Contents

The “real” contents list is the menu in the sidebar, but we need to keep track of what pages are planned and what are already available.

11 thoughts on “Contents”

  1. Thanks Soy, I’ve add that to the list – though may be a while before it actually appears, as haven’t covered it in the workshop.

  2. The people I’m working with have vast interests with N-mixture model. Perhaps we could add it in as a subtopic under ‘Population Estimation’…🤷🏻

    1. Thanks Bob. We haven’t actually included that in past workshops but it’s an important type of model and we should do so. It would probably need its own section as quite different to SCR.

      1. Hmm, that’s not quite true, we did include it in the Bangkok workshop in 2017 because folks asked for it. I’ll tidy that up and include.

  3. Although you have provided details of priors in each modelling approach, I was wondering whether it would be wise to insert a section on priors (somewhere in the probability section above). That way the readers would be prepared to face ‘priors’ in the later part of modelling. Just my thought! Great job in putting this together.

    1. There’s lots that can be said about priors (eg, see here), but I don’t think people need to digest all that before starting the orangutan example. Perhaps a short discussion of weak vs strong priors would be good at the start, plus more discussion along those lines for each model. Very weak priors seem to be the tradition in ecology, which I think is a shame: we should be making more use of existing information, at least if the results are to be used for management.

  4. Later in the community model, could we also add MSAM (like Yamaura et al., 2012 and Wearn et al., 2017 models)? Would that baffle the newest modellers? OR maybe we could save for those who want to explore on their own and not put in this tutorial. 🙂

    1. I’m not at all sure how (or indeed if) we address MSOMs or other advanced models. Should we just tell folks to work through Kéry & Royle (2016) chapter 11?

      I also have reservations about these more complex models. The published examples worked, but they do involve more and more assumptions and the estimates have wider and wider CrIs. We are trying to wring information out of a data set which may not be there. Most models will run just fine and give you some sort of estimate even with no data at all; the estimates are coming from the priors.

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