If I want to predict litterfall, what data do I need to collect, and what statistical model might I use?
For example, I might use the following coding to record phenological stage every other day, or perhaps once per week:
|value| description
|-----|--------------
| 0 | all leaves fallen
| 0.5 | most leaves fallen
| 1 | no more green in canopy half of leaves have fallen
| 2 | most leaves yellow or red few leaves have fallen
| 3 | noticeable reddening or yellowing, green still present
| 4 | summer condition
Once I have these data, I can also collect weather data. Now, say I want to predict the day on which each of the transitions (4→3, 3→2, etc) occur.
Many studies use a temperature metric of growing degree days GDD=(Tmax+Tmin)/2−Tbase, but I have also seen chiling days and photoperiod used to predict changes in phenological stage. I would like to develop a function (statistical model) f that would allow me to predict a date of state change from environemental variables, such as D4→3=f(GDD)
My questions:
- what controls litterfall? Is it photoperiod, temperature, other?
- do the controls vary by species?
- is there a "standard" approach to modelling senescence?
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