Summary
This is a technical reference handbook on mixed effects modelling and related statistical extensions for ecological applications, published as part of the Statistics for Biology and Health series. It provides practical guidance for implementing these methods in R, with worked examples spanning nested data, temporal and spatial dependence, and non-linear relationships across diverse ecological systems. The book serves as a methodological resource rather than an empirical study, addressing common violations of linear regression assumptions in ecological data.
Regional applicability
This is a general statistical methods textbook with no specific geographic focus. Its techniques are universally applicable to ecological research conducted in the United Kingdom or elsewhere; the Scottish farming catchment example demonstrates relevance to UK land-use and environmental monitoring contexts.
Key measures
Not applicable — this is a methods textbook, not an empirical study. The book illustrates statistical approaches including mixed models (GLMM), generalised additive models (GAM, GAMM), generalised estimating equations (GEE), and negative binomial models applied to count and binomial ecological data.
Outcomes reported
This is a methodological reference text providing practical guidance on mixed effects modelling and generalised linear/additive modelling approaches for ecological data. It demonstrates applications across diverse ecological systems including Antarctic birds, farming catchments, amphibians, deep-sea organisms, phytoplankton, honey bees, koalas, and badgers.
Topic tags
Dig deeper with Pulse AI.
Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.