EXAMINING CHANGES IN ARCTIC PLANT ASSEMBLAGES: PAST, PRESENT, AND FUTURE Mary S. Wisz, Loïc Pellissier, Lærke Stewart + collaborators
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1 EXAMINING CHANGES IN ARCTIC PLANT ASSEMBLAGES: PAST, PRESENT, AND FUTURE Mary S. Wisz, Loïc Pellissier, Lærke Stewart + collaborators VERSITET UNI Egevang
2 AARHUS OUTLINE: MODELLING CHANGE IN ARCTIC PLANT COMMUNITIES Big Questions: Climate, history, biotic interactions, and dispersal Emerging data and collaboration Greenland, North America, Eurasia Tools for solutions? SDM, structural equation models, mechanistic models Modelling patterns and processes; past present and future PhD project (Lærke) Post doc (Loïc)
3 AARHUS UNDERSTANDING AND PREDICTING CHANGES IN ARCTIC ECOSYSTEMS Y1 = β1x+γ 1Y 1 + e1 Y2 = β2x+γ 2Y 2 + e2 Yn = βnx+γ ny n + en Simonsen et al. 2011
4 QUESTIONS: GREENLAND (REGIONAL SCALE AND BEYOND?) Turnover: Which places and which groups of species will/ have experience/d the most/ least change? Consider: history, glaciation origin of species pools velocity of climate change functional groups dispersal strategy? Bay: phytogeographic origins Greenland s flora
5 WHAT SHAPES SPECIES ASSEMBLAGES? How do we quantify these relationships? Wisz et al in Press Biological Reviews
6 USING SPECIES DISTRIBUTION MODELLING TO PREDICT CURRENT AND FUTURE PLANT DISTRIBUTION Data collection Statistical modelling Spatial projection Water Temperature Stacking SDMs Communities Accounting for: Sp3 Sp2 Sp1 assemblages: past, present, and - futuret - Biotic interactions - Dispersal - Disturbances
7 SDM APPLICATIONS Scenarios Diversity and richness patterns Turnover Role of functional groups Dispersal Historical events (glaciation, etc) Species invasions Many, many others
8 CANDIDATE PREDICTOR VARIABLES Predictors Temperature Precipitation + winter precipitation Solar radiation Potential evapotranspiration Terrain (DEM elevation, slope, aspect) NDVI, NDWI, Snow (250 M) Bedrock Large mammals (caribou, muskox) Scenarios (DMI- Greenland) 25 km assuming 2 scenarios of glacial melt IPCC Historical DERIVATIVES/COMBINATIONS OF THESE RELAVANT FOR VEGETATION?
9 DESIGNING RELEVANT PREDICTORS FOR ARCTIC PLANT DISTRIBUTION Should 2-m air temperature or more proximal temperature (e.g. 5cm aboveground) be used?
10 AARHUS DESIGNING RELEVANT PREDICTORS FOR ARCTIC PLANTS USING THERMOLOGGERS a) NERO, vegetation transect, Nuuk, GL b) Design applied in the Swiss Alps
11 AARHUS VEGETATION DATA Details in this workshop Daniels Data Bay IAVD collaborators? CBIONET collaborators? Others?
12 But what about Greenland?
13 ARCTIC PLANT COMMUNITY MODELLING PHD PROJECT Lærke Stewart, Aarhus University (February 2012-January 2016) MS on the way: Funding: Greenland Climate Research Centre (500K), Greenland Ecosystem Monitoring (250K), NERI (250K) + AU (400K) Supervisors: Mary S. Wisz (main) Mads C. Forchhammer Jens-Christian Svenning Key-collaborators: Bay, Schmidt, Guisan Group UNIL, Skip Walker- IAVD, Daniels, Miska Luoto and hopefully others
14 AARHUS PHD PROJECT: AIMS PART 1 Coordinate effort with Daniels, Walker, IAVD, etc Collate existing information: recent past + contemporary. Data sources: Daniëls, CAFF, GEM, GBIF, ITEX, Back to the Future etc. Locations and functional traits Fieldwork and training Picture: arctic.ac.uk, modified by Stewart
15 PHD PROJECT: AIMS PART 2 Predicting contemporary distributions of plant species and assemblages in Greenland SDM methods Combining monitoring and biodiversity data New methods Herbivore distributions
16 PHD PROJECT: AIMS PART 3 How has glaciation and climate change shaped plant assemblages in Greenland for different species pools? SDM based on contemporary data predicted to historical scenarios Comparing historical, contemporary and future species assemblages in Greenland Stability + resilience of species, functional groups (e.g. source pools, dispersal or reproductive strategies) & habitats Comparing future scenario modelling results with ITEX warming experiments
17 PHD PROJECT: AIMS PART 4 How has circumpolar glaciation history since LGM influenced plant assemblages? Compare vegetation data to historical scenarios Which functional groups, dispersal strategies, etc predominate in a given region depending on velocity of climate change and glacial history? Multivariate stat analyses/sdm? Picture: Lomolino et al. 2010
18 CONCLUSION The biggest challenge is preparing the vegetation data. These must be harvested in the most efficient way how we can we contribute to the IAVD? Discuss Day 3 Flexible approach to project. Suggestions? Ecological and biogeographic questions are big but realistic progress can be made once the data are available
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