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Modelling the effect of changing snow cover regimes on alpine plant species distribution. Presented by Christophe Randin at the "Perth II: Global Change and the World's Mountains" conference in Perth, Scotland in September 2010.
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Christophe RANDIN, Jean-Pierre DEDIEU, Li LONG, Thomas DIRNBÖCK,
Ingrid KLEINBAUER, Raphael HUBACHER, Tobias JONAS,
Massimiliano ZAPPA and Stefan DULLINGER
Modelling the effect of changing snow cover
regimes on alpine plant species distribution
Photos: S.Dullinger
➭soil temperature & moisture
➭duration of the growing season
In turns, these factors control for nutrient
availability
Context: snow in the alpine
Snow cover distribution and duration ➔ most
critical drivers in the alpine / tundra
ecosystems
Snow cover affects:
Photos: C.Randin & N.Turland
2070-2100 in the Alps
Mean summer temperature may rise about 4°C (Raible et al. 2006)
snowpack: growing season may extend of
about 50–60 days at elevations above 2000–
2500 m a.s.l. (Beniston et al. 2006)
Trend already confirmed by satellite
observations: Increase of snow-free period caused by an earlier
snowmelt in spring over the last 30y (Dye 2002)
Temperature and snow cover duration will
both affect alpine plant diversity
Context: a warming world
Photos: C.Randin & N.Turland
Snowbed species (e.g. Salix herbacea, Gnaphalium
supinum) may be particularly endangered by
climate change because of the loss of their
habitat
They exhibit traits allowing to cope with a short
growing season:
• low carbon investment per unit of leaf area
• clonal reproduction
➮These specialized species show narrow
habitat niches (Schöb et al. 2009)
Gnaphalium
supinum
Salix herbacea
Context: snowbed species under
climate change
Photos: C.Randin & N.Turland; Uni Vienna
Photo: C.Randin
Aim of the project
Assess the effect of the
future climate change
on the distribution of
snowbed species Simulate a changing
snow cover
Quantify geographic
range contraction /
expansion of species
Temperature
0
1
Slope
8.1 2
- 2.3 48
… … …
Calibration data
GIS: Geographic
Information System P
res
en
ce
pro
ba
bil
ity
Slope [°]
Temperature [°C]
Pre
se
nc
e p
rob
ab
ilit
y
Statistical software:
Model calibration
Slope
Temperature
Presence
Absence
S. oppositifolia
Potential distribution
Species distribution models (SDMs)?
Species distribution models and climate change
scenarios
Potential distribution
2000
2025
2050
2080
2100
Temperature anomalies:
HadCM3 GCM (A1FI)
S. oppositifolia
Modeling framework
Comonly-used TC
variables
+ Snow-based variables from
simulated snow depth
GDD 0°C
Moisture index
Solar radiation
Slope & curvature
Number of snow days
Frost risk
Final snow accumulation day
19 snowbed species
19 “ridge” species
20 species with intermediate
preferences
1. Predictive power of models (Kappa, AUC & TSS): TC vs. TC+Snow-based models
2. Variable contribution (TC vs. Snow-based variables)
3. Predicted persistence of species under the A2 IPCC scenario
• 1 RCM MM5 2050
• RCM HirHam4 & GCM HadCM3 in 2100
Statistical model (calibration)
ENSEMBLE modeling / GBM Species P/A ~TC (+Snow-based variables)
Database
Evaluation with RS
Photo: D. Hohenwallner
Study sites
Snow-based predicting variables
Liston GE & Elder KE (2006) Journal of Hydrometeorology
SnowModel: a spatially distributed snow-
evolution model
Photos: N.Turland
Snow-based predicting variables
SnowModel: a spatially distributed snow-evolution model
Oct Nov Dec Jan Feb Mar Apr May Aug Sep Jun Jul
Validation of SnwoModel
Results: Model predictive power
Kappa / AUC / TSS (TC+Snow) > TC models
P < 0.01 P < 0.01
P < 0.01 P < 0.01
Results: variable contribution
Achillea clusiana
Typical snowbed species, quite frequent within its (small)
distribution range.
Dominating an own phytosociological community (Campanulo pullae-
Achilleetum clusianae)
Contribution of snow-based variables: >40% in the TC+Snow
model!
Crepis jacquinii
It is most typical for gaps in Carex firma swards with (fine-grained) scree
materials.
Contribution of snow-based variables: >25% in the TC+Snow model
Results: variable contribution
http://it.wikipedia.org
Results: persistence of species
MM5 - 2050
Potential regional persistence / species:
Nu
mb
er
of
sp
ec
ies
Persistence (%)
• Overall, more losers that winners
• Species from ridges more affected by surface loss
MM5 data source : A. Gobiet / Wegener Center, Austria.
Results: persistence of species
HadCM3
Nu
mb
er
of
sp
ec
ies
Persistence (%)
• Species from snowbed become more sensitive to changing conditions
Results: loss of connectivity between
potential suitable areas
NS
Achillea clusiana
% of pot. suitable
habitat: 92%
Loss of
connectivity: 67%
to 44%
HadCM3 A2
2100’s
Results: loss of connectivity between
potential suitable areas
• Ridge species may become rapidly exposed to
the effect of climate change (2050’s)
• Impacts on snowbed species may be buffered
(2050’s) but then become stronger at the end of
the century
• Nonspecialized species may be less affected
than specialized species (persistence and
connectivity)
Conclusions
Acknowledgments
Grant PBLAA—118505
Dr. Ioannis Xenarios
Thank you for your
attention!
Photos: C.Randin, N.Turland, Faculty Centre of Biodiversity; Uni Vienna