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. 2022 May;28(10):3275-3292.
doi: 10.1111/gcb.16121. Epub 2022 Feb 24.

Satellite observations document trends consistent with a boreal forest biome shift

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Satellite observations document trends consistent with a boreal forest biome shift

Logan T Berner et al. Glob Chang Biol. 2022 May.

Abstract

The boreal forest biome is a major component of Earth's biosphere and climate system that is projected to shift northward due to continued climate change over the coming century. Indicators of a biome shift will likely first be evident along the climatic margins of the boreal forest and include changes in vegetation productivity, mortality, and recruitment, as well as overall vegetation greenness. However, the extent to which a biome shift is already underway remains unclear because of the local nature of most field studies, sparsity of systematic ground-based ecological monitoring, and reliance on coarse resolution satellite observations. Here, we evaluated early indicators of a boreal forest biome shift using four decades of moderate resolution (30 m) satellite observations and biogeoclimatic spatial datasets. Specifically, we quantified interannual trends in annual maximum vegetation greenness using an ensemble of vegetation indices derived from Landsat observations at 100,000 sample sites in areas without signs of recent disturbance. We found vegetation greenness increased (greened) at 38 [29, 42] % and 22 [15, 26] % of sample sites from 1985 to 2019 and 2000 to 2019, whereas vegetation greenness decreased (browned) at 13 [9, 15] % and 15 [13, 19] % of sample sites during these respective periods [95% Monte Carlo confidence intervals]. Greening was thus 3.0 [2.6, 3.5] and 1.5 [0.8, 2.0] times more common than browning and primarily occurred in cold sparsely treed areas with high soil nitrogen and moderate summer warming. Conversely, browning primarily occurred in the climatically warmest margins of both the boreal forest biome and major forest types (e.g., evergreen conifer forests), especially in densely treed areas where summers became warmer and drier. These macroecological trends reflect underlying shifts in vegetation productivity, mortality, and recruitment that are consistent with early stages of a boreal biome shift.

Keywords: Arctic Boreal Vulnerability Experiment (ABoVE); Landsat; browning; climate change; ecotone; forest productivity; greening; tree mortality; tree recruitment; warming.

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Figures

FIGURE 1
FIGURE 1
Spatial extent of boreal forest study domain and locations of Landsat sample sites. (a) Boreal forest stretches across northern Eurasia and North America to form one of Earth's largest terrestrial biomes (15.1 million km2). The boreal study domain (green) included natural vegetation with low human pressure and no detectable disturbance since the early 1980s (8.4 million km2). Lands in the boreal forest that were masked from analysis are shown in white. (b,c) Locations of Landsat sample sites that were used for assessing changes in vegetation greenness from (b) 1985 to 2019 and (c) 2000 to 2019, shown here as the number of sample sites in a 30 x 30 km grid cell. Lands in the boreal forest without adequate data for time series analysis are shown in dark gray. The spatial extent of the boreal forest biome is from the World Wildlife Foundation's Terrestrial Ecoregions of the World dataset (Olson et al., 2001). Map Projection: North Pole Lambert Azimuthal Equal Area
FIGURE 2
FIGURE 2
Changes in vegetation greenness during recent decades across the boreal forest biome. Changes in vegetation greenness were assessed from 1985 to 2019 (top row) and 2000 to 2019 (bottom row) at sample sites in recently undisturbed boreal forest using Mann–Kendall trend tests and Theil–Sen slopes. For visualization, the sample sites were grouped (i.e., stratified) and their trends summarized by ecological land unit (ELU), where each ELU is a distinct combination of bioclimate, landform, lithology, and land cover (Sayre et al., 2014). (a, d) Prevalence of greening within each ELU, specifically the percent of sample sites where vegetation greenness significantly (α = .10) increased over each period. (b, e) Prevalence of browning within each ELU, specifically the percent of sample sites where vegetation greenness significantly (α = .10) decreased over each period. (c, f) Overall magnitude of change in vegetation greenness within each ELU characterized by the median total percent change in vegetation greenness over each period. Panels (a, b, d, e) characterize how common significant changes in vegetation greenness were within each ELU, while panels (c, f) characterize the typical magnitude of change in vegetation greenness. There were 431 ELUs in the study domain
FIGURE 3
FIGURE 3
Vegetation greenness trends related to tree cover. (a) Tree cover across the boreal forest biome. (b) Mean tree cover across sample sites where vegetation greenness significantly (α = .10) increased (greening), exhibited no trend, or decreased (browning) from 2000 to 2019. (c) Prevalence (i.e., relative frequency) of greening and browning along spatial gradients in tree cover binned at 1% increments. Correlation coefficients (r) between tree cover and the prevalence of greening and browning. Tree cover exceeded 68% at 1% of sample sites and these were excluded when correlations were computed given the small number of sample sites within each 1% tree cover bin. The panels depict best estimates (dots or lines) and 95% confidence intervals (whiskers or bands) derived from Monte Carlo simulations (n = 103). Tree cover data from the MODIS Vegetation Continuous Fields dataset (DiMiceli et al., 2021)
FIGURE 4
FIGURE 4
Vegetation greenness trends related to land cover class. (a) Distribution of land cover classes across the boreal forest. (b) Occurrence of sample sites in each land cover class where vegetation greenness significantly (α = .10) increased (greening), decreased (browning), or had no trend (none) from 2000 to 2019 based on Mann–Kendall trend tests. (c) Prevalence of sample sites with recent greening or browning in each land cover class. (d) Comparison between the prevalence of greening and browning across land cover classes. Land cover classes include evergreen needleleaf forest (ENF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forest (MF), and five colder nonforest classes. Note each land cover class is assigned a unique color that is consistently used. In (b) and (c), the land cover classes are ordered by total number of sample sites and prevalence of greening, respectively. Panels depict best estimates (bars, dots) and 95% confidence intervals derived from Monte Carlo simulations (n = 103). Land cover data from the European Space Agency's Climate Change Initiative Land Cover dataset (ESA, 2017)
FIGURE 5
FIGURE 5
Vegetation greenness trends related to summer warmth by land cover class. (a) Mean summer warmth index (SWI) from 2000 to 2019 with SWI computed as the annual sum of mean monthly air temperatures above 0°C. The SWI is an indicator of total annual heat load. (b) Cross‐site average of mean SWI for sample sites in each land cover class where vegetation greenness increased (greening), had no trend (none), or decreased (browning) from 2000 to 2019. Land cover classes are ordered from highest (top) to lowest (bottom) average mean SWI. Error bars are 95% confidence intervals derived from Monte Carlo simulations (n = 103). Black stars denote significant (α = .05) differences in cross‐site average of mean SWI between greening and browning classes based on permutation tests. The SWI was derived using TerraClimate data (Abatzoglou et al., 2018)
FIGURE 6
FIGURE 6
Environmental predictors of recent greening and browning. Random Forest models predicted with 77 [74, 81] % accuracy whether samples sites significantly (α = .10) greened or browned from 2000 to 2019 based on environmental predictors. (a) Variable importance of the six most important predictors as quantified by the mean decrease in accuracy, where a higher value indicates greater importance to classification accuracy. (b) Partial dependency plots show how classification probability varies with each predictor while holding all other predictors in the model at their average value. Climate means and changes (∆ for SWI and VPD) were for the period 2000–2019, except for mean annual soil temperature which was for the period 2000–2016 given available data. The panels depict best estimates (dots or lines) and 95% confidence intervals (whiskers or bands) derived from Monte Carlo simulations (n = 103)

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