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Different Metrics (different + metric)
Selected AbstractsOn quantitative measures of indirect interactionsECOLOGY LETTERS, Issue 4 2007Toshinori Okuyama Abstract Indirect effects, whether density-mediated (DMII) or trait-mediated (TMII), have been recognized as potentially important drivers of community dynamics. However, empirical studies that have attempted to detect TMII or to quantify the relative strength of DMII and TMII in short-term studies have used a range of different metrics. We review these studies and assess both the consistency of a variety of different metrics and their robustness to (or ability to detect) ecological phenomena such as the dependence of forager behaviour on conspecific density. Quantifying indirect effects over longer time scales when behaviour and population density vary is more challenging, but also necessary if we really intend to incorporate indirect effects into predictions of long-term community dynamics; we discuss some problems associated with this effort and conclude with general recommendations for quantifying indirect effects. [source] Size-independent growth in fishes: patterns, models and metricsJOURNAL OF FISH BIOLOGY, Issue 10 2008D. B. Sigourney A combination of a dynamic energy budget (DEB) model, field data on Atlantic salmon Salmo salar and brown trout Salmo trutta and laboratory data on Atlantic salmon was used to assess the underlying assumptions of three different metrics of growth including specific growth rate (G), standardized mass-specific growth rate (GS) and absolute growth rate in length (GL) in salmonids. Close agreement was found between predictions of the DEB model and the assumptions of linear growth in length and parabolic growth in mass. Field data comparing spring growth rates of age 1+ year and 2+ year Atlantic salmon demonstrated that in all years the larger age 2+ year fish exhibited a significantly lower G, but differences in growth in terms of GS and GL depended on the year examined. For brown trout, larger age 2+ year fish also consistently exhibited slower growth rates in terms of G but grew at similar rates as age 1+ year fish in terms of GS and GL. Laboratory results revealed that during the age 0+ year (autumn) the divergence in growth between future Atlantic salmon smolts and non-smolts was similar in terms of all three metrics with smolts displaying higher growth than non-smolts, however, both GS and GL indicated that smolts maintain relatively fast growth into the late autumn where G suggested that both smolts and non-smolts exhibit a sharp decrease in growth from October to November. During the spring, patterns of growth in length were significantly decoupled from patterns in growth in mass. Smolts maintained relatively fast growth though April in length but not in mass. These results suggest GS can be a useful alternative to G as a size-independent measure of growth rate in immature salmonids. In addition, during certain growth stanzas, GS may be highly correlated with GL. The decoupling of growth in mass from growth in length over ontogeny, however, may necessitate a combination of metrics to adequately describe variation in growth depending on ontogenetic stage particularly if life histories differ. [source] Naming speed and word familiarity as confounding factors in decodingJOURNAL OF RESEARCH IN READING, Issue 2 2002R. Malatesha Joshi The present investigation has three aims: (1) to establish a suitable composite index which combines speed and accuracy in the measurement of decoding skill; (2) to examine whether speed acts as a confounding factor in the measurement of decoding ability; and (3) to see whether familiarity with the word, as indicated by the ability to pronounce it, or lack of it acts as a confounding factor in the assessment of spelling skills. Three studies were conducted to fulfill these aims. In the first study, 33 children from Grade 2 were asked to name a list of 40 letters of the alphabet as quickly and as accurately as possible. A combined index of speed and accuracy was computed from these two sets of data using two formulas, which were based on the ,z' score and the mean variance score. A Pearson product-moment correlation coefficient of 0.94 was obtained between the results of the two formulas indicating that the two formulas yield almost identical results. In the second study, 37 fifth-graders were administered the word-attack sub-test of Woodcock Language Proficiency Battery. When the speed and accuracy composite index, based on the ,z formula' was applied to the word-attack scores, it was found that three children were slow decoders even though their scores were within the normal range. In the third study, 39 children from Grade 3 and 40 children from Grade 5 were asked to read aloud a list of words and then these words were administered as a spelling test. Subsequently, their spelling ability was assessed by computing the number of words they could both read and spell correctly. When familiarity of words was included as a factor in assessing spelling ability, five children in Grade 3 and three children in Grade 5 were found to be misclassified as poor spellers. This indicates that including word-naming speed and word familiarity (i.e. ability to pronounce) produce different metrics than when they are not. Inclusion of speed and familiarity factors in assessment can be helpful in avoiding false negatives and false positives. [source] Indicating ontology data quality, stability, and completeness throughout ontology evolutionJOURNAL OF SOFTWARE MAINTENANCE AND EVOLUTION: RESEARCH AND PRACTICE, Issue 1 2007Anthony M. Orme Abstract Many application areas today make use of ontologies. With the advent of semantic Web technologies, ontology based systems have become widespread. Developing an ontology is part of the necessary early development of an ontology-based system. Since the validity and quality of the ontology data directly affects the validity and quality of the system using the ontology, evolution of the ontology data directly affects the evolution and/or maintenance of the ontology-based systems that depend on and employ the ontology data. Our research examines the quality, completeness, and stability of ontology data as ontologies evolve. We propose a metrics suite, based on standard software quality concepts, to measure the complexity and cohesion of ontology data. First we theoretically validate our metrics. Then we examine empirically whether our metrics determine ontology data quality, by comparing them to human evaluator ratings. We conclude that several of our metrics successfully determine ontology complexity or cohesion. Finally, we examine, over evolving ontology data, whether our metrics determine ontology completeness and stability. We determine that various metrics reflect different kinds of changes. Our experiments indicate our metrics' measure ontology stability and completeness; however, interpretation of specific metrics values and the interaction of different metrics requires further study. Copyright © 2007 John Wiley & Sons, Ltd. [source] Emerging patterns in the comparative analysis of phylogenetic community structureMOLECULAR ECOLOGY, Issue 4 2009S. M. VAMOSI Abstract The analysis of the phylogenetic structure of communities can help reveal contemporary ecological interactions, as well as link community ecology with biogeography and the study of character evolution. The number of studies employing this broad approach has increased to the point where comparison of their results can now be used to highlight successes and deficiencies in the approach, and to detect emerging patterns in community organization. We review studies of the phylogenetic structure of communities of different major taxa and trophic levels, across different spatial and phylogenetic scales, and using different metrics and null models. Twenty-three of 39 studies (59%) find evidence for phylogenetic clustering in contemporary communities, but terrestrial and/or plant systems are heavily over-represented among published studies. Experimental investigations, although uncommon at present, hold promise for unravelling mechanisms underlying the phylogenetic community structure patterns observed in community surveys. We discuss the relationship between metrics of phylogenetic clustering and tree balance and explore the various emerging biases in taxonomy and pitfalls of scale. Finally, we look beyond one-dimensional metrics of phylogenetic structure towards multivariate descriptors that better capture the variety of ecological behaviours likely to be exhibited in communities of species with hundreds of millions of years of independent evolution. [source] | ||||||||||