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Modelling the effect of changing snow coverregimes on alpine plant species distribution  Christophe RANDIN, Jean-Pierre DE...
Context: snow in the alpineSnow cover distribution and duration ➔ mostcritical drivers in the alpine / tundraecosystemsSno...
Context: a warming world2070-2100 in the AlpsMean summer temperature may rise about 4°C(Raible et al. 2006) snowpack: gro...
Context: snowbed species under                                   climate changeSalix herbacea                 Snowbed spec...
Aim of the project                  Assess the effect of the                  future climate change                  on th...
Species distribution models (SDMs)?                                Statistical software:                                 C...
Species distribution models and climate change                                                        scenariosS. oppositi...
Database                                                          Modeling framework                                      ...
Study sitesPhoto: D. Hohenwallner
Snow-based predicting variablesSnowModel: a spatially distributed snow-evolution model                                    ...
Snow-based predicting variables SnowModel: a spatially distributed snow-evolution modelOct   Nov   Dec   Jan   Feb   Mar  ...
Validation of SnwoModel
Results: Model predictive power     P < 0.01                   P < 0.01Kappa / AUC / TSS (TC+Snow) > TC models     P < 0.0...
Results: variable contribution
Results: variable contributionAchillea clusianaTypical snowbed species, quite frequent within its (small) http://it.wikipe...
Results: persistence of species                                                                   MM5 - 2050Number of spec...
Results: persistence of species                                                                     HadCM3Number of specie...
Results: loss of connectivity between              potential suitable areas                            NS
Results: loss of connectivity between                                   potential suitable areasAchillea clusiana% of pot....
Conclusions• Ridge species may become rapidly exposed to  the effect of climate change (2050’s)• Impacts on snowbed specie...
Acknowledgments      Dr. Ioannis XenariosGrant PBLAA—118505
Thank you for your                           attention!Photos: C.Randin, N.Turland, Faculty Centre of Biodiversity; Uni Vi...
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Modelling the effect of changing snow cover regimes on alpine plant species distribution [Christophe Randin]

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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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Transcript of "Modelling the effect of changing snow cover regimes on alpine plant species distribution [Christophe Randin]"

  1. 1. Modelling the effect of changing snow coverregimes 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. 2. Context: snow in the alpineSnow cover distribution and duration ➔ mostcritical drivers in the alpine / tundraecosystemsSnow cover affects:➭soil temperature & moisture➭duration of the growing seasonIn turns, these factors control for nutrient availability Photos: C.Randin & N.Turland
  3. 3. Context: a warming world2070-2100 in the AlpsMean summer temperature may rise about 4°C(Raible et al. 2006) snowpack: growing season may extend ofabout 50–60 days at elevations above 2000–2500 m a.s.l.(Beniston et al. 2006)Trend already confirmed by satelliteobservations:Increase of snow-free period caused by an earliersnowmelt in spring over the last 30y (Dye 2002) Temperature and snow cover duration willboth affect alpine plant diversity Photos: C.Randin & N.Turland
  4. 4. Context: snowbed species under climate changeSalix 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. 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 speciesPhoto: C.Randin
  6. 6. Species distribution models (SDMs)? Statistical software: Calibration data Model calibration Slope Presence probability Temperature 0 8.1 2S. oppositifolia 1 - 2.3 48 … … … Temperature [°C] Slope Presence probability Temperature Slope [°] Presence GIS: Geographic Absence Information System Potential distribution
  7. 7. Species distribution models and climate change scenariosS. oppositifolia Temperature anomalies: HadCM3 GCM (A1FI) Potential distribution 2000 2025 2050 2080 2100
  8. 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 RSStatistical model (calibration) Species P/A ~TC (+Snow-based variables) ENSEMBLE modeling / GBM1. Predictive power of models (Kappa, AUC & TSS): TC vs. TC+Snow-based models2. 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. 9. Study sitesPhoto: D. Hohenwallner
  10. 10. Snow-based predicting variablesSnowModel: a spatially distributed snow-evolution model Photos: N.TurlandListon GE & Elder KE (2006) Journal of Hydrometeorology
  11. 11. Snow-based predicting variables SnowModel: a spatially distributed snow-evolution modelOct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
  12. 12. Validation of SnwoModel
  13. 13. Results: Model predictive power P < 0.01 P < 0.01Kappa / AUC / TSS (TC+Snow) > TC models P < 0.01 P < 0.01
  14. 14. Results: variable contribution
  15. 15. Results: variable contributionAchillea clusianaTypical snowbed species, quite frequent within its (small) http://it.wikipedia.orgdistribution range.Dominating an own phytosociological community (Campanulo pullae-Achilleetum clusianae)Contribution of snow-based variables: >40% in the TC+Snowmodel!Crepis jacquiniiIt is most typical for gaps in Carex firma swards with (fine-grained) screematerials.Contribution of snow-based variables: >25% in the TC+Snow model
  16. 16. Results: persistence of species MM5 - 2050Number 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. 17. Results: persistence of species HadCM3Number of species Persistence (%) • Species from snowbed become more sensitive to changing conditions
  18. 18. Results: loss of connectivity between potential suitable areas NS
  19. 19. Results: loss of connectivity between potential suitable areasAchillea clusiana% of pot. suitablehabitat: 92%Loss ofconnectivity: 67%to 44%HadCM3 A2 2100’s
  20. 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. 21. Acknowledgments Dr. Ioannis XenariosGrant PBLAA—118505
  22. 22. Thank you for your attention!Photos: C.Randin, N.Turland, Faculty Centre of Biodiversity; Uni Vienna
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