Predicting AspenPatch Growth Subject to Environmental Change - PowerPoint PPT Presentation

1 / 25

About This Presentation

Title:

Predicting AspenPatch Growth Subject to Environmental Change

Description:

These drive complex interactions among physiological processes at the leaf and branch levels. ... 'Tree Anatomy' Erv Evans, NC State Univ. Bud Dynamics ... – PowerPoint PPT presentation

Number of Views:13

Avg rating:3.0/5.0

Slides: 26

Provided by: kathry97

Category:

more less

Transcript and Presenter's Notes

Title: Predicting AspenPatch Growth Subject to Environmental Change

1
Predicting Aspen-Patch Growth Subject to
Environmental Change

Kathryn E. Lenz
Mathematics Statistics, U. Minnesota Duluth
Engineering Mathematics, U. Bristol, 9/04 12/04
Presented at Forest Research Seminar, 14/9/04
Research Agency of the Forestry Commission
Biometrics, Surveys Statistics Division, Alice
Holt Lodge, Wrecclesham-Farnham

2
Outline

Aspen FACE Experiment
ECOPHYS Overview (modeling component of Aspen
FACE)
Aspen-Patch Simulation
Consequences of Competition for Light
Bud Dynamics, Green-leaf drop, and Branch Death
Responsive to Light Competition Environmental
Change
Conclusions

3
Linking Modeling and Experimentation Aspen FACE

FACE - Free-air CO2 Enrichment
Large scale studies to assess the effects of
greenhouse gasses on the natural environment
Seven FACE installations in US, 15 worldwide

http//aspenface.mtu.edu
4
Aspen FACE Experiment CONTROL, ?O3, ?CO2 ,
?O3 ?CO2
5
Aspen FACE Experimental Design

Four treatments, 3 replicates
2x CO2
2x O3
2x CO2 O3
Control
Measure the response of trees, soil system,
insects and disease

6
ECOPHYS Process model for Populus

Genus Populus
P. tremuloides Trembling Aspen
Most widespread and economically important
species in eastern United States
P. eugenei (deltoides x nigra) and other hybrid
poplar clones
Grown for fiber and energy production
Populus has a long
history of physiological
and ecological research

2 yr old poplar plantation in Minnesota, USA
7
ECOPHYS Tree Simulation

George Host, Kathryn Lenz, Harlan Stech
NRRI UMD
Multi-year growth of poplar and aspen clones
Predict growth based on physiological factors and
environment
Time scales hour, day, year
Inputs Temp, PPFD, RH, CO2, O3, Latitude,
Genetics

8
Photosynthate Productivity Drives ECOPHYS Growth

For each leaf each hour
Calculate leaf-level sun and shade light
interception
Calculate photosynthetic rate variation of the
Harley model with ozone effects on photosynthetic
rate
M. Martin, G. Host, K. Lenz, J. Isebrands,
2001
Compute photosynthate produced, accumulate and
distributed daily.

9
Use and Transport of Photosynthate

Psyn used for growth and maintenance.
Leaf, internode, and root photosynthate
transportation base on radiotracer data and
source/sink relationships
Y. Guan MS thesis 2002
A. Laconite, J. Isebrands, G. Host 2002

10
0

Intercepted Light
Neighbors provide significant shade after 3
yrs.
Heterogeneous patch on Beowulf cluster, different
computer processors devoted to different trees in
patch. Trees communicate canopy architectures
daily
-- Harlan Stech,
Matt Zagrabelny

11
Consequences of Light Competition

Necessary for predicting long-term patterns of
growth within a patch of trees.
Individual tree genetics and environmental
growth histories.
These drive complex interactions among
physiological processes at the leaf and branch
levels.
Hypothesis These interacting processes can be
adequately simulated at hourly, daily, and
yearly time steps, taken over multiple-years of
simulated patch-growth.

12
Abundant Psyn ? GrowthInsufficient Psyn ?
Death

Disruption or interruption of conditions
necessary for psyn production ? low
photosynthesis ? low carbon availability ?
respiration needs unmet ? death
Why do trees die? Agricultural Extension
Service, U of Tennessee
Branches generating insufficient psyn die and
eventually fall off
Tree Anatomy Erv Evans, NC State Univ.

13
Bud Dynamics

Branchs bud-set timing predetermined in its
bud.
Each buds size (primordia) is determined by
its parent leafs and branchs productivity, LPI,
and genetics.
Buds where leaves are present at fall senescence.
If a bud is too small, then it dies.
Small surviving buds ? short shoots.
Largest bud(s) ? indeterminate branch(es).
Intermediate size bud ? determinate branch
with bud-set date corresponding to parent buds
size.

14
Green-Leaf Drop

A leaf drops if it cant meet its maintenance
(respiration) needs.
Each day for each leaf, compute weighted daily
average over current and past 14 days of net psyn
per unit leaf area
Beyond a threshold, probability of dropping
increases as weighted average decreases

15
Leaf Drop Algorithm

For each leaf
P(d) (net psyn(d))/LeafArea(d).
w(t) Aeat, t 0, -1, -2, -14
e.g. a 0.2, A 0.19
Pwa(d)
AP(d) Ae-aP(d-1) Ae-2aP(d-2)
Ae-14aP(d-14))
If Pwa(d) t (threshold)
the leaf will not drop that day.

16
Fading memory weights for a 0.1, 0.2, 0.3
17
Leaf Drop Algorithm continued

Uniform random ?
0 lt ? lt 1
for variability due to unmodelled dynamics
Leaf drops if
Pwa(d) lt t and ? 1eK ,
where K (Pwa(d) - t)d
e.g. d 3, t 0.05
If Pwa(d) lt t,
probability leaf drops is 1eK.

K vs 1eK
18
Green-wood Branch Death

Green-wood branch death is based on branch
productivity.
A branch withers if it doesnt produce enough
psyn to maintain its attachment to older wood.
Each day for each branch compute branchs rolling
weighted average net psyn over current day and
past 14 days.
Probability of branch death increases as
weighted average psyn decreases below
threshold

19
Green-Wood Death Algorithm

P(d) remaining psyn in a given branch after
all transport, and respiration processes
w(t) Aeat, where
A(1e-ae-2a e-14a) 1
Pwa(d)A(P(d)e-aP(d-1)e-2aP(d-2)
e-14aP(d-14))
If Pwa(d) t (threshold), branch will not die
that day.

20
Green-Wood Death Algorithm - continued

Uniform random ? such that 0 lt ? lt 1
for variability due to unmodelled dynamics
Branch dies if Pwa(d) lt t and ? 1eK
where K (Pwa(d) - t)d
e.g. d 5, t 0.03
If Pwa(d) lt t,
probability of branch death is 1eK

21
Older Wood Death

Older wood dies incrementally as supporting
leaves and green wood die.
Finally, the tree dies when all its branches are
dead.

22
Architectural Influences of Bud Set Parameters, 5
Years of Growth

Vary only the bud-set parameters t (threshold)
and d (probability factor).
Pictures generated by Kyle Roskoski
t 0, d 0 t 0, d 20
(turned off) (high probability
factor)
t 3, d 1 t 3, d 20
(high threshold) (both high)

23
? 0, ? 0
? 0, ? 20
? 3, ? 20
? 3, ? 1
24
Predicting Aspen-Patch Growth Subject to
Environmental Change

Genetics, environmental conditions, carbon
allocation patterns, phenological processes,
competition and other factors contribute to
aspen-patch growth.
These variables drive complex, multi-year dynamic
feedbacks and interdependencies among
phenological and growth processes.
Simulations such as ECOPHYS can be developed to
incorporate models of various physiological
processes and their responses to environmental
change.
Simulation models can aid in understanding
interactions among trees and the environment and
how these interactions produce aspen-patch growth.

25
Acknowledgements