Ecological Classifications (ecological + classification)

Distribution by Scientific Domains


Selected Abstracts


Water Framework Directive: ecological classification of Danish lakes

JOURNAL OF APPLIED ECOLOGY, Issue 4 2005
MARTIN SØNDERGAARD
Summary 1The European Water Framework Directive (WFD) requires that all European waterbodies are assigned to one of five ecological classes, based primarily on biological indicators, and that minimum good ecological quality is obtained by 2015. However, the directive provides only general guidance regarding indicator definitions and determination of boundaries between classes. 2We used chemical and biological data from 709 Danish lakes to investigate whether and how lake types respond differently to eutrophication. In the absence of well-defined reference conditions, lakes were grouped according to alkalinity and water depth, and the responses to eutrophication were ordered along a total phosphorus (TP) gradient to test the applicability of pre-defined boundaries. 3As a preliminary classification we suggest a TP-based classification into high, good, moderate, bad and poor ecological quality using 0,25, 25,50, 50,100, 100,200 and > 200 µg P L,1 boundaries for shallow lakes, and 0,12·5, 12·5,25, 25,50, 50,100 and > 100 µg P L,1 boundaries for deep lakes. Within each TP category, median values are used to define preliminary boundaries for the biological indicators. 4Most indicators responded strongly to increasing TP, but there were only minor differences between low and high alkalinity lakes and modest variations between deep and shallow lakes. The variability of indicators within a given TP range was, however, high, and for most indicators there was a considerable overlap between adjacent TP categories. Cyanophyte biomass, submerged macrophyte coverage, fish numbers and chlorophyll a were among the ,best' indicators, but their ability to separate different TP classes varied with TP. 5When using multiple indicators the risk that one or more indicators will indicate different ecological classes is high because of a high variability of all indicators within a specific TP class, and the ,one out , all out' principle in relation to indicators does not seem feasible. Alternatively a certain compliance level or a ,mean value' of the indicators can be used to define ecological classes. A precise ecological quality ratio (EQR) using values between 0 and 1 can be calculated based on the extent to which the total number of indicators meets the boundary conditions, as demonstrated from three Danish lakes. 6Synthesis and applications. The analysis of Danish lakes has identified a number of useful indicators for lake quality and has suggested a method for calculating an ecological quality ratio. However, it also demonstrates that the implementation of the Water Framework Directive faces several challenges: gradual rather than stepwise changes for all indicators, large variability of indicators within lake classes, and problems using the one out , all out principle for lake classification. [source]


Metagenomic signatures of 86 microbial and viral metagenomes

ENVIRONMENTAL MICROBIOLOGY, Issue 7 2009
Dana Willner
Summary Previous studies have shown that dinucleotide abundances capture the majority of variation in genome signatures and are useful for quantifying lateral gene transfer and building molecular phylogenies. Metagenomes contain a mixture of individual genomes, and might be expected to lack compositional signatures. In many metagenomic data sets the majority of sequences have no significant similarities to known sequences and are effectively excluded from subsequent analyses. To circumvent this limitation, di-, tri- and tetranucleotide abundances of 86 microbial and viral metagenomes consisting of short pyrosequencing reads were analysed to provide a method which includes all sequences that can be used in combination with other analysis to increase our knowledge about microbial and viral communities. Both principal component analysis and hierarchical clustering showed definitive groupings of metagenomes drawn from similar environments. Together these analyses showed that dinucleotide composition, as opposed to tri- and tetranucleotides, defines a metagenomic signature which can explain up to 80% of the variance between biomes, which is comparable to that obtained by functional genomics. Metagenomes with anomalous content were also identified using dinucleotide abundances. Subsequent analyses determined that these metagenomes were contaminated with exogenous DNA, suggesting that this approach is a useful metric for quality control. The predictive strength of the dinucleotide composition also opens the possibility of assigning ecological classifications to unknown fragments. Environmental selection may be responsible for this dinucleotide signature through direct selection of specific compositional signals; however, simulations suggest that the environment may select indirectly by promoting the increased abundance of a few dominant taxa. [source]


Improving the assessment of species compositional dissimilarity in a priori ecological classifications: evaluating map scale, sampling intensity and improvement in a hierarchical classification

APPLIED VEGETATION SCIENCE, Issue 4 2010
B.E. Lawson
Abstract Question: Can species compositional dissimilarity analyses be used to assess and improve the representation of biodiversity patterns in a priori ecological classifications? Location: The case study examined the northern-half of the South-east Queensland Bioregion, eastern Australia. Methods: Site-based floristic presence,absence data were used to construct species dissimilarity matrices (Kulczynski metric) for three levels of Queensland's bioregional hierarchy , subregions (1:500 000 scale), land zones (1:250 000 scale) and regional ecosystems (1:100 000 scale). Within- and between-class dissimilarities were compiled for each level to elucidate species compositional patterns. Randomized subsampling was used to determine the minimum site sampling intensity for each hierarchy level, and the effects of lumping and splitting illustrated for several classes. Results: Consistent dissimilarity estimates were obtained with five or more sites per regional ecosystem, 10 or more sites per land zone, and more than 15 sites per subregion. On average, subregions represented 4% dissimilarity in floristic composition, land zones approximately 10%, and regional ecosystems over 19%. Splitting classes with a low dissimilarity increased dissimilarity levels closer to average, while merging ecologically similar classes with high dissimilarities reduced dissimilarity levels closer to average levels. Conclusions: This approach demonstrates a robust and repeatable means of analysing species compositional dissimilarity, determining site sampling requirements for classifications and guiding decisions about ,lumping' or ,splitting' of classes. This will allow more informed decisions on selecting and improving classifications and map scales in an ecologically and statistically robust manner. [source]