The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

Significance: Schedules of survival, growth, and reproduction define life-history strategies across species. Understanding how life-history strategies are structured is fundamental to our understanding of the evolution, abundance, and distribution of species. We found that life-history strategies of 418 plant species worldwide are explained by an axis representing the pace of life and another representing the wide range of reproductive strategies. This framework predicts responses to perturbations and long-term population performance, showing great promise as a predictive tool for plant population responses to environmental change.

Summary: Schedules of survival, growth and reproduction are key life‐history traits. Data on how these traits vary among species and populations are fundamental to our understanding of the ecological conditions that have shaped plant evolution. Because these demographic schedules determine population growth or decline, such data help us understand how different biomes shape plant ecology, how plant populations and communities respond to global change and how to develop successful management tools for endangered or invasive species. Matrix population models summarize the life cycle components of survival, growth and reproduction, while explicitly acknowledging heterogeneity among classes of individuals in the population. Matrix models have comparable structures, and their emergent measures of population dynamics, such as population growth rate or mean life expectancy, have direct biological interpretations, facilitating comparisons among populations and species. Thousands of plant matrix population models have been parameterized from empirical data, but they are largely dispersed through peer‐reviewed and grey literature, and thus remain inaccessible for synthetic analysis. Here, we introduce the compadre Plant Matrix Database version 3.0, an open‐source online repository containing 468 studies from 598 species world‐wide (672 species hits, when accounting for species studied in more than one source), with a total of 5621 matrices. compadre also contains relevant ancillary information (e.g. ecoregion, growth form, taxonomy, phylogeny) that facilitates interpretation of the numerous demographic metrics that can be derived from the matrices. Synthesis. Large collections of data allow broad questions to be addressed at the global scale, for example, in genetics (genbank), functional plant ecology (try, bien, d3) and grassland community ecology (nutnet). Here, we present compadre, a similarly data‐rich and ecologically relevant resource for plant demography. Open access to this information, its frequent updates and its integration with other online resources will allow researchers to address timely and important ecological and evolutionary questions.

Summary: The open‐data scientific philosophy is being widely adopted and proving to promote considerable progress in ecology and evolution. Open‐data global data bases now exist on animal migration, species distribution, conservation status, etc. However, a gap exists for data on population dynamics spanning the rich diversity of the animal kingdom world‐wide. This information is fundamental to our understanding of the conditions that have shaped variation in animal life histories and their relationships with the environment, as well as the determinants of invasion and extinction. Matrix population models (MPMs) are among the most widely used demographic tools by animal ecologists. MPMs project population dynamics based on the reproduction, survival and development of individuals in a population over their life cycle. The outputs from MPMs have direct biological interpretations, facilitating comparisons among animal species as different as Caenorhabditis elegans, Loxodonta africana and Homo sapiens. Thousands of animal demographic records exist in the form of MPMs, but they are dispersed throughout the literature, rendering comparative analyses difficult. Here, we introduce the COMADRE Animal Matrix Database, an open‐data online repository, which in its version 1.0.0 contains data on 345 species world‐wide, from 402 studies with a total of 1625 population projection matrices. COMADRE also contains ancillary information (e.g. ecoregion, taxonomy, biogeography, etc.) that facilitates interpretation of the numerous demographic metrics that can be derived from its MPMs. We provide R code to some of these examples. Synthesis: We introduce the COMADRE Animal Matrix Database, a resource for animal demography. Its open‐data nature, together with its ancillary information, will facilitate comparative analysis, as will the growing availability of databases focusing on other aspects of the rich animal diversity, and tools to query and combine them. Through future frequent updates of COMADRE, and its integration with other online resources, we encourage animal ecologists to tackle global ecological and evolutionary questions with unprecedented sample size.

A peer-reviewed open access journal published by the National Institute of Environmental Health Sciences