Air temperature trends
The overall trend in MAAT was +0.97 °C per 30 years during the ~1949-2010 period for the five weather stations nearest the region (Supplementary material, Table 1). Trends for nine weather stations that encompass the broader region where historical change rates were determined varied from −0.2 to 1.3 °C per 30 years. Trends also varied by periods associated with cold and warm phases of the Pacific Decadal Oscillation (Hartmann and Wendler 2005), with predominant cooling trends during ~1949–1975 and more mixed trends during ~1976–2010. The overall period, however, which incorporates the large regime shift in 1976, showed strong warming. At Kotzebue, a coastal station nearest the study area, regression analysis indicated that MAAT has increased 1.7 °C from 1949 to 2010, for a 30-year rate of 0.83 °C (Supplementary material, Fig. 6).
Spatial modeling of temperature distributions by SNAP showed large variability in temperatures across the study area due to topography and ocean proximity, with the baseline average of MAATs for the period 1961–1990 ranging from −4.8 °C near the coast to −13.4 °C in the highest mountains. By their 2090–2099 period, projected increases in MAAT compared to the baseline varied by 5 to 6.5 °C across the study area. For our nonspatial modeling purposes, however, we assigned generalized temperature increases of 2, 4, and 6 °C across the entire study area for the 2010–2040, 2040–2070, and 2070–2100 periods, respectively.
Observed historical changes and drivers
We compiled a list of 243 potential transitions based on historical rates of ecotype changes documented by 402 observations from 8 studies; 10 transitions were supported by data from 5–6 separate referenced studies, 25 by 3–4 studies, and 60 by 1–2 studies (see Supplement material, Table 2). We also included 98 potential transitions that we hypothesized to occur based on our field experience. The number of transitions possible for each ecotype ranged from 1 (staying the same as a potential outcome, e.g., Alpine Mafic Barrens) to 11 (e.g., Upland Dwarf Birch-Tussock Shrub). We also attributed each transition to one of 23 geomorphic and ecological drivers likely to be influenced by climate change (Supplement material, Table 2, Fig. 7). Over a century-long period, an area can be affected by multiple transitions over the three periods, for example Upland Dwarf Birch-Tussock Shrub can be replaced by Upland Barrens-Thermokarst (with thermokarst as the driver), then by Upland Alder-Willow Tall Shrub (with early plant succession as the driver), and finally by Upland White Spruce Forest (with late plant succession as the driver).
Modeled ecosystem transitions
The time model, which assumes future transitions occur at the same rate as past transitions, projected a cumulative change (sum of all partial transitions for all ecotypes, or total area undergoing change) of 12.3 % across the region’s area (162,868 km2) over the three periods, while overall net change was 6.3 % (sum of all ecotypes gaining area, thus shift to other types) at the end of the three periods,. The cumulative change is larger than the net change because many changes are compensating. For example, the cumulative gains (4053 km2) in Upland White Spruce Forest mainly from post-fire succession and range expansion (partly aided by establishment in thaw slumps) is partially offset by losses (1837 km2) mainly by fire and thermokarst for a net gain of 2216 km2. Thus, cumulative change provides a measure of how dynamic the overall system is, while the net change indicates how these dynamic processes can lead to directional shifts over time. Over the next century, the models project 33 ecotypes gain area, 23 will lose area, and 4 have no change (Fig. 1). Note that during the time period (~1950–2010) from which the historical transition rates were developed, MAATs increased at a rate of 1 °C/30 years; at this linear rate, MAATs would be expected to increase by 3 °C from 2010 to 2100. Thus, the time model provides a baseline of the minimum areal changes in ecotypes projected to occur by 2100.
The temperature model using temperature increases of 2, 4, and 6 °C for the three periods estimated a cumulative change of 39.9 % and net change of 17.0 %. This model provides a high estimate of projected changes by assuming all transitions respond linearly to accelerating warming temperatures with no feedbacks or lags.
The rate-adjusted model, similar to the temperature model but with a change-rate factor, projected a cumulative change of 32.2 % and net change of 13.1 %. When comparing periods in the rate-adjusted model, the rate of cumulative change nearly tripled in response to the large increase in temperatures, increasing from 5.8 % during 2010–2040 to 15.7 % during 2070–2100. In this model (Supplemental Information, Table 2) the change-rate factor was adjusted to 0.5 for 87 transitions associated with biophysical drivers that were perceived to have numerous negative feedbacks and time lags (e.g. vegetation change by plant migration or succession), and to 1.5 for 32 transitions with drivers expected to have positive feedbacks (e.g., thermokarst, glacial melting), while 124 transitions had no adjustment (=1). Below, we present the results of the rate-adjusted model, where changes were mostly intermediate between the other two models, to simplify the discussion. We discuss results in terms of changes in: (1) area as a percent of the total study area to highlight changes that affect large areas; (2) relative percent for each ecotype to highlight changes that may affect small areas but may dramatically change the extent of particular ecotypes, with the potential for loss of rare ecotypes; and (3) by driver to identify factors most responsible for the changes. Results from the other two models are presented in Fig. 2.
Ecotypes projected to experience large increases in area as a percent of the total area in the rate-adjusted model (Fig. 2) include Lowland Black Spruce Forest (from forest expansion and post-fire succession), Upland Alder-Willow Tall Shrub (shrub expansion), Lowland Willow Low Shrub (shrub expansion and soil drainage), Upland White Spruce Forest (forest expansion), and Upland Willow Low Shrub (primary succession after thermokarst). Major losses are projected for Lowland Birch-Ericaceous-Willow Low Shrub (post-fire succession and forest expansion), Upland Birch-Ericaceous-Willow Low Shrub (due to thermokarst, fires, and shrub and forest expansion), Upland Dwarf Birch-Tussock Shrub (thermokarst, fires, and shrub and forest expansion), Upland Sedge-Dryas Meadow (thermokarst, shrub expansion, in-situ shrub dominance shifts, and acidification), Lowland Alder Tall Shrub (forest expansion) and Lowland Lake. Common ecotypes (>1000 km2) that showed little change include Alpine Acidic Barrens, Alpine Acidic Dryas Dwarf Shrub, Alpine Alkaline Barrens, Lowland Sedge-Dryas Meadow, and Riverine Alder-Willow Tall Shrub.
When grouped by broad physiognomic group (Fig. 3), the rate-adjusted model projects that forests will have the largest gain (4.1 %) and low shrubs the largest loss (−4.8 %). Tall shrubs show only a small gain (0.6 %). Although the model showed fairly large cumulative gains by tall shrubs, the net gain was substantially reduced by the replacement with forest types. Small losses are projected for dwarf shrubs (−0.5 %) and herbaceous meadows (−0.2 %). There were large differences among the models, however. The temperature model projected 3-fold higher gains than the time model for both forests (8.5 vs 3.1 %) and tall shrubs (1.7 vs 0.6 %).
When considering relative changes (% of initial area by ecotype), several ecotypes, often covering relatively small areas, had large projected increases by 2100 (Fig. 2), including: Lacustrine Bluejoint Meadow (3168 % increase, from lake drainage), Lowland Birch Forest (645 %, thermokarst, fires), Human Modified Barrens (532 %, gravel fill, mines), Lacustrine Willow Shrub (794 %, lake drainage), Upland Aspen Forest (648 %, warming south-facing slopes), Upland Barrens-Thermokarst (801 %), and Lowland Spruce-Birch Forest (645 %, late succession). Ecotypes with projected large relative decreases include: Upland Barrens-Landslides (−69 %, early succession), Lowland Birch-Ericaceous-Willow Low Shrub (−65 %, post-fire late succession, plant migration), Riverine Dryas Dwarf Shrub (−70 %, shrub expansion), Upland Birch Forest (−35 %, post-fire late succession to spruce), Coastal Brackish Sedge–Grass Meadow (−57 %, erosion and sedimentation), Lowland Alder Tall Shrub (−50 %, forest expansion, succession), and Alpine Snowfields and Glaciers (−57 %, melting).
Biophysical drivers of the ecotype transitions showed large differences in the percent of the total area that they affected (Fig. 4). In all models, most change was driven by drivers involving plant migration into new areas and outward expansion of tall shrubs and trees, post-fire succession, thermokarst (i.e., thaw slumps, detachment slides, thaw lakes, ice-wedge degradation), soil drainage associated with thawing permafrost, and river erosion and deposition. We differentiated the drivers of secondary succession associated with post-fire recovery and primary early and late succession related to colonization of newly exposed mineral surfaces, such as those created by fluvial deposits, thermokarst, and lake drainage. Plant migration/expansion involved shifts of tundra ecotypes (graminoids, dwarf shrub) into tall shrub (alder, tall willow) and forest ecotypes. Dominance shifting/infilling described transitions where preexisting low shrubs increased in height and abundance and overtopped dwarf shrubs and herbaceous plants to transition into low shrub ecotypes. Soil drainage characterized changes where active-layer thickening and permafrost thaw in upland, thaw-stable soils, allows supra-permafrost water to be lowered or drained away. Drainage and migration differentiated transitions where soil drainage simultaneously allowed migration of tall shrubs and trees onto soils that previously were too wet to be suitable. When comparing models during the final 2070–2100 period when MAATs were 6 °C warmer, the changes for many of the drivers in the temperature model were 3–9 fold higher than for the time model.
The current distribution of ecotypes that are projected to undergo large relative increases or decreases is presented in Fig. 5. It should be noted, however, that we could not identify specifically where these transitions will occur because locations of future disturbances (e.g., fire, thermokarst) are unpredictable and so these models are not spatially explicit. The map indicates that large decreases will occur in lowland ecotypes in the southern Brooks Range associated with fires, thermokarst, and succession, and that the forest and tall shrub ecotypes along the southern Brooks Range will be the source of moderate increases in those ecotypes in the future. In contrast, few changes are predicted for the high mountains of the Brooks Range.