Ecological forecasting in R
Tutorial 1: temporal random effects
Nicholas Clark, School of Veterinary Science, University of Queensland1
Exercises and associated data
The data and modelling objects created in this notebook can be downloaded directly to save computational time.
Users who wish to complete the exercises can download a small
template R script. Assuming you have already downloaded the
data objects above, this script will load all data objects so that the
steps used to create them are not necessary to tackle the exercises.
Load libraries and time series data
This tutorial relates to content covered in Lecture 1 and Lecture 2, and relies on the following packages for manipulating data, shaping time series, fitting dynamic regression models and plotting:
library(dplyr)
library(mvgam)
library(gratia)
library(ggplot2); theme_set(theme_classic())
library(marginaleffects)
library(viridis)We will work with time series of rodent captures from the Portal Project, a long-term monitoring study based near the town of Portal, Arizona. Researchers have been operating a standardized set of baited traps within 24 experimental plots at this site since the 1970’s. Sampling follows the lunar monthly cycle, with observations occurring on average about 28 days apart. However, missing observations do occur due to difficulties accessing the site (weather events, COVID disruptions etc…). You can read about the sampling protocol in this preprint by Ernest et al on the Biorxiv.
All data from the Portal Project are made openly available in
near real-time so that they can provide maximum benefit to scientific
research and outreach (a set of open-source software tools make data readily
accessible). These data are extremely rich, containing monthly
counts of rodent captures for >20 species. But rather than accessing
the raw data, we will use some data that I have already processed and
put into a simple, usable form
data("portal_data")As the data come pre-loaded with mvgam, you can read a
little about it in the help page using ?portal_data. Before
working with data, it is important to inspect how the data are
structured. There are various ways to inspect data in R; I
typically find the the glimpse() function in
dplyr to be useful for understanding how variables are
structured
dplyr::glimpse(portal_data)## Rows: 320
## Columns: 5
## $ time <int> 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, …
## $ series <fct> DM, DO, PB, PP, DM, DO, PB, PP, DM, DO, PB, PP, DM, DO, PB, …
## $ captures <int> 20, 2, 0, 0, NA, NA, NA, NA, 36, 5, 0, 0, 40, 3, 0, 1, 29, 3…
## $ ndvi_ma12 <dbl> -0.172144125, -0.172144125, -0.172144125, -0.172144125, -0.2…
## $ mintemp <dbl> -0.79633807, -0.79633807, -0.79633807, -0.79633807, -1.33471…We will analyse time series of captures for one specific rodent species, the Desert Pocket Mouse Chaetodipus penicillatus. This species is interesting in that it goes into a kind of “hibernation” during the colder months, leading to very low captures during the winter period