Pulse Brain · Growing Health Evidence Index
Tier 4 — Narrative / commentaryBook chapterConventional

Mixed effects models and extensions in ecology with R

Zuur AF; Ieno EN; Walker N; Saveliev AA; Smith GM

Statistics for Biology and Health · 2009

Read source ↗ All evidence

Summary

This is a technical reference work 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, and has been cited as a methodological foundation in biodiversity modelling work (e.g., Nature Communications British biodiversity scenarios). The book is not an empirical study but rather a tools-and-methods resource for quantitative ecology.

Regional applicability

As a statistical methods text, it has universal applicability across geographic regions. UK-based ecological researchers and those modelling British biodiversity scenarios have drawn upon its methodological approaches, as evidenced by its citation in Nature Communications biodiversity work.

Key measures

Not applicable — methodological reference rather than empirical study

Outcomes reported

The book presents methodological approaches and practical guidance for applying mixed effects models to ecological datasets using the R statistical programming environment.

Theme
Measurement & metrics
Subject
Measurement methods & nutrient profiling
Study type
Methodology
Study design
Methodology / Textbook
Source type
Book chapter
Status
Published
Geography
United Kingdom
System type
Other
DOI
10.1007/978-0-387-87458-6
Catalogue ID
IRmoq83nfn-ebb282

Topic tags

Pulse AI · ask about this record

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.