Arctic tundra shrubification: a review of mechanisms and impacts on ecosystem carbon balance

Multiple Earth-observing satellites provide evidence that summer normalized difference vegetation index (NDVI) widely increased (spectral greening) and locally decreased (spectral browning) during recent decades across the Arctic. Circum-Arctic spectral greening and browning trends have primarily been assessed during recent decades using coarse-resolution (∼8 km) summer NDVI derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor (Myneni et al 1997, Bhatt et al 2010, Beck and Goetz 2011, Guay et al 2014, Andersen and Andreassen 2020, Myers-Smith et al 2020). However, higher-resolution NDVI data sets from the (500 m) MODIS (Guay et al 2014, Jenkins et al 2020, Myers-Smith et al 2020) and (30 m) Landsat (Berner et al 2020) satellites have been increasingly used for circum-Arctic assessments. A recent AVHRR NDVI analysis found spectral greening and browning across 38% and 3%, respectively, of the Arctic from 1982 to 2014, with greening evident over large parts of the eastern Eurasian and North American Low Arctic (Park et al 2016). Xu et al (2019) used AVHRR NDVI to assess controls on spring greenup and compared these controls with those inferred from MODIS (Xu et al 2018). Here we update the Landsat NDVI trend analysis from Berner et al (2020) to extend from 2000 to 2020 (instead of to 2016; supplementary material). We find spectral greening and browning across 27% and 8% of the Arctic from 2000 to 2020, respectively, along with a 3.9% increase in mean Arctic NDVI during this period (Mann–Kendall trend test: p = 6.6 × 10⁻⁶, τ = 0.65, n = 21 years; figure 2). While the magnitude and spatial patterns of spectral greening and browning differ somewhat among satellite NDVI data sets (Guay et al 2014), these products nevertheless show overall spectral greening of the Arctic during recent decades (Berner et al 2020, Jenkins et al 2020, Myers-Smith et al 2020). Arctic spectral greening has been linked with increasing summer air and soil temperatures, permafrost thaw, and loss of sea ice (Bhatt et al 2010, Keenan and Riley 2018, Berner et al 2020, Peng et al 2020), and with increasing cover and growth of shrubs and other vascular plants (e.g. Forbes et al 2010, Fraser et al 2011, Frost et al 2014, Andreu-Hayles et al 2020).

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In tundra ecosystems, summer NDVI tracks broad spatial patterns of plant productivity (Boelman et al 2003, Street et al 2007, Kushida et al 2015, Sweet et al 2015, Berner et al 2020) and aboveground biomass (Boelman et al 2003, Walker 2003, Jia et al 2006, Raynolds et al 2012, Johansen and Tømmervik 2014, Berner et al 2018). The leafy canopies of tall deciduous shrubs can strongly affect summer NDVI (Riedel et al 2005), and thus tundra with greater deciduous shrub cover (Boelman et al 2011, Blok et al 2011b, Pattison et al 2015) and aboveground biomass (Jia et al 2003, Riedel et al 2005, Kushida et al 2009, 2015, Greaves et al 2016, Berner et al 2018) tends to have higher summer NDVI. Nevertheless, aboveground biomass corresponded much more strongly with drone-derived canopy height than NDVI in a shrub tundra landscape. These results indicate that shrub dynamics may not entirely be captured by NDVI records at landscape scales (Cunliffe et al 2020). Moreover, some tundra systems (e.g. wetlands) can have high summer NDVI despite few, if any, shrubs (Bartsch et al 2020), and relationships between summer NDVI and tundra biophysical characteristics are typically non-linear and scale dependent (Cunliffe et al 2020, Myers-Smith et al 2020).

Satellite-observed spectral greening relates to increasing shrub cover and growth in parts of the Arctic. Spectral greening has been directly linked in several studies with increasing shrub cover mapped using repeat high-resolution aerial photos and satellite imagery. For example, Landsat NDVI strongly increased from 1984 to 2014 in Siberian alder (Alnus viridis) patches that established since the 1960s across five tundra landscapes in northwestern Siberia (Frost et al 2014). Similarly, Landsat NDVI increased from 1985 to 2011 widely across a study area in the Western Canadian Arctic and was linked to increasing cover of mountain alder (Alnus crispa) and dwarf birch (Betula nana and B. glandulosa) (Fraser et al 2014). Historical reliance on coarse-resolution AVHRR NDVI datasets for spectral greening analyses has hindered direct comparisons with shrub expansion mapped with high-resolution aerial photos or satellite imagery. However, moderate- and high-resolution satellite NDVI time series are increasingly being used to assess Arctic tundra vegetation productivity changes (e.g. Arndt et al 2019, Berner et al 2020) and hold considerable promise to further elucidate how changes in shrub cover contribute to spectral greening in the Arctic.

Satellite-derived summer NDVI time series have also been linked with interannual variability in shrub growth at locations across the Arctic. For instance, summer NDVI time series have been shown to covary with annual shoot elongation of an evergreen shrub, Arctic bell-heather (Cassiope tetragona), and with annual radial growth of three deciduous shrub genera that are widespread in the Low Arctic: willow (Salix spp.), alder (Alnus spp.), and birch (Betula spp.) (e.g. Weijers et al 2018b, Andreu-Hayles et al 2020, Berner et al 2020). AVHRR NDVI time series strongly correlated with annual growth of Woolly willow (Salix lanata) from 1981 to 2005 across a tundra landscape in northwestern Russia (Forbes et al 2010). Subsequent studies linked AVHRR NDVI time series with annual shrub growth across other tundra landscapes in Russia (Blok et al 2011a, Macias-Fauria et al 2012), Alaska (Andreu-Hayles et al 2020), and Canada (Ropars et al 2015, Weijers et al 2018a). Comparisons between Landsat NDVI_max and 22 shrub growth chronologies synthesized from six Arctic countries revealed moderate correlations (median Spearman correlation (r_s) = 0.42) (Berner et al 2020). Several studies also documented positive trends in annual shrub growth concurrent with remotely sensed spectral greening trends (Forbes et al 2010, Ropars et al 2015, Andreu-Hayles et al 2020).

Nevertheless, neither AVHRR, MODIS, nor Landsat NDVI correspond with shrub growth in all tundra landscapes (Blok et al 2011a, Andreu-Hayles et al 2020, Berner et al 2020, Myers-Smith et al 2020), potentially because shrubs are but one component of varying dominance in plant communities that are typically intermixed within a mosaic of land cover types (Forbes et al 2010, Myers-Smith et al 2020). It also remains unclear how shrub radial growth or shoot elongation relates to changes in shrub leaf area, biomass, or landscape productivity (Andreu-Hayles et al 2020, Myers-Smith et al 2020). As a result, covariation between remotely sensed NDVI and shrub growth suggests increasing shrub growth could contribute to spectral greening, but the overall contribution of increasing shrub growth to greening remains uncertain relative to contributions from other PFTs in the Arctic.

Tundra shrubs have undergone conspicuous increases in cover, abundance, height, and growth concurrent with warming trends during the past decades in parts of the Arctic (Tape et al 2006, Rundqvist et al 2011, Myers-Smith et al 2011b, Normand et al 2013, Frost and Epstein 2014, Andreu-Hayles et al 2020, García Criado et al 2020). Species undergoing these increases include birch (Betula spp.), alder (Alnus spp.) and willow (Salix spp.) (Myers-Smith et al 2011a, Lantz et al 2013, Frost and Epstein 2014, Andruko et al 2020), dwarf evergreen shrubs including Arctic bell-heather (C. tetragona) and cowberry (Vaccinium vitis-idaea), and semi-deciduous shrubs such as Arctic avens (Dryas integrifolia) (Wilson and Nilsson 2009, Vowles et al 2017). Shrubification occurs through infilling of existing patches, increasing growth, and advancing shrublines (Myers-Smith et al 2011a).

These ecological changes have been documented in many parts of the Arctic (figure 2) through ecological monitoring (Rundqvist et al 2011), dendroecology (Boulanger-Lapointe et al 2014, Andreu-Hayles et al 2020), repeat oblique photography (Tape et al 2006, Lantz et al 2013), and high-resolution airborne and satellite remote sensing (Frost et al 2013, Moffat et al 2016). Site-level studies have reported substantial increases in shrub cover across Arctic sites, including Alaska, USA (Hollister et al 2005), northern Canada (Hill and Henry 2011), Greenland (Callaghan et al 2011), and Sweden (Becher et al 2018), among many others. While there have been many studies that reported recent shrub expansion, we note that there may be publication bias in reporting findings that exhibit changes in shrub cover. For instance, stable or decreasing shrub cover or growth has been found using ecological monitoring at many sites in the International Tundra Experiment dataset (Elmendorf et al 2012a, Bjorkman et al 2018) and reported in sites in northern Alaska and southeast Greenland (Daniëls and de Molenaar 2011, Villarreal et al 2012), among many others.

Repeat oblique photography has revealed tundra shrubification in Alaska (Sturm et al 2001b , Tape et al 2006, 2012, Brodie et al 2019), western Canada (Danby et al 2011, Mackay and Burn 2011, Moffat et al 2016), eastern Canada (Fraser et al 2011, Tremblay et al 2012), and southwest Greenland (Jørgensen et al 2013). Most of these studies relied on ground-based oblique photographs. Tape et al (2006), from an analysis of 202 pairs of oblique aerial photographs collected between ∼1950 and ∼2000, found widespread expansion of alder, willow, and dwarf birch along hillslopes and valley bottoms on the Alaskan North Slope.

For example, analysis of aerial photos from 1980 to 2013 showed tall shrub and dwarf shrub cover increased at 55% and 74%, respectively, of 38 study sites across the Tuktoyaktuk Coastlands in the Northwest Territories, Canada (Moffat et al 2016). On the other side of the Arctic, shrub cover change was evaluated at ten study sites spanning northern Siberia using high-resolution photographs from Cold-war era spy satellites (1965–1969) and recent (2009–2011) high-resolution commercial satellite imagery (Frost and Epstein 2014, Frost et al 2014). Shrub cover exhibited little net change (−0.8%) at one study site but increased 5%–26% (mean = 13%) across the other nine study sites, particularly in landscape positions with active disturbance regimes (e.g. permafrost-related patterned-ground, floodplains, hillslopes) (Frost and Epstein 2014). While attention is often paid to areas with shrub expansion, aerial photos also reveal areas with little to no change in shrub cover during recent decades (e.g. Plante et al 2014, Jorgenson et al 2018). Overall, high-resolution remote sensing analyses illustrate extensive increases in shrub cover during the last four to seven decades in the Arctic, while also underscoring that change did not uniformly occur across tundra landscapes.

Warming temperatures have been reported at biome-wide scales across the tundra (IPCC 2013, AMAP 2017) and are associated with increasing shrub cover across the Arctic (Myers-Smith et al 2015). While Arctic plant communities are generally sensitive to warming, the responses are site-dependent and heterogeneous (Hollister et al 2005a, Bjorkman et al 2020, García Criado et al 2020, Myers-Smith et al 2020). Shrub species differ substantially in their responses to climate change because of a variety of factors other than warming (e.g. site conditions, soil moisture, snow-dynamics, plant-specific responses) (García Criado et al 2020, Myers-Smith et al 2020). Patterns of plant community responses to warming are consistent between monitoring and experimental warming methods, although space-for-time approaches do not appear to be appropriate for quantifying the rate or magnitude of change (Elmendorf et al 2012a, 2012b, 2015) and short- and long-term responses are expected to differ (Bouskill et al 2020). The most apparent link between climate and shrub expansion is the correlation of temperature with shrub growth, abundance, and recruitment. Observational studies, based on remote or ground surveys over time or space and on warming experiments, have found higher shrub growth and recruitment with warmer temperatures. The trend of warmer summers correlates with shrub expansion across the Arctic tundra (Myers-Smith and Hik 2018, Weijers et al 2018b, Berner et al 2020).

The response of shrub growth to warming is spatially heterogeneous, however, with higher temperature sensitivity in the European Arctic than in North America and at sites with greater soil moisture and taller shrubs (Myers-Smith et al 2015). Plant and ring width growth that correlate with summer NDVI (section 2.2) have also been found to correlate with summer temperatures (Myers-Smith et al 2015). Annual growth of alder and willows have been found to be climate sensitive around the circumpolar Arctic (Myers-Smith et al 2015) including in Arctic Alaska (Tape et al 2012, Andreu-Hayles et al 2020), northwest Russia (Forbes et al 2010), Arctic Canada (Boulanger-Lapointe et al 2014, Myers-Smith and Hik 2018, Weijers et al 2018a), and Greenland, Norway and Svalbard (Jørgensen et al 2015, Weijers et al 2018a). These trends are corroborated by large-scale dendroecological syntheses (Myers-Smith et al 2015).

Temperature manipulation experiments allow more controlled investigation of warming impacts on shrub productivity. Meta-analyses and most experiments report that warming promotes growth (Walker et al 2006) and germination (i.e. seed biomass, cumulative germination, germination rate, peak germination (Klady et al 2011)) unless moisture or other conditions are limiting. Experimental warming can also increase seedling mortality, resulting in no net effect on establishment (Milbau et al 2017). Biome-wide remote sensing and modeling also confirm that warming is leading to more favorable conditions for shrubs. For example, the extent of Arctic areas where vegetation is limited by temperature declined over the past three decades (Keenan and Riley 2018). Although warming tends to expand shrub ranges into currently colder locations, it can also cause contraction at the warmer or southern edge of current shrub ranges (Bokhorst et al 2018), due to population-level variation in temperature optima (Kueppers et al 2017) or due to treeline advance, as seen between 1900 and 2008 (Harsch et al 2009).

Soil moisture, snow dynamics, and other climate-related factors can also influence shrub growth and establishment (Martin et al 2017). Furthermore, air temperature often covaries with soil moisture, snow dynamics, active layer depth, nutrient availability, and other environmental conditions that affect shrub success (figure 1). These co-varying factors are difficult to control in observational studies and receive less assessment compared to air temperature (Martin et al 2017, Myers-Smith et al 2019a). When these factors have been investigated, their interactions with the warming effect were variable. Multiple studies have reported greater shrub expansion in more moist sites. For example, Naito and Cairns (2011) found that shrubs expand more into areas with higher topographic wetness index and closer to the riverbank in the North Slope of Alaska. Boulanger-Lapointe et al (2014) found willow cover and seedling density was high in sites with elevated soil moisture but limited by water availability in dry sites in the High Arctic of Greenland and Canada. García Criado et al (2020) found that tundra woody cover changed more rapidly in wetter sites. Higher temperature sensitivity of shrub growth was found for wetter vs drier sites (Myers-Smith et al 2015, Ackerman et al 2017). Field surveys and warming experiments indicate that moisture limitation can reduce shrub growth, recruitment, and abundance (Elmendorf et al 2012a, Myers-Smith et al 2015, Li et al 2016, Ackerman et al 2017). Across all of these studies, the availability of soil moisture strongly interacts with warming. Soil moisture also plays a critical role in determining the trajectory of vegetation under warming (Elmendorf et al 2012b, Ackerman et al 2017, Bjorkman et al 2018). Bjorkman et al (2018), in a large-scale analysis of the relationships between plant traits, warming, and soil moisture, highlighted the importance of soil moisture and concluded that the trajectory of changes in plant traits and ecosystem function under future warming would depend on soil moisture.

As glaciers retreat and permanent snow cover shrinks, reduced melt-water supply in the growing season might induce moisture limitation in extended regions (Boulanger-Lapointe et al 2014). Warming can lead to earlier snowmelt, which can affect soil moisture and lengthen the growing season, which have been found to promote shrub growth in some studies (Hill and Henry 2011, Wilcox et al 2019). However, because snowpack protects shrub shoots and seedlings from damage caused by fungal and insect attacks and frost, earlier snowmelt due to warming has also been found to offset some positive impacts of warming (Bokhorst et al 2009, Wheeler et al 2016).

Topography is an important distal controller of vegetation growth in many ecosystems. The more proximal vegetation growth controllers influenced by topography include soil moisture, snowpack redistributions, lateral nutrient and oxygen fluxes, relatively static soil properties (e.g. texture (perhaps affected by erosion), depth), soil redox and nutrient states, snow cover and properties, disturbance, and light (figure 1). Although these factors affect all ecosystems, the conditions in tundra systems (e.g. permafrost, patterned ground, thermokarst) lead to unique controls on shrub growth. The three observed categories of recent shrub increases (i.e. infilling, growth increases, and range spread) identified by Myers-Smith et al (2011a) will each be affected by different combinations of these mechanisms. However, there are relatively fewer observational studies that quantify the relative importance of these mechanistic controls than in, e.g. temperate forests.

Observations of relationships between tundra shrub cover change and topographically driven processes have been used to infer mechanisms affecting these interactions. Many studies (Chapin et al 1988, Epstein et al 2004, Naito and Cairns 2011, Myers-Smith et al 2015, Lara et al 2018, Campbell et al 2020) indicate that tundra plant productivity is strongly affected by topographically driven hydrology. Additional observed factors associated with topography that affect shrub growth include snow properties (Boulanger-Lapointe et al 2016), thermokarst in patterned ground (Frost et al 2013, Huebner and Bret-Harte 2019), soil properties, and cryoturbation (Ropars and Boudreau 2012, Frost et al 2014, Swanson 2015).

A number of land models have been applied to analyze tundra shrub dynamics (Epstein et al 2000, Euskirchen et al 2009, Lawrence and Swenson 2011, Bonfils et al 2012, Miller and Smith 2012, Zhang et al 2013, Druel et al 2019), but none of these models explicitly represent topographical variation at the relevant spatial scales. We identified only a few studies that have applied land models resolving topographic gradients that evaluated the effects on vegetation. Mekonnen et al (2021b) showed that hillslope topography and thereby hydrology strongly controlled historical and future shrub growth. At the hill crest, canopy water stress and low plant nitrogen uptake led to low modeled shrub biomass. In the mid-slope position, intermediate soil water content reduced shrub water and nitrogen stress, leading to higher shrub biomass. In the lower-slope position, saturated soil conditions reduced soil oxygen concentrations, nutrient availability and uptake, and plant biomass. An analysis with simulations that ignored topographical gradients and gridcell inter-connectivity underestimated mean shrub biomass and over- or under-estimated shrub productivity at the various hillslope positions.

The emergent patterns of shrub expansion responses to climate warming and changes in soil moisture summarized above interact with a range of processes, including permafrost thaw, nutrient cycling, and disturbance, as discussed in the following sections.

Thawing of permafrost (Brown and Romanovsky 2008, Schuur et al 2015, Hugelius et al 2020, Mekonnen et al 2021a) and thermokarst development (Schuur et al 2007, Jones et al 2015) may alter soil moisture and thermal regimes and thus affect nutrient availability. Evidence from many Arctic sites suggests that a deeper active layer promotes shrub expansion (Martin et al 2017). Recent and projected warming are expected to deepen the active layer heterogeneously depending on climate and soil properties, and thereby increase nutrient availability (Mekonnen et al 2018b). These environmental conditions, though connected to air temperature through ecosystem and climate feedbacks, directly influence shrub growth and establishment.

Thawing of ice-rich permafrost or the melting of massive ice may lead to a landscape deformation process resulting in thermokarst development (van Everdingen 2005). Alterations of the ground surface, such as ground temperature, thaw depth, and soil moisture could provide favorable conditions for shrub growth (Schuur et al 2007, Lantz et al 2010, Frost et al 2013). Thermokarst disturbances of tundra surfaces will increase access to bare soils initially covered by a thick organic mat and could increase germination of shrub species (Lloyd et al 2003, Lantz 2017, Mikhailov 2020). Observational studies have reported increased soil temperature, thaw depth, nutrient availability, and snowpack in sites associated with thermokarst subsidence and landform deformation, and concurrent increases in shrub growth (Lloyd et al 2003), but the effects vary with thermokarst type and process (supplementary material). In thermokarst depressions on tundra hills with harsher conditions, protection from wind and frost burn may play essential roles in shrub expansion (Lantz 2017).

As high-latitude soils warm, availability of plant nutrients (i.e. nitrogen and phosphorus) is expected to increase (figure 3). This increase stems from kinetically controlled mineralization rates, which are strong functions of temperature (Nadelhoffer et al 1991, Blok et al 2018), and deepening active-layer exposing previously frozen organic matter (Salmon et al 2018) for decomposition, nutrient mineralization, and plant assimilation (Keuper et al 2017, Blume-Werry et al 2019, Hewitt et al 2020). Furthermore, observations of increasing wintertime respiration, particularly under deeper snowpack, could play an important role in releasing nutrients over the 21st century (Schimel et al 2004, Natali et al 2019). However, whether nutrients released during subnivean activity are assimilated by plants or lost hydrologically during snowmelt remains an open question (Grogan and Jonasson 2003, Edwards et al 2006, Koven et al 2015, Riley et al 2018).

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How higher nutrient availability shapes vegetation composition and productivity has been examined directly through nutrient manipulation experiments (Mack et al 2004, DeMarco et al 2014b, Prager et al 2020) and indirectly through long-term observations across spatial gradients (Pelletier et al 2019). In general, increased nutrient availability enhances shrub productivity, coverage, and biomass (Shaver and Chapin 1980, Mack et al 2004), which can lead to taller shrubs with higher leaf nitrogen content (Bjorkman et al 2018, Prager et al 2020). A feedback loop may subsequently emerge (figure 3) whereby increased shrub biomass and height under warmer temperatures and elevated nutrient availability can lead to a deepening snowpack, insulating the under-snow soil, and further increasing nutrient mineralization rates and nutrient availability into the summer (Chapin et al 2005, Sturm et al 2005b, Bjorkman et al 2018, Hicks et al 2020). However, interactions between snow depth and belowground activity are complex. A controlled manipulation study showed no impact on nutrient cycling of a deepening snowpack (Myers-Smith and Hik 2013), while litter quantity and quality were likely more significant factors driving decomposition (DeMarco et al 2014a).

Traits that control shrub-ectomycorrhizal (ECM) associations may also provide mechanistic insight into the competitive dynamics of tundra vegetation. For instance, mycorrhizal networks exist in tundra and facilitate belowground inter-plant carbon transfers, and thus may alter competitive abilities (Deslippe et al 2011, Deslippe and Simard 2011). Tundra shrubs, and associated ECM fungi, have been shown to have higher maximum uptake rates (V_MAX) than graminoids (Zhu et al 2016), and allocate significant resources towards fine root biomass and therefore nutrient uptake capacity (Vamerali et al 2003, Iversen et al 2015). Such competitiveness for nutrients could explain consistent trait responses across nutrient-manipulation studies. For example, in a recent study examining plant traits under nutrient fertilization, Prager et al (2020) found that only deciduous shrubs had significantly greater leaf N at high levels of nutrient addition. Furthermore, under similar nutrient-enrichment conditions, deciduous shrubs, but not graminoids, increased foliar N (Heskel et al 2012). Similarly, Bret-Harte et al (2001) noted that elevated nutrient availability increased shrub leaf area index and canopy density, resulting in light limitation of understory species. These relationships imply that high foliar nutrient levels and the ability to acquire soil nutrients are beneficial traits in high-latitude environments. However, Bjorkman et al (2018) noted that traits such as leaf nitrogen content generally decreased with warming in dry sites, while increasing in wetter conditions in analyses at the community-level.

Finally, the importance of post-growing season nutrient uptake has become increasingly clear in recent years (Riley et al 2018). Belowground plant activity (e.g. root growth) continues after the cessation of photosynthetic activity (Iversen et al 2015, Blume-Werry et al 2016), while nutrient uptake continues into the winter (Chapin and Bloom 1976, Andresen and Michelsen 2005). Shrubs have been noted to be particularly active during spring, relative to other tundra plants (Weih 2000, Larsen et al 2012), which could lead shifts in phenology under a warmer climate (Mekonnen et al 2018b, Oberbauer et al 2013).

Wildfires can burn and remove shrubs, and thus reduce post-fire shrub populations in the short-term. Wildfires often alter soil temperature (Jiang et al 2015), surface litter and soil organic carbon stocks (Grosse et al 2011, Mack et al 2011, Chen et al 2021), and seedbed quality, thus affecting regeneration and shrub establishment (Lantz et al 2010, Bret-Harte et al 2013). Removal of an insulating surface litter layer increases the active layer depth (Iwahana et al 2016, Michaelides et al 2019). These factors may alter soil organic carbon stocks, soil moisture, and nutrient dynamics and thus affect post-fire competition and successional trajectories in the tundra. Fire may also impact shrub performance by altering mycorrhizal symbionts. Hewitt et al (2013) concluded that the resprouting strategy of tundra shrubs makes the dominant mycorrhizal fungi more resilient to fire by maintaining an inoculum source on the landscape after fire. As a result, resprouting shrubs may facilitate post-fire vegetation regeneration and potentially shrub expansion under future warming and fire regimes.

Wildfires increase shrub expansion in tundra regions over multi-decadal timescales, although short-term observations of disturbed tundra indicate negative influences of wildfires on shrubs. Based on paleoecological studies (e.g. Higuera et al 2008, Hu et al 2010) and model simulations (Rupp et al 2000, Mekonnen et al 2019, Bouskill et al 2020), the predicted frequent and larger wildfires in the Arctic under future climate are expected to increase Arctic shrub expansion. Several studies across the tundra reported increases in shrub growth and distribution in older burn scars, although post-fire shrub recovery may last for more than ten years. For instance, graminoid biomass was shown to recover four years after the Anaktuvuk River Fire (ARF) on the North Slope, Alaska (Bret-Harte et al 2013). However, shrubs did not recover to pre-fire conditions (Jandt et al 2012, Bret-Harte et al 2013) or were recovering slowly by the fourth year after the ARF (Jandt et al 2012). Narita et al (2015) and Iwahana et al (2016) observed vegetation and thaw depth changes five to ten years after a 2002 tundra fire on the Seward Peninsula, Alaska. They found evergreen shrub cover was still substantially lower five years after the fire and had not recovered ten years after the fire. In contrast, graminoid and deciduous shrub cover had increased over the same period. The long-term increase in shrubs following fire was also shown in other studies on the Seward Peninsula, Alaska and in Western Siberia (Racine et al 2004, Heim et al 2019). These studies show that fire promotes shrub growth and expansion and thus alters ecosystem carbon balance, although fire may reduce shrub growth in the short term.

Short-term increases in active layer depth are a common feature following tundra wildfires. Depending on site conditions, post-fire thaw depth may recover to the level of unburned sites in about ten years (Iwahana et al 2016), or persist longer (Rocha et al 2012), while increased shrub growth and expansion may continue for decades. Spatial variation of active layer depth may also be related to plant community composition, with deeper thaw corresponding to graminoid-rich areas and shallower thaw corresponding to shrub-rich areas (Narita et al 2015). The active layer depth returned to pre-fire levels ten years after the 2002 Kougarok fire, Seward Peninsula, Alaska (Narita et al 2015, Iwahana et al 2016). Active layer was deepest (52.3 cm) 10–11 years following a fire and gradually returned to unburned levels thereafter.

Some areas burned in recent Kougarok fires experienced thermokarst, especially polygonal depressions along lines of ice-wedges (Iwahana et al 2016, Tsuyuzaki et al 2018). Frost et al (2020) studied vegetation changes after 1971–1972, 1985, 2006–2007, and 2015 tundra fires on the Yukon-Kuskokwim Delta, Alaska. Shrub cover was lower in younger fire scars (one to three years) than adjacent unburned areas, but higher shrub cover occurred at sites with older fires (10–46 years). Using radiocarbon dating, aerial photography, and climate proxy data, Jones et al (2013) identified tundra fires that likely occurred between AD 1880 and 1920 on the North Slope, Alaska. They found degradation of ice-rich permafrost and increased shrub vegetation with taller canopy height than surrounding unburned areas. In the Mackenzie Delta Uplands, Lantz et al (2013) also found the highest shrub coverages (92%–99%) in old-burned (about 40 years) tundra among their studied tundra areas. These results suggest changes in recovery times of thaw depth and shrub biomass following fire may subsequently alter post-fire successional trajectories and ecosystem carbon balance.

Herbivory is an important factor that affects shrub growth in the tundra (Olofsson et al 2004, Post and Pedersen 2008, Tape 2011). Several exclosure experiments have demonstrated that herbivory may alter vegetation composition (Pajunen et al 2008, Ravolainen et al 2011) and reduce climate-driven shrub expansion (Olofsson et al 2009). For instance, in a ten year field experiment with permanent plots with treatments of reindeer only vs all mammalian (small mammals and reindeer) exclosures at four forest–tundra ecotone locations in northern Fennoscandia, shrub abundance was generally shown to increase with herbivore exclusions (Olofsson et al 2009). In a three year exclosure experiment in a Low Arctic site in Norway, the biomass of forbs, deciduous shrubs, and herbaceous plants were shown to increase by 40%–50% in the absence of herbivory (Ravolainen et al 2011). The effects on different tundra PFTs may also vary with herbivores. For instance, rodents may prefer mosses and dwarf shrubs (Moen et al 1993, Dahlgren et al 2007), while reindeer and wild caribou were shown to prefer deciduous shrubs and lichens (Herder et al 2003, Post and Pedersen 2008). Herbivores can also affect seedbed quality, seedling establishment, and growth of tundra plants (Munier et al 2010). These processes can strongly alter tundra PFT prevalence, suggesting that herbivore abundance and distribution may have a direct impact on shrub expansion across the tundra.

Climate warming may have direct and indirect effects on plant–herbivore interactions (Olofsson et al 2009) and on changes in the abundance of herbivores and their predators (Ims and Fuglei 2005). Shrub expansion and shifts in vegetation composition driven by climate warming can alter the composition and quality of forage for herbivores (Kitti et al 2006, Doiron et al 2014). Increases in shrub cover may also increase snowpack height that may affect timing of snowmelt, growing season length, and forage access to herbivores (Berg et al 2008). Indirectly, climate warming may also alter predator–prey interactions, thus altering herbivore populations (Legagneux et al 2014) and their impact on shrubs.

Warmer climate, enhanced nutrient cycling (which will lead to increased nutrient availability), and disturbance modify competitive interactions of tundra plants and thereby may result in changes in relative shrub abundance (Mack et al 2004, DeMarco et al 2014b, Zamin et al 2014, Prager et al 2020). Competitive interactions among PFTs, as mediated through functional traits, strongly affect community assembly through competition for light, water, and nutrients under changing climate (Soudzilovskaia et al 2013, Myers-Smith et al 2019b). Several plant traits (e.g. plant height, leaf nutrients and optical properties, phenology, morphology, root traits, and axial hydraulic resistance) are known to differ among tundra PFTs (Chapin et al 1996a, Iversen et al 2015, Bjorkman et al 2018, Myers-Smith et al 2019b, Thomas et al 2019). As a result, tundra PFTs differ in their abilities to acquire and retain resources. Across ecosystems, carbon and nutrient investment and retention strategies in leaves, stems, and roots partly explain PFTs' competitive abilities under changing climate (Chapin et al 1996a, Westoby et al 2002, Wright et al 2004, Soudzilovskaia et al 2013). Differences in traits may also affect emergent PFT variation in phenology, irradiance, CO₂ fixation rate, and water uptake and thereby each PFT's competitive growth. Structural traits such as plant height respond strongly to changes in growing conditions in tundra ecosystems (Bjorkman et al 2018), yet do not differ strongly among PFTs in tundra plants (Thomas et al 2019).

With increases in nutrient availability, such as those expected over the 21st century (Mekonnen et al 2018b), shrubs may grow faster leading to greater carbon gains per N investment, resulting in higher woody carbon stocks (Sistla et al 2013), with longer turnover times and higher plant carbon to nitrogen (C:N) ratios (Weintraub and Schimel 2005). Shrubs associated with symbiotic N₂ fixation (Densmore 2005, Salmon et al 2019) may also compete more effectively due to their independent supply of N.

The rapid growth of shrubs with greater height and leaf area (e.g. Hudson et al 2011, Elmendorf et al 2012a) have led to the competitive exclusion of shade-intolerant species, such as lichens and mosses (Cornelissen et al 2001, Walker et al 2006, Pajunen et al 2011, Elmendorf et al 2012a, Fraser et al 2014) at some warming experiment sites, further increasing shrubs' ability to compete. While exclusion via light has not been observed at all sites (e.g. Elmendorf et al 2012a), light competition can impact plant functional diversity and community structure. Plant etiolation responses to light attenuation is an important trait that controls their ability to effectively compete under shading (Havström et al 1993, Chapin et al 1996b). Thus, traits that control maximum canopy height and greater carbon uptake may result in shrubs being more competitive than other PFTs in tundra ecosystems in a warmer climate (Mekonnen et al 2018b).

Several studies reported increases in plant carbon uptake inferred from Arctic greening and increases in shrub biomass (section 2). Circum-Arctic trends inferred based on multi-decadal changes in remote sensing observations (Rouse et al 1974) indicate increases in plant productivity (Tucker et al 2001, Olthof et al 2008, Verbyla 2008) and shrub growth and biomass (Frost et al 2013, Moffat et al 2016) across much of the Arctic tundra biome during the past decades (sections 2.2 and 2.3; figure 2). Observed increases in carbon uptake and thus shrub growth and biomass were also shown based on a systematic literature review updated from García Criado et al (2020) (figure 2). These results have been supported with a shrub ring width chronology analysis (Blok et al 2011b) that showed increases in shrub growth. Dendroecological measurements indicate changes in shrub radial growth and establishment at sites across the Arctic (Myers-Smith et al 2015). Shrub radial growth has been found to commonly relate to above-ground biomass Moullec et al 2019 and remotely sensed NDVI (Forbes et al 2010, Ropars et al 2015, Andreu-Hayles et al 2020, Berner et al 2020, Myers-Smith et al 2020), though not always (Weijers et al 2018b).

Further, results from field and remote sensing observations were corroborated by several natural and artificial warming experiments. Most of these experiments have shown an increase in shrub net primary production. For instance, long-term plot observations from 46 sites across the tundra (Elmendorf et al 2012b) showed overall increases in height and abundance of shrubs, although responses varied with site conditions (Myers-Smith et al 2015). In other warming experiments across the tundra, increases in height and biomass of tall deciduous shrubs were shown (van Wijk et al 2004, Walker et al 2006, Zamin and Grogan 2012, Sistla et al 2013). Gains in net primary productivity that led to shrub growth were primarily driven by enhanced CO₂ fixation via higher N mineralization, deeper thaw depth, and thus increased nutrient availability (Campioli et al 2013, DeMarco et al 2014b). Nutrient availability and plant productivity may further be enhanced by biological N₂ fixation through symbiotic associations with bacteria (e.g. Alder), and primed by increased root carbon allocation (Rhoades et al 2001, Densmore 2005). This robust range of observations and warming experiments suggest enhanced plant carbon uptake that led to increased biomass of shrubs in response to warming.

Expansion of shrubs also increases carbon losses through ecosystem respiration (Parker et al 2015, Phillips and Wurzburger 2019) by increasing litter inputs (Liston et al 2002, Myers-Smith and Hik 2013, Christiansen et al 2018b, Kropp et al 2018) and active layer depths (Blok et al 2010, Frost et al 2018, Wilcox et al 2019). The net ecosystem carbon balance in a shrub-dominated site depends on contrasting responses of ecosystem carbon uptake vs respiration. Several meta-analyses of long-term ecosystem warming experiments (Arft et al 1999, Dormann and Woodin 2002, Rustad et al 2001, Walker et al 2006, Elmendorf et al 2012a) have shown that ecosystem responses to warming are spatially heterogeneous and dependent on the climate zone, site conditions (e.g. local topography, soil properties, surface and subsurface hydrology), and PFT composition (via litter inputs). Since woody shrubs have the highest C:N ratio among tundra PFTs and their woody litter decomposes relatively slowly, their relative increase across the Arctic may enhance ecosystem carbon storage (Weintraub and Schimel 2005, Heskel et al 2013). However, increases in net carbon uptake from shrub expansion may be offset by concurrent increases in ecosystem respiration (Biasi et al 2008). In-situ measurements in shrub-dominated sites indicate contrasting responses of shrub biomass and soil organic carbon. Shrub expansion and the subsequent increase in biomass (Berner et al 2018, García Criado et al 2020) and litter inputs (Elmendorf et al 2012b) may alter decomposition rates of soil organic carbon (Myers-Smith and Hik 2013, Sistla et al 2013, Parker et al 2015, Lynch et al 2018, Gagnon et al 2019, Christiansen et al 2018a). Flux data from 21 sites across the Arctic and boreal ecosystems have shown a strong ecosystem carbon sink for sites dominated by shrubs vs herbaceous plants (Cahoon et al 2012). However, sites with greater summer soil temperatures were shown to be carbon sources. These results suggest that shrub expansion impacts on net ecosystem exchange is site specific, and dependent on changes in biomass vs soil organic carbon stocks. We note that, although several studies in the literature reported effects of shrub expansion on biomass and decomposition of soil organic carbon (e.g. Christiansen et al 2018a, Gagnon et al 2019, Lynch et al 2018), the effects on net biome productivity have not been widely measured.

Shrubs affect ecosystem carbon balances indirectly through effects on the surface energy balance and snowpack accumulation (Liston et al 2002, Marsh et al 2010, Nowinski et al 2010, Myers-Smith and Hik 2013). Increases in tall shrubs that grow above the snowpack reduce albedo, altering the energy balance and thus snowmelt timing (Marsh et al 2010). Sturm et al (2005a) showed from measurements at five tundra sites in Alaska that sites dominated by tall shrubs resulted in a 30% reduction in winter time albedo, compared to sites with dwarf shrubs underneath the snowpack. Increases in canopy net radiation from reduced albedo by tall shrubs may alter seasonal carbon uptake (Livensperger et al 2016, Lafleur and Humphreys 2018). Changes in spring albedo may also affect snowmelt timing (Sturm et al 2005a, Marsh et al 2010), thus resulting in earlier leaf-out and carbon uptake (Bonfils et al 2012, Livensperger et al 2016). While earlier snow-melt results in more spring snow-free days and greening (Livensperger et al 2016), it may also reduce dwarf-shrub growth, likely related to adverse effects of temperature on the early growing season (Wheeler et al 2016). Thus, reduced albedo that leads to earlier snowmelt may have a contrasting impact on spring carbon uptake.

Tall deciduous shrubs can accumulate snow redistributed by wind across a landscape (Liston et al 2002, Pomeroy et al 2006). Snow fence experiments at Arctic tundra sites show that deeper snowpack promotes winter soil warming (Nobrega and Grogan 2007, Joshua Leffler and Welker 2013). Deeper snowpack insulates the soil surface, resulting in warmer soil during winter (Paradis et al 2016) and thus increased soil organic matter (SOM) decomposition (Sturm et al 2001a). Warmer soil may accelerate wintertime ecosystem respiration that may substantially contribute to non-growing season carbon loss (Natali et al 2019), but may also increase nutrient availability to plants and facilitate higher growing season biomass gains (Riley et al 2018). Deeper snow may also deepen the active layer (Nowinski et al 2010) and increase soil wetness and thus methane production (Blanc‐Betes et al 2016). While snow accumulation may enhance winter soil warming (Myers-Smith and Hik 2013) and active layer depth (Nowinski et al 2010), shrub expansion was also reported to cool soils (Myers-Smith and Hik 2013) and reduce summer permafrost thaw (Blok et al 2010) from shading of the soil surface by the greater canopy cover. These interactions may result in lower summer soil temperatures, decomposition rates, and summertime nutrient availability (Myers-Smith and Hik 2013). Thus, alteration of snowpack dynamics affects seasonal soil temperature, nutrient dynamics, and rates of ecosystem respiration.

Long-term plots show increases in tall deciduous shrub growth leading to greater litter inputs (Elmendorf et al 2012b). However, increased shrub litter inputs have contrasting effects on net ecosystem carbon exchange. Shrub growth may be enhanced from greater litter inputs, which can accelerate SOM decomposition, thus increasing nutrient availability (Buckeridge et al 2010). Rapid decomposition of shrub litter may also increase SOM carbon losses through heterotrophic respiration (Nielsen et al 2019, Phillips and Wurzburger 2019). Wintertime shrub litter decomposition was shown to be accelerated from deeper snowpack that resulted in warmer soil, although spring warming, under drier conditions, was reported to reduce litter decomposition rates (Blok et al 2016). The net effect of increased shrub litter on ecosystem carbon balances depends on litter quality and quantity. For instance, Christiansen et al (2018b) reported greater litter carbon losses in tall vs low birch shrubs at Daring Lake, a mesic Arctic tundra site in Canada. Changes in litter decomposition rates and the subsequent effects on carbon uptake may alter ecosystem carbon residence times (Parker et al 2015, Ravn et al 2020).

We highlight two major gaps in the observational literature of climate controls on shrub expansion. First, while shrub expansion is controlled by multiple climatic and environmental conditions, most studies have focused on the direct impacts of warming or soil moisture. Yet research also suggests that warming may not be the dominant control on growth or establishment when other factors such as soil moisture, snow dynamics, permafrost thaw, nutrient cycling, and biotic activity are also considered or controlled (Martin et al 2017, Lett and Dorrepaal 2018, Myers-Smith et al 2019b). As such, there is a need for multifactorial experiments and observational analysis. Disentangling impacts of multiple factors will contribute to a better assessment of the relative influences of positive and negative feedbacks under warming, which are key to projecting future rates of shrub expansion (Myers-Smith et al 2011a, 2015). Second, most analyses are based on relatively short-term observations (<25 years) or substitute spatial patterns for longitudinal studies to quantify temporal responses. The limited time range and reliance on space-for-time approaches pose challenges in addressing time lags in shrub response to variation in environmental conditions (Büntgen et al 2015) and to adequately representing long-term responses (Elmendorf et al 2012a, Martin et al 2017, Bouskill et al 2020).

The strong interplay between shrub expansion and nutrient availability underscores a critical role for soil microbes (figure 3). Microbial communities can promote shrub expansion through, for example, the mining of nitrogen from organic compounds in response to rhizodeposition (Hicks et al 2020, Street et al 2020). However, shifts in tundra vegetation, including shrub expansion, can alter the composition and abundance of microbial functional guilds (Wallenstein et al 2007, Eskelinen et al 2009, Shi et al 2015). Shrub expansion can modify the quantity, quality, and chemical composition of SOM (McLaren et al 2017) due to increased root (Brüggemann et al 2011) and litter production (Cornelissen et al 2007), and rhizodeposition (Street et al 2020). Despite strong functional redundancy (Louca et al 2018), shifts in microbial community composition within the tundra can lead to changes in metabolic function, including, for example, increased carbohydrate utilization (Johnston et al 2019). Nonetheless, impacts on the tundra carbon cycle remain uncertain, with evidence for and against the priming of existing SOM under higher rhizodeposition and litter production (Lynch et al 2018, Hicks et al 2020, Street et al 2020).

Of particular significance to tundra carbon and nutrient cycling is the potential change in fungal–plant interactions that could emerge under shrub expansion (Clemmensen et al 2006, Bennett and Classen 2020). ECM and ericoid mycorrhizal fungi, typically partnered with deciduous and evergreen shrubs respectively (Deslippe et al 2011, Deslippe and Simard 2011, Vowles and Björk 2019, Hicks et al 2020), play an important role in the acquisition and transfer of nutrients and water to the plant (Read and Perez-Moreno 2003, Fernandez and Kennedy 2016, Hewitt et al 2020). However, the feedback to soil carbon stability remains uncertain, with evidence both for and against increased decomposition rates attributable to changes in the abundance and composition of mycorrhizal fungal (Fernandez and Kennedy 2016).

By definition, shrub reproduction and establishment refer to range shifts or infilling, but controls on these processes are not well-described in the literature. It is less clear how seed germination and seedling establishment, potential bottlenecks for Arctic shrub expansion, will respond to a warmer climate (Büntgen et al 2015, Milbau et al 2017, Myers-Smith and Hik 2018). Factors controlling the recruitment of new shrub individuals need further study to predict shrubline advance and changes in PFTs driven by climate and disturbance. More studies are needed that evaluate site-specific controls on regeneration, such as seed production, suitable microsite availability, recruitment, seedling survival, and establishment (Šenfeldr and Treml 2020). The conditions that control seedling survivorship are not always the same as those that control growth of mature shrubs, yet there is little overlap among studies of these two processes (Büntgen et al 2015, Angers-Blondin et al 2018, Myers-Smith and Hik 2018). It is relatively easy to conduct manipulation experiments with seeds and seedlings to examine the controls on germination and seedling survivorship (Angers-Blondin et al 2018). It is also easy, at the other end of the demographic spectrum, to conduct field and aerial surveys of mature or emergent shrub biomass and abundance (Tape et al 2006, Lantz et al 2013, Myers-Smith and Hik 2018) to examine correlations with disturbance and climate. Pulses of recruitment can be determined from age distributions of adult shrubs derived from dendroecological approaches (Boulanger-Lapointe et al 2014, Büntgen et al 2015, Myers-Smith and Hik 2018, Andreu-Hayles et al 2020). However, there remains a demographic gap, and difference in outcome metrics, among studies, between seedling survivorship on the one hand and growth of mature shrubs on the other. Thus, we suggest there is a need for future research on seed dispersal, and processes that control shrub success between initial establishment (i.e. <5 years) and reaching mature individuals, such as seedling competition for light and nutrients, all of which may limit long-term shrub establishment success as well as growth.

While the literature clearly documents effects of wildfire on shrub biomass, litter layer, and active layer depth (see section 3.6), many gaps remain in understanding and predicting the impacts of wildfire and other disturbances on shrub expansion. Recent severe fires and repeated burns in the Arctic prompt the need to further study the effects of fire intensity and frequency on shrub expansion. For example, the effects of more severe fires on soil fungi reduced seedling performance in an Alaskan site, raising the possibility that fire–fungal–plant interactions may counteract positive aspects of fire on establishment in tundra (Hewitt et al 2016).

Ecosystem interactions can reduce or amplify the effects of fires on shrubification. For example, shrub density may be increased by wildfire and in turn shrub biomass provides fuel for fires which can promote further shrub colonization (Higuera et al 2008, Bret-Harte et al 2013). Likewise, interactions among shrub canopy, the moss layer, and permafrost thaw can dampen or exacerbate the effect of fire on active layer depth perturbations, leading to more or less shrub colonization and growth. One consequence of the importance of these internal interactions is that tundra fires may have opposite influences on shrubification depending on antecedent conditions, hydrology (e.g. drainage and slope), and nitrogen availability.

It is difficult to conduct whole-system manipulation or observational studies to determine the ultimate net effect of fire on shrubs because of the long time scales and multiple processes involved, and difficulty maintaining adequate control or untreated systems (Bouskill et al 2020). An alternative approach is to conduct more narrowly-aimed experiments to quantify the response or effect size for separate components of the ecosystem response (such as effect of fire on seed viability or seedling survival), and integrate what is learned in process-rich models. Ideally, these experimental approaches would allow for replication and evaluation in different landscapes. Moreover, model sensitivity analyses could help prioritize which processes to study.

In this section, we describe modeling needs most relevant to simulate changes in vegetation composition that lead to tundra shrub expansion and alter the ecosystem carbon balance. Although models vary in structure and parameterization, we highlight below the key process representations and modeling needs such as (a) tundra PFT traits, (b) topography, hydrology, and thermal dynamics, and snow–shrub interactions, and (c) synthesized observations for model benchmarking.

5.5.1. Model representation of tundra PFT traits and mechanisms

5.5.2. Research gap: model representation of tundra PFTs

5.5.3. Model representation of tundra topography, hydrology, and thermal dynamics, and snow–shrub interactions.

5.5.4. Development of synthesized observations for model benchmarking