increased with oat cultivars as revealed by quantitative PCR. Our results shed light on the complex interactions of cereal CHO with intestinal bacterial ecophysiology and the possible impact on host health.