David D. Hofmann

The Okavango Delta is one of the world’s most pristine ecosystems, hosting a large diversity of animal and plant species. It represents the planet’s largest inland delta and is characterized by significant seasonal fluctuations of its flood extent. In this blog post, I will introduce you to a simple, yet effective approach to remote sense the Delta’s flood extent using MODIS satellite imagery and the floodmapr R-package.

During my last season of field work in Botswana, we encountered a pack of wild dogs where some individuals carried a wire snare around their neck. Luckily, with the aid of a wildlife veterinarian, we were able to remove the snares.

Hidden Markov Movement Models (HMMMs) are increasingly popular in movement ecology to learn more about unobserved behavioral modes from telemetry data. In this blog post, we will simulate telemetry data with known movement parameters and estimate the parameters and associated behavioral modes using the moveHMM package.

When you visualize spatial data on a map, it is often useful to plot a satellite image in the background. Hence, I would like to show you can use R to access and download satellite imagery that is free of charge. Once downloaded, we will plot the imagery using either base plot, ggplot, or the magnificent tmap package.

When we fit a statistical model, we usually scale our covariates to a mean of zero and standard deviation of one. But how does this actually influence the model and how does scaling change the way in which we need to interpret resulting model coefficients? Moreover, can we backtransform model coefficients from the scaled into the unscaled world? In this blog post, we’ll take a detailed look at scaling and examine why we scale, how to scale, and how to backtransform a model containing scaled covariates.