Looking forward to an exciting ICCB! I'm presenting some recent work using deepSSF to simulate animal movement and dynamic distributions in 'Landscape & Spatial Ecology II', Wed 8:30-10:30am. Preprint here: www.biorxiv.org/content/10.1... and code and supp info here: swforrest.github.io/deepSSF/.
The latest release of the {mvgam} #rstats 📦 has hit CRAN. Plenty of exciting new features including Joint Species Distribution Models, support for the full range of Gaussian Process kernels available in the {brms} 📦 and plenty more nicholasjclark.github.io/mvgam/news/i...
Animal movement is complex, depends on many interacting factors, and can be hard to accurately predict. Here we present a novel approach using a deep learning step selection framework: tinyurl.com/2em9yyjb, with lots of supporting info and code! swforrest.github.io/deepSSF/
Hi, thanks for the comment. We cited the main R packages we used for data processing and the statistical analyses (terra, amt, TwoStepClogit), but you're right that we didn't cite packages such as ggplot2, which was an oversight. I depend on ggplot2 and co so I'll be more careful in the future!
Stoked to have a cover photo for @ecography.bsky.social! In our paper doi.org/10.1111/ecog... we included temporal dynamics into SSFs, resulting in daily patterns of movement and habitat selection. Simulating gave us dynamic spatial predictions across the landscape. Code! github.com/swforrest/dy...
