University of Connecticut

About: University of Connecticut is a education organization based out in Storrs, Connecticut, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35297 authors who have published 81224 publications receiving 2952682 citations. The organization is also known as: UConn & Storrs Agricultural School.

The integrative review: updated methodology

Abstract: Aim. The aim of this paper is to distinguish the integrative review method from other review methods and to propose methodological strategies specific to the integrative review method to enhance the rigour of the process. Background. Recent evidence-based practice initiatives have increased the need for and the production of all types of reviews of the literature (integrative reviews, systematic reviews, meta-analyses, and qualitative reviews). The integrative review method is the only approach that allows for the combination of diverse methodologies (for example, experimental and non-experimental research), and has the potential to play a greater role in evidence-based practice for nursing. With respect to the integrative review method, strategies to enhance data collection and extraction have been developed; however, methods of analysis, synthesis, and conclusion drawing remain poorly formulated. Discussion. A modified framework for research reviews is presented to address issues specific to the integrative review method. Issues related to specifying the review purpose, searching the literature, evaluating data from primary sources, analysing data, and presenting the results are discussed. Data analysis methods of qualitative research are proposed as strategies that enhance the rigour of combining diverse methodologies as well as empirical and theoretical sources in an integrative review. Conclusion. An updated integrative review method has the potential to allow for diverse primary research methods to become a greater part of evidence-based practice initiatives.

A protocol for data exploration to avoid common statistical problems

Abstract: Summary 1. While teaching statistics to ecologists, the lead authors of this paper have noticed common statistical problems. If a random sample of their work (including scientific papers) produced before doing these courses were selected, half would probably contain violations of the underlying assumptions of the statistical techniques employed. 2. Some violations have little impact on the results or ecological conclusions; yet others increase type I or type II errors, potentially resulting in wrong ecological conclusions. Most of these violations can be avoided by applying better data exploration. These problems are especially troublesome in applied ecology, where management and policy decisions are often at stake. 3. Here, we provide a protocol for data exploration; discuss current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables; and provide advice on how to address these problems when they arise. We also address misconceptions about normality, and provide advice on data transformations. 4. Data exploration avoids type I and type II errors, among other problems, thereby reducing the chance of making wrong ecological conclusions and poor recommendations. It is therefore essential for good quality management and policy based on statistical analyses.

Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness

Abstract: Species richness is a fundamental measurement of community and regional diversity, and it underlies many ecological models and conservation strategies. In spite of its importance, ecologists have not always appreciated the effects of abundance and sampling effort on richness measures and comparisons. We survey a series of common pitfalls in quantifying and comparing taxon richness. These pitfalls can be largely avoided by using accumulation and rarefaction curves, which may be based on either individuals or samples. These taxon sampling curves contain the basic information for valid richness comparisons, including category‐subcategory ratios (species-to-genus and species-toindividual ratios). Rarefaction methods ‐ both sample-based and individual-based ‐ allow for meaningful standardization and comparison of datasets. Standardizing data sets by area or sampling effort may produce very different results compared to standardizing by number of individuals collected, and it is not always clear which measure of diversity is more appropriate. Asymptotic richness estimators provide lower-bound estimates for taxon-rich groups such as tropical arthropods, in which observed richness rarely reaches an asymptote, despite intensive sampling. Recent examples of diversity studies of tropical trees, stream invertebrates, and herbaceous plants emphasize the importance of carefully quantifying species richness using taxon sampling curves.

Evolution, weighting, and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers

Abstract: DNA-sequence data from the mitochondrial genome are being used with increasing frequency to estimate phylogenetic relationships among animal taxa. The advantage to using DNA-sequence data is that many of the processes governing the evolution and inheritance of DNA are already understood. DNA data, however, do not guarantee the correct phylogenetic tree because of problems associated with shared ancestral polymorphisms and multiple substitutions at single nucleotide sites. Knowledge of evolutionary processes can be used to improve estimates of patterns of relationships and can help to assess the phylogenetic usefulness of individual genes and nucleotides. This article reviews molecular processes, discusses the correction of genetic distances and the weighting of DNA data, and provides an assessment of the phylogenetic usefulness of specific mitochondrial genes. The Appendix presents a compilation of conserved polymerase chain reaction primers that can be used to amplify virtually any gene in the mitochondrial genome. DNA data sets vary tremendously in degree of phylogenetic usefulness. Correction or weighting (or both) of DNA-sequence data based on level of variability can improve results in some cases. Gene choice is of critical importance. For studies of relationships among closely related species, the use of ribosomal genes can be problematic, whereas unconstrained sites in protein coding genes appear to have fewer problems. In addition, information from studies of amino acid substitutions in rapidly evolving genes may help to decipher close relationships. For intermediate levels of divergence where silent sites contain many multiple hits, amino acid changes can be useful for construction phylogenetic relationships. For deep levels of divergence, protein coding genes may be saturated at the amino acid level and highly conserved regions of ribosomal RNA and transfer RNA genes may be useful. Because of the arbitrariness of taxonomic categories, no sweeping generalizations can be made about the taxonomic rank at which particular genes are useful. As more DNA-sequence data accumulate, we will be able to gain an even better understanding of the way in which genes and species evolve.