Climate-Biome Shifts in Climate-Biome Shifts in Alaska and Western Alaska and Western CanadaCanadaCurrent Results and Final StepsOctober 2011

ParticipantsParticipantsScenarios Network for Alaska Planning (SNAP), University of Alaska Fairbanks

EWHALE lab, Institute of Arctic Biology, University of Alaska Fairbanks

US Fish and Wildlife ServiceThe Nature ConservancyDucks Unlimited CanadaGovernment of the Northwest TerritoriesGovernment of CanadaOther invited experts

OverviewOverview This project is intended to:

◦ 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. 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.

Goals of this meetingGoals of this meetingReview Project GoalsSummary of project backgroundRefresher on modeling methods and

dataUpdate on decisions and progress thus

farDiscussion and decisions from group:

◦Finalizing and processing results◦Discuss data delivery and formats◦Other issues?

Timeline for completion

The Scenarios Network for Alaska The Scenarios Network for Alaska and Arctic Planning (SNAP)and Arctic Planning (SNAP)

SNAP is a collaborative network of the SNAP is a collaborative network of the University of Alaska, state, federal, and local University of Alaska, state, federal, and local agencies, NGOs, and industry partners. agencies, NGOs, and industry partners.

Its mission is to provide timely access to Its mission is to provide timely access to scenarios of future conditions in Alaska for scenarios of future conditions in Alaska for more effective planning by decision-makers, more effective planning by decision-makers, communities, and industry.communities, and industry.

SNAP uses data for 5 of 15 models that performed best for Alaska, Yukon, and NWT

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

Phase I: Alaska modelPhase I: Alaska modelMapped shifts in potential biomes based on current climate Mapped shifts in potential biomes based on current climate envelopes for six Alaskan biomes and six Canadian Ecozonesenvelopes for six Alaskan biomes and six Canadian Ecozones

http://geogratis.cgdi.gc.ca/geogratis/en/collection/detail.do?id=43617

Phase I Results:Potential Change: Current - Phase I Results:Potential Change: Current - 21002100(Noting that actual species shifts lag behind climate (Noting that actual species shifts lag behind climate shifts)shifts)

Improvements over Phase Improvements over Phase IIExtend scope to northwestern CanadaUse all 12 months of data, not just 2Eliminate 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

Other ImprovementsComparison with multiple landcover

classification categories◦ Eliminates over-reliance on one categorization

scheme◦ Utilizes divergent methods of classification

Recalibration of all SNAP downscaling to reconcile discontinuities between US and Canada and errors in A2 and B1 data (not used in Phase 1)*

* Note that this has caused project delays, but the very extensive work involved will NOT be charged to project funders.

Temperature Average Mosaic 2050

Sampling ExtentSampling Extent

Cluster analysisCluster 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.

Step 1: Create a Dissimilarity Step 1: Create a Dissimilarity MatrixMatrix

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”

(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.

Methods: Partitioning Around Methods: Partitioning Around Medoids (PAM)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

Resolution limitationsResolution limitationsData 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.

Solution to resolution Solution to resolution limitationslimitations

The solution to both resolution and clustering limitations was to cluster across the whole region using CRU data, which is available for the entire area, but to project future climate-biomes using PRISM, where available, to maximize resolution and sensitivity to slope, aspect, and proximity to coastlines.

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?

Sample cluster analysis showing 5 clusters, based on CRU 10’ climatologies. This level of detail was deemed too simplistic to meet the needs of end users.

Sample cluster analysis showing 30 clusters, based on CRU 10’ climatologies. This level of detail was deemed too complex to meet the needs of end users, as well as too fine-scale for the inherent uncertainties of the data.

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Number of Clusters Returned

Mean silhouette width for varying numbers of clusters between 3 and 50. High values in the selected range between 10 and 20 occur at 11, 17, and 18.

Eighteen-cluster map for the entire study area. This cluster number was selected in order to maximize both the distinctness of each cluster and the utility to land managers and other stakeholders.

Cluster certainty based on silhouette width. Note that certainty is lowest along boundaries.

Assessing the clustersBox plots, rose

plots, and other direct temp/precip depictions

Congruence with existing land cover classification by modal values

Congruence with land cover classification by percent

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AKClustPAM

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Rose Plots Showing the “Shape” of clusters

Line Graphs – clear, simple, familiar

NALCMS Land Cover, 2005North American Land Change

Monitoring System (Canada, Mexico and US)

Based on monthly composites of 2005 MODIS imagery

250m resolution;19 classesSeparating shrubland,

grasslands, deciduous forests and evergreen forests, as well as open vs closed canopies.

Does not distinguish boreal forest, temperate forest and rain forest, and defines tundra as either “grassland” or “sparse.”

 

AVHRR Landcover, 1995USGS – NOAA data13 categories with clear distinctions

between forest, shrubs, and grasslands.

“Dwarf shrub” and “herbaceous” categories help define tundra

Forested areas are defined primarily as deciduous or needle-leaf.

GlobCover 2009ESA JRC initiative300m MERIS sensor, ENVISAT

satelliteSimilar strengths and

weaknesses to AVHRR

Alaska Biomes and Canadian Ecozones

Nowacki et al. 2001 and Envirnoment CanadaDiffers fundamentally from other 3 classification

systems -- not based on remote sensing data but rather on a combination of observed data and interpolated data.

Based on not only cover type but also functional and morphological details, e.g. ecosystem function, soil type, bedrock, wildlife.

Dominant GlobCover 2009 land cover by cluster number. All land cover categories that occur in 15% or more of a given cluster are included.

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Permanent snow and ice

Water bodies

Sparse (<15%) vegetation

Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)Mosaic grassland (50-70%) / forest or shrubland (20-50%)

Dominant AVHRR land cover types by cluster number. All land cover categories that occur in 15% or more of a given cluster are included.

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Bare Ground

Cropland

Grassland

Open Shrubland

Closed Shrubland

Wooded Grassland

Woodland

Mixed Forest

Deciduous Broadleaf Forest

Dominant Alaska biomes and Canadian Ecozones by cluster number. Alaska biomes are adapted from Nowacki et al. 2001; Canadian ecozones are defined by Natural Resources Canada. All categories that occur in 15% or more of a given cluster are included.

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Montane Cordillera

Pacific Maritime

Boreal Cordillera

Taiga Cordillera

Prairie

Boreal PLain

Boreal Shield

Taiga Shield

Taiga Plain

Southern Arctic

Northern Arctic

Western Tundra

Climate-biome characteristics

1. The coldest cliome. Northern Arctic sparsely vegetated tundra with up to 25% bare ground and ice, with an extremely short growing season.

2. Cold northern arctic tundra, but primarily vegetated

3. More densely vegetated arctic tundra with up to 40% shrubs but no tree cover

4. Arctic tundra with denser vegetation, a longer growing season, and more shrub cover including some small trees

5. Dry sparsely vegetated southern arctic tundra

6. Northern boreal/southern arctic shrubland, with an open canopy and short growing season

7. Northern boreal coniferous woodland, open canopy

8. Dry boreal wooded grasslands — mixed coniferous forests and grasses

9. Mixed boreal forest, interior climate

10.Boreal forest with coastal influence and intermixed grass and tundra

11.Cold northern boreal/subarctic forest

12.More densely forested closed-canopy boreal

13.Dry, sparsely vegetated northern boreal

14.Interior boreal, densely forested

15.Warmer boreal zone with mixed evergreen and deciduous

16.Southern boreal, mixed forest17.Coastal rainforest, wet, more

temperate18.Prairie and grasslands

Data selected from SNAP models

Available climate data from SNAP includes output for each of the five best-performing GCMs as well as a composite (mean) of all five models for all months of all years to 2100, for each of three emission scenarios as defined by the IPCC: A1B, A2, B1

Selected three future time periods (2030-2039; 2060-2069 and 2090-2099). Decadal averages deemed more useful than single years for capturing trends.

Chose to provide outputs from all five models plus composite in A1B, and all three emission scenarios with composite.

Defining refugia and areas of greatest change 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.

Thresholds for what constitutes greatest change◦ Sites that shift climatically to match non-

adjacent biomes can be interpreted as a proxy for magnitude of change

◦ Areas that shift to a climate-biome with different dominant land cover types according to AVHRR, GlobCover, etc.

Interpreting confidence in refugia/ areas of change

◦Only consider areas selected as refugia in the majority (or all?) of the climate models

◦RandomForest assigns a ranking value to each of pixel that can be used to identify the model confidence

◦Treat boundary zones (low silhouette values) between cliomes as belonging to either cliome

Data and Product DeliveryFormal report Report submitted to the FWS Journal of

Fish & Wildlife Management or to a peer reviewed journal

Executive summaryAdditional short/simplified/regional

versions?PPT presentations – for what audiences?Posters, talks, and additional

publications?

Timeline for completionRemaining salary provided by grants

will not be withdrawn until project completion

Remaining modeling/mapping steps◦ Finalizing projection results◦ Defining refugia/areas of change◦ Hoping to meet current Dec 31 goal

Product creation◦ Simultaneous with above◦ Likely to require group feedback, final

edits, copy editing and printing in early 2012