Estimate Trophic Position - Two Source Model

Our Objectives

The purpose of this vignette is to learn how to estimate trophic position of a species using stable isotope data (δ¹³C and δ¹⁵N). We can estimate trophic position using a two source model that is based on equations from Post (2002) and Vander Zaden and Vadeboncoeur (2002).

Trophic Position Model

The equations for a two source model consists of the following:

$$ \alpha = \frac{(\delta^{13}C_c - \delta^{13}C_{b2})}{(\delta^{13}C_{b1}-\delta^{13}C_{b2})} $$

δ¹⁵C_c is the isotope value of the consumer, δ¹⁵C_b1 is the mean isotope value of the first baseline, δ¹⁵C_b2 is the mean isotope value of the second baseline. For aquatic ecosystems, often δ¹⁵C_b1 is from a benthic source and δ¹⁵C_b2 is from a pelagic source. Lastly, α is the proportion of carbon that comes from each source and should be bound by 0 and 1. We will see later that this does not always happen which can be problematic with Heuvel et al., (2024) proposing a new method to correct (i.e., scale) these values. Estimates of α are used in the two source trophic postion equation below.

$$ \text{Trophic Position} = \lambda + \frac{(\delta^{15}N_c - [(\delta^{15}N_{b1} \times \alpha) - (\delta^{15}N_{b1} \times (1 - \alpha))]}{\Delta N} $$

Where λ is the trophic position of the baseline (e.g., 2), δ¹⁵N_c is the δ¹⁵N of the consumer, δ¹⁵N_b1 is the mean δ¹⁵N of the first baseline (e.g., benthic), δ¹⁵N_b2 is the mean δ¹⁵N of the second baseline (e.g., pelagic), α is estimated above, and ΔN is the trophic enrichment factor (e.g., 3.4).

There is a variation of this model that uses a mixing model to consider different trophic position for each baseline (λ). The equation replaces λ with the following:

λ = (λ₁ × α) − (λ₂ × (1 − α))

Where λ₁ is the trophic level of the first baseline (e.g., 2), λ₂ is the trophic level of the second baseline (e.g., 2.5), and α is from above. Only use this replacement equation for λ if you have baselines from two different trophic levels.

Bayesian model

To use these model with a Bayesian framework, we need to rearrange the equation for α to the following:

δ¹³C_c = α × (δ¹³C_b1 − δ¹³C_b2) + δ¹³C_b2

Estimates of α are then used in the rearranged equation for trophic position below.

δ¹⁵N_c = ΔN × (Trophic Position − λ) + δ¹⁵N_b1 × α + δ¹⁵N_b2 × (1 − α)

The function two_source_model() uses both of these rearranged equation. If using baselines from two different trophic levels, you can set the argument lambda to 2 to replace λ (l1) with the mixing model for λ above.

Vignette structure

First we need to organize the data prior to running the model. To do this work we will use {dplyr} and {tidyr} but we could also use {data.table}.

When running the model we will use {trps} and {brms}.

Once we have run the model we will use {bayesplot} to assess models and then extract posterior draws using {tidybayes}. Posterior distributions will be plotted using {ggplot2} and {ggdist} with colours provided by {viridis}.

Load packages

First we load all the packages needed to carry out the analysis.

{
library(bayesplot)
library(brms)
library(dplyr)
library(ggplot2)
library(ggdist)
library(grid)
library(tidybayes)
library(tidyr)
library(trps)
library(viridis)
}

Assess data

In {trps} we have several data sets, they include stable isotope data (δ¹³C and δ¹⁵N) for a consumer, lake trout (Salvelinus namaycush), a benthic baseline, amphipods, and a pelagic baseline, dreissenids, for an ecoregion in Lake Ontario.

Consumer data

We check out each data set with the first being the consumer.

consumer_iso
#> # A tibble: 30 × 4
#>    common_name ecoregion  d13c  d15n
#>    <fct>       <fct>     <dbl> <dbl>
#>  1 Lake Trout  Embayment -22.9  15.9
#>  2 Lake Trout  Embayment -22.5  16.2
#>  3 Lake Trout  Embayment -22.8  17.0
#>  4 Lake Trout  Embayment -22.3  16.6
#>  5 Lake Trout  Embayment -22.5  16.6
#>  6 Lake Trout  Embayment -22.3  16.6
#>  7 Lake Trout  Embayment -22.3  16.6
#>  8 Lake Trout  Embayment -22.5  16.2
#>  9 Lake Trout  Embayment -22.9  16.4
#> 10 Lake Trout  Embayment -22.3  16.3
#> # ℹ 20 more rows

We can see that this data set contains the common_name of the consumer, the ecoregion samples were collected from, and δ¹³C (d13c) and δ¹⁵N (d15n).

Baseline data

Next we check out the benthic baseline data set.

baseline_1_iso
#> # A tibble: 14 × 5
#>    common_name ecoregion d13c_b1 d15n_b1    id
#>    <fct>       <fct>       <dbl>   <dbl> <int>
#>  1 Amphipoda   Embayment   -26.2    8.44     1
#>  2 Amphipoda   Embayment   -26.6    8.77     2
#>  3 Amphipoda   Embayment   -26.0    8.05     3
#>  4 Amphipoda   Embayment   -22.1   13.6      4
#>  5 Amphipoda   Embayment   -24.3    6.99     5
#>  6 Amphipoda   Embayment   -22.1    7.95     6
#>  7 Amphipoda   Embayment   -24.7    7.37     7
#>  8 Amphipoda   Embayment   -26.6    6.93     8
#>  9 Amphipoda   Embayment   -24.6    6.97     9
#> 10 Amphipoda   Embayment   -22.1    7.95    10
#> 11 Amphipoda   Embayment   -24.7    7.37    11
#> 12 Amphipoda   Embayment   -22.1    7.95    12
#> 13 Amphipoda   Embayment   -24.7    7.37    13
#> 14 Amphipoda   Embayment   -26.9   10.2     14

We can see that this data set contains the common_name of the baseline, the ecoregion samples were collected from, and δ¹³C (d13c_b1) and δ¹⁵N (d15n_b1).

Next we check out the pelagic baseline data set.

baseline_2_iso
#> # A tibble: 12 × 4
#>    common_name ecoregion d13c_b2 d15n_b2
#>    <fct>       <fct>       <dbl>   <dbl>
#>  1 Dreissenids Embayment   -23.4    7.81
#>  2 Dreissenids Embayment   -22.9    7.61
#>  3 Dreissenids Embayment   -22.7    7.32
#>  4 Dreissenids Embayment   -23.4    7.81
#>  5 Dreissenids Embayment   -22.9    7.61
#>  6 Dreissenids Embayment   -22.7    7.32
#>  7 Dreissenids Embayment   -23.4    7.81
#>  8 Dreissenids Embayment   -22.9    7.61
#>  9 Dreissenids Embayment   -22.7    7.32
#> 10 Dreissenids Embayment   -26.9   10.2 
#> 11 Dreissenids Embayment   -23.5    7.68
#> 12 Dreissenids Embayment   -23.7    7.64

We can see that this data set contains the common_name of the baseline, the ecoregion samples were collected from, and δ¹³C (d13c_b2) and δ¹⁵N (d15n_b2).

Organizing data

Now that we understand the data we need to combine both data sets to estimate trophic position for our consumer.

To do this we first need to make an id column in each data set, which will allow us to join them together. We first arrange() the data by ecoregion and common_name. Next we group_by() the same variables, and add id for each grouping using row_number(). Always ungroup() the data.frame after using group_by(). Lastly, we use dplyr::select() to rearrange the order of the columns.

Consumer data

Let’s first add id to consumer_iso data frame.

consumer_iso_2 <- consumer_iso %>% 
  arrange(ecoregion, common_name) %>% 
  group_by(ecoregion, common_name) %>% 
  mutate(
    id = row_number()
  ) %>% 
  ungroup() %>% 
  dplyr::select(id, common_name:d15n)

You will notice that I have renamed this object to consumer_iso_2 this is because we are modifying consumer_iso and should make a new object. I have continued with the same renaming nomenclature for objects below.

Baseline 1 data

Next let’s add id to baseline_1_iso data frame. For joining purposes we are going to drop common_name from this data frame.

baseline_1_iso_2 <- baseline_1_iso %>% 
  arrange(ecoregion, common_name) %>% 
  group_by(ecoregion, common_name) %>% 
  mutate(
    id = row_number()
  ) %>% 
  ungroup() %>% 
  dplyr::select(id, ecoregion:d15n_b1)

Baseline 2 data

Next let’s add id to baseline_2_iso data frame. For joining purposes we are going to drop common_name from this data frame.

baseline_2_iso_2 <- baseline_2_iso %>% 
  arrange(ecoregion, common_name) %>% 
  group_by(ecoregion, common_name) %>% 
  mutate(
    id = row_number()
  ) %>% 
  ungroup() %>% 
  dplyr::select(id, ecoregion:d15n_b2)

Joining isotope data

Now that we have the consumer and baseline data sets in a consistent format we can join them by "id" and "ecoregion" using left_join() from {dplyr}.

combined_iso_ts <- consumer_iso_2 %>% 
  left_join(baseline_1_iso_2, by = c("id", "ecoregion")) %>% 
  left_join(baseline_2_iso_2, by = c("id", "ecoregion"))

We can see that we have successfully combined our consumer and baseline data. We need to do one last thing prior to analyzing the data, and that is calculate the mean δ¹³C (c1 and c2) and δ¹⁵N (n1 and n2) for the baselines and add in the constant λ (l1) to our data frame. We do this by using groub_by() to group the data by our two groups, then using mutate() and mean() to calculate the mean values.

Important note, to run the model successfully, columns need to be named d13c, c1, c2, d15n, n1, n2, and l1 with l2 needed if using two λs.

combined_iso_ts_1 <- combined_iso_ts %>% 
  group_by(ecoregion, common_name) %>% 
  mutate(
    c1 = mean(d13c_b1, na.rm = TRUE),
    n1 = mean(d15n_b1, na.rm = TRUE),
    c2 = mean(d13c_b2, na.rm = TRUE),
    n2 = mean(d15n_b2, na.rm = TRUE),
    l1 = 2
  ) %>% 
  ungroup()

Let’s view our combined data.

combined_iso_ts_1
#> # A tibble: 30 × 14
#>       id common_name ecoregion  d13c  d15n d13c_b1 d15n_b1 d13c_b2 d15n_b2    c1
#>    <int> <fct>       <fct>     <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>
#>  1     1 Lake Trout  Embayment -22.9  15.9   -26.2    8.44   -23.4    7.81 -24.6
#>  2     2 Lake Trout  Embayment -22.5  16.2   -26.6    8.77   -22.9    7.61 -24.6
#>  3     3 Lake Trout  Embayment -22.8  17.0   -26.0    8.05   -22.7    7.32 -24.6
#>  4     4 Lake Trout  Embayment -22.3  16.6   -22.1   13.6    -23.4    7.81 -24.6
#>  5     5 Lake Trout  Embayment -22.5  16.6   -24.3    6.99   -22.9    7.61 -24.6
#>  6     6 Lake Trout  Embayment -22.3  16.6   -22.1    7.95   -22.7    7.32 -24.6
#>  7     7 Lake Trout  Embayment -22.3  16.6   -24.7    7.37   -23.4    7.81 -24.6
#>  8     8 Lake Trout  Embayment -22.5  16.2   -26.6    6.93   -22.9    7.61 -24.6
#>  9     9 Lake Trout  Embayment -22.9  16.4   -24.6    6.97   -22.7    7.32 -24.6
#> 10    10 Lake Trout  Embayment -22.3  16.3   -22.1    7.95   -26.9   10.2  -24.6
#> # ℹ 20 more rows
#> # ℹ 4 more variables: n1 <dbl>, c2 <dbl>, n2 <dbl>, l1 <dbl>

It is now ready to be analyzed!

Bayesian Analysis

We can now estimate trophic position for lake trout in an ecoregion of Lake Ontario.

There are a few things to know about running a Bayesian analysis, I suggest reading these resources:

Priors

Bayesian analyses rely on supplying uninformed or informed prior distributions for each parameter (coefficient; predictor) in the model. The default informed priors for a two source model are the following, α is bound by 0 and 1 and assumes an unformed beta distribution (α = 1 and β = 1), ΔN assumes a normal distribution (dn; μ = 3.4; σ = 0.25), trophic position assumes a uniform distribution (lower bound = 2 and upper bound = 10), σ assumes a uniform distribution (lower bound = 0 and upper bound = 10), and if informed priors are desired for δ¹³C_b1 and δ¹³C_b2 (c1 and c2; μ = −21 and −26; σ = 1), and δ¹⁵N_b1 and δ¹⁵N_b2 (n1 and n2; μ = 8 and 9.5; σ = 1), we can set the argument bp to TRUE in all two_source_ functions.

You can change these default priors using two_source_priors_params(), however, I would suggest becoming familiar with Bayesian analyses, your study species, and system prior to adjusting these values.

Model convergence

It is important to always run the model with at least 2 chains. If the model does not converge you can try to increase the following:

The amount of samples that are burned-in (discarded; in brm() this can be controlled by the argument warmup)
The number of iterative samples retained (in brm() this can be controlled by the argument iter).
The number of samples drawn (in brm() this is controlled by the argument thin).
The adapt_delta value using control = list(adapt_delta = 0.95).

When assessing the model we want R̂ to be 1 or within 0.05 of 1, which indicates that the variance among and within chains are equal (see {rstan} documentation on R̂), a high value for effective sample size (ESS), trace plots to look “grassy” or “caterpillar like,” and posterior distributions to look relatively normal.

Estimating trophic position

We will use functions from {trps} that drop into a {brms} model. These functions are two_source_model() which provides brm() the formula structure needed to run a one source model. Next brm() needs the structure of the priors which is supplied to the prior argument using two_source_priors(). Lastly, values for these priors are supplied through the stanvars argument using two_source_priors_params(). You can adjust the mean (μ), variance (σ), or upper and lower bounds (lb and ub) for each prior of the model using two_source_priors_params(), however, only adjust priors if you have a good grasp of Bayesian frameworks and your study system and species.

Model

Let’s run the model!

m <- brm(
  formula = two_source_model(),
  prior = two_source_priors(),
  stanvars = two_source_priors_params(),
  data = combined_iso_ts_1,
  family = gaussian(),
  chains = 2,
  iter = 4000,
  warmup = 1000,
  cores = 4,
  seed = 4,
  control = list(adapt_delta = 0.95)
)
#> Compiling Stan program...
#> Start sampling

Model output

Let’s view the summary of the model.

m
#>  Family: MV(gaussian, gaussian) 
#>   Links: mu = identity; sigma = identity
#>          mu = identity; sigma = identity 
#> Formula: d13c ~ alpha * (c1 - c2) + c2 
#>          alpha ~ 1
#>          d15n ~ dn * (tp - l1) + n1 * alpha + n2 * (1 - alpha) 
#>          alpha ~ 1
#>          tp ~ 1
#>          dn ~ 1
#>    Data: combined_iso_ts_1 (Number of observations: 30) 
#>   Draws: 2 chains, each with iter = 4000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 6000
#> 
#> Regression Coefficients:
#>                      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
#> d13c_alpha_Intercept     0.05      0.05     0.00     0.18 1.00     2805
#> d15n_alpha_Intercept     0.49      0.29     0.02     0.97 1.00     4393
#> d15n_tp_Intercept        4.57      0.21     4.20     5.02 1.00     2486
#> d15n_dn_Intercept        3.38      0.25     2.89     3.85 1.00     2562
#>                      Tail_ESS
#> d13c_alpha_Intercept     2113
#> d15n_alpha_Intercept     3571
#> d15n_tp_Intercept        2928
#> d15n_dn_Intercept        2720
#> 
#> Further Distributional Parameters:
#>            Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma_d13c     1.40      0.20     1.07     1.84 1.00     4034     3944
#> sigma_d15n     0.66      0.11     0.49     0.93 1.00     3809     3066
#> 
#> Residual Correlations: 
#>                   Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> rescor(d13c,d15n)     0.20      0.31    -0.44     0.71 1.00     2768     2882
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).

We can see that R̂ is 1 meaning that variance among and within chains are equal (see {rstan} documentation on R̂) and that ESS is quite large. Overall, this means the model is converging and fitting accordingly.

Trace plots

Let’s view trace plots and posterior distributions for the model.

plot(m)

We can see that the trace plots look “grassy” meaning the model is converging!

Predictive posterior check

δ¹³C

We can check how well the model is predicting the δ¹³C of the consumer using pp_check() from {bayesplot}.

pp_check(m, resp = "d13c")
#> Using 10 posterior draws for ppc type 'dens_overlay' by default.

We can see that posteriors draws (y_rep; light lines) are not effectively modeling δ¹⁵N of the consumer (y; dark line). We can correct (i.e., scale) these values using another model two_source_model_ar() that uses an equation in Heuvel et al., (2024) that corrects (i.e., scales) α providing better and more meaningful estimates. I will demostrate how to use this model in another vignette.

δ¹⁵N

Next We can check how well the model is predicting the δ¹⁵N of the consumer using pp_check() from {bayesplot}.

pp_check(m, resp = "d15n")
#> Using 10 posterior draws for ppc type 'dens_overlay' by default.

We can see that posteriors draws (y_rep; light lines) are effectively modeling δ¹⁵N of the consumer (y; dark line).

Posterior draws

Let’s again look at the summary output from the model.

m
#>  Family: MV(gaussian, gaussian) 
#>   Links: mu = identity; sigma = identity
#>          mu = identity; sigma = identity 
#> Formula: d13c ~ alpha * (c1 - c2) + c2 
#>          alpha ~ 1
#>          d15n ~ dn * (tp - l1) + n1 * alpha + n2 * (1 - alpha) 
#>          alpha ~ 1
#>          tp ~ 1
#>          dn ~ 1
#>    Data: combined_iso_ts_1 (Number of observations: 30) 
#>   Draws: 2 chains, each with iter = 4000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 6000
#> 
#> Regression Coefficients:
#>                      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
#> d13c_alpha_Intercept     0.05      0.05     0.00     0.18 1.00     2805
#> d15n_alpha_Intercept     0.49      0.29     0.02     0.97 1.00     4393
#> d15n_tp_Intercept        4.57      0.21     4.20     5.02 1.00     2486
#> d15n_dn_Intercept        3.38      0.25     2.89     3.85 1.00     2562
#>                      Tail_ESS
#> d13c_alpha_Intercept     2113
#> d15n_alpha_Intercept     3571
#> d15n_tp_Intercept        2928
#> d15n_dn_Intercept        2720
#> 
#> Further Distributional Parameters:
#>            Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma_d13c     1.40      0.20     1.07     1.84 1.00     4034     3944
#> sigma_d15n     0.66      0.11     0.49     0.93 1.00     3809     3066
#> 
#> Residual Correlations: 
#>                   Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> rescor(d13c,d15n)     0.20      0.31    -0.44     0.71 1.00     2768     2882
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).

We can see that α is estimated to be 0.05 with l-95% CI of 0.00 and u-95% CI of 0.19. These values do not make a lot sense as this indicates the lake trout are heavily using benthic resource which we know from previous work is not true. In another vignette I’ll demonstrate how to use a model that corrects or scales α appropriately using an equation in Heuvel et al. (2024).

Moving down to the trophic position model we can see ΔN is estimated to be 3.38 with l-95% CI of 2.89, and u-95% CI of 3.85. If we move down to trophic position (tp) we see trophic position is estimated to be 4.57 with l-95% CI of 4.20, and u-95% CI of 5.02.

Extract posterior draws

We use functions from {tidybayes} to do this work. First we look at the the names of the variables we want to extract using get_variables().

get_variables(m)
#>  [1] "b_d13c_alpha_Intercept" "b_d15n_alpha_Intercept" "b_d15n_tp_Intercept"   
#>  [4] "b_d15n_dn_Intercept"    "sigma_d13c"             "sigma_d15n"            
#>  [7] "rescor__d13c__d15n"     "lprior"                 "lp__"                  
#> [10] "accept_stat__"          "stepsize__"             "treedepth__"           
#> [13] "n_leapfrog__"           "divergent__"            "energy__"

You will notice that "b_d13c_alpha_Intercept" and "b_d15n_tp_Intercept" are the names of the variable that we are wanting to extract. We extract posterior draws using gather_draws(), and rename "b_d15n_tp_Intercept" to tp and "b_d13c_alpha_Intercept" to alpha.

post_draws <- m %>% 
  gather_draws(b_d13c_alpha_Intercept, b_d15n_tp_Intercept) %>% 
  mutate(
    ecoregion = "Embayment",
    common_name = "Lake Trout",
    .variable = case_when(
      .variable %in% "b_d15n_tp_Intercept" ~ "tp",
      .variable %in% "b_d13c_alpha_Intercept" ~ "alpha"
    )
  ) %>% 
  dplyr::select(common_name, ecoregion, .chain:.value)

Let’s view the post_draws

post_draws
#> # A tibble: 12,000 × 7
#> # Groups:   .variable [2]
#>    common_name ecoregion .chain .iteration .draw .variable   .value
#>    <chr>       <chr>      <int>      <int> <int> <chr>        <dbl>
#>  1 Lake Trout  Embayment      1          1     1 alpha     0.00464 
#>  2 Lake Trout  Embayment      1          2     2 alpha     0.0472  
#>  3 Lake Trout  Embayment      1          3     3 alpha     0.0153  
#>  4 Lake Trout  Embayment      1          4     4 alpha     0.0236  
#>  5 Lake Trout  Embayment      1          5     5 alpha     0.0212  
#>  6 Lake Trout  Embayment      1          6     6 alpha     0.00227 
#>  7 Lake Trout  Embayment      1          7     7 alpha     0.00194 
#>  8 Lake Trout  Embayment      1          8     8 alpha     0.0634  
#>  9 Lake Trout  Embayment      1          9     9 alpha     0.000480
#> 10 Lake Trout  Embayment      1         10    10 alpha     0.000134
#> # ℹ 11,990 more rows

We can see that this consists of seven variables:

ecoregion
common_name
.chain
.iteration (number of sample after burn-in)
.draw (number of samples from iter)
.variable (this will have different variables depending on what is supplied to gather_draws())
.value (estimated value)

Extracting credible intervals

Considering we are likely using this information for a paper or presentation, it is nice to be able to report the median and credible intervals (e.g., equal-tailed intervals; ETI). We can extract and export these values using gather_draws() and median_qi from {tidybayes}.

We rename d15n_tp_Intercept to tp and b_d13c_alpha_Intercept to alpha, add the grouping columns, round all columns that are numeric to two decimal points using mutate_if(), and rearrange the order of the columns using dplyr::select().

medians_ci <- m %>%
        gather_draws(b_d13c_alpha_Intercept, 
                     b_d15n_tp_Intercept) %>%
        median_qi() %>% 
  mutate(
    ecoregion = "Embayment", 
    common_name = "Lake Trout", 
    .variable = case_when(
      .variable %in% "b_d15n_tp_Intercept" ~ "tp",
      .variable %in% "b_d13c_alpha_Intercept" ~ "alpha"
    )
  ) %>% 
  mutate_if(is.numeric, round, digits = 2)

Let’s view the output.

medians_ci
#> # A tibble: 2 × 9
#>   .variable .value .lower .upper .width .point .interval ecoregion common_name
#>   <chr>      <dbl>  <dbl>  <dbl>  <dbl> <chr>  <chr>     <chr>     <chr>      
#> 1 alpha       0.03    0     0.18   0.95 median qi        Embayment Lake Trout 
#> 2 tp          4.56    4.2   5.02   0.95 median qi        Embayment Lake Trout

I like to use {openxlsx} to export these values into a table that I can use for presentations and papers. For the vignette I am not going to demonstrate how to do this but please check out {openxlsx}.

Plotting posterior distributions – single species or group

Now that we have our posterior draws extracted we can plot them. To analyze a single species or group, I like using density plots.

Density plot

For this example we first plot the density for posterior draws using geom_density().

ggplot(data = post_draws, aes(x = .value)) + 
  geom_density() + 
  facet_wrap(~ .variable, scale = "free") +
  theme_bw(base_size = 15) +
  theme(
    panel.grid = element_blank(), 
    strip.background = element_blank(),
  ) + 
  labs(
    x = "P(Estimate | X)", 
    y = "Density"
  )

Point interval

Next we plot it as a point interval plot using stat_pointinterval().

ggplot(data = post_draws, aes(y = .value, 
                              x = common_name)) + 
  stat_pointinterval() + 
   facet_wrap(~ .variable, scale = "free") +
  theme_bw(base_size = 15) +
  theme(
    panel.grid = element_blank(), 
    strip.background = element_blank(),
  ) + 
  labs(
    x = "P(Estimate | X)", 
    y = "Density"
  )

Congratulations we have estimated the trophic position for Lake Trout!

Again, you will notice estimates of α do not really make sense which we can correct (i.e., scale) using another model two_source_model_ar() that uses an equation in Heuvel et al. (2024).

You can use iterative process to produce estimates of trophic position for more than one group (e.g., comparing trophic position among species or in this case different ecoregions) using iteratstive processes that are demonstrated in estimate trophic position - one source - multiple groups.

Estimate Trophic Position - Two Source Model

Our Objectives

Trophic Position Model

Bayesian model

Vignette structure

Load packages

Assess data

Consumer data

Baseline data

Organizing data

Consumer data

Baseline 1 data

Baseline 2 data

Joining isotope data

Bayesian Analysis

Priors

Model convergence

Estimating trophic position

Model

Model output

Trace plots

Predictive posterior check

δ13C

δ15N

Posterior draws

Extract posterior draws

Extracting credible intervals

Plotting posterior distributions – single species or group

Density plot

Point interval

δ¹³C

δ¹⁵N