DOI: 10.5061/dryad.ncjsxkszw
title: Forests on the move: Tracking climate-related treeline changes in mountains of the northeastern United States

description: Aim 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.   Location White Mountain
 National Forest (New Hampshire) and Baxter State Park (Maine), USA.
   Taxon High-elevation trees – Abies balsamea, Picea mariana, and
 Betula cordata.    Methods 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.
   Results 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).   Main conclusions 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.
description: <em>Remote sensing analysis</em>
 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 &lt; 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 &gt;20 m in diameter in any direction
 (determined in ArcGIS) and included an individual &gt;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.   <em>Field
 surveys</em> 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
 m<sup>2</sup>) 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
 &gt; 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 &gt;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 (&lt;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%).  
 <em>Spatial data processing</em>
 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).
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