Predict the resilience of black spruce, Douglas-fir, eastern hemlock, Alaska birch, pinyon pine, ponderosa pine, sugar maple, quaking aspen, white bark pine and white oak to climate change.
Predict the resilience of black spruce, Douglas-fir, eastern hemlock, Alaska birch, pinyon pine, ponderosa pine, sugar maple, quaking aspen, white bark pine and white oak to climate change.
Biomass partitioning, leaf area index, and canopy greenness: the Good, the BA...remkoduursma
Seminar presented to the Hawkesbury Institute for the Environment's weekly seminar series on 28 October 2015. Topics include a global database of plant biomass and allometry, leaf area index at the EucFACE, and canopy greenness as measured with phenocams.
Interannual and decadal variations of Antarctic ice shelves using multi-mission satellite radar altimetry, and links with oceanic and atmospheric forcings
Solar ghosts: Weighing the evidence for sunspot cycles in fossil treesScott St. George
In their study of tree rings from the Chemnitz Fossil Forest (Germany), Luthardt and Rößler (2017) claim to identify a regular near-11-yr cyclicity in growth, and present that pattern as evidence of the influence of the Schwabe solar cycle (Usokin and Mursula, 2003) on climate and forest productivity during the early Permian. If correctly interpreted, these fossil tree rings would indicate the sunspot cycle was the dominant influence on interannual variability in Earth’s climate during this period and that it has been a consistent aspect of our Sun’s behavior for at least the past 300 m.y. We argue the fossil tree-ring record from Chemnitz does not constitute reliable evidence of solar activity during the Permian because the individual tree-ring sequences are not correctly aligned (dendrochronologically dated) and, as a result, the mean ring-width composite is not a meaningful estimate of year-to-year variations in tree growth in this ancient forest.
LATE QUATERNARY STRATIGRAPHIC EVOLUTION OF THE NORTHERN GULF OF MEXICO MARGINDaniel Matranga
Abstract: This volume presents results from several high-resolution stratigraphic investigations of late Quaternary strata of the northern Gulf of Mexico, from the Apalachicola River to the Rio Grande. The studies characterize deposition and strata formation associated with different fluvial and deltaic systems during the most recent glacioeustatic cycle (approximately 120 ka to present).
This problem represents an interesting opportunity for scientists and statisticians to collaborate since the problem is too big for either community. The science is not well established, although fairly sophisticated ice flow models exist. They are even becoming relevant to explain some of the complexity seen in observational data. At the same time, the complex phenomena we see in observations may not be particularly relevant to assessing the risks of significant increases in sea level rise over the near future. The talk will review what we have learned about this problem through the PISCEES SciDAC project. This problem is rich with challenges and opportunities, particularly for realigning how our two communities engage each other. The talk will review the computational, scientific, and mathematical "reality checks" that might stop any reasonable person from considering this topic further. I then will point out how each of these challenges could be mitigated if these different perspectives were better integrated.
Biomass partitioning, leaf area index, and canopy greenness: the Good, the BA...remkoduursma
Seminar presented to the Hawkesbury Institute for the Environment's weekly seminar series on 28 October 2015. Topics include a global database of plant biomass and allometry, leaf area index at the EucFACE, and canopy greenness as measured with phenocams.
Interannual and decadal variations of Antarctic ice shelves using multi-mission satellite radar altimetry, and links with oceanic and atmospheric forcings
Solar ghosts: Weighing the evidence for sunspot cycles in fossil treesScott St. George
In their study of tree rings from the Chemnitz Fossil Forest (Germany), Luthardt and Rößler (2017) claim to identify a regular near-11-yr cyclicity in growth, and present that pattern as evidence of the influence of the Schwabe solar cycle (Usokin and Mursula, 2003) on climate and forest productivity during the early Permian. If correctly interpreted, these fossil tree rings would indicate the sunspot cycle was the dominant influence on interannual variability in Earth’s climate during this period and that it has been a consistent aspect of our Sun’s behavior for at least the past 300 m.y. We argue the fossil tree-ring record from Chemnitz does not constitute reliable evidence of solar activity during the Permian because the individual tree-ring sequences are not correctly aligned (dendrochronologically dated) and, as a result, the mean ring-width composite is not a meaningful estimate of year-to-year variations in tree growth in this ancient forest.
LATE QUATERNARY STRATIGRAPHIC EVOLUTION OF THE NORTHERN GULF OF MEXICO MARGINDaniel Matranga
Abstract: This volume presents results from several high-resolution stratigraphic investigations of late Quaternary strata of the northern Gulf of Mexico, from the Apalachicola River to the Rio Grande. The studies characterize deposition and strata formation associated with different fluvial and deltaic systems during the most recent glacioeustatic cycle (approximately 120 ka to present).
This problem represents an interesting opportunity for scientists and statisticians to collaborate since the problem is too big for either community. The science is not well established, although fairly sophisticated ice flow models exist. They are even becoming relevant to explain some of the complexity seen in observational data. At the same time, the complex phenomena we see in observations may not be particularly relevant to assessing the risks of significant increases in sea level rise over the near future. The talk will review what we have learned about this problem through the PISCEES SciDAC project. This problem is rich with challenges and opportunities, particularly for realigning how our two communities engage each other. The talk will review the computational, scientific, and mathematical "reality checks" that might stop any reasonable person from considering this topic further. I then will point out how each of these challenges could be mitigated if these different perspectives were better integrated.
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Tree Advancement Patterns
1. TREELINE ADVANCE PATTERNS OF ALASKAN WHITE SPRUCETREELINE ADVANCE PATTERNS OF ALASKAN WHITE SPRUCE
Keena Auld, Kenneth Chadwell, Turner Glasgow, David Cairns, and Keith Gaddis
Texas A&M University Department of Geography, College Station, Texas
Is P. glauca treeline advancing?
Question
Hypothesis
1. If treeline is advancing, trees
establishment date will decrease (become
more recent) as elevation increases
2. If treeline advance is not occurring, then
there will be no relationship between
elevation and tree establishment date
Methods
Lab Methods
In the fall of 2015, we
measured the
establishment date of
each tree by counting
the tree rings on
extracted tree cores.
Abstract
Many recent studies have indicated altitudinal
treeline advance throughout the world due to global
climatic changes. These advances have major
implications for the assembly and survival of
species throughout the world that may become
marginalized as their habitat is destroyed by this
advance. Given this fact, we set out to examine
treeline advance patterns in the dominant North
American treeline forming species, white spruce
(Picea glauca) throughout south-central Alaska.
We examined Dendrochronological data rom 17
sites and have found a variable arrangement of
treeline pattern across the study area with some
sites showing a clear pattern of advance and others
maintaining stabile over the recent past.
Background
Treeline is an ecotone
where the forest biome
transitions to a tundra at
the highest elevation point
where trees can grow
Changes in climate have a
direct link the global treeline
position. Long winters limit
establishment of seeds and
permafrost prevents deep roots
from penetrating the soil. As
global climates have warmed,
forest biomes have began to
encroach on that of the tundra
leading to treeline advance.
Study System
White spruce (Picea glauca) is
native to the boreal forest of
North America. It has
evergreen needles
accompanied by narrow,
oblong cones. The species
disperses through seeds or
layering (where lower branches
take root to form new trunks
that later separate from the
maternal tree forming a clone).
The area used in this study was south-
central Alaska. The climate of region is
subarctic with temperatures averaging -12
to 18 ˚C throughout the year. Climatic data
has indicated a warming trend in this area.
This region is heavily forested, with high
mountainous terrain.
Results Conclusion
Treelines are advancing
• 5/17 show definite treeline advance
• 3-6 sites show younger trees at treeline
• Recent advance over a short spatial scale
Topography determines advance
• The correlation between tree age and
distance from treeline was explained by
the slope at the sample site.
• Low sloped sites have greater treeline
advance rates.
Acknowledgements
Future Work
How does climate influence advance?
• We will determine if contemporary climate
to influence advance rates.
• We will use climate modelling to identify if
areas with greatest climatic change have
experienced greater advance.
• We will analyze additional topographic
variables (slope, aspect, curvature)
How do dispersal and reproduction
affect influence treeline advance?
• We will relate this work to our existing
analysis of dispersal and reproductive
pattern using genetics.
Treeline Advance
Infilling
No Pattern
We would like to thank Rachel Clausing, Zac
Harlow, and Michelle Lee for tireless field collection.
Clint Magill provided guidance and laboratory
resources to conduct genetic work. Parveen Chhetri
assisted in lab work and analysis.
1840
1860
1880
1900
1920
1940
1960
1980
2000
2020
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 16 P-value > 0.05
1900
1920
1940
1960
1980
2000
2020
2040
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 20 P-value > 0.05
1750
1800
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 25 P-value > 0.05
1900
1920
1940
1960
1980
2000
2020
2040
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 28 P-value > 0.05
1750
1800
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 32 P-value > 0.05
1750
1800
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 33 Adjusted R-squred: 0.08
P-value < 0.05
1450
1550
1650
1750
1850
1950
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 34 P-value > 0.05
1860
1880
1900
1920
1940
1960
1980
2000
2020
2040
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 41 Adjusted R-squred: 0.27
P-value < 0.001
1650
1700
1750
1800
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 36 Adjusted R-squred: 0.10
P-value < 0.05
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EStablishmentYear
Distance From Treeline (m)
Site 43 P-value > 0.05
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 46 P-value > 0.05
1650
1700
1750
1800
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 55 P-value > 0.05
1900
1920
1940
1960
1980
2000
2020
2040
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 58 Adjusted R-squred: 0.25
P-value < 0.001
1800
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmnetYear
Distance From Treeline (m)
Site 68 Adjusted R-squred: 0.28
P-value < 0.001
1860
1880
1900
1920
1940
1960
1980
2000
2020
2040
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 71 P-value > 0.05
1800
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 75 Adjusted R-squred: 0.19
P-value < 0.001
1650
1700
1750
1800
1850
1900
1950
2000
2050
0 100 200 300 400 500 600
EstablishmentYear
Distance From Treeline (m)
Site 76 P-value > 0.05
Field Methods
In the summer of 2015,
we sampled cores from
trees spaced on 500 m
transects parallel and
perpendicular to
treeline. Five trees were
sampled every 50 m.
We collected a total of
1,870 white spruce tree
cores across 17 sites.
Analysis
We used linear regression to
examine the age of trees
relative to the distance from
treeline. An additional analysis
examining topographic effects
was conducted using logistic
regression
y = 0.0569ln(x) + 0.0805
R² = 0.3586
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
CorrelationCoefficient(r)
Slope of Sample Site
Does topography influence treeline advance ?
We conducted a
preliminary analysis
to examine
topographic effects
(slope at each
sample site) on
treeline advance,
using a logistic
regression.