The Alaska Climate-Biome Shift Project (AK Cliomes) and the Yukon and Northwest Territories (NWT) Climate-Biome Shift Project (Ca Cliomes) were collaborative efforts that used clustering methodology, existing land cover designations, and historical and projected climate data to identify areas of Alaska, the Yukon, and NWT that are likely to undergo the greatest or least ecological pressure, given climate change. Project results and data are presented in the final report. These results are intended to serve as a framework for research and planning by land managers and other stakeholders with an interest in ecological and socioeconomic sustainability.
The Alaska project was funded by led the U.S. Fish and Wildlife Service (USFWS), with Karen Murphy as project lead, and the Canadian project was funded by The Nature Conservancy's Canada Project, Ducks Unlimited, and the Governments of YT and NWT, with Evie Whitten as project lead. Data and analysis were provided by the University of Alaska Fairbanks (UAF) Scenarios Network for Alaska and Arctic Planning (SNAP) program and Ecological Wildlife Habitat Data Analysis for the Land and Seascape Laboratory (EWHALE) lab, with Nancy Fresco, Michael Lindgren, and Falk Huettmann as project leads. Further input was provided by stakeholders from other interested organizations.
“Cliomes” can be considered to be broadly defined regions of temperature and precipitation patterns that reflect assemblages of species and vegetation communities (biomes) that occur or might be expected to occur based on linkages with climate conditions. They are not the same as actual biomes, since actual species shift incorporates significant and variable lag times, as well as factors not directly linked to climate. However, results serve as indicators of potential change and/or stress to ecosystems, and can help guide stakeholders in the management of areas of greatest and lowest resilience to changing climate.
Using climate projection data from SNAP and input from project leaders and partici pants, we modeled projected changes in statewide climate-biomes (cliomes). The primary data used were SNAP climate models based on downscaled global projections, which provided baseline climate data and future pro jections. Analysis of these data involved use of the Partitioning Around Medoids (PAM) clustering methodology, which defined regions of similar temperature and precipitation based on a Random Forests™ (Breiman, 2001) generated proximity matrix. Each cluster was used to define one cliome. Thus, for the purposes of this project, clusters are synonymous with cliomes. Further, the Random Forests™ algorithm was used to take the PAM classification and predict the spatial configuration of the cliomes given a changing climate. Alaska and the Yukon were modeled at 2km resolution. These fine-scale data are not available for NWT, so outputs for this territory are at 18.4 km resolution. For all areas, we addressed the inevitable uncertainty of climate projections by analyzing outputs for five different downscaled General Circulation Models (GCMs) as well as for a composite (average) of all five, and for three different greenhouse-gas emissions scenarios (B2, A1B, and A2), as defined for by Nakicenovic et. al. (2001).
The AK Cliomes and Ca Cliomes projects modeled projected shifts in climate-biomes based on current and historical climatic conditions and pro jected climate change. The eighteen cliomes used in this project were identified using the combined Random Forests™ and PAM clustering algorithms, which are defined by 24 input variables (monthly mean temperature and precipitation) used to create each cluster. They were also assessed via comparisons with four existing land-classification schemes for North America (NALCMS Land cover, AVHRR Land cover, GlobCover 2009, and a combination of the Unified Ecoregions of Alaska described by Nowacki et al. 2001 and Canadian Ecozones). We used Random Forests™ to model projected spatial shifts in climate-biomes, based on SNAP projections for monthly mean temperature and precipitation for the decades 2001–2009, 2030–2039, 2060–2069, and 2090–2099.
The results of this modeling effort show that profound changes can be expected across the study area, with most regions experiencing at least one cliome shift by the end of the century, and some areas shifting three times. Although results differed according to which GCM was used and which emissions scenario was selected, the general patterns of change were relatively robust. These patterns involved a northward movement of cliomes, with arctic clusters shrinking or disappearing, interior boreal and taiga clusters shifting, and clusters currently found only outside of the study area appearing in Alaska, the Yukon, and the Northwest Territories. Cliomes that are currently typical of the central and southern potions of British Columbia, Alberta, and Saskatchewan are likely to become prevalent in a large percentage of the study area. This can be interpreted as their being a high likelihood for changing precipitation/temperature conditions which may be beneficial to some existing plant and animal species and placing some under high stress.
We analyzed all outputs for resilience (defined as lack of projected cliome shift) and vulnerability (defined as multiple cliome shifts over time). The most resilient regions are projected to be the coastal rainforest of southcentral and southeast Alaska, and the most vulnerable areas are projected to be interior and arctic regions, with the exception of the islands of the NWT. However, these conclusions may be affected by the relative dissimilarity of the coastal rainforest to any other cluster in North America; species change may be less there simply because surrounding cliomes differ so greatly.
The ramifications of these projected changes for land managers and local residents are varied, and depend on the mandates and goals of the organizations and agencies involved. By linking species-specific data and local details of landscape ecology to these projections, land managers can make informed decisions about how to adapt to a changing landscape in an active manner.