https://github.com/emilio-berti/hattian
https://github.com/emilio-berti/hattian
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/emilio-berti/hattian
- Owner: emilio-berti
- License: other
- Created: 2024-07-31T15:24:49.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-11-08T16:34:12.000Z (7 months ago)
- Last Synced: 2025-01-21T04:28:08.972Z (4 months ago)
- Language: C++
- Size: 87.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# hattian
The goal of hattian is to model energy fluxes worldwide applying the conceptual workflow from Antunes et al. (2024).
## Installation
You can install the development version of hattian from [GitHub](https://github.com/) with:
``` r
devtools::install_github("emilio-berti/hattian")
```
hattian depends on big packages, such as stan and Rcpp, and it takes some time to install. Be patient...
You can install all dependencies before install hattian by running:
```{r install, eval=FALSE}
install.packages(
c(
"loo", "Rcpp", "RcppParallel", "rstan", "rstantools", "BH",
"RcppEigen", "StanHeaders", "rgbif", "RandomForest"
)
)
```
Load the package:
```{r load}
library(hattian)
```
# Datasets included in hattian
Hattian comes with # datasets:
1. `mammals`, the body mass (g) of mammals EltonTraits.
2. `birds`, the body mass (g) of birds from EltonTraits.
3. `amphibians`, the maximum body mass (g) of amphibians from AmphiBIO.
4. `tetraeu`, the metweb of tetrapods of Europe.
From more details type `?` in R, e.g. `?mammals`.
# Foodweb inference models
Hattian contains # models to infer foodwebs:
1. A scaling niche model, from Yeakel et al. (2014).
2. A variable niche width model, from Li et al. (2023).
## Scaling niche model (Yeakel et al., 2014)
This is the simplest model implemented in hattian.
The probability that predator $j$ predates on prey $i$ is given by their body mass ratio:
$$
P(A_{ij} = 1) = \frac{p}{1 + p}
$$
where
$$
p = exp( a_1 + a_2 log_{10}(\frac{m_j}{m_i}) + a_3 (log_{10}(\frac{m_j}{m_i}))^2 )
$$
and $m_i$ and $m_j$ are the body masses if prey and predator, respectively.
This model is coded in stan and is called using `yeakel_stan()`:
```{r yeakel}
# create community
S <- 20
m <- sort(runif(S, 1, 1e2))
fw <- matrix(NA, nrow = S, ncol = S)
# set parameters
a1 <- 1
a2 <- 3
a3 <- -10
# create web accordingly
for (j in seq_along(m)) {
fw[, j] <- exp(a1 + a2 * log10(m[j] / m) + a3 * log10(m[j] / m)^2)
}
fw <- fw / (1 + fw)
# sample probabilities
fw <- rbinom(length(fw), 1, fw)
fw <- matrix(fw, nrow = S, ncol = S)
show_web(fw)
# fit model
fit_yeakel <- yeakel_stan(
fw, m,
warmup = 250, iter = 500, #to speed up vignette
cores = 4, refresh = 250
)
posterior <- rstan::extract(fit_yeakel, c("a1", "a2", "a3"))
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(2, 2), mar = c(4, 4, 2, 2))
for (i in seq_along(posterior)) {
plot(
density(posterior[[i]]),
main = names(posterior)[i],
xlab = "Posterior"
)
}
par(oldpar)
```
## Variable niche width model (Li et al., 2023)
This model is a conceptual variation of the Yeakel (2014) model.
In Yeakel's model, the relative optimal prey size is constant for all predators, with niche width determined by the parameter `a3`.
In Li's model, instead, the optimal prey size ($\mu_j$) and niche width $\sigma_j$ of a predator $j$ scale allometrically with predators' size.
Specifically, for a predator $j$, its optimal size prey is:
$$
\mu_j = \alpha_0 + \alpha_1 log_{10}(m_j)
$$
its niche width is:
$$
\sigma_j = exp(\beta_0 + \beta_1 log_{10}(m_j))
$$
and the maximum probability of interaction is:
$$
\theta_j = logit^{-1}(\gamma_0 + \gamma_1 log_{10}(m_j))
$$
The probability of interaction is then calculated as:
$$
P(A_{ij} = 1) = \theta_j exp( - \frac{(log_{10}(m_j/m_i) - \mu_j)^2}{2\sigma_j^2} )
$$
```{r li}
# create community
S <- 20
m <- sort(runif(S, 1, 1e2))
fw <- matrix(NA, nrow = S, ncol = S)
# set parameters
alpha0 <- -3
alpha1 <- 2
beta0 <- -3
beta1 <- 0.75
gamma0 <- 0.1
gamma1 <- -0.1
# create web accordingly
for (j in seq_along(m)) {
mu <- alpha0 + alpha1 * log10(m[j])
sigma <- exp(beta0 + beta1 * log10(m[j]))
theta <- exp( (gamma0 + gamma1 * log10(m[j])) / (1 + gamma0 + gamma1 * log10(m[j])) )
fw[, j] <- theta * exp( - ( (log10(m[j] / m) - mu)^2 / 2 / sigma^2 ) )
}
# sample probabilities
fw <- rbinom(length(fw), 1, fw)
fw <- matrix(fw, nrow = S, ncol = S)
show_web(fw)
# fit model
fit_li <- li_stan(
fw, m,
warmup = 250, iter = 500, #to speed up vignette
cores = 4, refresh = 250
)
pars <- c(
"alpha0", "alpha1",
"beta0", "beta1",
"gamma0", "gamma1"
)
posterior <- rstan::extract(fit_li, pars)
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(3, 2), mar = c(4, 4, 2, 2))
for (i in seq_along(posterior)) {
plot(
density(posterior[[i]]),
main = names(posterior)[i],
xlab = "Posterior"
)
}
par(oldpar)
```
# References
Antunes, A. C., Berti, E., Brose, U., Hirt, M. R., Karger, D. N., O’Connor, L. M., ... & Gauzens, B. (2024). Linking biodiversity, ecosystem function, and Nature’s contributions to people: a macroecological energy flux perspective. Trends in Ecology & Evolution.
Li, J., Luo, M., Wang, S., Gauzens, B., Hirt, M. R., Rosenbaum, B., & Brose, U. (2023). A size‐constrained feeding‐niche model distinguishes predation patterns between aquatic and terrestrial food webs. Ecology Letters, 26(1), 76-86.
Yeakel, J. D., Pires, M. M., Rudolf, L., Dominy, N. J., Koch, P. L., Guimarães Jr, P. R., & Gross, T. (2014). Collapse of an ecological network in Ancient Egypt. Proceedings of the National Academy of Sciences, 111(40), 14472-14477.