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.

1. Modelling the effect of changing snow cover
regimes on alpine plant species distribution
Christophe RANDIN, Jean-Pierre DEDIEU, Li LONG, Thomas DIRNBÖCK,
Ingrid KLEINBAUER, Raphael HUBACHER, Tobias JONAS,
Massimiliano ZAPPA and Stefan DULLINGER

2. Context: snow in the alpine
Snow cover distribution and duration ➔ most
critical drivers in the alpine / tundra
ecosystems
Snow cover affects:
➭soil temperature & moisture
➭duration of the growing season
In turns, these factors control for nutrient
availability
Photos: C.Randin & N.Turland

3. Context: a warming world
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
Photos: C.Randin & N.Turland

4. Context: snowbed species under
climate change
Salix herbacea
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
Gnaphalium
supinum • clonal reproduction
➮These specialized species show narrow
habitat niches (Schöb et al. 2009)
Photos: C.Randin & N.Turland; Uni Vienna

5. 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
Photo: C.Randin

6. Species distribution models (SDMs)? Statistical software:
Calibration data Model calibration
Slope
Presence probability
Temperature
0 8.1 2
S. oppositifolia
1 - 2.3 48
… … …
Temperature [°C]
Slope
Presence probability
Temperature
Slope [°]
Presence
GIS: Geographic Absence
Information System Potential distribution

7. Species distribution models and climate change
scenarios
S. oppositifolia
Temperature anomalies:
HadCM3 GCM (A1FI)
Potential distribution
2000
2025
2050
2080
2100

8. Database
Modeling framework
Comonly-used TC
19 snowbed species variables
19 “ridge” species GDD 0°C
20 species with intermediate Moisture index
Solar radiation
preferences Slope & curvature
Number of snow days + Snow-based variables from
Frost risk
Final snow accumulation day simulated snow depth
Evaluation with RS
Statistical model (calibration)
Species P/A ~TC (+Snow-based variables) ENSEMBLE modeling / GBM
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

9. Study sites
Photo: D. Hohenwallner

10. Snow-based predicting variables
SnowModel: a spatially distributed snow-
evolution model
Photos: N.Turland
Liston GE & Elder KE (2006) Journal of Hydrometeorology

11. Snow-based predicting variables
SnowModel: a spatially distributed snow-evolution model
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

12. Validation of SnwoModel

13. Results: Model predictive power
P < 0.01 P < 0.01
Kappa / AUC / TSS (TC+Snow) > TC models
P < 0.01
P < 0.01

14. Results: variable contribution

15. Results: variable contribution
Achillea clusiana
Typical snowbed species, quite frequent within its (small) http://it.wikipedia.org
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

16. Results: persistence of species
MM5 - 2050
Number of species
Persistence (%)
Potential regional persistence / species:
• Overall, more losers that winners
• Species from ridges more affected by surface loss
MM5 data source : A. Gobiet / Wegener Center, Austria.

17. Results: persistence of species
HadCM3
Number of species
Persistence (%)
• Species from snowbed become more sensitive to changing conditions

18. Results: loss of connectivity between
potential suitable areas
NS

19. Results: loss of connectivity between
potential suitable areas
Achillea clusiana
% of pot. suitable
habitat: 92%
Loss of
connectivity: 67%
to 44%
HadCM3 A2
2100’s

20. Conclusions
• 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)

21. Acknowledgments
Dr. Ioannis Xenarios
Grant PBLAA—118505

22. Thank you for your
attention!
Photos: C.Randin, N.Turland, Faculty Centre of Biodiversity; Uni Vienna