nickboffa
commented
4 months ago So in this paper (https://nsojournals.onlinelibrary.wiley.com/doi/10.1111/j.0906-7590.2008.05457.x), they mention near the end
"One way to reduce such extrapolation would be to use a mechanistic approach to define areas in which the species cannot persist (as we have done here). Once these areas are defined, pseudo-absence points could be generated within them and these pseudo-absence points could then inform a correlative model. In addition, output layers of mechanistic models could be used as input layers for correlative models. Such mechanistically-derived layers are composites of multiple environmental conditions transformed through the traits of the organism into one composite variable that directly reflects fitness impacts, providing highly proximal variables for modelling (sensu Austin 2002). Thus, mechanistic approaches can be used in synergy with correlative approaches. Such synergies would potentially extract distribution information from both the basic physiology of the organism as well as its current known range, and would be a valuable avenue for further research."
So, I decided the best way to inform everything by the primary literature is to try combine mechanistic models with the correlative approach I was using (which has been done by quite a few people in many different ways, e.g. https://royalsocietypublishing.org/doi/10.1098/rspb.2007.0114).
The problem was (and still is), that the output layers to their mechanistic models are not accessible. That said, their figures are (https://www.ecography.org/sites/ecography.org/files/appendix/e5457.pdf), so I tried to read the data from the figure myself, and was fairly(?) successful. This meant I had a layer for how good each area is for cane toad breeding. I've been fiddling with exactly how to incorporate this data into the model, but at the very least this is using the primary literature.
Though these articles are old, I haven't found any since that incorporated these suggestions. Cane toad modelling shifted to other aims, other than just 'where will the toads end up'
I can see you've done lots of cool data analyses, but at this point I wonder a little bit what it's for.
This would be a good time to revisit your questions and aims, make sure you can do a project which is genuinely motivated by the primary literature, and isn't something which has obviously been done before.
With your niche model and the agent-based model, I suspect there's lots you can do, but setting out really clear questions and aims (and reading a few papers to make sure they're legit) is a good thing to do before you go much further.