Introduction

Ellner, Stephen P.; Childs, Dylan Z.; Rees, Mark

doi:10.1007/978-3-319-28893-2_1

Cited by 1 publication

(6 citation statements)

References 46 publications

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“…The fourth and final step in an Rpadrino workflow is to conduct the analyses of interest (Figure 1, Box 1). Rpadrino provides functions to extract per-capita growth rates, eigenvectors (Caswell 2001, Ellner, Childs & Rees 2016 Ch. 2, demonstrated in Box 1), assess convergence to asymptotic dynamics (Caswell 2001), compute mean kernels for stochastic IPMs (Ellner, Childs & Rees Ch 7), and modify existing IPMs with new parameter values and functional forms.…”

Section: Rpadrino and Ipmrmentioning

confidence: 99%

“…Jones et al 2014). The intermediate life table conversion steps may not be necessary, as many of the same life history traits and population level parameters may be calculated from all of these models (Caswell 2001, Ellner, Childs & Rees 2016). Furthermore, recent publications combine biotic and abiotic interactions into demographic models providing a robust theoretical toolbox for exploring species responses to environmental drivers such as climate change ( e.g.…”

Section: Opportunities and Future Directionsmentioning

confidence: 99%

“…Indeed, there is a rich history of using such structured population models across a variety of subdisciplines in ecology ( e.g. Leslie 1945, Caswell 2001, Easterling et al 2000, Adler et al 2010, Ellner et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…clonal reproduction). Each kernel may be comprised of any number of vital rate functions (Ellner, Childs, & Rees 2016). Analytical solutions to the integrals in Eq 1 are not available (Ellner & Rees 2006).…”

Section: An Introduction To Ipms and Padrinomentioning

confidence: 99%

“…Analytical solutions to the integrals in Eq 1 are not available (Ellner & Rees 2006). Therefore, the integrals are numerically approximated, resulting in a large iteration matrix (typically ranging from 45 × 45 to 5000 × 5000 in dimension, SC Levin unpublished data), and then some quantities of interest are computed (Ellner, Childs & Rees 2016).…”

Section: An Introduction To Ipms and Padrinomentioning

confidence: 99%

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Rpadrino: an R package to access and use PADRINO, an open access database of Integral Projection Models

Levin

Evers

Potter

et al. 2022

Preprint

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Discrete time structured population projection models are an important tool for studying population dynamics. Within this field, Integral Projection Models (IPMs) have become a popular method for studying populations structured by continuously distributed traits (e.g. height, weight). Databases of discrete time, discrete state structured population models, for example DATLife (life tables) and COMPADRE & COMADRE (matrix population models), have made quantitative syntheses straightforward to implement. These efforts allow researchers to address questions in both basic and applied ecology and evolutionary biology. There are now over 300 peer-reviewed publications containing IPMs. We describe a novel framework to quickly reconstruct these models for subsequent analyses using Rpadrino R package, which serves as an interface to PADRINO, a new database of structured population models.We introduce an R package, Rpadrino, which enables users to download, subset, reconstruct, and extend published IPMs. Rpadrino makes use of recently created software, ipmr, to provide an engine to reconstruct a wide array of IPMs from their symbolic representations and conduct subsequent analyses. Rpadrino and ipmr are extensively documented to help users learn their usage.Rpadrino currently enables users to reconstruct 280 IPMs from 40 publications that describe the demography of 14 animal and 26 plant species. All of these IPMs are tested to ensure they reproduce published estimates. Rpadrino provides an interface to augment PADRINO with external data and modify parameter values, creating a platform to extend models beyond their original purpose while retaining full reproducibility.PADRINO and Rpadrino provide a toolbox for asking new questions and conducting syntheses with peer-reviewed published IPMs. Rpadrino provides a user-friendly interface so researchers do not need to worry about the database structure or syntax, and can focus on their research questions and analyses. Additionally, Rpadrino is thoroughly documented, and provides numerous examples of how to perform analyses which are not included in the package’s functionality.

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Section: Rpadrino and Ipmrmentioning

confidence: 99%

Section: Opportunities and Future Directionsmentioning

confidence: 99%