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lefko3 : Analysing individual history through size‐classified matrix population models
Author(s) -
Shefferson Richard P.,
Kurokawa Shun,
Ehrlén Johan
Publication year - 2021
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13526
Subject(s) - computer science , r package , workflow , population , projection (relational algebra) , selection (genetic algorithm) , matrix (chemical analysis) , function (biology) , population size , population model , data mining , algorithm , machine learning , database , computational science , biology , materials science , demography , evolutionary biology , sociology , composite material
The histories of individuals impact the dynamics of their populations. Matrix projection models (MPMs) are used to analyse population dynamics, but are not structured to incorporate these influences. Historical MPMs (hMPM) were developed to incorporate these impacts, but their complexity has left them little used. We developed r package lefko3 to provide simple, quick methods to estimate and analyse hMPMs, as well as ahistorical MPMs. Package lefko3 handles the entire workflow from dataset organization to the construction and analysis of hMPMs. Dataset management functions reorganize most demographic data formats, and matrix creation functions estimate both raw and function‐based matrices. Vital rates may be modelled as mixed or generalized linear models, with model selection protocols involving current best practices. The core kernels are binaries allowing even matrices with over 10,000 rows and columns to be estimated quickly without parallelization. We also include functions to conduct basic deterministic projection analyses. Package lefko3 , available on CRAN, dramatically reduces the difficulties in testing the impacts of individual history on population dynamics. We provide three vignettes to showcase how hMPMs can be developed and analysed.