Project reorganization (Swiss part)  TASK 7&8: Bronwyn (content), Achilleas (tech. support), Janine (senior expert)  TASK 9 tbd  UG project coordination/ communication/ administration until end of FORECOM

Forest cover time series Swiss Alps first results  Time series based on historical maps for -SA (1850/1880/1940/[1970]/current)  Trends and trajectories  Test reliability

Maps (the Swiss Alps) o Dufour Map Original Survey (~1850, scale 1: – 1:50 000) o Siegfried Map (edition 1880 and 1940, scale 1: – 1:50 000) o Landeskarte der Schweiz (1970s and current state, 1: )

(ha) persistent loss increase

Forest transition GR GL URI OW NW portion of total landscape

Historic map comparison: Methodological challenges Test for consistency: Minimal Mapping Unit Reliability of trajectories Comparison with independent sources Trajectory ( ) Portion of forest in %87.9% % % % %94.7% %99.4% % %100%

OrthophotoHistorical Map Terrestrial Photo Comparison -spatial overlay -identification of error types Comparison -qualitative assessment -areas with good/bad agreement Hypothesis generation -topography -morphology Hypothesis test Application - Accurracy map large extent Vectorization of forest cover information

TASK 6: Drivers of past forest cover change concept and first results for the Swiss Alps TASK 6: Estimation of climate change and land use contribution to past forest cover change Research aims: disentangling land use and climate effects for the past forest cover trajectories at different spatial and temporal scales Compare drivers in Swiss Alps and Polish Carpathians

1 ha raster (n=970’000) Target variable: Loss/gain (binary) Administrative units Communities (n=199) Districts (n=15) Cantons (n=5) Target variable: change in forest proportion (abs/rel) context climate/ topography socioeconomics Test different combinations of drivers at different spatial resolutions Potential drivers Scale of analysis

Topographical Data parameterunitcalculation/transformationsourcestatus Altitudem aslDHM100ready Slopedegree calculated from DHM 100ready Norhtness(-1,1) cos ((aspect in degrees * PI)/180) calculated from DHM 100ready Eastness(-1,1) sin ((aspect in degrees * PI)/180) calculated from DHM 100ready

Socioeconomic Data An extensive sample of socioeconomic data has been compiled for all 199 communities within FORECOM study area by Marc Herrmann (data to be jointly used in AlpPast/FORDYNCH and FORECOM) Parameters include information on population (inc. Age distribution), accessibility (road/railway), agriculture, employment sectors, commuters etc. Not full set of parameters available for all periods (most go back to 1930) Transferability of approach and comparability -> identify minimal set of parameters available for CH and PL

Socioeconomic Data populationagricultureeconomyaccessibility N peopleN farms employees per econ sectorBy railroad (0/1) Age classes (0- 14/15-60/60+)Farming area By road (major roads only) Animals (LU/small cattles) Selection based on hypothesis Availability Poland ?

Context Data Contextual variables include information that is determined by location. Some variables are clearly related to the biological system (distance to forest edge) others to socio-economy (distance to road/settlement)

Climate Data Basic data set ( ) Monthly temperature and precipitation downscaled to 100m resolution Historical data ( ) Calculate anomalies to reconstructed historical time series; monthly temperature (Luterbacher), seasonal precipitation (seasonal,Pauling). Spatial Interpolation (100m grid) Final data ( ) Mean values for temperature and precipitation (periods same as for fcc) Mean annual DDsum

context climate/topo socioeconomics Test different combinations at different scales 1 ha raster (n=969’700) Target variable: forest loss/forest gain Administrative units Target variable: change in forest cover proportion Drivers

Drivers of forest gain Adj D2 Model: GLM (binomial) stepwise, sample: 10’000 non- forest pixels at t1 Target variable: forest gain (yes/no) Explanatory variables: exposition (northeness/eastness), altitude, slope, distance to forest edge at 1st time step, distance to settlement

Drivers of forest loss Adj D2 Model: GLM (binomial) stepwise, sample 10’000 forest pixels at t1 Target variable: forest gain (yes/no) Explanatory variables: exposition (northeness/eastness), altitude, slope, distance to forest edge at 1st time step, distance to settlement

Explaining forest cover by topography and previous forest cover? Adj D2 Model: GLM (binomial) stepwise, sample 10’000 of all pixels Target variable: forest (yes/no) Explanatory variables: exposition (northeness/eastness), altitude, slope, forest cover at previous time step

Problem of spatial autocorrelation Example modelling gain Sample size 10’000 -> 819 Model performance (Adj D2) > km distance threshold

context climate/topo socioeconomics Test different combinations at different scales 1 ha raster (n=969’700) Target variable: forest loss/forest gain Administrative units Target variable: change in forest cover proportion Drivers

Appropriate admin unit? Forest cover vs. Population change ( relative changes ) Communities (n=199) Districts (n=15) Relatively strong correlation with proportion of older people (60+) at district level Forest cover change population change

Does population changes drive forest cover change? What is the appropriate resolution (admin unit)? Absolute vs relative changes (fc and pc) Time lag between pop change and forest cover change?