ipmr: Flexibly implement integral projection models in R
Integral projection models (IPMs) are an important tool for studying the dynamics of populations structured by one or more continuous traits (e.g. size, height, body mass). Researchers use IPMs to investigate questions ranging from linking drivers to population dynamics, planning conservation and ma...
| Auteurs principaux: | , , , , , |
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| Format: | Journal article |
| Langue: | English |
| Publié: |
Wiley
2021
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| _version_ | 1826310737343021056 |
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| author | Levin, SC Childs, DZ Compagnoni, A Evers, S Knight, TM Salguero-Gomez, R |
| author_facet | Levin, SC Childs, DZ Compagnoni, A Evers, S Knight, TM Salguero-Gomez, R |
| author_sort | Levin, SC |
| collection | OXFORD |
| description | Integral projection models (IPMs) are an important tool for studying the dynamics of populations structured by one or more continuous traits (e.g. size, height, body mass). Researchers use IPMs to investigate questions ranging from linking drivers to population dynamics, planning conservation and management strategies, and quantifying selective pressures in natural populations. The popularity of stage-structured population models has been supported by R scripts and packages (e.g. IPMpack, popbio, popdemo, lefko3) aimed at ecologists, which have introduced a broad repertoire of functionality and outputs. However, pressing ecological, evolutionary and conservation biology topics require developing more complex IPMs, and considerably more expertise to implement them. Here, we introduce ipmr, a flexible R package for building, analysing and interpreting IPMs.
The ipmr framework relies on the mathematical notation of the models to express them in code format. Additionally, this package decouples the model parameterization step from the model implementation step. The latter point substantially increases ipmr's flexibility to model complex life cycles and demographic processes.
ipmr can handle a wide variety of models, including those that incorporate density dependence, discretely and continuously varying stochastic environments, and multiple continuous and/or discrete traits. ipmr can accommodate models with individuals cross-classified by age and size. Furthermore, the package provides methods for demographic analyses (e.g. asymptotic and stochastic growth rates) and visualization (e.g. kernel plotting).
ipmr is a flexible R package for integral projection models. The package substantially reduces the amount of time required to implement general IPMs. We also provide extensive documentation with six vignettes and help files, accessible from an R session and online.
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| first_indexed | 2024-03-07T07:56:25Z |
| format | Journal article |
| id | oxford-uuid:aa7e40fc-9f83-46db-86cf-c3d3bf12d1c0 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:56:25Z |
| publishDate | 2021 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:aa7e40fc-9f83-46db-86cf-c3d3bf12d1c02023-08-23T15:38:20Zipmr: Flexibly implement integral projection models in RJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:aa7e40fc-9f83-46db-86cf-c3d3bf12d1c0EnglishSymplectic ElementsWiley2021Levin, SCChilds, DZCompagnoni, AEvers, SKnight, TMSalguero-Gomez, RIntegral projection models (IPMs) are an important tool for studying the dynamics of populations structured by one or more continuous traits (e.g. size, height, body mass). Researchers use IPMs to investigate questions ranging from linking drivers to population dynamics, planning conservation and management strategies, and quantifying selective pressures in natural populations. The popularity of stage-structured population models has been supported by R scripts and packages (e.g. IPMpack, popbio, popdemo, lefko3) aimed at ecologists, which have introduced a broad repertoire of functionality and outputs. However, pressing ecological, evolutionary and conservation biology topics require developing more complex IPMs, and considerably more expertise to implement them. Here, we introduce ipmr, a flexible R package for building, analysing and interpreting IPMs. The ipmr framework relies on the mathematical notation of the models to express them in code format. Additionally, this package decouples the model parameterization step from the model implementation step. The latter point substantially increases ipmr's flexibility to model complex life cycles and demographic processes. ipmr can handle a wide variety of models, including those that incorporate density dependence, discretely and continuously varying stochastic environments, and multiple continuous and/or discrete traits. ipmr can accommodate models with individuals cross-classified by age and size. Furthermore, the package provides methods for demographic analyses (e.g. asymptotic and stochastic growth rates) and visualization (e.g. kernel plotting). ipmr is a flexible R package for integral projection models. The package substantially reduces the amount of time required to implement general IPMs. We also provide extensive documentation with six vignettes and help files, accessible from an R session and online. |
| spellingShingle | Levin, SC Childs, DZ Compagnoni, A Evers, S Knight, TM Salguero-Gomez, R ipmr: Flexibly implement integral projection models in R |
| title | ipmr: Flexibly implement integral projection models in R |
| title_full | ipmr: Flexibly implement integral projection models in R |
| title_fullStr | ipmr: Flexibly implement integral projection models in R |
| title_full_unstemmed | ipmr: Flexibly implement integral projection models in R |
| title_short | ipmr: Flexibly implement integral projection models in R |
| title_sort | ipmr flexibly implement integral projection models in r |
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