The N Idaho bear dataset is great and should definitely be a central case study in one of the lectures.  In addition, there are some really fantastic slides associated with this dataset.

There is a lot of overlap between exercises 8 and 11 realated to this dataset (slides ~13-30, 44-50 in lecture 11 are also in lecture 8).  Also the final results are presented in both lectures.

Couple of ideas that might help reduce redundancy:

1) Cover causal modeling more generally, or with examples from ecology in Lecture 11.

2) De-emphasize the Cushman et al paper in lecture 8 and bring out some of the other least-cost path/distance approach papers in landscape genetics as examples.

3) It would likely be way too much information, but collapse 8 and 11 into a single lecture opening up the slot for other topics.

It also seems that the Cushman et al paper should be read as the
reading assignment associated with the first time the dataset is
discussed, and not later in the serries.

Yes, we'll figure out which of the slides go in which lecture once the final order of lectures has been decided. Yes, the paper should be assigned once (early).

Helene