Please provide a reproducible .Rmd script that answers all questions below and produces all of your analysis and plots. Some reminders:
The discipline of landscape ecology frequently postulates that the spatial pattern of habitat is important, in addition to local characteristics such as patch area, vegetation type, and climate. Westphal et al (2003) analyzed data from the South Australian Bird Atlas using a series of landscape pattern metrics estimated at 3 spatial scales. They concluded that landscape structure had a positive effect on many bird species. However, this dataset was never designed to be analyzed using logistic regression, and consequently their conclusions were somewhat weak, and badly compromised by model selection uncertainty. They used AIC rather than AICc, because the number of datapoints was relatively large (n=499) compared to the number of parameters in the most complex model (K = 5) so n/K ~ 100 and AIC is probably adequate. However the number of models considered (R = 45) is quite large. In addition, because of the way the Atlas data were archived, it was not possible to directly compare the effect of local patch variables to landscape pattern covariates.
At the same time, Dr. Scott Field and colleagues of the University of Adelaide collected an independent data set at 34 woodland sites in the Mt. Lofty Ranges using a standard 2 ha, 20 min timed count procedure in each of 2 years. Field et al. (2002) describe some of the issues related to designing this survey; we have data for 34 of the 38 sites in that study. The bird data has sites and years in rows, and bird species in columns; for a given site in a particular year, a 1 indicates that the species was observed at least once out of three visits to the site, and a zero indicates that the species was not observed in three visits. This provides some correction for the problem of false negatives. FYI: if you’re thinking “duh–these folks should have just used an occupancy model,” check out the date of the OG occupancy modeling papers (e.g., MacKenzie et al. 2002 in Ecology).
Here are citations for the papers mentioned above:
Do not use the analysis presented in these papers, although you can refer to the paper for the ecological background.
We have two datasets for this homework. First, we have the bird occurrence data in mlrbird:
We also have landscape pattern variables similar to those used by Westphal et al at 2 km, 5 km, and 10 km scales, as well as three “local” covariates. Each of the landscape variables starts with the name, followed by 2k, 5k, or 10K according to which buffer size was used. The variables used by Westphal et al are indicated with an asterisk, and “local” covariates are indicated by two asterisks. These landscape data are in mlrland. Note that these data have NOT been modified to reduce correlations among the variables as Westphal et al described (This is a hint, maybe).
NOTE: There are twice as many rows in mlrbird than mlrland. This is because the bird surveys were conducted over two years, but landscape variables were only collected once… How should you deal with this?
# List of packages necessary to run this script:
require(librarian, quietly = TRUE)
shelf(tidyverse, cowplot,
AICcmodavg, # for aic.tab, model averaging, etc.
mgcv, # for qq.gam
quiet = TRUE,
lib = tempdir())
# Load data:
mlrbird <-
read.csv("https://github.com/LivingLandscapes/Course_EcologicalModeling/raw/master/data/mlrbird.csv")
mlrland <-
read.csv("https://github.com/LivingLandscapes/Course_EcologicalModeling/raw/master/data/mlrland.csv")
# NOTE: You will need to join these datasets to conduct the analysis.