1. PRADEEP KUMAR CHIEF CONSERVATOR OF FORESTS FORESTS, ENVIRONMENT AND WILDLIFE MANAGEMENT DEPARTMENT SIKKIM

2. 9 million sqkm of the Earth’s surface, 23 %, In India 9 states 63%

6. Optical VEGETATION

8. SL_N ODIST Com -No. Site- ID Plot _ID Tree _ID Local_N ame Botanical Name Volume equation (by FSI) CBH(c m) Dia(c m) VolumeSP_gravityTree Biomass 0.223350.510.11391 1 EgangR gangB 1 Bhusu k111Gobrey Echinocarpu s desycarpus V/D2=0.25564/D2- 0.030418/D+0.0012897350111.41 3.62564 0.00000 2 EgangR gangB 1 Bhusu k112Tarsing Belischmiedi a sikkimensis V=0.51191- 1.78643*√D+11.19974*D220063.66 2.597210.451.16874 3 EgangR gangB 1 Bhusu k113 Titey Chanp Michelia cathcartii Hk.f.& T. V/D2*H=0.00667/D2*H+0.32 94919060.48 0.11489 0.00000 4 EgangR gangB 1 Bhusu k114Kawlo Machilus gramminean a V=0.12652- 0.018037*D+0.000956*D221066.85 0.235430.510.12007 5 EgangR gangB 1 Bhusu k115Gobrey Echinocarpu s desycarpus V/D2=0.25564/D2- 0.030418/D+0.001289721568.44 0.11650 0.00000 6 EgangR gangB 1 Bhusu k116Kawlo Machilus gramminean a V=0.12652- 0.018037*D+0.000956*D218057.30 0.058970.700.04122 0.024670.480.01189

12. But foreshortening, layover and shadowing limit the application

13. LIDAR

14. As quoted by the company Weighing less than 10kg, LiDAR platform called the “Phoenix AL-2” combines the latest UAV, LiDAR and GNSS technology. Could prove to be a cost effective, accurate and safe micro-mapping solution.

15. “As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.” ― Albert EinsteinAlbert Einstein

16. NEED TO UNDERSTAND WHAT IS GOING TO HAPPEN RATHER THAN JUMPSTARTING TO ADAPTATION

17. Model Current and Future Climate Current Species Distribution Develop algorithms Model Future Distribution Understand what is going to happen ADAPTATION BASED ON SCIENCE, NOT ON PERCEPTIONS

18. In India >90 species of Rhodo. 36 ~40 species in Sikkim. State tree of Sikkim R. niveum.

19. MODELING PROCEDURE ‘Mechanistically’ or ‘Correlatively’ Maxent is a maximum entropy based machine learning program that estimates the probability distribution for a species’ occurrence by finding the probability distribution of maximum entropy based on environmental constraints distribution.

20. MODELING PROCEDURE All the bioclimatic layers in file format ASCII were used with resolution of 30ARC seconds. 70% were used in calibrating the model and remaining 30% were used for testing the model. 112+63 locations

21. Bioclimatic variables BIO1 = Annual Mean Temperature BIO5 = Max Temperature of Warmest Month BIO13 = Precipitation of Wettest Month BIO15 = Precipitation Seasonality (Coefficient of Variation) BIO6 = Min Temperature of Coldest Month

22. Test Statistics For threshold independent assessment ROC analysis, which characterizes performance of model at all possible thresholds by a single number AUC was used. The ROC describes the relationship between (sensitivity) and the (1 – specificity).

23. CLIMATE DATASET WorldClim database developed by Hijmans et el. Data resolution 30 seconds (0.93 km x 0.93km = 0.86 km 2 at equator Statistically downscaled datasets obtained from International Centre for Tropical Agriculture 2010 originally downloaded from the IPCC data portal and re-processed using a spline interpolation algorithm of the anomalies

24. CLIMATE DATASET contd. The future climate change scenario pertained to HadClim Emission scenario SRES-A1B (corresponding to A1: Maximum energy requirements -emissions differentiated dependent on fuel sources. B: Balance across sources). Altitude not used in the modelling

25. Representation of the Maxent model for current distribution of Rhododendron

26. Projection of the Maxent model for Rhododendron onto the environmental variables for future climate

28. AUC Analysis through ROC Curve

29. “Life must be lived forward, But understood backward” -Kierkegaard PAST CLIMATE RECONSTRUCION

30. Reconstructed late summer temperature (July-September) from Abies densa of Eastern Himalaya Some marked cool and warm period in this reconstructed series Cool Period A.D. 1781-1792 A.D. 1881-1810 (-0.31 o C) A.D. 1827-1836 A.D. 1850-1859 A.D. 1893-1902 A.D. 1929-1938 A.D. 1970-1978 Warm Period A.D. 1813-1822 A.D. 1938-1846 A.D. 1905-1914 A.D. 1960-1969 A.D. 1978-1987 (+0.25 o C) Markedly cool late summer A.D. 1782-1786, A.D. 1830-1831 A.D. 1899, A.D. 1933, A.D. 1975 Much warmer summers A.D. 1777-1779, A.D. 1817, A.D. 1843, A.D. 1904-1906, A.D. 1926-1927, A.D. 1980-1982 Bhattacharyya, A., Chaudhary, V., 2003.

31. Abies densa Forest in and around Zema Sample collected during 2008: 73 cores from 39 trees Preliminary result: Chronology extending from AD 1628-2007 (need further correction of the samples )

33. QUANTITATIVE CLIMATE RECONSTRUCTION BASED ON POLLEN DATA Contemporary climate data Calibration dataset Transfer Function Modern Pollen Fossil Pollen Pollen Diagram Reconstruction Interpolated climate dataset at each surface pollen site. Correspondence Analysis (CA) Detrended Correspondence Analysis (DCA) Principal Component Analysis (PCA) Redundancy Analysis (RDA) Canonical Correspondence Analysis (CCA) Weighted Averaging Partial Least Square (WA-PLS) Principal Component Regression, Correspondence Analysis Regression Modern Analog Technique