GC51D-0780. Geologic and geomorphic controls of altitudinal treeline in the Canadian Rocky Mountains
M. Macias Fauria & E. A. Johnson, Biogeoscience Institute, University of Calgary
HYPOTHESIS
INTRODUCTION
Conventionally, altitudinal treelines are considered cold ecotones, that is, a
transitional environment in which trees give way to prostrate and non-woody life
forms due to cold growing season temperatures (1-3).
Such hypothesis is backed by both site experiments in treeline environments and
the observation that treeline altitude increases with decreasing latitude globally.
Current global warming is thus expected to strongly affect treeline environments,
as they are supposed to be highly sensitive to changes in temperature.
Indeed, global increases in treeline elevation are forecasted under a warmer planet
(1).
MATERIAL AND METHODS
A binary matrix of tree presence/absence was obtained from digitally processing RGB
georeferenced orthophotos for a ~ 200 Km 2 area located in the Front Ranges of the Canadian Rocky Mountains (1400-2900 m.a.s.l.).
Mountain environments are highly heterogeneous and rugged terrain, in
which the availability of suitable sites for tree growth might be limited by
geological and geomorphologic processes.
The area was selected as it represents the range of mountain morphologies and materials
which compose the Canadian Rockies Front Ranges.
Traditionally, treeline studies tend to focus exclusively on temperature and
ignore other constraints, such as the ones related with the physical characteristics of the landscape.
A LiDAR based Digital Elevation Model (DEM) was produced for the area, which enabled,
together with intensive ground thruthing, the construction of a geomorphologic map.
We hypothesize that a multi-scale (in both time and space) process
competition involving topographical shelter, substrate and water
availability, and temperature ultimately set the distribution of suitable sites
where trees can establish and survive in the altitudinal treeline.
A spatially explicit model based on logistic regression on a set of topographical,
geomorphologic, and climatic variables, was built in order to:
- Predict tree presence/absence over 10m pixels.
- Analyze the relative contribution of the different predictors to the final tree position.
Limestone
Probability of
tree presence
1.0
12000
Number of pixels
GENERALIZED MIXED
LOGISTIC MODEL
OF
TREE
PRESENCE/ABSENCE
8000
6000
4000
2000
0
0
0
0.2
0.4
0.6
0.8
1
Probability of tree presence
Number of observed treed pixels
Total number of pixels
Ratio treed/total Difference (Nº pixels)
¯
The relation between the
number of treed pixels and
the total number of pixels in
the landscape varies as a
function of the predicted
probability of presence of
trees.
10000
Shale
5000
The difference between treed
and total number of pixels increases at low predicted
probabilities, as expected.
4000
3000
2000
1000
0
0
1
0.2
0.4
0.6
0.8
The high agreement
between predicted and
observed tree cover
demonstrates the good
performance of the model.
y = 1.0298*x - 0.017542
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
However, when treeline is analyzed at a landscape scale, only 3 to 6 % of the
climatic-sensitive treeline area contains trees.
The remaining treelines occur at lower elevations, and are mostly limited by
the physical characteristics of the terrain.
1
Cox & Snell R Squared: 0.529
Nagelkerke R Squared: 0.718
% Correct Classification: 88.5
5,000
CONCLUSIONS AND SIGNIFICANCE
10,000 Meters
RESULTS
Tree presence is positively related to recessive shale substrates, growing season temperatures,
cuesta-shaped mountains, regolith depth, and colluvial slopes.
Human structures, channels and alluvial floodplains were excluded from the analyses.
Traditionally, treeline studies have focused on sites known to be sensitive to
temperature, such as moderate slopes with deep regolith (e.g. 4).
1
Probability of tree presence
0
RGB to binary matrix
Climatic-limited treeline has
been empirically shown to be
located at average growing
season temperatures between
5.5ºC and 7.5ºC (2).
Most treeline in our study
area occurs well under
this area, especially
in the limestone ranges.
The presence of trees in the uppermost part of these forests largely depends on the existence
of suitable conditions mostly linked to topography and climate. Such places are the result of
geomorphologic processes acting on a framework set by the structural geology of the region.
We predict that the appearance of new sites suitable for tree growth does not depend on short
(i.e. yearly to decadal) time scales but on longer ones (i.e. centuries to millennia).
Mapping the 5.5ºC - 7.5ºC growing season mean T normally used to explain the position of
treeline (2) reveals that most of the treeline in the study area does not reach the temperature limited zone, especially over limestone.
Projections of future treeline dynamics at ecosystem or regional scales should take into
account the nature and significance of the sites where traditional treeline studies have been
performed.
Geomorphology alone performs better than growing season T as a predictor of tree presence
(82.9% vs. 79.0% of correct classification, Nagelkerke R Squared 0.480 vs. 0.439). A model
computed on topographical parameters only performs even better (86.6% and 0.666).
Ratio treed/total
Temperature, albeit being an important parameter for tree presence, is not even the strongest
single parameter determining the final presence/absence of a tree, which is more related to
substrate type.
Slopes with very specific characteristics (very rare in our study area, but potentially more
abundant in other geological environments) may exhibit suitable habitats above the current
treeline susceptible to being colonized by trees in a scenario of climate amelioration. We
argue that these precisely constitute the slopes over which most treeline studies have been
carried out (bias towards positive evidence).
Growing season T alone is a poorer predictor than physical parameters of the terrain,
even when elevation is not taken into account as a predictor.
1
1
x = 1.061*x - 0.029806
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
Growing Season Temperature
0
0.2
0.4
y = 1.0155*x - 0.023897
0.8
0.6
0.8
1
0
Topography (without elevation)
0
0.2
Probability of tree presence
0.4
0.6
0.8
1
(1) Holtmeier & Broll, 2005. Glob. Ecol. Biogeogr. 14, 395–410.
(2) Körner, 2003. Alpine Plant Life - Functional Plant Ecology of High
Mountain Ecosystems, 2nd Ed. Springer, Heidelberg.
(3) Körner & Paulsen, 2004. J. Biogeogr. 31, 713–732.
(4) Wiegand et al., 2006 J. Ecol., 94, 880–892.