1. Climate Change and Biome Shifts in Alaska and Western Canada Current Results and Modeling Options December 2010

2. Participants Scenarios Network for Alaska Planning (SNAP), University of Alaska Fairbanks EWHALE lab, Institute of Arctic Biology, University of Alaska Fairbanks US Fish and Wildlife Service The Nature Conservancy Ducks Unlimited Canada Government of the Northwest Territories Government of Canada Other invited experts

3. Goals of this meeting Review Project Goals Summary of project background Explanation of modeling methods and data Update on progress thus far Discussion and decisions from group: ◦ Confirm clustering inputs (24 predictor variables) ◦ Confirm resolution for clustering and re-projection (CRU vs PRISM) ◦ Select number of clusters (15-20) ◦ Select land cover comparisons, data and methods ◦ Choose future decades to model ◦ Confirm emissions scenarios (A1B, A2, B1) ◦ Discuss data delivery and formats ◦ Other issues? Review Project timeline

4. Overview This project is intended to: ◦ a) develop climate and vegetation based biomes for Alaska, the Yukon and the Northwest Territories based on data, and ◦ b) based on the climate data, identify areas that are least likely to change and those that are most likely to change over the next 100 years. This project builds,and makes use of, work previously conducted by SNAP, EWHALE, USFWS, TNC, and other partners. The completed analysis will be used by partners involved in protected areas, land use, and sustainable land use planning, e.g. connectivity.

5. Overall objectives Develop climate and vegetation based biomes (based on cluster analysis) for AK, Yukon, NWT, and areas to the south that may represent future climatic conditions for AK,Yukon or NWT. Model potential climate-induced biome shift. Based on model results, identify areas that are least or most likely to change over the next 10-90 years. Provide maps, data, and a written report summarizing, supporting, and displaying these findings.

6. The Scenarios Network for Alaska and Arctic Planning (SNAP) SNAP is a collaborative network of the University of Alaska, state, federal, and local agencies, NGOs, and industry partners. SNAP is a collaborative network of the University of Alaska, state, federal, and local agencies, NGOs, and industry partners. Its mission is to provide timely access to scenarios of future conditions in Alaska for more effective planning by decision-makers, communities, and industry. Its mission is to provide timely access to scenarios of future conditions in Alaska for more effective planning by decision-makers, communities, and industry.

7. SNAP uses data for 5 of 15 models that performed best for Alaska and northern latitudes PRISM downscaled to 2 km resolution OR CRU downscaled to 10 minutes (18.4 km) Monthly temp and precip from 1900 to 2100 (historical CRU + projected) 5 models x 3 emission scenarios Available as maps, graphs, charts, raw data On line, downloadable, in Google Earth, or in printable formats No data yet: ◦ Extreme events ◦ Snowpack ◦ Coastal/Oceans SNAP Projections: based on IPCC models

8. Phase I: Alaska model Mapped shifts in potential biomes based on current climate envelopes for six Alaskan biomes and six Canadian Ecozones http://geogratis.cgdi.gc.ca/geogratis/en/collection/detail.do?id=4361 8

9. Phase I Results:Potential Change: Current - 2100 (Noting that actual species shifts lag behind climate shifts)

10. Improvements over Phase I Extend scope to northwestern Canada Use all 12 months of data, not just 2 Eliminate pre-defined biome/ecozone categories in favor of model-defined groupings (clusters) ◦ Eliminates false line at US/Canada border ◦ Creates groups with greatest degree of intra-group and inter-group dissimilarity ◦ Gets around the problem of imperfect mapping of vegetation and ecosystem types ◦ Allows for comparison and/or validation against existing maps of vegetation and ecosystems

11. Sampling Extent

12. Cluster analysis Cluster analysis is the assignment of a set of observations into subsets so that observations in the same cluster are similar in some sense. Clustering is a method of “unsupervised learning” (the model teaches itself, and finds the major breaks) Clustering is common for statistical data analysis used in many fields The choice of which clusters to merge or split is determined by a linkage criterion (distance metrics), which is a function of the pairwise distances between observations. Cutting the tree at a given height will give a clustering at a selected precision.

13. Step 1: Create a Dissimilarity Matrix Distance measure determines how the similarity of two elements is calculated. Some elements may be close to one another according to one distance and farther away according to another. In our modeling efforts, all 24 variables are given equal weight, and all distances are calculated in “24- dimensional space” using RandomForest (similarity matrix, proximity matrix, distance matrix get converted into each other) Taxicab geometry versus Euclidean distance: The red, blue, and yellow lines have the same length in taxicab geometry for the same route. In Euclidean geometry, the green line has length 6×√2 ≈ 8.48, and is the unique shortest path.

14. Methods: Partitioning Around Medoids (PAM) The dissimilarity matrix describes pairwise distinction between objects. The algorithm PAM computes representative objects, called medoids whose average dissimilarity to all the objects in the cluster is minimal Each object of the data set is assigned to the nearest medoid. PAM is more robust than the well-known kmeans algorithm, because it minimizes a sum of dissimilarities instead of a sum of squared Euclidean distances, thereby reducing the influence of outliers. PAM is a standard procedure

15. Clustering limitations PAM must compare every data point to every other data point in the dissimilarity matrix (created by RandomForest), and create medoids Adding additional data points affects processing requirements exponentially Thus, in creating clusters, we were limited to approximately 20,000 data points, a fraction of the possible samples. Total area is approximately 19 million square kilometers This meant selecting one data point for approximately every 20 km by 20 km

16. Resolution limitations Data are not available at the same resolution for the entire area ◦ for Alaska, Yukon, and BC, SNAP uses 1961-1990 climatologies from PRISM, at 2 km, ◦ for all other regions of Canada SNAP uses climatologies for the same time period from CRU, at 10 minutes lat/long (~18.4 km) ◦ In clustering these data, both the difference in scale and the difference in gridding algorithms led to artificial incongruities across boundaries. ◦ One solution to both resolution and clustering limitations is to cluster across the whole region using CRU data, which is available for the entire area.

17. Re-Sampling to overcome AK & Can differences (=> as it applies to many GIS datasets) Different Pixel Resolutions

18. Different Pixel Resolutions resolved…. Re-Sampling to overcome AK & Can differences (=> as it applies to many GIS datasets)

19. PRISM data Unlike other statistical methods in use today, PRISM was written by a meteorologist specifically to address climate Moving-window regression of climate vs. elevation for each grid cell Uses nearby station observations Spatial climate knowledge base weights stations in the regression function by their physiographic similarity to the target grid cell PRISM is well-suited to mountainous regions, because the effects of terrain on climate play a central role in the model's conceptual framework The primary effect of orography on a given mountain slope is to cause precipitation to vary strongly with elevation. The topographic facet is an important climatic unit and elevation is a primary driver of climate patterns PRISM quality depends on DEM

20. PRISM: 5 clusters Coastal vs interior, northern vs southern Note: colors on all the following cluster maps are arbitrary, and are chosen merely to be distinct from one another.

21. PRISM: 10 clusters Aleutians and coastal rainforest become distinct

22. PRISM: 15 clusters Latitudinal patterns in AK and BC

23. PRISM: 20 clusters Highest points of Brooks Range separate from coastal plain and lower foothills How many clusters can be justified?

24. CRU data The station climate statistics were interpolated using thin- plate smoothing splines (ANUSPLIN) Trivariate thin-plate spline surfaces were fitted as functions of latitude, longitude and elevation to the station data The inclusion of elevation as a co-predictor adds considerable skill to the interpolation, enabling topographic controls on climate Local topographic effects such as rain shadows cannot be resolved unless: (1) a predictor that is a proxy for this influence is incorporated in the interpolation, and/or (2) there are sufficient stations to capture this local dependency as a function of latitude, longitude and elevation. In regions with sparse data, the station networks used to create these data sets are clearly unable to capture this sort of detail

25. CRU data alone: 5 clusters Strong latitudinal banding

26. CRU data alone: 10 clusters Weather station anomaly?

27. CRU data alone: 15 clusters Latitudinal banding persists, but more variability and east/west break

28. CRU data alone: 20 clusters How many clusters can be justified?

29. Re-projecting CRU clusters to PRISM CRU is available for entire study area, and offers a good fit at a broader scale PRISM offers a better fit at fine scales, with better accuracy re altitude but is not fully available for the study area Best of both: ◦ Cluster results from CRU data were used to train an RF classification model. ◦ RF then classified the full PRISM datasets (where available) according to these clusters ◦ This referred as DOWNSCALING

30. Comparison of results using various methods The following results were derived from the following clustering and downscaling groups: ◦ Created clusters using 15km sample of 2km PRISM data, and downscaled to the full PRISM dataset at 2km resolution over AK, YT, BC. ◦ Created clusters using 20km sample of 10min CRU data, and downscaled using the 2km PRISM data over AK, YT, BC.

31. Comparison of results: 5 clusters Trained to PRISM data, and re- projected to PRISM Trained to CRU, re- projected to PRISM data

32. Comparison of results: 10 clusters Trained to PRISM data Trained to CRU, re- projected to PRISM data Trained to PRISM data, and re- projected to PRISM

33. Comparison of results: 15 clusters Trained to PRISM data Trained to CRU, re- projected to PRISM data Trained to PRISM data, and re- projected to PRISM

34. Comparison of results: 20 clusters Trained to PRISM data Trained to CRU, re- projected to PRISM data Trained to PRISM data, and re- projected to PRISM

35. Assessing the clusters Box plots Congruence with existing land cover classification by modal values Congruence with land cover classification by percent Other metrics?

36. January precipitationJanuary temperature February precipitationFebruary temperature

37. July precipitationJuly temperature October precipitationOctober temperature

38. Landcover in Alaska and Canada Viereck Nowacki Canadian Ecoregions NLDC Landfire LANDSAT AVHRR MODIS NDVI Greenness North America Landcover

39. ValueLandcover 0Water 1Evergreen Needleleaf Forest 2Evergreen Broadleaf Forest 3Deciduous Needleleaf Forest 4Deciduous Broadleaf Forest 5Mixed Forest 6Woodland 7Wooded Grassland 8Closed Shrubland 9Open Shrubland 10Grassland 11Cropland 12Bare Ground 13Urban and Built AVHRR Land Cover (1km)

40. AVHRR Landcover Overlaid with 15 Cluster Polygons

41. AVHRR_LC 1 - Evergreen Needleleaf Forest 6 - Woodland 8 - Closed Shrubland 9 - Open Shrubland 10 - Grassland 15 Cluster Solution (10min CRU) With Most Common AVHRR Landcover Class Displayed Within Each Cluster Area The logic here is that each cluster has the mode response displayed within it using a “winner- take-all” methodology

42. How Pure Are These New Clusters with Regard to AVHRR Landcover?

43. Boreal Cordillera Boreal PLain Boreal Shield Hudson Plain Montane Cordillera Northern Arctic Pacific Maritime Prairie Southern Arctic Taiga Cordillera Taiga Plain Taiga Shield Canada Ecozones

44. How Pure Are These New Clusters with Regard to Canada’s Ecozones?

45. Canada Ecozones – With 15 Cluster Solution Polygons Overlaid “Winner-take-all” Type of Mode Reclassification Boreal Cordillera Boreal PLain Boreal Shield Hudson Plain Montane Cordillera Northern Arctic Pacific Maritime Prairie Southern Arctic Taiga Cordillera Taiga Plain Taiga Shield

46. Northern Arctic Southern Arctic Taiga Plain Taiga Sheild Boreal Sheild Boreal Plain Prairie Taiga Cordillera Boreal Cordillera Pacific Maritime Montane Cordillera 15 Cluster Solution with Mode Response From Canada Ecozones as Identifier of New Clusters – With Canada Ecozones Polygons Overlaid “Winner-take-all” Type of Mode Reclassification

47. Alaska Ecoregions - Nowacki LEVEL_2 Alaska Range Transition Aleutian Meadows Arctic Tundra Bering Taiga Bering Tundra Coast Mountains Transition Coastal Rainforests Intermontane Boreal Pacific Mountains Transition

48. 15 Cluster Solution Mode Response of Alaska Ecoregions – Nowacki [Level 2] Alaska Range Transition Aleutian Meadows Arctic Tundra Bering Taiga No MODE Value Coastal Rainforests Intermontane Boreal Pacific Mountains Transition

49. How Pure Are These New Clusters with Regard to Alaska Ecoregions (Nowacki)?

50. 15 Cluster Solution of Alaska Ecoregions – With Nowacki [Level 2] Ecoregions Polygons Overlaid

51. How many clusters? Choice is mathematically somewhat arbitrary, since all splits are valid Some groupings likely to more closely match existing land cover classifications How many clusters are defensible? How large a biome shift is “really” a shift from the conservation perspective? Multiple numbers of clusters to explore this, e.g. 15 and 20?

52. 16 clusters (CRU, not downscaled )

53. 17 clusters (CRU, not downscaled)

54. 18 clusters (CRU, not downscaled)

55. 19 clusters (CRU, not downscaled)

56. 16 clusters [trained at 10min (CRU) and down-modeled at 10min (CRU)]

57. 17 clusters [trained at 10min (CRU) and down-modeled at 10min (CRU)]

58. 18 clusters [trained at 10min (CRU) and down-modeled at 10min (CRU)]

59. 19 clusters [trained at 10min (CRU) and down-modeled at 10min (CRU)]

60. 16 clusters [trained at 10min (CRU) and down-modelled at 2km PRISM (AK, YT, BC)]

61. 17 clusters [trained at 10min (CRU) and down-modelled at 2km PRISM (AK, YT, BC)]

62. 18 clusters [trained at 10min (CRU) and down-modelled at 2km PRISM (AK, YT, BC)]

63. 19 clusters [trained at 10min (CRU) and down-modelled at 2km PRISM (AK, YT, BC)]

64. What Does The Future Look Like? At the 15 cluster solution Using A1B temperature and precipitation data for Canada and Alaska we can visualize the predicted shifting of biomes through time. Time steps: 2000-2009, 2030-2039, 2060- 2069, 2090-2099 All predictor 24 variables included

65. Alaska Canada Study Extent 2000-2009 --15 clusters Note: future projections for the project will NOT be done over this full extent, but only for AK, YT, NWT, and a limited boundary area. Results for the eastern and southern portions shown here are invalid because no clusters have been allowed to shift in from outside these boundaries.

66. 2000-2039 – 15 clusters

67. 2060 - 2069 – 15 clusters

68. 2090 - 2099 – 15 clusters

69. Data choices: SNAP models Available climate data from SNAP include output for each of the five best-performing GCM models as well as a composite (mean) of all five models Minimum of three future time periods (e.g. 2030- 2039; 2060-2069 and 2090-2099) -- are these periods optimal? Will we use just the composite model? Choice of emission scenario as defined by the IPCC: A1B, A2, B1 – A2 and A1B, or just A1B?

70. Modeling choices: Model variability and extreme years RandomForest can inform researchers of which variables, or models, of the complex multivariate set are most important in defining future distributions ◦ Can run 6 different climate models independently so results can be compared ◦ All 6 model variables can be entered simultaneously within RandomForest so the software can select between models and variables. ◦ Top 5 SNAP models perform differently in different areas of Alaska. ◦ Geographic tag to explain how the different GCM models perform in different regions of the state. ◦ Incorporate the important thresholds or ‘tipping points’ that are often defined by extreme climate years, while avoiding the reliance on just a single year’s modeled data.

71. Modeling choices: Defining change, defining refugia The decadal results from RandomForest will be analyzed to determine which grid cells are projected to remain within the same biome climate envelope over the time periods. Confidence in these areas ◦ Only consider areas selected as refugia in the majority of the climate models ◦ RandomForest assigns a ranking value to each of pixel that can be used to identify the model confidence ◦ Sites that shift climatically to match non-adjacent biomes can be interpreted as a proxy for magnitude of change

72. Timeline Initiation meeting with experts and stakeholders to review approaches for developing the existing biome data layer: May 5th 2010 Team leader meetings/teleconferences for AK and Canada projects to make key decisions regarding clustering methods and spatial resolution. Autumn 2010 Initial clustering results and sample projection data: December 2010 Project update and stakeholder meeting. Decisions to be made include time steps for analysis, emission scenarios, and composite vs single models: December 14th, 2010 Project update, progress report and stakeholder meeting to determine thresholds for the analysis of refugia and areas of extreme change: April 2011 Close-out meeting with experts and stakeholders: September 2011 Final report, manuscript draft, digital data (including metadata): September 30th 2011

73. Deliverables Complete set (spatial ArcGIS map files, metadata and appropriate data streams) of GIS models for the Alaska & Canadian biomes Progress report describing final derived biomes and the quantitative differences between their climate envelopes. Complete set of GIS models predicting future biome change at four time steps Progress report describing the methods and selection process to create the final predicted biomes Complete set of GIS models defining areas of refugia, and how frequently other areas within Alaska change at the 3 future time steps (i.e. “resilience”) Report submitted to the FWS Journal of Fish & Wildlife Management or to a peer reviewed journal. The FWS Journal of Fish & Wildlife Management is an electronic journal sponsored by FWS.