Model-based thinking for community ecology

[1]  David I. Warton,et al.  The fourth‐corner solution – using predictive models to understand how species traits interact with the environment , 2014 .

[2]  Richard Arnold,et al.  Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection , 2014, Comput. Stat. Data Anal..

[3]  Geof H. Givens,et al.  Modelling biological regions from multi‐species and environmental data , 2013 .

[4]  Wim A. Ozinga,et al.  Selecting traits that explain species–environment relationships: a generalized linear mixed model approach , 2013 .

[5]  Francis K C Hui,et al.  To mix or not to mix: comparing the predictive performance of mixture models vs. separate species distribution models. , 2013, Ecology.

[6]  Maureen C. Kennedy,et al.  Applied statistics in ecology: common pitfalls and simple solutions , 2013 .

[7]  David I. Warton,et al.  Finite Mixture of Regression Modeling for High-Dimensional Count and Biomass Data in Ecology , 2013 .

[8]  William K. Morris,et al.  The role of functional traits in species distributions revealed through a hierarchical model , 2012 .

[9]  Yi Wang,et al.  mvabund– an R package for model‐based analysis of multivariate abundance data , 2012 .

[10]  Julian D. Olden,et al.  Assessing transferability of ecological models: an underappreciated aspect of statistical validation , 2012 .

[11]  D. Warton,et al.  Distance‐based multivariate analyses confound location and dispersion effects , 2012 .

[12]  Anthony R. Ives,et al.  Generalized linear mixed models for phylogenetic analyses of community structure , 2011 .

[13]  David I Warton,et al.  Regularized Sandwich Estimators for Analysis of High‐Dimensional Data Using Generalized Estimating Equations , 2011, Biometrics.

[14]  Scott D. Foster,et al.  Model based grouping of species across environmental gradients , 2011 .

[15]  Otso Ovaskainen,et al.  Making more out of sparse data: hierarchical modeling of species communities. , 2011, Ecology.

[16]  M. Bravington,et al.  Graphical Diagnostics for Markov Models for Categorical Data , 2011 .

[17]  Francis K C Hui,et al.  The arcsine is asinine: the analysis of proportions in ecology. , 2011, Ecology.

[18]  R. Christensen.  Regression Models for Ordinal Data Introducing R-package ordinal , 2011 .

[19]  Alain F. Zuur,et al.  A protocol for data exploration to avoid common statistical problems , 2010 .

[20]  Robert B. O'Hara,et al.  Do not log‐transform count data , 2010 .

[21]  T. Yee.  The VGAM Package for Categorical Data Analysis , 2010 .

[22]  Catherine A Calder,et al.  Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. , 2009, Ecological applications : a publication of the Ecological Society of America.

[23]  Mollie E. Brooks,et al.  Generalized linear mixed models: a practical guide for ecology and evolution. , 2009, Trends in ecology & evolution.

[24]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[25]  D. Warton.  Raw data graphing: an informative but under‐utilized tool for the analysis of multivariate abundances , 2008 .

[26]  Andrew P. Robinson,et al.  Randomization, Bootstrap and Monte Carlo Methods in Biology , 2007 .

[27]  Rampal S Etienne,et al.  A neutral sampling formula for multiple samples and an 'exact' test of neutrality. , 2007, Ecology letters.

[28]  Jane Elith,et al.  Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines , 2007 .

[29]  Antoine Guisan,et al.  Spatial modelling of biodiversity at the community level , 2006 .

[30]  M. Azevedo,et al.  Species-richness patterns in space, depth, and time (1989-1999) of the Portuguese fauna sampled by bottom trawl , 2006 .

[31]  J. Shuster.  Diagnostics for assumptions in moderate to large simple clinical trials: do they really help? , 2005, Statistics in medicine.

[32]  A. Gelfand,et al.  Modelling species diversity through species level hierarchical modelling , 2005 .

[33]  Alan David Hutson,et al.  Resampling Methods for Dependent Data , 2004, Technometrics.

[34]  H. Hudson,et al.  A MANOVA STATISTIC IS JUST AS POWERFUL AS DISTANCE-BASED STATISTICS, FOR MULTIVARIATE ABUNDANCES , 2004 .

[35]  D. W. Goodall,et al.  Non-linear ordination in several dimensions , 1982, Vegetatio.

[36]  Anja Vogler,et al.  An Introduction to Multivariate Statistical Analysis , 2004 .

[37]  Cajo J. F. ter Braak,et al.  Bayesian model-based cluster analysis for predicting macrofaunal communities , 2003 .

[38]  Marti J. Anderson,et al.  A new method for non-parametric multivariate analysis of variance in ecology , 2001 .

[39]  David R. Anderson,et al.  Model Selection and Inference: A Practical Information-Theoretic Approach , 2001 .

[40]  N. Draper,et al.  Applied Regression Analysis: Draper/Applied Regression Analysis , 1998 .

[41]  A. W. Kemp,et al.  Randomization, Bootstrap and Monte Carlo Methods in Biology , 1997 .

[42]  Peter K. Dunn,et al.  Randomized Quantile Residuals , 1996 .

[43]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[44]  M. Braga,et al.  Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[45]  M. Hill,et al.  Data analysis in community and landscape ecology , 1987 .

[46]  C. Braak.  Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis , 1986 .

[47]  P. McCullagh.  Regression Models for Ordinal Data , 1980 .

[48]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[49]  Hugh G. Gauch,et al.  Ordination of Vegetation Samples by Gaussian Species Distributions , 1974 .

[50]  D. M. Ellis,et al.  Applied Regression Analysis , 1968 .

[51]  T. W. Anderson,et al.  An Introduction to Multivariate Statistical Analysis , 1959 .

[52]  C. W. Dilke.  Statistics of the Defence Expenditure of the Chief Military and Naval Powers , 1891 .