Density dependent model classes are now implemented. This vignette will get more details shortly. For now, see the example below:

This example will probably look a lot like the ones from other vignettes. It assumes that density dependence is modeled as a fixed effect in survival and recruit production models, and assumes there is no density dependence in growth or probability of reproducing models. The survival (\(s(z, N, \theta)\)/`s_yr`

), growth (\(g(z',z, \theta)\)/ `g_yr`

), and number of recruit models (\(f_s(z, N, \theta)\)/`f_s_yr`

) have year-specific intercepts as well.

The mathematical form for the IPM is below:

\(n(z', t+1) = K(z', z, N, \theta)n(z, t)dz\)

\(N = \int_L^Un(z,t)dz\)

\(K(z', z, N) = P(z', z, N, \theta) + F(z', z, N, \theta)\)

Here, \(N\) represents the total population size, and \(\theta\) denotes the year specific intercepts. The kernel values fluctuate as a function of these at each iteration of the model.

The \(P(z', z, N)\) kernel is comprised of a density independent function for growth (Eq 6-7) and a density dependent function for survival (Eq 5):

\(P(z', z, N) = s(z, N) * g(z', z)\)

\(Logit(s(z, N)) = \alpha_s + \alpha_s^{yr} + \beta_s^z * z + \beta_s^{N} * N\)

\(g(z', z, \theta) \sim Norm(\mu_g(z, \theta), \sigma_g)\)

\(\mu_g(z, \theta) = \alpha_g + \alpha_g^{yr} + \beta_g^z * z\)

The \(F(z',z, N, \theta)\) kernel is comprised of a density independent function for recruit size (Eq 10) and probability of reproducing (Eq 9), and a density dependent function for number of recruits produced by parents (Eq 11).

\(F(z', z, N, \theta) = f_r(z) * f_s(z, N, \theta) + f_d(z')\)

\(Logit(f_r(z)) = \alpha_{f_r} + \beta_{f_r}^z * z\)

\(f_d(z') \sim Norm(\mu_{f_d}, \sigma_{f_d})\)

\(Log(f_s(z, N, \theta)) = \alpha_{f_s} + \alpha_{f_s}^{yr} + \beta_{f_s}^z * z + \beta_{f_s}^N * N\)

We’ll simulate a 50 year time series using hypothetical parameter values. The fixed parameter values are created as with a density independent model. The difference is that we now have two more parameters: `s_dd`

, and `f_s_dd`

. These are the coefficients that correspond to \(\beta_s^N\) and \(\beta_{f_s}^N\), respectively. The chunk below initializes the data list object, which we name `params`

.

```
library(ipmr)
= list(
data_list s_int = 1.03,
s_slope = 2.2,
s_dd = -0.7,
g_int = 8,
g_slope = 0.92,
sd_g = 0.9,
f_r_int = 0.09,
f_r_slope = 0.05,
f_s_int = 0.1,
f_s_slope = 0.005,
f_s_dd = -0.03,
mu_fd = 9,
sd_fd = 2
)
# Now, simulate some random intercepts for growth, survival, and offspring production
<- rnorm(5, 0, 0.3)
g_r_int <- rnorm(5, 0, 0.7)
s_r_int <- rnorm(5, 0, 0.2)
f_s_r_int
<- paste("r_", 1:5, sep = "")
nms
names(g_r_int) <- paste("g_", nms, sep = "")
names(s_r_int) <- paste("s_", nms, sep = "")
names(f_s_r_int) <- paste("f_s_", nms, sep = "")
<- c(data_list, g_r_int, s_r_int, f_s_r_int) params
```

Next, we initialize the model using `init_ipm`

. The difference is that the second argument is now changed to `"dd"`

to denote that this is a density dependent model.

`<- init_ipm(sim_gen = "simple", di_dd = "dd", det_stoch = "det") dd_ipm `

Once we’ve done that, we’re ready to begin specifying the kernel forms. One previously not mentioned aspect of `define_pop_state()`

is that, in addition to defining initial conditions, 2 additional helper variables are generated: `n_stateVariable_t`

and `n_stateVariable_t_1`

. These can be used to reference the population states in vital rate and/or kernel expressions.

These will look very similar to the ones we specified for density-independent models, except that we now include the term `s_dd * sum(n_size_t) * d_size`

in the survival expression. `sum(n_size_t) * d_size`

is the syntax `ipmr`

uses to denote total population size. Further down, there is an example of how to use subsets of the trait distribution.

```
<- define_kernel(
dd_ipm proto_ipm = dd_ipm,
name = "P_yr",
formula = s_yr * g_yr,
family = "CC",
s_yr = plogis(s_int + s_r_yr + s_slope * size_1 + s_dd * sum(n_size_t) * d_size),
g_yr = dnorm(size_2, g_mu_yr, sd_g),
g_mu_yr = g_int + g_r_yr + g_slope * size_1,
data_list = params,
states = list(c("size")),
has_hier_effs = TRUE,
levels_hier_effs = list(yr = 1:5),
evict_cor = TRUE,
evict_fun = truncated_distributions("norm", "g_yr")
)
```

Other than the inclusion of the density dependent term in the survival expression, this should look quite similar to the density-independent kernel-resampled models from the Introduction vignette. We are now ready to continue defining the \(F(z',z,N,\theta)\) kernel.

```
<- define_kernel(
dd_ipm proto_ipm = dd_ipm,
name = "F_yr",
formula = f_r * f_s_yr * f_d,
family = "CC",
f_r = plogis(f_r_int + f_r_slope * size_1),
f_s_yr = exp(f_s_int + f_s_r_yr + f_s_slope * size_1 + f_s_dd * sum(n_size_t) * d_size),
f_d = dnorm(size_2, mu_fd, sd_fd),
data_list = params,
states = list(c("size")),
has_hier_effs = TRUE,
levels_hier_effs = list(yr = 1:5),
evict_cor = TRUE,
evict_fun = truncated_distributions("norm", "f_d")
)
```

Again, we’ve add the `f_s_dd * sum(n_size_t) * d_size`

to the expression for `f_s_yr`

, but otherwise, not much is different from how we’ve defined density independent models. The rest of the model definition process is unchanged.

```
<- dd_ipm %>%
dd_ipm define_impl(
make_impl_args_list(
kernel_names = c("P_yr", "F_yr"),
int_rule = rep("midpoint", 2),
state_start = rep("size", 2),
state_end = rep("size", 2)
)%>%
) define_domains(
size = c(0, 50, 200)
%>%
) define_pop_state(
n_size = runif(200)
%>%
) make_ipm(
iterate = TRUE,
iterations = 50,
kernel_seq = sample(1:5, 50, replace = TRUE)
)
```

`lambda`

methods are defined for all density-dependent models as well. It is fairly straightforward to plot population sizes for these models by extracting the column sums of the arrays in `pop_state`

.

```
<- lambda(dd_ipm, type_lambda = "all")
time_step_lams <- lambda(dd_ipm, type_lambda = "stochastic", burn_in = 0.15)
stoch_lam
<- colSums(dd_ipm$pop_state$n_size)
pop_sizes
plot(pop_sizes, type = "l")
```