Pattern & Process of Tree Mortality Waves in the Mountains of the Southwestern United States. Presented by Alison Macalady at the "Perth II: Global Change and the World's Mountains" conference in Perth, Scotland in September 2010.
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Pattern & Process of Tree Mortality Waves in the Mountains of the Southwestern United States [Alison Macalady]
1. Pattern & Process of Tree
Mortality Waves in the
Mountains of the Southwestern
United States
Alison Macalady1 & Harald Bugmann2,1
1 Laboratory of Tree-Ring Research, University of Arizona
2 Forest Ecology, ETH Zürich, Switzerland
Photo: Craig Allen
5. Growth-mortality models
1
Mortality
Probability ?
0
Index based on radial growth
CCR >80%, (e.g. Bigler & Bugmann 2004, Ecol Appl)
– growth level over past few years
– growth trend over past years to decades
– growth sensitivity
6. Research questions
Can the probability of piñon
mortality under drought be
accurately modeled using
indices derived from diameter
growth?
What do growth-mortality
models reveal about the
drivers of tree mortality
through space and time?
9. Tree growth – typical patterns
Large Low growth
SEV 2000s release/recovery before death
of L trees!
Divergence of L
TRP 2000s and D trees
incited by 1950s
drought
10. Fitting mortality models: one site
Sevilleta, 1950s
Internal validation: 60% fitting, 40% testing
500 simulations
11. Fitting mortality models: all sites
Site/period Variable AU ROC CCR
mean
SEV 1950s 0.89 78.7%
sensitivity 50
mean
BNM 1950s 0.92 82.0%
sensitivity 25
recent
SEV 2000s 0.83 75.3%
growth 3
BNM 2000s – – –
growth
TRP 2000s 0.67 59.6%
difference 15
12. Validating mortality models
Calibration data [shown is CCR]
Validation SEV 1950s BNM 1950s SEV 2000s
SEV 1950s – 73.1 77.4
BNM 1950s 77.4 – 60.0
SEV 2000s 55.9 61.7 –
BNM 2000s 31.6 16.7 14.3
TRP 2000s 53.4 55.9 52.5
13. What’s going on?
High model accuracies associated with 1950’s and SEV
2000’s data reflect a chronic stress signal associated with
mortality risk
•Best predictors reflect the resource status of the trees over
different time periods.
•Supports carbon starvation mechanism of mortality
Lack of fit in N 2000’s models suggests other processes.
•Acute drought stress
•Increased temps driving accelerated bark beetle/fungi dynamics?
•Carbon allocation to defensive compounds (Kane and Kolb 2010,
Oikos)?
14. Conclusions
Strong influence of acute
drought stress and/or bark
beetle/fungi dynamics at
northern sites in the 2000’s
Differences in space and time
an early indicator of global
change?
Challenges of predicting
mortality under drought
15. Acknowledgements…
Acknowledgements
Craig Allen, Julio Betancourt, Tom Swetnam, Dave Breshears,
Kay Beeley, Collin Haffey, Greg Pederson, Derek Murrow, Chris
Baisan, Rex Adams, Alex Arizpe, Christof Bigler
Financial support
Science Foundation Arizona, US DOE GREF (AM)
ETH Zürich, UA Lab. Tree-Ring Research, Haury Fellowship
(HB)