This document summarizes research on the effects of calcium (Ca), nitrogen (N), and phosphorus (P) additions on tree transpiration rates. The research found that: 1) A 2014 study found that a one-time addition of calcium silicate (CaSiO3) increased transpiration over the control plots, especially for yellow birch trees. Transpiration rates also varied between tree species and sites. 2) A 2015 study found that adding phosphorus (P) alone decreased transpiration rates in white birch trees compared to other treatments. 3) Overall, the research suggests that calcium and phosphorus can impact tree water use and transpiration rates. Precise measurements of sap flow

1. Sap flow : Calcium, and what
about N and P?

2. Background
• Hubbard Brook whole-watershed transpiration response
to 1999 wollastonite (CaSiO3)
(M. Green, A. Bailey, S. Bailey, J. Battles, J. Campbell, C. Driscoll,
T. Fahey, L. Lepine, G. Likens, S. Ollinger, P. Schaberg, 2013)

3. Background - MELNHE
• 2011 – Extra (5th) plot receiving a one-time
CaSiO3 application was added to a subset of
MELNHE sites (Green et al.)
• 2014 –Transpiration measurements on
Control and CaSiO3 plots at Bartlett C8,
Hubbard Brook (old), and Jeffers Brook
(old) - YB, SM, BE
• 2015 – Measurements on five treatments (Con,
N, P, NP, CaSiO3) at Bartlett C6 - WB

4. About measuring transpiration . . .
• Granier method: reference probe 10 cm below a heated probe,
measures temperature difference (ΔT) (Granier, 1987)
• Measurements collected by data logger every 30 seconds,
average recorded every 15 minutes
• ΔT converted to sapflux (Js, g x m2 x s-1) using BaseLiner
software (Oren and Parashkevov, 2012)

5. Research Questions:
• Does CaSiO3 addition increase
transpiration?
– Expect that it does, possibly due to
increased xylem and fine root growth
– 2014 data was 3 years post-application;
2015 data is 4 years
• Do N and P impact transpiration?
• Does transpiration vary by species?

6. Field Methods
• Install probes, hook up data logger and
batteries
• Visit data logger routinely to download
and check data

7. Statistical Analysis
• Three data sets
• 2014 SM, YB on C8 (June), HB(July), JB (Aug)
• 2014 BE (pooled C8, HB)
• 2015 WB on C6 (5 treatments)
• Mixed Model Analysis
• Random: Time (curvilinear)
• Explanatory: Weather covariates vs PET
• Predictors: treatment, species

8. • 3 weather
covariates
was a better
model fit
• Single PET
covariate was
an easier
model fit

9. Results – YB-SM Treatment Effects
(2014)
Type 3 fixed effects
Site p= 0.01
Treatment p= 0.26
Site*Trt*Time p= 0.03

10. Results – YB-SM Species Effects
(2014)
Type 3 fixed effects
Species p=0.09
Species*time p=0.001
Site*Sp*Time p= 0.001

11. Results – BE Treatment Effects
(2014)
Type 3 fixed effects
Treat p=0.42
Treat * Time p=0.05

12. Results – WB Treatment Effects
(2015)
Type 3 fixed effects
Treatment p=0.12
Treatment*Time p= 0.01
Treatment*Time2 p = 0.08

13. Results- Summary
• 2014 (YB, SM, BE)
– CaSiO3 increased transpiration over Con
– YB had higher transpiration than SM (BE even higher)
– YB-SM interacted with time and site/date
• 2015 (WB)
– P decreased transpiration in WB
– WB in mid-age stand had higher transpiration (except P!)
than anything in 2014
• Thank you HB Sta 1 and Bartlett Flux Tower!
– Explanatory effects were immensely important in being
able to detect effects of interest

14. Acknowledgements
• Michele Pruyn!!!!!
• Sophie Harrison
• Adam Wild
• Mark Green
• NSRC
• Ruth Yanai
• Matt Vadeboncoeur
• 2015 Shoestring Crew