Ecosystems https://doi.org/10.1007/s10021-020-00506-7 Ó 2020 Springer Science+Business Media, LLC, part of Springer Nature

High Arctic Vegetation Change Mediated by Hydrological Conditions

T. Kiyo F. Campbell,1* Trevor C. Lantz,1 Robert H. Fraser,2 and Danica Hogan3

1School of Environmental Studies, University of Victoria, PO Box 1700 STN CSC, Victoria, British Columbia V8W 2Y2, Canada; 2Centre for Mapping and Earth Observation Canada, Natural Resources Canada, 560 Rochester Street, Ottawa, Ontario K1S 5K2, Canada; 3Environment and Climate Change Canada, Canadian Wildlife Service, Nova Plaza. 5019 – 52nd Street, PO Box 2310, Yellowknife, Northern Territory X1A 2P7, Canada

ABSTRACT Increasing air temperatures are driving widespread ing productivity levels can be attributed to changes to Arctic vegetation. In the high Arctic, increasing biomass of the plant communities in these changes are patchy and the causes of both upland and lowland habitats. Our analysis heterogeneity are not well understood. In this also shows that the magnitude of greening is study, we explore the determinants of high Arctic mediated by terrain characteristics related to soil vegetation change over the last three decades on moisture. Shifts in tundra vegetation will impact Banks Island, Northwest Territories. We used wildlife habitat quality, surface energy balance, Landsat imagery (1984–2014) to map long-term permafrost dynamics, and the carbon cycle; addi- trends in vegetation productivity and regional tional research is needed to explore the effects of spatial data to investigate the relationships between more productive vegetation communities on these trends in productivity and terrain position. Field processes in the high Arctic. sampling investigated vegetation community com- position in different habitat types. Our analysis Key words: Climate change; Landsat; Remote shows that vegetation productivity changes are sensing; Primary production; Tundra ecosystems; substantial on Banks Island, where productivity has Protected areas. increased across about 80% of the study area. Ris-

HIGHLIGHTS  Greening trends are more common in upland habitats than in lowland habitats.  The magnitude of greening is strongly influenced  Vegetation productivity is increasing across the by moisture conditions. Banks Island, NT, high Arctic ecosystem.

INTRODUCTION Received 3 October 2019; accepted 5 April 2020 Rapidly warming temperatures are driving wide- Electronic supplementary material: The online version of this article spread changes to vegetation in many Arctic re- (https://doi.org/10.1007/s10021-020-00506-7) contains supplementary gions (Kaplan and New 2006; Lantz and others material, which is available to authorized users. 2010; Myers-Smith and others 2011; Fraser and Author Contributions TKFC and TCL took part in conceptualization and design; TKFC, TCL, and RHF performed research; TKFC analyzed others 2014a; Ju and Masek 2016). Plot-based data; TKFC, TCL, RHF, and DH contributed to methods; and TKFC, TCL, studies in the high Arctic show that vegetation RHF, and DH wrote the manuscript. productivity is increasing (Hudson and Henry 2009; *Corresponding author; e-mail: [email protected] T. K. F. Campbell and others

Hill and Henry 2011), but remote sensing studies sponses to climate change (Fraser and others 2018; indicate that changes are regionally variable, with Campbell and others 2018). Over the last few extensive greening (increases in satellite-derived decades, lowland areas across Banks Island have vegetation productivity), browning (decreases in experienced a decline in surface water through the satellite-derived vegetation productivity), and sta- drying of shallow tundra ponds (Campbell and bility (no change in vegetation productivity) evi- others 2018), whereas upland areas have shown an dent across the high Arctic (Guay and others 2014; increase in surface water driven by the develop- Ju and Masek 2016; Edwards and Treitz 2017). ment of new ice-wedge melt ponds (Fraser and Heterogeneity in productivity trends is likely re- others 2018). A paleoecological study of high Arctic lated to continental differences in temperature wetlands during a period of mid-twentieth-century (Raynolds and others 2008; Reichle and others warming also showed changes in moisture and 2018), but landscape-scale differences in biophysi- vegetation conditions that depended on wetland cal variables (for example, soil moisture, surface type (Sim and others 2019). Given the magnitude water, vegetation type, snow conditions, and her- of the recent hydrological changes on Banks Island, bivory) may be contributing to the variation in shifting moisture conditions could be creating vegetation responses to warming (Kotanen and diverging vegetation productivity trends among Jefferies 1997; Myers-Smith and others 2015; Ca- terrain types. meron and Lantz 2016; Martin and others 2017; In this study, we combined the analysis of Bjorkman and others 2019; Sim and others 2019). moderate resolution (30 m) Landsat satellite ima- Satellite-derived trends can also be influenced by gery (1984–2014) and regional spatial data with the interaction between the biophysical variation field sampling of vegetation communities to of a landscape and the spectral, spatial, and tem- examine the regional scale determinants of high poral resolution of the data being used (Campbell Arctic vegetation change over the last three dec- and others 2018; Myers-Smith and others 2020). ades. We hypothesized that: (1) changes in vege- Understanding the factors controlling high Arctic tation productivity would differ by habitat vegetation change is important because shifts in characteristics and topographic conditions and (2) vegetation will impact wildlife habitat quality, changes in moisture would be positively associated surface energy balance, permafrost dynamics, and with trends in vegetation productivity. the carbon cycle (Harding and others 2002; Chapin and others 2005; Gornall and others 2007; Fraser STUDY AREA and others 2014a; Fisher and others 2016). In high Arctic ecosystems, topographic position is Banks Island is the westernmost island in the a primary determinant of soil moisture and plant Canadian Arctic Archipelago and part of the Inu- community composition and productivity (Barrett vialuit Settlement Region in the Northwest Terri- and Teeri 1973; Bliss 1977; Woo and Young 1997, tories. The community of Sachs Harbour is the only 2006; Ecosystem Classification Group 2013; Becker permanent settlement on the island, with a popu- and others 2016). Upland terrain (for example, lation of approximately 100 residents. Located steep slopes, hilltops, and plateaus) has low soil within the northern or high Arctic ecozone, this moisture, vegetation cover, and organic soil area has a harsh climate with a mean annual development relative to poorly drained lowland temperature of - 12.8 °C at Sachs Harbour. Sum- terrain (for example, flats, alluvial terraces, or areas mers are short, with average daily temperatures adjacent to water bodies) (Bliss 1977; Hines and rising above freezing for only 3 months of the year, others 2010; Ecosystem Classification Group 2013). peaking at 6.6 °C in July. Average annual precipi- Upland plant communities are often sparsely veg- tation is 151.5 mm, with 38% falling as rain be- etated by prostrate dwarf shrubs and sedges, tween June and September (Environment and whereas lowland communities are dominated by Climate Change Canada 2019). Mean annual hydrophilic sedges, grasses, and mosses (Cody temperature has increased by 3.5 °C since 1956, 2000; CAVM Team 2003). These large differences while summer precipitation and maximum snow in soil and vegetation conditions suggest that water equivalent before spring melt have changed topographic position is likely to influence the nat- minimally (Fraser and others 2018; Mudryk and ure of climate-driven vegetation change in the high others 2018). Arctic. This study focuses on Banks Island Migratory Recent studies on Banks Island, Northwest Ter- Bird Sanctuary No. 1 (BIMBS1), which covers ritories, show that upland and lowland terrain 20,517 km2 of the southwestern side of the island types have exhibited different hydrological re- (Figure 1) and is the second-largest federally pro- Hydrology-Mediated Vegetation Change

Figure 1. Map of Banks Island Migratory Bird Sanctuary No. 1, showing habitat types from the land cover classification by Hines and others (2010). Inset map at the upper left corner shows the study area on Banks Island outlined with a red box (Color figure online). tected migratory bird sanctuary in Canada (Hines BIMBS1 was created in 1961 to protect migratory and others 2010; Ecosystem Classification Group birds and their habitat on Banks Island. BIMBS1 2013). BIMBS1 is characterized by gently rolling provides important nesting habitat for many uplands intersected by numerous west flowing migratory birds, including lesser snow geese (Chen rivers with wide floodplains. Alluvial terraces in caerulescens caerulescens), black brant (Branta bernicla river valleys are dotted with thousands of shallow nigricans), and king eiders (Somateria spectabilis). The ponds and have nearly continuous vegetation main nesting colony for over 95% of the western cover dominated by sedges, grasses, and mosses Arctic lesser snow goose population is located at the (Hines and others 2010; Ecosystem Classification confluence of the Egg and Big Rivers, within the Group 2013). Upland areas are occupied by sparse migratory bird sanctuary (Hines and others 2010; to continuous dwarf shrub and herb tundra Ecosystem Classification Group 2013). This colony (Ecosystem Classification Group 2013). The island has almost tripled in population since 1976 (Hines is underlain by continuous permafrost (French and others 2010; Kerbes and others 2014). 2016), and ice-wedge polygons, non-sorted circles and stripes, and turf hummocks are widespread (Ecosystem Classification Group 2013). T. K. F. Campbell and others

METHODS and others 2014b; Olthof and others 2015; Bro- naugh and Werner 2019). Cumulative Julian Day, Landsat Trends starting from the first Landsat image in the time To explore changes in vegetation productivity be- series, was used as the explanatory variable in these tween 1984 and 2014, we analyzed a time series of analyses. Theil–Sen regression is a nonparametric near-annual Tasseled Cap (TC) transformed Land- alternative to ordinary least-squares regression that sat images. This time series was composed of 154 uses the median of all possible pairwise slopes, in- 30-m-resolution images captured by the Landsat 5 stead of the mean. The rank-based Mann–Kendall TM and Landsat 7 ETM + sensors between 1984 test of significance is calculated by comparison with and 2014 (Table S1). To reduce the influence of all possible pairwise slopes (Kendall and Stewart changing phenology, image acquisition dates were 1967). restricted to between July 10 and August 15 of each year. Due to cloud cover and data availability, 1 to Landscape Analysis 10 images were available each year. These images Our use of moderate resolution (30 m) satellite were balanced over the time series to optimize data data in this study allowed for better exploration of coverage while avoiding systematic changes in the the ecological processes mediating observed chan- number of images over time (Figure S1 and ges. Specifically, we divided the bird sanctuary into Table S1). Images were calibrated to top-of-atmo- seven habitat types, which we classified as either spheric reflectance using USGS coefficients upland or lowland habitats. This was done using a (Chander and others 2009), and clouds, cloud 30-m-resolution land-cover classification devel- shadows, and scan lines were masked out. oped by Hines and others (2010). The classification The Tasseled Cap Greenness (TCG) and Wetness was derived from Landsat 5 TM images acquired on (TCW) indices were calculated for each Landsat July 6, 1990, and August 12, 1995, included 11 image using Landsat bands 1–5 and 7 and estab- habitat types in total, and had an overall accuracy lished coefficients (Crist and Cicone 1984; Huang of 88%. The dry–mesic tundra, hummocky tundra, and others 2002). Tasseled Cap Greenness (TCG) and dwarf shrub–herb tundra habitat types were uses the difference between near-infrared and vis- grouped and classified as upland habitats (Fig- ible bands, making it suitable for measuring green ure 2). The lowland pond complex, wet meadow, vegetation. TCG is strongly correlated with Nor- moist meadow, and degraded lowland habitat types malized Difference Vegetation Index (NDVI) and were grouped and classified as lowland habitats has shown similar results in Arctic and sub-Arctic (Figure 2). The water-based, unvegetated, and terrain types (Fraser and others 2011; Raynolds and unknown land-cover types from the Hines and Walker 2016). In this study, TCG was used as op- others (2010) land-cover classification were not posed to NDVI, because of NDVI’s greater reliance considered in this analysis. We calculated the area on near-infrared wavelengths, which may impact within each of the seven habitat types exhibiting performance in areas of changing surface water and significant greening, stable, and browning trends soil moisture conditions (Goswami and others and used a Chi-square test to determine whether 2011; Lin and others 2011; Elmendorf and others the frequency of pixel trends deviated from values 2012; Raynolds and Walker 2016). The use of expected based on the area covered by each habitat additional Landsat bands in the TCG index can re- type. We calculated the expected number of duce noise caused by varying soil and moisture greening, browning, and nonsignificantly trended conditions (Crist and Cicone 1984; Huete and pixels in each habitat type by multiplying the others 1994). Tasseled Cap Wetness (TCW) con- number of pixels in each trend category with the trasts shortwave infrared with visible and near-in- number of pixels in the habitat type and dividing frared bands which makes it sensitive to water by the sample size. The Chi-square test used a surfaces, soil moisture, and plant moisture (Kauth random subset of 300,000 pixels from upland and Thomas 1976; Crist and Cicone 1984). TCW habitats and 300,000 from lowland habitats. has been used successfully to map changes in sur- To examine the biophysical factors associated face water on western Banks Island (Campbell and with vegetative greening and browning in upland others 2018). and lowland habitats, we created separate random To determine per-pixel trends and test their sta- forest regression models for upland and lowland tistical significance, Theil–Sen regression and the habitat classifications, using the significant TCG rank-based Mann–Kendall test were used (Hollan- trend slope as the response variable. Models were der and Wolfe 1973; Best and Roberts 1975; Fraser constructed using a random subset of 300,000 Hydrology-Mediated Vegetation Change

Figure 2. Example photographs from BIMBS1 of the habitat types considered in this study, as well as the dominant vascular plants found in each. Habitat types taken from the Hines and others (2010) land-cover classification of BIMBS1. Upland habitat types are shown in the upper row, whereas lowland habitat types are shown in the bottom row of the figure (Color figure online).

pixels for each habitat classification and the ‘ran- explained the most variance in TCG trends for both domForest’ package (Liaw and Wiener 2002), in R upland and lowland models (Liaw and Wiener software version 3.3.2 (R Core Team 2016). 2002). Variable importance was calculated as the Explanatory variables included: significant TCW percentage decrease in accuracy when each trend slope, latitude, elevation, topographic slope, explanatory variable was removed from the model; flow accumulation, distance from the coast, and the difference in accuracy was calculated as the habitat type (from Hines and others 2010). Eleva- mean difference between the prediction errors of tion data were taken from the 5-m-resolution the out-of-bag data and the prediction errors after ArcticDEM, referenced to the WGS84 ellipsoid permuting each explanatory variable, normalized (Porter and others 2018). Topographic slope was by the standard deviation of the differences (Liaw calculated using the ArcticDEM and the slope tool and Wiener 2002). We also used partial depen- from the ArcMap (10.4.1) surface toolset. Flow dence plots to explore how the relationship be- accumulation was calculated using the ArcticDEM tween TCG trend and explanatory variables and the fill, flow direction, and flow accumulation differed between upland and lowland habitat types. tools from the ArcMap (10.4.1) hydrology toolset Partial dependence plots show the average pre- (Jenson and Domingue 1988). The flow accumu- dicted value of the response variable, at different lation variable was log(x + 1)-transformed to en- levels of a given explanatory variable, with the sure normality. Distance from the coast was influence of the other explanatory variables aver- calculated using the Euclidean distance from a aged (Friedman 2001). polyline, manually delineated along the western and southern coasts of BIMBS1. Spatial data that Habitat Vegetation Communities did not match the 30 m Landsat spatial resolution To characterize plant communities in the study were aggregated using the mean value within a area, detailed plant cover data were collected in the 30 m by 30 m area. field over two summers (July 2017 and July–Au- The variable importance function from the gust 2018). In 2017, data were collected at 16 ‘randomForest’ package (Liaw and Wiener 2002) locations within BIMBS1. Survey sites in 2017 was used to determine which explanatory variables consisted of a single 100 m transect, oriented in a T. K. F. Campbell and others

Table 1. Observed and Expected Numbers of Greening, Browning, and Nonsignificantly Trended Pixels by Habitat Type, Based on the Chi-square Analysis of a Sample of 600,000 Pixels

Browning Stable Greening

Observed Expected Observed Expected Observed Expected

Lowland pond complex 423* 90.7 11,301* 5267.5 13,726* 20,091.8 Wet meadow 509* 190.1 18,582* 11,039.5 34,247* 42,108.4 Moist meadow 765 780.0 57,336* 45,305.6 160,795* 172,810.4 Degraded lowland 193* 8.3 1278* 479.4 845* 1828.4 Dry–mesic tundra 17* 112.2 3223* 6514.5 28,235* 24,848.4 Hummocky tundra 14* 56.2 1339* 3262.5 14,410* 12,444.3 Dwarf shrub–herb tundra 217* 900.7 31,125* 52,315.0 221,420* 199,546.3 Column totals 2138 2138 124,184 124,184 473,678 473,678

Bold numbers indicate that the observed number of pixels exceeds the expected number. Numbers with an asterisk indicate a significant difference from the expected value, using a Bonferroni correction (p < 0.0012) (Color figure online)

north–south direction. The percent cover of dwarf TCG Trend Contributions from Landsat shrubs, forbs, graminoids, bryophytes, and lichens Bands was visually estimated within 50 cm2 quadrats at 10-m intervals along each transect. In 2018, data To better understand the nature of vegetation were collected at 40 locations within BIMBS1. change occurring on Banks Island, the contribu- Survey sites in 2018 consisted of four 50 m tran- tions of each Landsat spectral band to the TCG sects intersecting at 0 m and oriented toward the trends were investigated using two random forest cardinal directions. The cover of dwarf shrubs, regression models (Liaw and Wiener 2002; R Core forbs, graminoids, bryophytes, and lichens were Team 2016). As previously, one model was created visually estimated to species within 50 cm2 quad- for upland habitats and one for lowland habitats, rats at the 25 m mark of each transect (Abraham using a random subset of 300,000 pixels from the 2014). To minimize the influence of observer error respective upland–lowland classification. Explana- in percent cover estimations, data were binned tory variables included the trend slopes for Landsat within cover ranges (0–0.9, 1–5, 6–25, 26–50, 51– bands 1–5 and 7. Per-pixel trends over the time 75, 76–100). To ensure that the data from both series (1984–2014) were calculated using Theil–Sen years were compatible, species were grouped into regression (Fraser and others 2014b; Olthof and functional groups (vascular species found in others 2015). All significant and nonsignificant Table S2–S4). trends were considered in this analysis. The vari- To characterize and compare the community able importance function was used to determine composition among habitat types, we used a non- the wavelengths that contributed most to changes metric multidimensional scaling (NMDS) analysis. in TCG (Liaw and Wiener 2002). If no major This analysis used a log(x + 1) matrix of the percent influences from the bands existed, then importance cover of each functional group. The NMDS analysis values would resemble the TC conversion coeffi- was conducted using the ‘Vegan’ package (Oksa- cients (Huang and others 2002). Partial depen- nen and others 2017) in R version 3.3.2 (R Core dence plots were used to visualize the relationships Team 2016). An analysis of similarities (ANOSIM) between TCG and band trends, and to investigate test was used to evaluate the dissimilarity of com- whether these relationships differed in upland and munity composition among upland and lowland lowland habitats (Friedman 2001). habitat types. The ‘envfit’ function in ‘Vegan’ was Landsat TM and ETM + bands 1, 3, and 4 are used to explore associations between TC trends and known to be useful wavelengths for detecting community composition (Oksanen and others vegetation conditions (Crist and Cicone 1984; 2017). Plots that fell within water bodies or desic- Huete and others 1994; May and others 2018). cated pond basins were excluded from this analysis, Band 1 detects visible blue wavelengths and cor- as they were either entirely water or represented relates well with the other visible wavelengths in land cover with a different origin and substrate. measuring green vegetation (Crist and Cicone 1984). However, band 1 is particularly useful in Hydrology-Mediated Vegetation Change reducing noise caused by varying soil and atmo- bands 5 or 7 could indicate vegetation change is an spheric conditions (Huete and others 1994); high artifact of a transition to or from water cover. band 1 importance could indicate that apparent vegetation change is an artifact of changes in soil RESULTS and atmospheric conditions. Band 3 measures vis- ible red wavelengths. Plant materials reflect more Landsat Trends and Landscape Analysis visible red light if they have lower chlorophyll Roughly 80% of the study area (15,628.08 km2) content or if they are dry or desiccated (Crist and exhibited a significant positive TCG trend from Cicone 1984; May and others 2018). High band 3 1984 to 2014. Areas that showed no significant importance could indicate that vegetation change is TCG trend were also common, accounting for 20% related to an increase in vegetation with lower (3943.39 km2) of the study area. Stable areas were chlorophyll content (i.e., a change in vegetation especially prevalent in lowland habitats. Browning type), or substantial changes in moisture avail- pixels were uncommon, making up only 0.3% ability. Band 4 detects near-infrared (NIR) wave- (67.58 km2) of the study area. Browning pixels lengths, which strongly correlate with plant were located primarily in lowland habitat areas biomass (Crist and Cicone 1984). Bands 5 and 7 (87% of browning pixels; Table 1) like the Bernard detect short-wave infrared (SWIR) radiation, which River valley at the northern border of BIMBS1 and is known to be sensitive to the presence of surface at the Egg River nesting colony in the Big River water (Olthof and others 2015). High importance of valley (Figure 3B). The extent of greening also

Figure 3. (A) Significant TCG trend slopes (p < 0.05) across the study area of BIMBS1. (B) Enlarged inset showing an alluvial terrace adjacent to the Bernard River. (C) Enlarged inset showing an upland area northeast of the Egg River Snow Goose nesting colony. (D) The proportion of pixels with significant positive (greening) or negative (browning) TCG trend slopes (p < 0.05) in each habitat type; pixels were classified as stable (gray) if there was no significant trend. Habitat types include: dry–mesic tundra (MT), hummocky tundra (HT), dwarf shrub–herb tundra (ST), lowland pond complex (PC), wet meadow (WM), moist meadow (MM), and degraded lowland (DL) (Color figure online). T. K. F. Campbell and others differed in upland and lowland habitats. The pro- by 94% more than the next most important vari- portion of significantly greening pixels in the three able. upland habitats ranged from 87 to 92%, whereas The partial dependence plot for habitat type the proportion of significantly greening pixels in shows that upland habitat types had consistently the lowland habitats ranged from 35 to 74% (Fig- higher predicted TCG trends, relative to lowland ure 3). A Chi-square test confirmed that the habitat types. Hummocky tundra had the highest amount of greening, browning, and stability was predicted TCG trend, and the degraded lowland and associated with the upland and lowland classifica- pond complex habitat types had the lowest pre- tions (p < 0.001) (Table 1). All lowland habitat dicted TCG trends (Figure 5A). The partial depen- types, except moist meadow, had more browning dence plots for TCW trend show that higher and stable pixels, and fewer greening pixels than greening was associated with pixels where surface would be expected based on the proportion of the water and soil moisture were also increasing (Fig- study area covered by each habitat type. Moist ure 5B). This effect was intensified in lowland areas meadow had the expected amount of browning where TCG showed larger increases and decreases pixels, but more stable pixels than expected and in response to changes in TCW. fewer greening pixels than expected. All upland Topographic slope had opposite effects on TCG habitat types had more greening, and fewer trends in upland versus lowland habitats. In upland browning and stable pixels than expected (Ta- habitats, TCG trends were highest in flatter areas, ble 1). while in lowland habitats, flat areas had the lowest The random forest models parameterized to TCG trends (Figure 5C). In both upland and low- predict changes in TCG explained a similar pro- land habitats, increases in TCG were higher at high portion of the total variance in upland (R2 = 0.418) levels of flow accumulation (Figure 5D). The par- and lowland (R2 = 0.385) habitats, but the impor- tial dependence plots for latitude show that in- tance of explanatory variables differed between creases in TCG were highest at mid-latitudes for models. The most important variables in the upland upland habits and low-to-mid-latitudes for lowland model were (1) flow accumulation, (2) habitat habitats (Figure 5E). The partial dependence plots type, (3) topographic slope, and (4) TCW slope, for distance from the coast show that inland areas which all increased model accuracy by over 100% had higher average greening (Figure 5F). (Figure 4). The most important variables in the lowland model were (1) TCW slope, (2) topo- Habitat Vegetation Communities graphic slope, (3) habitat type, (4) latitude, (5) The NMDS ordination plotted in Figure 6 shows distance from the coast, and (6) flow accumulation, considerable heterogeneity among habitat types, which all increased model accuracy by over 100% but a clear distinction between upland and lowland (Figure 4). TCW slope was particularly important habitats. A pairwise comparison of the upland and in the lowland model, increasing model accuracy lowland sites produced an RANOSIM statistic of 0.442

Figure 4. Variable importance for the upland and lowland random forest models explaining vegetation change with moisture and terrain related variables. Variable importance is measured as the percentage increase in accuracy when individual explanatory variables are included in the model; calculated using the mean difference between the prediction errors of the out-of-bag data and the prediction errors after permuting each explanatory variable, normalized by the standard error of the differences (Liaw and Wiener 2002) (Color figure online). Hydrology-Mediated Vegetation Change

Figure 5. Partial dependence plots showing the most important explanatory variables from both upland and lowland random forest regressions. Class variables are shown as bar graphs, while continuous variables are shown as line graphs. Red bars and lines represent the lowland model, and blue bars and lines represent the upland model: (A) habitat type, (B) TCW trend slope, (C) topographic slope, (D) flow accumulation, (E) latitude, and (F) distance from the coast. The y-axis shows the mean predicted TCG trend slope, at different levels of the given explanatory variable, with the influence of the other explanatory variables averaged (Friedman 2001). Habitat types include: dry–mesic tundra (MT), hummocky tundra (HT), dwarf shrub–herb tundra (ST), lowland pond complex (PC), wet meadow (WM), moist meadow (MM), and degraded lowland (DL) (Color figure online).

(p < 0.001), indicating moderate separation be- cated by the vectors shown in Figure 6, which are tween upland and lowland community composi- oriented perpendicular to the separation between tion. Upland communities were associated with upland and lowland habitats. The ordination of high forb and lichen cover, and lowland commu- community composition also shows that upland nities were associated with the dominance of quadrats associated with increasing TCG had high bryophytes. Dwarf shrubs and graminoids were forb cover and highly variable cover of dwarf shrub common in both uplands and lowlands, as indi- and graminoids, whereas lowland quadrats associ- T. K. F. Campbell and others

Figure 6. NMDS ordination (stress = 0.0511) plot showing similarity and dissimilarity of vegetation community composition among habitat types. Each point (n = 319) on the ordination represents an individual field quadrat, and its proximity to other points represents the similarity in plant community composition, with close meaning more similar. Ellipses represent the standard deviation of NMDS1 and NMDS2 for each habitat type. The black arrows show the direction and magnitude of significant associations between functional group cover and the NMDS scores for each quadrat. The brown arrows show the direction and magnitude of significant associations between the TC trend slopes and the NMDS scores for each quadrat (Color figure online). ated with increasing TCG had high forb and gra- variance in upland (R2 = 0.878) and lowland minoid cover. Lower TCG slopes were associated (R2 = 0.911) habitat types. In both models, Landsat with bryophyte dominated lowland habitats. band 4 was over five times more important than any other band (Figure 7). The order of importance TCG Trend Contributions from Landsat did not match TC conversion coefficient values Bands (Huang and others 2002). The partial dependence plot (Figure 8D) shows a positive association be- Random forest models parameterized to determine tween the trend slopes of TCG and band 4, sug- the contributions of each Landsat band to the TCG gesting that the increase in TCG is being driven by trends both explained a large proportion of the total increases in plant biomass.

DISCUSSION The widespread nature of the greening on western Banks Island suggests that this change is the pro- duct of rapid increases in air temperature observed across the western Arctic (Hansen and others 2010; Vincent and others 2015). Since 1950, mean sum- mer (June–August) temperatures have increased by 1–2 °C and mean spring (March–May) and fall (September–November) temperatures have in- creased by 2–4 °C (Hansen and others 2010; NASA Figure 7. Variable importance for the upland and GISS 2014). These changes are larger than the lowland random forest models explaining TCG pixel difference in average temperature between some trends with Landsat band trends. Importance is measured northern and southern Arctic ecoregions in the as the percentage increase in accuracy when each Northwest Territories (Ecosystem Classification explanatory variable is included in the model; Group 2012; Ecosystem Classification Group 2013). calculated using the mean difference between the Ground-based studies using experimental warm- prediction errors of the out-of-bag data and the ing, repeat survey, and space-for-time approaches prediction errors after permuting each explanatory all suggest that tundra vegetation change is linked variable, normalized by the standard deviation of the differences (Liaw and Wiener 2002) (Color to increasing air and ground temperatures, which figure online). can extend the length of the growing season and Hydrology-Mediated Vegetation Change

Figure 8. Partial dependence plots for each Landsat band trend slope, from both upland and lowland random forest regression models. Red lines represent the lowland model and blue lines represent the upland model: (A) band 1, (B) band 2, (C) band 3, (D) band 4, (E) band 5, and (F) band 7. The y-axis shows the mean predicted TCG trend slope, at different levels of the given explanatory variable, with the influence of the other explanatory variables averaged (Friedman 2001) (Color figure online). increase the level of plant available nutrients upland habitats, flow accumulation, which is a (Chapin and others 1995; Walker and others 2006; proxy for the discharge of overland and subsurface Elmendorf and others 2015). flow, was the most important factor in explaining The correspondence between hydrological indi- changes in productivity. The observation that cator variables and the magnitude of greening higher greening occurred in areas with high flow suggests that climate-driven vegetation change is accumulation levels indicates that tundra vegeta- being mediated by moisture availability across the tion receiving moisture and nutrients from upslope study area. Variables related to moisture potential, areas can more effectively capitalize on increased such as flow accumulation, slope, and TCW trends, summer temperatures compared to drier areas were among the most important predictors of (Billings and Mooney 1968; Webber 1978; Guo and variation in productivity trends within BIMBS1. In others 2012; Atkinson and Treitz 2013; Johansson T. K. F. Campbell and others and others 2013; Becker and others 2016; Niittynen and others 2018). Overgrazing from lesser snow and Luoto 2017). This is consistent with research geese, which can reduce aboveground biomass and by Naito and Cairns (2011) and Tape and others is known to be occurring in some areas of BIMBS1, (2012), showing that shrub expansion on the North may also be contributing to the areas of decreasing Slope of Alaska was greatest in drainage areas with productivity (Jefferies and others 1979; Kotanen large potential for moisture accumulation. Flow and Jefferies 1997; Hines and others 2010). pathways are likely to be particularly important on Our analysis suggests that trends in TCG are Banks Island where the spring freshet is the largest linked to the growth of existing vegetation, rather annual influx of water and one of the only times of than shifts in the abundance of some functional the year that surface runoff occurs (Lewkowicz and groups (Figures 6, 7,and8). Variation in commu- French 1982). The association between greening nity composition at upland sites was not correlated and topographic slope in the upland habitats also with the trend in TCG, indicating that greening is shows that greening was highest in flatter areas not associated with increases in the dominance of a where surface water can accumulate (Riedlinger single functional group as has been described in the and Berkes 2001; Fraser and others 2018). In low Arctic (Tremblay and others 2012; Fraser and lowland habitat types, the TCW trend, which de- others 2014a; Frost and Epstein 2014; Moffat and scribes long-term changes in moisture, was the others 2016). This explanation is also consistent most important factor in explaining productivity with warming experiments in high Arctic tundra, changes. Our observation that greening was high- which show increases in biomass from existing est in lowland areas with increasing moisture vegetation, and little evidence of species replace- trends is also consistent with numerous studies, ments (Hudson and Henry 2009; Hill and Henry indicating that soil moisture can strongly influence 2011). Taken together, this suggests that the northern vegetation change (Elmendorf and others greening we observed in upland habitats is likely 2012; Myers-Smith and others 2015; Cameron and the result of increases in forb and dwarf shrub Lantz 2016). biomass. Greening in lowland habitats has probably Our observation that greening was less wide- been driven by the growth of forbs and graminoids. spread in lowland habitats also suggests that soil Browning in lowland habitats is likely related to moisture mediates the response of tundra vegeta- decreases in forbs and graminoids exposing bare tion to warming. Water bodies and wetlands are peat or a bryophyte carpet (Hines and others 2010). more common in lowland habitats (Ecosystem Although still vegetated, an exposed bryophyte Classification Group 2013), where higher moisture carpet is also more susceptible to desiccation, availability has facilitated the long-term develop- leading to reduced productivity and discoloration ment of productive plant communities dominated (Proctor 2000; Proctor and Tuba 2002; May and by hydrophilic sedges, grasses, and mosses (Cody others 2018). Future research should investigate 2000; CAVM Team 2003; Woo and Young 2006). It whether species dominance is shifting toward more is likely that plant communities in these habitats moisture-tolerant species within functional groups. are less responsive to warming because productiv- Changes in the productivity of tundra ecosystems ity is already relatively high in lowland areas (Hines are significant because they are likely to impact and others 2010; Ecosystem Classification Group surface energy balance, permafrost and active layer 2013) and there is less potential for vegetation dynamics, and wildlife habitat quality (Harding and expansion and greening. This is consistent with our others 2002; Chapin and others 2005; Gornall and observation that greening in lowland habitats was others 2007; Fraser and others 2014a; Fisher and highest on slopes, which are likely to have inter- others 2016). Increasing vegetation biomass may mediate moisture levels and lower vegetation affect surface energy balance, as denser vegetation cover. can decrease summer and winter albedo, con- Discrete areas of vegetation browning in lowland tributing to further warming trends (Chapin and habitats indicate that long-term changes in mois- others 2005; Loranty and others 2011). Changes in ture may be contributing to reductions in above- vegetation can also alter permafrost conditions and ground biomass and productivity in some areas. terrain stability (Gornall and others 2007; Kokelj Lowland areas with significant reductions in TCW and Jorgenson 2013; Fisher and others 2016). experienced the lowest average greening levels. Additional research on the nature of high Arctic Recent research also shows that lowland pond greening is vital because vegetation canopy density complexes and degraded lowlands, which had the and moss layer characteristics have a substantial highest proportions of browning pixels, have influence on active layer thickness and the quan- experienced a large loss of surface water (Campbell tity of carbon stored in permafrost (Gornall and Hydrology-Mediated Vegetation Change others 2007; Fisher and others 2016). A range of the Canadian Wildlife Service - Yellowknife, and species (lesser snow geese, black brant, king eider, the Sachs Harbour Hunters and Trappers Commit- Peary caribou, and muskox) rely on BIMBS1, par- tee. We thank Marie Fast, Megan Ross, Eric Reed, ticularly lowland habitats, for forage and protection and Cindy Wood from the Canadian Wildlife Ser- during nesting and calving periods (Slattery and vice, as well as Trevor Lucas, from the Sachs Har- Alisauskas 2007; Sachs Harbour and Joint Secre- bour Hunters and Trappers Committee, for their tariat 2008). Understanding the nature of vegeta- support with fieldwork and logistics. We also thank tive change, especially shifts in the abundance of Ian Olthof for assistance with the collection and forage plants, is also critical to evaluate the impacts processing of remote sensing data. This research of climate change on wildlife within this protected was funded by: the Polar Continental Shelf Pro- area and to enable effective management of the gram; the Natural Sciences and Engineering Re- Migratory Bird Sanctuary. Effective conservation search Council of Canada; ArcticNet; the Northern and management of the wildlife resources within Scientific Training Program; the Canadian Space the sanctuary are particularly important to the Agency Government Related Initiatives Program Inuvialuit of Sachs Harbour, who rely on many of (GRIP); the Canadian Wildlife Service; and the the species within BIMBS1 for cultural and sub- University of Victoria. sistence purposes (Sachs Harbour and Joint Secre- tariat 2008). REFERENCES

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