Closed teixeirak closed 4 years ago
@teixeirak in terms of figure 4, I'm unsure about the best precipitation variable to include. Precipitation seasonality is not a good predictor, so I don't think we want to put that in (not significant for most variables). Annual precipitation from WorldClim and from ForC show completely different patterns (see below), so I don't really trust either of them (!). The results using data from WorldClim certainly seem a lot less reliable. I don't know if it's worth trying to download another set of precipitation data from a different source to compare..?
I'll exclude vapour pressure. VPD doesn't come out as a great predictor for any of the variables, but I could include it; other variables that come out as reasonably good predictors that could be included are annual frost days or potential evapotranspiration.
Please use the map
variable (data from ForC). The big difference is that these data range up to higher values, and C cycling tends to be reduced in those very wet ecosystems. Very high precipitation is generally more localized (i.e., you wouldn't get 6000 mm yr-1 over an entire grid cell, but you get it in places where topography is conducive to high ppt). Most global gridded products, WorldClim included, have spatial resolution that is too course to capture these areas with very high precipitation.
@teixeirak what do you think of this combination of climate variables on the graph? I've only included four response variables because it was getting hard to read the graphs with the quantity of points if more than that were plotted, but I can change the set/add more if you think others would be more meaningful.
@teixeirak shall I remove vapour pressure from the analysis entirely, and not bother including it in the paper? I've included variables that we tested which turned out to be bad predictors, but if you think that vapour pressure is scientifically a bit meaningless, shall I just leave it out?
Please use the
map
variable (data from ForC). The big difference is that these data range up to higher values, and C cycling tends to be reduced in those very wet ecosystems. Very high precipitation is generally more localized (i.e., you wouldn't get 6000 mm yr-1 over an entire grid cell, but you get it in places where topography is conducive to high ppt). Most global gridded products, WorldClim included, have spatial resolution that is too course to capture these areas with very high precipitation.
@beckybanbury, I should add... we also want to give precedence to the mat
variable.
@teixeirak I had already prioritised that!
Great! Just wanted to make sure.
@teixeirak shall I remove vapour pressure from the analysis entirely, and not bother including it in the paper? I've included variables that we tested which turned out to be bad predictors, but if you think that vapour pressure is scientifically a bit meaningless, shall I just leave it out?
Yeah, I think I'd leave it out.
@teixeirak what do you think of this combination of climate variables on the graph? I've only included four response variables because it was getting hard to read the graphs with the quantity of points if more than that were plotted, but I can change the set/add more if you think others would be more meaningful.
Sorry for the delayed reply on this. A few thoughts:
I guess what I'm saying is that the set of variables you showed above was great!
NEW ROUND:
General
Figure 1 (map)
Figure 2 (flux closure) - looks good!
Figure 3 (latitude)
Figure 4 (MAT-MAP)
Figure 5 (multiple climate variables
Table 1
()
is very intuitive, and it also overlaps in meaning with no
. Consider replacing with no
or n.s.
analyses
tables
figures
general:
Fig. 1 (map):
Fig. 2 (all variables scaled):
Fig. 3 (stacked plots):
Fig. 4 (responses to climate variables):
Fig. 5 (ratio tests):