atredennick
commented
9 years ago Ran simulations with one vital rate per scenario having climate coefficients set to 0. This is actually pretty hard to interpret. New strategy: run simulations where ALL vital rate regressions have climate effects set to 0 except for one, sequentially. This can be interpreted as the influence of climate on equilibrium cover via each vital rate. Adding rather than removing makes more intuitive since, b/c if we remove, then there are still two vital rates contributing climate effects.
Here's what the figure looks like now; not easy to interpret...
It is difficult to get an overall picture of sensitivities of each vital rate to climate given the many covariates and interactions. So, why not employ a strategy like Chu and Adler 2015, but instead of removing niches sequentially for each vital rate, remove their climate dependence? For example, for each climate perturbation, run it once where climate effects are set to 0 to growth, then for survival, then for recruitment. Compare these to runs with climate effects included to see which one causes the biggest shift in equilibrium cover. This could help explain inconsistent forecasts between IPM and QBM: if a certain vital rate is very sensitive that isn't picked up by the QBM...