The map extent is the Arctic, defined as the Arctic Bioclimate Zone, the area of the Earth with tundra vegetation and an Arctic climate and Arctic flora. It excludes tundra regions that lack an Arctic flora, such as the boreal oceanic areas of Iceland, the Aleutian Island, and the alpine tundra regions south of latitudinal tree line. Tundra is a physiognomic descriptor of low-growing vegetation beyond the cold limit of tree growth, both at high elevation (alpine tundra) and at high latitude (arctic tundra). Tundra vegetation types are composed of various combinations of herbaceous plants, shrubs, mosses and lichens. Tree line defines the southern limit of the Arctic Bioclimate Zone. In some regions of the Arctic, especially Canada and Chukotka, the forest tundra transition is gradual and interpretation of treeline directly from the AVHRR imagery was not possible. Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Elvebakk, A. 1999. Bioclimate delimitation and subdivisions of the Arctic. Pages 81-112 in I. Nordal and V. Y. Razzhivin, editors. The Species Concept in the High North - A Panarctic Flora Initiative. The Norwegian Academy of Science and Letters, Oslo. Yurtsev, B. A. 1994. Floristic divisions of the Arctic. Journal of Vegetation Science 5:765-776.

These are map products depicting modeled treeline dynamics. The left panel indicates modeled treeline dynamics from a single 2014 baseline year to the year 2100. The right panel indicates basal area accumulation on a 1km x 1km pixel basis during the year 2100, which gives an indication where possible further treeline advance may occur beyond 2100.

The source datasets used to create these maps can be found here: https://catalog.snap.uaf.edu/geonetwork/srv/eng/catalog.search#/metadata/53b35453-7b88-4ea7-8321-5447f8926c48

ALFRESCO is a landscape scale fire and vegetation dynamics model. These specific outputs are from the Integrated Ecosystem Model (IEM) project, and are from the linear coupled version using AR4/CMIP3 and AR5/CMIP5 climate inputs (IEM Generation 1a).

These outputs include data from model rep 171(AR4/CMIP3) and rep 26(AR5/CMIP5), referred to as the “best rep” out of 200 replicates. The best rep was chosen through comparing ALFRESCO’s historical fire outputs to observed historical fire patterns. Single rep analysis is not recommended as a best practice, but can be used to visualize possible changes.

The IEM Generation 1 is driven by outputs from 4 climate models, and two emission scenarios: AR4/CMIP3 SRES A1B CCCMA-CGCMS-3.1 MPI-ECHAM5

AR5/CMIP5 RCP 8.5 MRI-CGCM3 NCAR-CCSM4

A ReadMe file has been uploaded to accompany the dataset.

The planimetric data was compiled by The Sanborn Map Company, Inc for the Metropolitan District and is based on an aerial flight performed in April 2015. In addition, the City's GIS staff has been updating limited planimetric features based on information on file in various City departments. The planimetric data has also been updated in 2016 and yearly to current based on spring aerial flights by EagleView.

Data type: multipoint shape file (ESRI format) of 20 sample plots

Sampling design: 20 sample plots (10 upper treelines + 10 lower treelines) along the Toiyabe range of Nevada, US

Data sources and methods: on-screen photointerpretation of high-resolution (30 cm) aerial photographs (Bing Maps Microsoft Virtual Earth; year 2012) available from the ArcGIS Online service (ESRI, 2012). The approach consists of a manual segmentation of the image at a map scale ranging between 1:400 and 1:600 to identify tree crown polygons, and subsequently the canopy cover and the centroid of each detected tree. The minimum mapping unit (MMU) adopted in the image analysis was 0.78 m2 corresponding to a crown radius of at least 0.5 m.

Validation: The 20 vector maps resulting from this process were validated through accuracy assessment by ground control plots (GCPs) collected in the field during June and July of 2015. The accuracy of the image classification was obtained by calculating the number of correctly classified trees (matches =M), trees detected in the image but absent on the field (false presence = FP) and trees present on the field but not detected on the image (false absence = FA). The final accuracy, calculated on a total of 104 ground control points (GCPs) was 83% total matches, 10% false absences and 7% false presences.

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. Methods Remote sensing analysis 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 surveys 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%).

Spatial data processing 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).

We sampled Google Earth aerial images to get a representative and globally distributed dataset of treeline locations. Google Earth images are available to everyone, but may not be automatically downloaded and processed according to Google's license terms. Since we only wanted to detect tree individuals, we evaluated the aerial images manually by hand.

Doing so, we scaled Google Earth’s GUI interface to a buffer size of approximately 6000 m from a perspective of 100 m (+/- 20 m) above Earth’s surface. Within this buffer zone, we took coordinates and elevation of the highest realized treeline locations. In some remote areas of Russia and Canada, individual trees were not identifiable due to insufficient image resolution. If this was the case, no treeline was sampled, unless we detected another visible treeline within the 6,000 m buffer and took this next highest treeline. We did not apply an automated image processing approach. We calculated mass elevation effect as the distance to the nearest mountain chain limits. Continentality was assessed by the distance to the nearest coastline. Isolation was calculated by the nearest distance of a mountain chain to another mountain chain within a comparable elevational band.

Methods The file global-treeline-data.csv contains the whole data set. Please find further information about the data set in the README.md. Please download both files and load the .csv file into your stats software, e.g. R.

Purpose: create a map of potential alpine habitat in the western USA as a basis for future studies in ecology and biogeography.

Location: all mountains in the continental USA west of 104° longitude.

Procedure: manually identify treeline elevations; interpolate a surface of these elevations; intersect the surface with a 90-m resolution DEM; record all areas above these elevations as projected alpine habitat; for display, map the area recorded as alpine at 90-m resolution.

Products: a map for display; a dataset of elevations of treeline at 268 points on 66 mountain ranges in the western USA (61) and Canada (5); a dataset of points recorded as above treeline at 90-m resolution.

Accurate tree maps of 10 heterogenous treeline ecotones of 9 ha each, spanning a broad longitudinal gradient representative of the Western, Central, and Eastern Italian Alps.

Aim: The spatial patterns of tree populations reflect multiple ecological processes. However, little is known whether these patterns mediate responses to climate in marginal tree populations as those forming alpine treelines. Harsh conditions at these ecotones imply the existence of positive interactions which should lead to tree clustering. In fact, densification in response to climate warming is more widely reported than upward shifts in most treelines. This suggests that more intense tree-to-tree interactions could buffer the treeline responses to climate warming, resulting in low treeline shift rates.

Location: Central Himalayas.

Methods: We examined influence of tree-to-tree interactions on the responsiveness of treelines to climate warming by analyzing a network of 17 treeline sites located across the central Himalayas, and encompassing a wide longitudinal gradient characterized by increasing precipitation eastwards. We quantified the changes in density and the spatial patterns of three 50-year age classes of the two main tree species found at treeline (Betula utilis and Abies spectabilis), and related them to reconstructed shifts in treeline elevation.

Results: Young trees showed clustering near the treeline, while older trees tended to show random spatial distribution. Clustering decreased as climate conditions ameliorated, i.e. in the wetter eastern sites. A negative association between upward treeline shift rate and clustering indicates that tree aggregation weakens treeline responsiveness to climate warming. Thus, warming-induced drought stress tends to lower treeline shift rates by enhancing clustering.

Main conclusions: Our results highlight the complexity and contingency of site-dependent treeline responses to climate. Hence, to advance our understanding on treeline processes, we should consider both direct and indirect influences of relevant biotic (tree-to-tree interactions) and abiotic (climate) drivers of treeline dynamics.

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 pot..., Remote sensing analysis 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 ..., Associated csv's require R (or Excel) to be loaded and for data to be analyzed.Â

Understanding the key mechanisms that control northern treelines is important to accurately predict biome shifts and terrestrial feedbacks to climate. At a global scale, it has long been observed that elevational and latitudinal treelines occur at similar mean growing season air temperature (GSAT) isotherms, inspiring the growth limitation hypothesis (GLH) that cold GSAT limits aboveground growth of treeline trees, with mean treeline GSAT ~6-7 degrees celsius (°C). Treelines with mean GSAT warmer than 6-7 °C may indicate other limiting factors. Many treelines globally are not advancing despite warming, and other climate variables are rarely considered at broad scales. Our goals were to test whether current boreal treelines in northern Alaska correspond with the GLH isotherm, determine which environmental factors are most predictive of treeline presence, and to identify areas beyond the current treeline where advance is most likely. We digitized ~12,400 kilometers (km) of treelines (greater than 26K points) and computed seasonal climate variables across northern Alaska. We then built a generalized additive model predicting treeline presence to identify key factors determining treeline. Two metrics of mean GSAT at Alaska’s northern treelines were consistently warmer than the 6-7 °C isotherm (means of 8.5 °C and 9.3 °C), indicating that direct physiological limitation from low GSAT is unlikely to explain the position of treelines in northern Alaska. Our final model included cumulative growing degree-days, near-surface (≤ 1 meters (m)) permafrost probability, and growing season total precipitation, which together may represent the importance of soil temperature. Our results indicate that mean GSAT may not be the primary driver of treeline in northern Alaska or that its effect is mediated by other more proximate, and possibly non-climatic, controls. Our model predicts treeline potential in several areas beyond current treelines, pointing to possible routes of treeline advance if unconstrained by non-climatic factors.

The position of the Arctic treeline is an important regulator of land surface energy budgets, ecosystem-atmosphere carbon cycling, wildlife habitat and availability of subsistence resources to local communities. The prevailing hypothesis states that treeline position is determined by air temperature during the growing season. Because trees are taller than tundra vegetation and their canopy extends above the warmer boundary layer, their tissues are colder than tundra vegetation. These colder conditions are hypothesized to limit cell division and growth, such that seedlings are unable to grow into trees. However, our early work revealed that air temperature is warmer than previously thought near the Arctic treeline in Alaska and the indirect effects of temperature on soil nutrient availability may be important determinants of tree growth. We hypothesized that cold soils at treeline, particularly during winter, limit microbial activity and nutrient availability to the point where trees are barely able to survive and grow. This dataset contains tree-ring measurements made on increment cores collected in late August of 2021 at three treelines that varied in soil moisture and tundra vegetation near the John River in the central Brooks Range (Gunsight = dry heath tundra, Eagle Creek = mesic shrub tundra. Sheep Pond = wet sedge tundra). Our objective was to relate the tree-ring data to the weather record for Bettles, Alaska and test the hypothesis that winters with greater precipitation and/or deeper snow were associated with greater tree growth.

The Circumpolar Arctic Vegetation Map (CAVM) is a geoecological map (front) of the entire Arctic with a unified legend (back). It is the first vegetation map of an entire global biome at a comparable resolution. It was funded by the US National Science Foundation (OPP-9908-829), the US Fish & Wildlife Service, the US Geological Survey and the US Bureau of Land Management. The CAVM region is north of the climatic limit of trees and is characterized by an arctic climate, arctic flora, and tundra vegetation. It excludes tundra regions than have a boreal flora such as the boreal oceanic areas of Iceland and the Aleutian Islands and alpine tundra south of the latitudinal treeline. The map was published at 1:7.5 million scale and displays the vegetation using 15 units (CAVM Team 2003, legend details: www.arcticatlas.org/maps/themes/cp/cpvg). The methods used to make the map are described in Walker et al. 2005. The CAVM is a polygon (vector) map. The GIS data are in shapefile format, and include fields for bioclimate subzone, floristic province, lake cover, landscape, substrate chemistry and vegetation category. There is also a landscape age shapefile which was created after the publication of the CAVM (Raynolds et al. 2009) In addition, there are a number of raster maps of the same extent (the Arctic), based on satellite data from the Advanced High Resolution Radiometer (AVHRR) instruments. These include the false color-infrared and NDVI images which formed the base maps for the CAVM mapping effort (Walker et al. 2005, Raynolds et al. 2006), a recent biomass map (Raynolds et al. 2012), biomass trends (Epstein et al. 2012), NDVI trends (Bhatt et al. 2010), and Summer Warmth Index (Raynolds et al. 2008). Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation Layer References CAVM Team. 2003. Circumpolar Arctic Vegetation Map, scale 1:7 500 000. Conservation of Arctic Flora and Fauna (CAFF) Map No. 1. U.S. Fish and Wildlife Service, Anchorage, Alaska. Bhatt, U. S., D. A. Walker, M. K. Raynolds, J. C. Comiso, H. E. Epstein, G. J. Jia, R. Gens, J. E. Pinzon, C. J. Tucker, C. E. Tweedie, and P. J. Webber. 2010. Circumpolar arctic tundra vegetation change is linked to sea ice decline. Earth Interactions 14:1-20. doi: 10.1175/2010EI1315.1171. Epstein, H. E., M. K. Raynolds, D. A. Walker, U. S. Bhatt, C. J. Tucker, and J. E. Pinzon. 2012. Dynamics of aboveground phytomass of the circumpolar arctic tundra during the past three decades. Environmental Research Letters 7:015506 (015512 pp). Raynolds, M. K., D. A. Walker, and H. A. Maier. 2006. NDVI patterns and phytomass distribution in the circumpolar Arctic. Remote Sensing of Environment 102:271-281. Raynolds, M. K., J. C. Comiso, D. A. Walker, and D. Verbyla. 2008. Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI. Remote Sensing of Environment 112:1884-1894. Raynolds, M. K. and D. A. Walker. 2009. The effects of deglaciation on circumpolar distribution of arctic vegetation. Canadian Journal of Remote Sensing 35:118-129. Raynolds, M. K. 2009. A geobotanical analysis of circumpolar arctic vegetation, climate, and substrate. PhD Thesis, University of Alaska, Fairbanks. Raynolds, M. K., D. A. Walker, H. E. Epstein, J. E. Pinzon, and C. J. Tucker. 2012. A new estimate of tundra-biome phytomass from trans-Arctic field data and AVHRR NDVI. Remote Sensing Letters 3:403-411. Raynolds, M. K., D. A. Walker, A. Balser, C. Bay, M. W. Campbell, M. M. Cherosov, F. J. A. Daniëls, P. B. Eidesen, K. A. Ermokhina, G. V. Frost, B. Jedrzejek, M. T. Jorgenson, B. E. Kennedy, S. S. Kholod, I. A. Lavrinenko, O. Lavrinenko, B. Magnússon, S. Metúsalemsson, I. Olthof, I. N. Pospelov, E. B. Pospelova, D. Pouliot, V. Y. Razzhivin, G. Schaepman-Strub, J. Šibík, M. Y. Telyatnikov, and E. Troeva. 2019. A raster version of the Circumpolar Arctic Vegetation Map (CAVM). Remote Sensing of Environment 232:111297. Walker, D. A., M. K. Raynolds, F. J. A. Daniels, E. Einarsson, A. Elvebakk, W. A. Gould, A. E. Katenin, S. S. Kholod, C. J. Markon, E. S. Melnikov, N. G. Moskalenko, S. S. Talbot, B. A. Yurtsev, and CAVM Team. 2005. The Circumpolar Arctic Vegetation Map. Journal of Vegetation Science 16:267-282.

Cover rankings of ground cover during 2019, 2020, 2021 and 2022 as sampled within n = 553 5-meter (m) radius plots (area = 78.5 square meters), each centered on a selected white spruce adult called "Focal Tree." The purpose of this dataset was to examine spatial variation in ground cover (less than 0.2-0.3 m tall) of the most important genera across the Brooks Range and in relation to local microclimates. It also provides a baseline for future ground cover rankings to determine changes in abundance. Taxa were ranked in order of cover from Rank1 with the most cover down to Rank5 with the lowest cover in most cases.

The distributions of many high-elevation tree species have shifted as a result of recent climate change; however, there is substantial variability in the movement of alpine treelines at local to regional scales. In this study, we derive records of tree growth and establishment from nine alpine treeline ecotones in the Canadian Rocky Mountains, characterise the influence of seasonal climate variables on four tree species (Abies lasiocarpa, Larix lyallii, Picea engelmannii, Pinus albicaulis) and estimate the degree to which treeline movement in the twentieth century has lagged or exceeded the rate predicted by recent temperature warming. The growth and establishment records revealed a widespread increase in radial growth, establishment frequency and stand density beginning in the mid-twentieth century. Coinciding with a period of warming summer temperatures and favourable moisture availability, these changes appear to have supported upslope treeline advance at all sites (range, 0.23–2.00 m/year; mean, 0.83 + 0.67 m/ year). However, relationships with seasonal climate variables varied between species, and the rates of treeline movement lagged those of temperature warming in most cases. These results indicate that future climate change impacts on treelines in the region are likely to be moderated by species composition and to occur more slowly than anticipated based on temperature warming alone.

This dataset include the main data and the R code for the paper "Intensifying neighbouring tree competition suppresses tree growth at the eastern Tibetan treeline".

Measurements of treeline white (Picea glauca) and black (P. mariana) spruce abundance during 2019, 2020, 2021 and 2022 as sampled within n = 695 5-meter (m) radius plots (area = 78.5 square meters), each centered on a selected white spruce adult called "Focal Tree". Measurements included height of stems between 0.5 and 1.4 m tall and diameter at 1.4 m for individuals taller than 1.4 m. Those individuals taller than 1.4 m tall were also stem mapped in polar coordinates with r = distance and theta as magnetic direction from a focal center white spruce tree. The purpose of this dataset was to examine spatial variation in treeline white spruce densities and basal area across the Brooks Range and in relation to local microclimates. It also provides a baseline for future stem mapping to determine changes in abundance.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Aim

Most studies focusing on the alpine treeline responses to climate warming have used either the tree densification within the ecotone or its elevational upshift as indicators. However, it is acknowledged that the relation between densification and upshift is spatially heterogeneous, making inferences and comparability among studies tricky. The lack of consistent empirical evidence on this potential mismatch and its drivers leads us to focus on this issue in this study. The aim was twofold: (i) to quantify the mismatch between the two processes at a regional scale, and (ii) to identify its site-specific determinants.

Location

French eastern Pyrenees

Time period

1953-2015

Major taxa studied

Pinus uncinata (Ramond ex DC)

Methods

An object-oriented supervised classification procedure was performed on historical (1953) and current (2015) air-photos. Based on the resulting rasters, densification of the treeline ecotone and upward shift of the treeline were estimated at the two dates in 191 sites, then standardized, before finally being compared. Three site clusters were derived (no mismatch, densification prevalence, upshift prevalence). After having characterized their spatial patterns through join count statistics, a multinomial logistic regression model was computed to identify the correlates of these clusters among a list of site variables.

Results

No spatial pattern among the categories of responses emerges at a local scale, but buffers with no mismatch tend to aggregate at a larger scale. Changes in minimum air temperatures, site elevation, mean slope, slope morphometry and lithology appear as significant drivers of the observed mismatch, implying that the relation between densification and elevational upshift is context-specific.

Main conclusions

Our findings suggest that both densification and upshift should be considered in quantitative analyses of treeline spatial dynamics, since these two ecological processes are not controlled by the same drivers.