This repository contains data and code for a manuscript by Eric Scott and Emilio Bruna written for submission to The American Naturalist tentatively titled: Context-dependent consequences of including lagged effects in demographic models.

The idea here is to investigate the consequences of including lagged effects (such as those modeled by distributed lag non-linear models) into population models (integral projection models, IPMs). We’ll compare population growth rates (lambda) with deterministic, stochastic (matrix shuffling), and lagged effects IPMs.

The most recent draft of the manuscript is available here.

Notes for collaborators

Roadmap

Before June 3:

• Finish building all IPMs
• Document functions and _targets.R
• Calculate bootstrapped 95% CIs around all lambdas
• Finish draft of methods section
• Draft tables & figures
• Archive draft of code in Zenodo

Future:

• Finish writing and revising
• Submit

Reproducibility

This research compendium has been developed using the statistical programming language R. To work with the compendium, you will need installed on your computer the R software itself and RStudio Desktop.

You can download the compendium as a zip file from from this URL: master.zip.

To run the compendium and reproduce all outputs:

• Open the project in RStudio by double-clicking the .Rproj file.
• This project uses renv for package management, which should be installed automatically when opening the project. Run renv::restore() to install all required packages.
• This project uses targets for workflow management. The functions used in the project are defined in R/ and the steps are defined in _targets.R and summarized in the schematic below. Run targets::tar_make() or targets::tar_make_clustermq() from the R console to run all code and produce all outputs.

graph LR
  subgraph Legend
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    x0a52b03877696646([""Outdated""]):::outdated --- x5b3426b4c7fa7dbc([""Started""]):::started
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    xbf4603d6c2c2ad6b([""Stem""]):::none --- x70a5fa6bea6f298d[""Pattern""]:::none
  end
  subgraph Graph
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    x2d3035f88bd10bcd(["proto_ipm_dlnm_cf"]):::uptodate --> xad111d9a6de9849e(["ipm_dlnm_cf"]):::uptodate
    x30536edc201e3ffd(["year_seq"]):::uptodate --> xad111d9a6de9849e(["ipm_dlnm_cf"]):::uptodate
    x0aed4093939f76f0(["data_cf"]):::uptodate --> x4453e3f37167c52e["lambda_bt_det_cf"]:::uptodate
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    xf6ff88faae11e235(["lambda_bt_det_ff_batch"]):::uptodate --> xa66f96c499139e98["lambda_bt_det_ff"]:::uptodate
    x2bc7a3254fee9de8(["vit_other_ff"]):::uptodate --> xa66f96c499139e98["lambda_bt_det_ff"]:::uptodate
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    x29048af5562090f1(["data_ff"]):::uptodate --> x3f5dae9caaa8de3d(["vit_list_det_ff"]):::uptodate
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NOTES:

• The targets lambda_bt_dlnm_ff and lambda_bt_dlnm_cf will take a very long time to run (days), so you may want to comment those out before attempting to reproduce results.
• If you want to make use of HPC, follow instructions here to customize templates and options.