Apresentação

Apresentação Dossiê - Educação midiática: experiências e desafios no contexto iberoamerican

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Copyright © 2009 The Authors. Copyright © ECOGRAPHY 2009.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling 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

Gamificación en Iberoamérica. Experiencias desde la comunicación y la educación

La presente obra capitular es el resultado de las investigaciones sobre las aplicaciones de la gamificación en contextos múltiples, emergentes provenientes de las comunicaciones presentadas en el Simposio 06 del III Congreso Internacional Comunicación y Pensamiento (Sevilla, España), así como de aquellas presentadas por los miembros del Gamelab UPS, del Proyecto I+D+i Coordinado “Competencias mediáticas de la ciudadanía en medios digitales emergentes (smartphones y tablets): Prácticas innovadoras y estrategias educomunicativas en contextos múltiples” (EDU2015-64015-C3-1-R) (MINECO/FEDER), de la “Red de Educación Mediática” del Programa Estatal de Investigación Científica-Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento (EDU2016-81772-REDT), financiados por el Fondo Europeo de Desarrollo Regional (FEDER) y Ministerio de Economía y Competitividad de España. En este sentido se busca construir, desde una mirada dual desde Europa y América Latina el primer libro iberoamericano de gamificación, avalado por el Gamelab de la Universidad Politécnica Salesiana (Ecuador), el Proyecto I+D+i EDU2015-64015-C3-1-R, la Red Interuniversitaria Euroamericana de Investigación sobre Competencias Mediáticas para la Ciudadanía (Alfamed), el Laboratorio de Estudios en Comunicación (Ladecom) y el Grupo de Investigación Ágora (PAI-HUM-648) de la Universidad de Huelva (España) y el Grupo de Investigación Estructura, Historia y Contenidos de la Comunicación GREHCCO

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Copyright © 2009 The Authors. Copyright © ECOGRAPHY 2009.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling 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