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Compositional Dissimilarity (compositional + dissimilarity)
Selected AbstractsThe ED strategy: how species-level surrogates indicate general biodiversity patterns through an ,environmental diversity' perspectiveJOURNAL OF BIOGEOGRAPHY, Issue 8 2004D. P. Faith Abstract Biodiversity assessment requires that we use surrogate information in practice to indicate more general biodiversity patterns. ,ED' refers to a surrogates framework that can link species data and environmental information based on a robust relationship of compositional dissimilarities to ordinations that indicate underlying environmental variation. In an example analysis of species and environmental data from Panama, the environmental and spatial variables that correlate with an hybrid multi-dimensional scaling ordination were able to explain 83% of the variation in the corresponding Bray Curtis dissimilarities. The assumptions of ED also provide the rationale for its use of p-median optimization criteria to measure biodiversity patterns among sites in a region. M.B. Araújo, P.J. Densham & P.H. Williams (2004, Journal of Biogeography31, 1) have re-named ED as ,AD' in their evaluation of the surrogacy value of ED based on European species data. Because lessons from previous work on ED options consequently may have been neglected, we use a corroboration framework to investigate the evidence and ,background knowledge' presented in their evaluations of ED. Investigations focus on the possibility that their weak corroboration of ED surrogacy (non-significance of target species recovery relative to a null model) may be a consequence of Araújo et al.'s use of particular evidence and randomizations. We illustrate how their use of discrete ED, and not the recommended continuous ED, may have produced unnecessarily poor species recovery values. Further, possible poor optimization of their MDS ordinations, due to small numbers of simulations and/or low resolution of stress values appears to have provided a possible poor basis for ED application and, consequently, may have unnecessarily favoured non-corroboration results. Consideration of Araújo et al.'s randomizations suggests that acknowledged sampling biases in the European data have not only artefactually promoted the non-significance of ED recovery values, but also artefactually elevated the significance of competing species surrogates recovery values. We conclude that little credence should be given to the comparisons of ED and species-based complementarity sets presented in M.B. Araújo, P.J. Densham & P.H. Williams (2004, Journal of Biogeography31, 1), unless the factors outlined here can be analysed for their effects on results. We discuss the lessons concerning surrogates evaluation emerging from our investigations, calling for better provision in such studies of the background information that can allow (i) critical examination of evidence (both at the initial corroboration and re-evaluation stages), and (ii) greater synthesis of lessons about the pitfalls of different forms of evidence in different contexts. [source] Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessmentDIVERSITY AND DISTRIBUTIONS, Issue 3 2007Simon Ferrier ABSTRACT Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate-change impact assessment. [source] Scale sensitivity of synthetic long-term vegetation time series derived through overlay of short-term field recordsJOURNAL OF VEGETATION SCIENCE, Issue 4 2007Otto Wildi Abstract Questions: Is change in cover of dominant species driving the velocity of succession or is it species turnover (1)? Is the length of the time-step chosen in sampling affecting our recognition of the long-term rate of change (2)1 Location: 74 permanent plots located in the Swiss National Park, SE Switzerland, ca. 1900 m a.s.l. Methods: We superimpose several time-series from permanent plots to one single series solely based on compositional dissimilarity. As shown earlier (Wildi & Schütz 2000) this results in a synthetic series covering about 400 to 650 yr length. Continuous power transformation of cover-percentage scores is used to test if the dominance or the presence-absence of species is governing secondary succession from pasture to forest. The effect of time step length is tested by sub-samples of the time series. Results: Altering the weight of presence-absence versus dominance of species affects the emerging time frame, while altering time step length is uncritical. Where species turnover is fast, different performance scales yield similar results. When cover change in dominant species prevails, the solutions vary considerably. Ordinations reveal that the synthetic time series seek for shortest paths of the temporal pattern whereas in the real system longer lasting alternatives exist. Conclusions: Superimposing time series differs from the classical space-for-time substitution approach. It is a computation-based method to investigate temporal patterns of hundreds of years fitting between direct monitoring (usually limited to decades) and the analysis of proxy-data (for time spans of thousands of years and more). [source] Improving the assessment of species compositional dissimilarity in a priori ecological classifications: evaluating map scale, sampling intensity and improvement in a hierarchical classificationAPPLIED VEGETATION SCIENCE, Issue 4 2010B.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] | ||||||||||