diff --git a/R/make_lambda_table.R b/R/make_lambda_table.R index 8c56e96..d66a803 100644 --- a/R/make_lambda_table.R +++ b/R/make_lambda_table.R @@ -13,7 +13,9 @@ #' det_ff = ipm_det_ff, #' det_cf = ipm_det_cf, #' stoch_ff = ipm_stoch_ff, -#' stoch_cf = ipm_stoch_cf +#' stoch_cf = ipm_stoch_cf, +#' dlnm_ff = ipm_dlnm_ff, +#' dlnm_cf = ipm_dlnm_cf #' ) #' #' #bt_list should look like this: @@ -21,11 +23,14 @@ #' det_ff = lambda_bt_det_ff, #' det_cf = lambda_bt_det_cf, #' stoch_ff = lambda_bt_stoch_ff, -#' stoch_cf = lambda_bt_stoch_cf -#' # dlnm_ff = lambda_bt_dlnm_ff, -#' # dlnm_cf = lambda_bt_dlnm_cf +#' stoch_cf = lambda_bt_stoch_cf, +#' dlnm_ff = lambda_bt_dlnm_ff, +#' dlnm_cf = lambda_bt_dlnm_cf #' ) #' +#' #load the above targets with tar_load(starts_with("lambda")) and +#' tar_load(starts_with("ipm")) +#' #' make_lambda_table(ipm_list, bt_list, alpha = 0.05) make_lambda_table <- function(ipm_list, bt_list, alpha = 0.05) { diff --git a/docs/paper.Rmd b/docs/paper.Rmd index b1495bb..2fe241d 100644 --- a/docs/paper.Rmd +++ b/docs/paper.Rmd @@ -333,18 +333,20 @@ lambda_table %>% c( "det" = "Deterministic", "stoch" = "Stochastic, kernel-resampled", - "DLNM" = "Stochastic, parameter-resampled" + "dlnm" = "Stochastic, DLNM" ) )) %>% - select(IPM = ipm, Habitat = habitat, "$\\lambda$" = lambda) %>% + select(IPM = ipm, habitat, lambda) %>% + pivot_wider(names_from = "habitat", values_from = lambda) %>% + pandoc.table( digits = 5, keep.trailing.zeros = TRUE, - justify = c("left", "left", "right"), + justify = c("left", "right", "right"), full_width = FALSE, - caption = "(\\#tab:lambdas) Population growth rates for continuous forest (CF) and forest fragments (FF) under different kinds of IPMs with bootstrapped, bias-corrected, 95% confidence intervals." + caption = "(\\#tab:lambdas) Population growth rates (lambda) for continuous forest (CF) and forest fragments (FF) under different kinds of IPMs with bootstrapped, bias-corrected, 95% confidence intervals." ) - +#TODO: in caption possibly explain that lambdas are calculated differently for deterministic and stochastic models ``` # Figures @@ -360,6 +362,7 @@ knitr::include_graphics(path, rel_path = FALSE) ```{r pop-states} #| fig.cap = "Relative proportions of plant sizes in the first 250 iterations of the IPM simulations. Stacked area charts (A) show the relative size/stage distribution of plants in continuous forest (CF, top row) and forest fragments (FF, bottom row) in each of the three IPMs (columns). The proportion of each size class in CF and FF for each iteration is shown in B with the first 30 iterations removed to not include transient dynamics. A 1:1 line is plotted in black. Size categories include seedlings (a discrete category in the IPMs), pre-reproductive 1 (log(size) 0–2.5) that have low average survival (< 0.9) and a near 0 probability of flowering, pre-reproductive 2 (log(size) 2.5–4.5) that have a higher average survival probabilty (> 0.8) and a near 0 probability of flowering, reproductive 1 (log(size) 4.5–6) that have a high average survival probability (>0.95) and a lower flowering probability (< 0.25), and reproductive 2 (log(size) 6+) that have a high average survival probability (>0.95) and higher flowering proability (> 0.2)." + knitr::include_graphics(here(fig_pop_states), rel_path = FALSE) ``` diff --git a/docs/paper.html b/docs/paper.html index c09ba24..93f4504 100644 --- a/docs/paper.html +++ b/docs/paper.html @@ -628,7 +628,7 @@

Context-dependent consequences of including lagged effects in demographic models

Eric R. Scott1,✉, and Emilio M. Bruna1,2,3

-

24 June, 2022

+

04 August, 2022

@@ -766,6 +766,7 @@

Acknowledgments

We thank Sam Levin for his help with the ipmr package. Financial support was provided by the U.S. National Science Foundation (awards ____, and ____). +This article is publication no. – – in the BDFFP Technical series. The authors declare no conflicts of interest.

@@ -1012,49 +1013,50 @@

Tables

- - +
Table 2: Population growth rates for continuous forest (CF) and forest fragments (FF) under different kinds of IPMs with bootstrapped, bias-corrected, 95% confidence intervals.
+- - - + - - - - - - - - - - - - - - + + +
Table 2: Population growth rates (lambda) for continuous forest (CF) and forest fragments (FF) under different kinds of IPMs with bootstrapped, bias-corrected, 95% confidence intervals. (continued below)
IPMHabitat\(\lambda\)FF
DeterministicFF 0.9778 (0.9736, 0.9823)
DeterministicCF0.9897 (0.9877, 0.9920)
Stochastic, kernel-resampledFF 0.9787 (0.9735, 0.9835)
Stochastic, kernel-resampledCF0.9913 (0.9892, 0.9939)
dlnmFFStochastic, DLNM 0.9595 (0.9459, 0.9689)
+ +++ + + + + + + + + + - - + + + @@ -1063,13 +1065,13 @@

Tables

Figures

-Lifecycle diagram of *Heliconia acuminata*. Each transition is associated with an equation for a vital rate function. The functions shown on the diagram correspond to those used to construct a general, density-independent, deterministic IPM. The table below shows the equivalent equations for stochastic, kernel-resampled IPMs and stochastic, parameter-resampled IPMs. +Lifecycle diagram of *Heliconia acuminata*. Each transition is associated with an equation for a vital rate function. The functions shown on the diagram correspond to those used to construct a general, density-independent, deterministic IPM. The table below shows the equivalent equations for stochastic, kernel-resampled IPMs and stochastic, parameter-resampled IPMs.

Figure 1: Lifecycle diagram of Heliconia acuminata. Each transition is associated with an equation for a vital rate function. The functions shown on the diagram correspond to those used to construct a general, density-independent, deterministic IPM. The table below shows the equivalent equations for stochastic, kernel-resampled IPMs and stochastic, parameter-resampled IPMs.

-Relative proportions of plant sizes in the first 250 iterations of the IPM simulations. Stacked area charts (A) show the relative size/stage distribution of plants in continuous forest (CF, top row) and forest fragments (FF, bottom row) in each of the three IPMs (columns). The proportion of each size class in CF and FF for each iteration is shown in B with the first 30 iterations removed to not include transient dynamics. A 1:1 line is plotted in black. Size categories include seedlings (a discrete category in the IPMs), pre-reproductive 1 (log(size) 0–2.5) that have low average survival (< 0.9) and a near 0 probability of flowering, pre-reproductive 2 (log(size) 2.5–4.5) that have a higher average survival probabilty (> 0.8) and a near 0 probability of flowering, reproductive 1 (log(size) 4.5–6) that have a high average survival probability (>0.95) and a lower flowering probability (< 0.25), and reproductive 2 (log(size) 6+) that have a high average survival probability (>0.95) and higher flowering proability (> 0.2). +Relative proportions of plant sizes in the first 250 iterations of the IPM simulations. Stacked area charts (A) show the relative size/stage distribution of plants in continuous forest (CF, top row) and forest fragments (FF, bottom row) in each of the three IPMs (columns). The proportion of each size class in CF and FF for each iteration is shown in B with the first 30 iterations removed to not include transient dynamics. A 1:1 line is plotted in black. Size categories include seedlings (a discrete category in the IPMs), pre-reproductive 1 (log(size) 0–2.5) that have low average survival (< 0.9) and a near 0 probability of flowering, pre-reproductive 2 (log(size) 2.5–4.5) that have a higher average survival probabilty (> 0.8) and a near 0 probability of flowering, reproductive 1 (log(size) 4.5–6) that have a high average survival probability (>0.95) and a lower flowering probability (< 0.25), and reproductive 2 (log(size) 6+) that have a high average survival probability (>0.95) and higher flowering proability (> 0.2).

Figure 2: Relative proportions of plant sizes in the first 250 iterations of the IPM simulations. Stacked area charts (A) show the relative size/stage distribution of plants in continuous forest (CF, top row) and forest fragments (FF, bottom row) in each of the three IPMs (columns). The proportion of each size class in CF and FF for each iteration is shown in B with the first 30 iterations removed to not include transient dynamics. A 1:1 line is plotted in black. Size categories include seedlings (a discrete category in the IPMs), pre-reproductive 1 (log(size) 0–2.5) that have low average survival (< 0.9) and a near 0 probability of flowering, pre-reproductive 2 (log(size) 2.5–4.5) that have a higher average survival probabilty (> 0.8) and a near 0 probability of flowering, reproductive 1 (log(size) 4.5–6) that have a high average survival probability (>0.95) and a lower flowering probability (< 0.25), and reproductive 2 (log(size) 6+) that have a high average survival probability (>0.95) and higher flowering proability (> 0.2).

CF
0.9897 (0.9877, 0.9920)
dlnmCF0.9913 (0.9892, 0.9939)
0.9795 (0.9752, 0.9867)