Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions.

 

White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA.

 

High-elevation trees – Abies balsamea, Picea mariana, and Betula cordata. 

 

We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional potential drivers of treeline locations. We used multiple linear regression models to examine the importance of both topographic and climatic variables on treeline advance.

 

Regional treelines have significantly shifted upslope over the past several decades (on average by 3 m/decade). Diffuse treelines (low tree densities and temperature limited) experienced significantly greater upslope shifts (5 m/decade) compared to other treeline forms, suggesting that both climate warming and treeline demography are important drivers of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R² = 0.32).

 

The observed advance of regional treelines suggests that climate warming induces upslope treeline shifts particularly at higher elevations where greater upslope shifts occurred in areas with lower AGDD. Overall, our findings suggest that diffuse treelines at high-elevations are more a of a result of climate warming than other alpine treeline ecotones and thus they can serve as key indicators of ongoing climatic changes.

Physical copies of true color high resolution historical aerial imagery (sub-meter resolution) were acquired from the Appalachian Mountain Club (AMC) and the USFS White Mountain National Forest Headquarters. Imagery for the Presidential Range was taken in 1978 and Katahdin imagery was taken in 1991. Hard copy images were scanned and converted to TIFF format at 300 dpi (resulting in 0.5 m resolution images). Spatial analyses of change in treeline positions over time were enabled by acquiring high resolution 2018 false-color near-infrared imagery from the National Agriculture Inventory Program (NAIP 2021). Both sets of imagery were taken during summer months (1:40,000 scale). Using ArcGIS 10.8 (ESRI 2011, Redlands, CA, USA), historic imagery was ortho- and georectified to newer imagery via a spline function along 60 ground control points, and then converted into one orthomosaic image (RMSE < 1m). Exact error was always below 5 m for each individual image.

 

All areas above treeline were manually digitized based on observed tree cover for both sets of images, and the resulting polygons were converted to raster format at 2 m resolution (all raster pixels within each polygon had a value of 1). We identified forest cover only as areas with overlapping crowns and seen as green reflectance in historic imagery and red reflectance in contemporary false-color near-infrared imagery (no visible bare earth or easily identified alpine vegetation). Isolated tree island edges were also digitized and included as treeline if they were >20 m in diameter in any direction (determined in ArcGIS) and included an individual >2 m in height as validated in the field. Alpine rasters were aligned to and multiplied by Lidar-derived digital elevation models (DEMs; 2 m resolution) acquired from New Hampshire and Maine state GIS repositories in order to determine treeline elevations. A total of 400 random sample points (200 for each range, using the ArcGIS random sample point tool) were placed along the outer boundary of the alpine rasters derived from our contemporary imagery, and for each of them we established a paired point at the nearest location along the alpine raster boundary derived from our historic imagery.

 

Field sampling was carried out in the summer of 2021 to characterize tree demography and demographic variation among different treeline forms identified from the current imagery. A subset of contemporary points from our GIS-based sample point pairs (n = 54, 33 in the Presidential Range, 21 in the Katahdin Range, see above) were selected using a random number generator to serve as sites for establishing belt transects. Each belt transect was 100 m in length and 4 m wide (2 m on either side of transect for a total area of 400 m²) and perpendicular to elevation contours, spanning the ecotone between closed forest interior and open alpine habitat. The start of each transect (the lowest elevation on the transect, set as 0 m) was located 50 m downslope (straight-line distance) of contemporary sample points. The start and end of each belt transect were recorded using a Garmin GPSMAP 64 (Garmin, Olathe, Kansas, USA). Each tree > 0.1 m in height with a stem rooted within the transect was recorded noting species, basal diameter (10 cm from the ground), height, horizontal distance from the transect, and distance along the transect (to estimate stem density of trees). Slope, aspect, elevation, and soil depth to bedrock (using a metal soil probe) were recorded at 20 m intervals along the belt transect centerline (0 m, 20 m, 40 m, 60 m, 80 m, 100 m).

 

For all belt transects, treeline form was assigned based on visual assessments (based on changes in tree height and density across the ecotone). Additionally, we visited a majority of our other accessible contemporary random sample points (~80%) in order to assign treeline form and ground-truth remote sensed treeline classifications. For all visited sample points we took a new GPS point at the field-verified treeline location (continuous canopy cover and at least one individual >2 m in height) nearest to our random sample points (assigned from our treeline delineation procedure). The new points were compared to the original sample point locations and assessed for accuracy (measuring linear distance between points). Eye-level photos of treelines were taken at all sample points to keep a permanent record of treeline appearance. We stress that because tree height could not be extracted or field validated from our historic imagery, some krummholz individuals (<2 m) may have been present above our treeline delineation using our classification scheme. Out of all 400 sample point pairs across both the Presidentials and Katahdin, 88 were classified as abrupt (22%), 70 as diffuse (17.5%), 84 as island (21%), and 162 as krummholz (40.5%).

 

To examine the factors potentially influencing the spatial dynamics of treeline advance, both climatological and topographical variables were extracted for the Presidential Range. We could not conduct a similar analysis for Katahdin given the lack of fine-scale climatological data in that area. Elevation was extracted from 2 m state produced DEMs. Using the Spatial Analyst toolbox in ArcGIS, topographical variables such as slope, aspect, and curvature (measure of convex or concave shape of the terrain ranging between -4 and 4) were extracted from our DEMs. Circular aspect data (measured in degrees, 0-360⁰) were converted to radians and linearized (east and west = 1, north and south = 0).

 

Before linearization, aspect values were used to calculate degree difference from prevailing wind (DDPW - 290˚) and degree difference from south (DDS - 180˚) variables. DDPW is a proxy for exposure to strong winds that can cause both direct physical damage and damage from icing, as well as a proxy for the potential for snow accumulation. The prevailing wind direction for the Presidential range (290˚) was based on wind measurements from the Mount Washington Observatory. DDS is a proxy for the amount of direct solar radiation (in the northern hemisphere). Average monthly mean, maximum, and minimum temperatures as well as annual accumulated growing degree days (AGDD) were calculated from an array of 34 HOBO dataloggers (Onset Computer Corporation, Bourne, MA, USA) placed at various elevations and adjacent to Appalachian Mountain Club buildings in the White Mountains of New Hampshire. HOBO loggers have recorded hourly air temperature at ground level (0 m height) continuously since 2007. Air temperature means and AGDD were calculated from HOBO logger data; for AGDD calculations we used a base temperature of 4˚C, consistent with other studies examining growth patterns of balsam fir, the dominant species within studied treelines. AGDD was calculated as the accumulated maximum value of growing degree days (GDD) in a year.

 

Gridded maps (90 m spatial resolution) of mean annual temperature (Tmean, between 2007 and 2020) and AGDD for the Presidential Range region were produced using a cokriging interpolation method. To do this, temperatures and AGDD response variables were first checked for normality using qq-plots. Next, correlation between response variables and potential covariates was assessed; both elevation and aspect were highly correlated with HOBO derived temperature and AGDD. We used normal-score simple cokriging with a stable semi-variogram model to interpolate (prediction map) climate variables over the entire spatial extent of the Presidential Range (RMSE ~ 1 for both Tmean and AGDD). Mean annual precipitation was estimated from 30-year normal PRISM climate data (1991-2020; PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu).