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Contiene: Limitations of linear regression applied on ecological data. Things are not always linear; additive modelling. Dealing with heterogeneity. Mixed effects modelling for nested data. Violation of independence. Meet the exponential family. GLM and GAM for count data. GLM and GAM for absence-presence and proportional data. Zero-truncated and zero-inflated models for count data. Generalised estimation equations. GLMM and GAMM. Estimating trends for antarctic birds in relation. Large-scale impacts of land-use change in a scottish, farming catchment. Negative binomial GAM and GAMM to analyse amphibian roadkills. Addtive mixed modelling applied on deep-sea pelagic bioluminescent organisms. Additive mixed modelling applied on phytoplankton time series data. Mixed effects modelling applied on american foulbrood affecting honey bees larvae. Three-way nested data for age determination techniques applied to cetaceans. GLMM applied on the spatial distribution of koalas in a fragmented landscape. A comparison of GLM, GEE and GLMM aplied to badger activity data. Incorporating temporal correlation in seal abundance data with MCMC. Required pre-knowledge: a linear regression and additive modelling example