Rcompadre and Rage—Two R packages to facilitate the use of the COMPADRE and COMADRE databases and calculation of life‐history traits from matrix population models

Jones, Owen R.; Barks, Patrick; Stott, Iain; James, Tamora D.; Levin, Sam; Petry, William K.; Capdevila, Pol; Che‐Castaldo, Judy; Jackson, John; Römer, Gesa; Schuette, Caroline; Thomas, Chelsea C.; Salguero‐Gómez, Roberto

doi:10.1111/2041-210x.13792

Cited by 31 publications

(37 citation statements)

References 50 publications

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“…For models from previous analyses that were stage-based (80 of 400 in COMADRE; 148 of 150 in COMPADRE), we converted them to age-based matrices. The stage-to-age transformation was based on life table projections of the stage matrix (Jones et al ., 2022). We constructed Leslie matrices using survival vectors (px) and fertility vectors (mx) for ages within 90% of the starting population size based on the survivorship curve (lx).…”

Section: Methods: Keyfitz Entropymentioning

confidence: 99%

A note on discretising Keyfitz entropy

Vries

Bernard

Salguero‐Gómez

2022

Preprint

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Keyfitz entropy is a widely used metric to quantify the shape of survivorship of populations, from plants, to animals, and microbes. Keyfitz entropy values < 1 correspond to life histories with an increasing mortality rate with age (i.e., actuarial senescence), whereas values > 1 correspond to species with a decreasing mortality rate with age (negative senescence), and a Keyfitz entropy of exactly 1 corresponds to a constant mortality rate with age. Keyfitz entropy was originally defined using a continuous-time model, and has since been discretised to facilitate its calculation from discrete-time demographic data. In this short note, we show that the previously used discretisation of the continuous-time metric does not preserve the relationship with increasing, decreasing, or constant mortality rates. To resolve this discrepancy, we propose a new discrete-time formula for Keyfitz entropy for age-classified life histories. We show that this new method of discretisation preserves the relationship with increasing, decreasing, or constant mortality rates. We analyse the relationship between the original and the new discretisation, and we find that the existing metric tends to underestimate Keyfitz entropy for both short-lived species and long-lived species, thereby introducing a consistent bias. To conclude, to avoid biases when classifying life histories as (non-)senescent, we suggest researchers use either the new metric proposed here, or one of the many previously suggested survivorship shape metrics applicable to discrete-time demographic data such as Gini coefficient or Hayley's median.

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Section: Methods: Keyfitz Entropymentioning

confidence: 99%

A note on discretising Keyfitz entropy

Vries

Bernard

Salguero‐Gómez

2022

Preprint

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“…[87]). Many tools are available to analytically obtain life-history traits from these types of structured demographic data, even if the original publications did not report such traits [88,89]. Age-or stage-structured models are best suited for easily observable components of populations, in species lacking complex interactions among individuals of either sex.…”

Section: Future Stepsmentioning

confidence: 99%

A sex skew in life-history research: the problem of missing males

et al. 2022

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Life-history strategies are diverse. While understanding this diversity is a fundamental aim of evolutionary biology and biodemography, life-history data for some traits—in particular, age-dependent reproductive investment—are biased towards females. While other authors have highlighted this sex skew, the general scale of this bias has not been quantified and its impact on our understanding of evolutionary ecology has not been discussed. This review summarizes why the sexes can evolve different life-history strategies. The scale of the sex skew is then discussed and its magnitude compared between taxonomic groups, laboratory and field studies, and through time. We discuss the consequences of this sex skew for evolutionary and ecological research. In particular, this sex bias means that we cannot test some core evolutionary theory. Additionally, this skew could obscure or drive trends in data and hinder our ability to develop effective conservation strategies. We finally highlight some ways through which this skew could be addressed to help us better understand broad patterns in life-history strategies.

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“…Compagnoni et al 2021b). For example, one could use IPMs from PADRINO and matrix population models from COMPADRE and COMADRE to create life tables (Jones et al 2021), which could then be combined with life tables from DATLife for further analysis ( e.g. Jones et al 2014).…”

Section: Opportunities and Future Directionsmentioning

confidence: 99%

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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Rcompadre and Rage—Two R packages to facilitate the use of the COMPADRE and COMADRE databases and calculation of life‐history traits from matrix population models

Cited by 31 publications

References 50 publications

A note on discretising Keyfitz entropy

A note on discretising Keyfitz entropy

A sex skew in life-history research: the problem of missing males

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

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