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5 resultados encontrados para: AUTOR: Kitching, Ian J.
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Resumen en español

A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modeling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; ‘‘OLS models’’ hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts.

Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation.


Resumen en: Inglés |
Resumen en inglés

We compiled 46 broadscale data sets of species richness for a wide range of terrestrial plant, invertebrate, and ectothermic vertebrate groups in all parts of the world to test the ability of metabolic theory to account for observed diversity gradients. The theory makes two related predictions: (1) In-transformed richness is linearly associated with a linear, inverse transformation of annual temperature, and (2) the slope of the relationship is near -0.65. Of the 46 data sets, 14 had no significant relationship; of the remaining 32, nine were linear, meeting prediction 1. Model I (ordinary least squares, OLS) and model II (reduced major axis, RMA) regressions then tested the linear slopes against prediction 2. In the 23 data sets having nonlinear relationships between richness and temperature, split-line regression divided the data into linear components, and regressions were done on each component to test prediction 2 for subsets of the data.

Of the 46 data sets analyzed in their entirety using OLS regression, one was consistent with metabolic theory (meeting both predictions), and one was possibly consistent. Using RMA regression, no data sets were consistent. Of 67 analyses of prediction 2 using OLS regression on all linear data sets and subsets, two were consistent with the prediction, and four were possibly consistent. Using RMA regression, one was consistent (albeit weakly), and four were possibly consistent. We also found that the relationship between richness and temperature is both taxonomically and geographically conditional, and there is no evidence for a universal response of diversity to temperature. Meta-analyses confirmed significant heterogeneity in slopes among data sets, and the combined slopes across studies were significantly lower than the range of slopes predicted by metabolic theory based on both OLS and RMA regressions. We conclude that metabolic theory, as currently formulated, is a poor predictor of observed diversity gradients in most terrestrial systems.


3.
Libro
Hawkmoths of the world: an annotated and illustrated revisionary checklist (lepidoptera: sphingidae) / by Ian J. Kitching and Jean Marie Cadiou
Kitching, Ian J. ; Cadiou, Jean Marie (coaut.) (1941-) ;
London : The Natural History Museum , 2000
Clasificación: C/595.781 / K5
Bibliotecas: San Cristóbal
Cerrar
SIBE San Cristóbal
SAC003305 (Prestado)
Disponibles para prestamo: 0

4.
Libro
Cladistics: the theory and practice of parsimony analysis / Ian J. Kitching, Peter L. Forey, Christopher J. Humphries and David M. Williams
Kitching, Ian J. ; Forey, Peter L. (coaut.) ; Humphries, Christopher J. (coaut.) ; Williams, David M. (coaut.) ;
Oxford : Oxford University , 1998
Clasificación: 575 / C5
Bibliotecas: Chetumal , Tapachula
Cerrar
SIBE Chetumal
ECO030000972 (Disponible) , ECO030007785 (Disponible)
Disponibles para prestamo: 2
Cerrar
SIBE Tapachula
ECO020001455 (Disponible)
Disponibles para prestamo: 1

5.
Libro
Cladistics: a practical course in systematics / Peter L. Forey, Christopher J. Humphries, ...[et al.]
Forey, Peter L. ; Humphries, Christopher J. (coaut.) ; Kitching, Ian J. (coaut.) ; Scotland, Robert W. (coaut.) ;
Oxford, England : Clarendon Press , 1992
Clasificación: 575 / C43
Bibliotecas: Tapachula
Cerrar
SIBE Tapachula
TAA001406 (Disponible)
Disponibles para prestamo: 1
Resumen en: Inglés |
Resumen en inglés

Systematics underpins all of biology. Cladistics is a method of systematic classification that has become applied to comparative studies in all fields of biology. In cladistics, the genealogies that are reconstructed are based on common ancestry rather than on overall similarity. True phylogenetic relationships are thus revealed. To meet the need for training, the Systematics Association has sponsored a short course on cladistics. The interest sparked by this course was such that the course material has been gathered together in this book, now available in paperback. To introduce the subject, the principle of parsimony and methods for character coding and determining character polarity are first presented. Methods of cladistic tree-building follow, and tree statistics are detailed. Alternatives to parsimony, molecular applications of cladistics, and the relevance of fossils are then discussed. The concluding chapters review two important topics in cladistics: cladistic biogeography and the implementation of cladistics results in systematics. This book provides an up-to-date account of the techniques of modern cladistics, written in a clear, readable style. As such, it should be an invaluable text for all students interest in systematics and comparative studies.