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