An innovative index for separating the effects of temperature and precipitation on global vegetation change

Temperature and precipitation changes are among the vital climatic driving forces of global vegetation change. However, the strategy to separate the relative contributions of these two critical climatic factors is still lacking. Here, we propose an index CRTP (contribution ratio of temperature and precipitation) to quantify their impacts on vegetation and then construct the CRTP classification prediction models based on climatic, geographic, and environmental factors using the Random Forest classifier. We find that precipitation predominates more than 70% of the significant vegetation change, mainly located in the low and middle latitudes during 2000-2021. Precipitation will remain the dominant climatic factor affecting global vegetation change in the coming six decades, whereas areas with temperature-dominated vegetation change will expand under higher radiative forcings. Hopefully, the promising index CRTP will be applied in the research about climatic attribution for regional vegetation degradation, monitoring drought-type conversion, and alarming the potential ecological risk.

Variability in Tree-ring Width and NDVI Responses to Climate at a Landscape Level

Inter-annual climatically driven growth variability of above-ground biomass compartments (for example, tree stems and foliage) controls the intensity of carbon sequestration into forest ecosystems. However, understanding the differences between the climatic response of stem and foliage at the landscape level is limited. In this study, we examined the climate-growth response of stem and leaf biomass and their relationship for Pinus sylvestris (PISY) and Picea abies (PCAB) in topographically complex landscapes. We used tree-ring width chronologies and time series of the normalized difference vegetation index (NDVI) derived from high-resolution Landsat scenes as proxies for stem and leaf biomass, respectively. We then compared growth variability and climate-growth relationships of both biomass proxies between topographical categories. Our results show that the responses of tree rings to climate differ significantly from those found in NDVI, with the stronger climatic signal observed in tree rings. Topography had distinct but species-specific effects: At moisture-limited PISY stands, stem biomass (tree rings) was strongly topographically driven, and leaf biomass (NDVI) was relatively insensitive to topographic variability. In landscapes close to the climatic optimum of PCAB, the relationship between stem and leaf biomass was weak, and their correlations with climate were often inverse, with no significant effects of topography. Different climatic signals from NDVI and tree rings suggest that the response of canopy and stem growth to climate change might be decoupled. Furthermore, our results hint toward different prioritizations of biomass allocation in trees under stressful conditions which might change allometric relationships between individual tree compartments in the long term.

Modelling Fagus sylvatica stem growth along a wide thermal gradient in Italy by incorporating dendroclimatic classification and land surface phenology metrics

Calibrating land surface phenology (LSP) with tree rings is important to model spatio-temporal variations in forest productivity. We used MODIS (resolution: 250 m) NDVI, WDRVI and EVI series 2000-2014 to derive LSP metrics quantifying phenophase timing and canopy photosynthetic rates of 26 European beech forests covering a large thermal gradient (5-16 °C) in Italy. Average phenophase timing changed greatly with site temperature (e.g. growing season 70 days longer at low- than high-elevation); average VI values were affected by precipitation. An annual temperature about 12 °C (c. 1100 m asl) represented a bioclimatic threshold dividing warm from cold beech forests, distinguished by different phenology-BAI (basal area increment) relationships and LSP trends. Cold forests showed decreasing VI values (browning) and delayed phenophases and had negative BAI slopes. Warmer forests tended to increase VI (greening), and positive BAI slopes. NDVI peak, commonly used in global trend assessments, changed with elevation in agreement with changes in wood production. A cross-validation modelling approach demonstrated the ability of LSP to predict average BAI and its interannual variability. Merging sites into bioclimatic groups improved models by amplifying the signal in growth or LSP. NDVI had highest performances when informing on BAI trends; WDRVI and EVI were mostly selected for modelling mean and interannual BAI. WDRVI association with tree rings, tested in this study for the first time, showed that this VI is highly promising for studying forest dynamics. MODIS LSP can quantify forest functioning changes across landscapes and model interannual spatial variations and trends in productivity dynamics under climate change.

Characterizing Seasonal Radial Growth Dynamics of Balsam Fir in a Cold Environment Using Continuous Dendrometric Data: A Case Study in a 12-Year Soil Warming Experiment

Historical temperature records reveal that the boreal forest has been subjected to a significant lengthening of the thermal growing season since the middle of the last century, and climate models predict that this lengthening will continue in the future. Nevertheless, the potential phenological response of trees to changes in growing season length remains relatively undocumented, particularly for evergreen boreal tree species growing in cold environments. Here, we used the recently defined zero growth (ZG) concept to extract and characterize the metrics of seasonal radial growth dynamics for 12 balsam fir trees subjected to a 12-year soil warming experiment using high resolution radius dendrometer measurements. The ZG concept provides an accurate determination of growth seasonality (onset, cessation, duration, growth rates, and total growth) for these slow-growing trees characterized by significant shrinkage in tree diameter due to dehydration in the winter. Our analysis revealed that, on average, growth onset starts at day 152 ± 7 (±1 SE, 31 May–1 June) and ceases at day 244 ± 27 (31 August–1 September), for a growing season duration of about 3 months (93 ± 26 days) over a 12-year period. Growing season duration is mainly determined by growth cessation, while growth onset varies little between years. A large part (80%) of the total growth occurs in the first 50 days of the growing season. Given the dynamics of growth, early growth cessation (shorter growing season) results in a higher average seasonal growth rate, meaning that longer growing seasons are not necessarily associated with greater tree growth. Soil warming induces earlier growth cessation, but increases the mean tree growth rate by 18.1% and the total annual growth by 9.1%, on average, as compared to the control trees. Our results suggest that a higher soil temperature for warmed trees contributes to providing better growth conditions and higher growth rates in the early growing season, when the soil temperature is low and the soil water content is elevated because of snowmelt. Attaining a critical soil temperature earlier, coupled with lower soil water content, may have contributed to the earlier growth cessation and shorter growing season of warmed trees.

Biophysical Determinants of Shifting Tundra Vegetation Productivity in the Beaufort Delta Region of Canada

Temperature increases across the circumpolar north have driven rapid increases in vegetation productivity, often described as ‘greening’. These changes have been widespread, but spatial variation in their pattern and magnitude suggests that biophysical factors also influence the response of tundra vegetation to climate warming. In this study, we used field sampling of soils and vegetation and random forests modeling to identify the determinants of trends in Landsat-derived Enhanced Vegetation Index, a surrogate for productivity, in the Beaufort Delta region of Canada between 1984 and 2016. This region has experienced notable change, with over 71% of the Tuktoyaktuk Coastlands and over 66% of the Yukon North Slope exhibiting statistically significant greening. Using both classification and regression random forests analyses, we show that increases in productivity have been more widespread and rapid at low-to-moderate elevations and in areas dominated by till blanket and glaciofluvial deposits, suggesting that nutrient and moisture availability mediate the impact of climate warming on tundra vegetation. Rapid greening in shrub-dominated vegetation types and observed increases in the cover of low and tall shrub cover (4.8% and 6.0%) also indicate that regional changes have been driven by shifts in the abundance of these functional groups. Our findings demonstrate the utility of random forests models for identifying regional drivers of tundra vegetation change. To obtain additional fine-grained insights on drivers of increased tundra productivity, we recommend future research combine spatially comprehensive time series satellite data (as used herein) with samples of high spatial resolution imagery and integrated field investigations.

Unexpected greening in a boreal permafrost peatland undergoing forest loss is partially attributable to tree species turnover

Time series of vegetation indices derived from satellite imagery are useful in measuring vegetation response to climate warming in remote northern regions. These indices show that productivity is generally declining in the boreal forest, but it is unclear which components of boreal vegetation are driving these trends. We aimed to compare trends in the normalized difference vegetation index (NDVI) to forest growth and demographic data taken from a 10 ha mapped plot located in a spruce-dominated boreal peatland. We used microcores to quantify recent growth trends and tree census data to characterize mortality and recruitment rates of the three dominant tree species. We then compared spatial patterns in growth and demography to patterns in Landsat-derived maximum NDVI trends (1984-2019) in 78 pixels that fell within the plot. We found that NDVI trends were predominantly positive (i.e., "greening") in spite of the ongoing loss of black spruce (the dominant species; 80% of stems) from the plot. The magnitude of these trends correlated positively with black spruce growth trends, but was also governed to a large extent by tree mortality and recruitment. Greening trends were weaker (lower slope) in areas with high larch mortality, and high turnover of spruce and birch, but stronger (higher slope) in areas with high larch recruitment. Larch dominance is currently low (~11% of stems), but it is increasing in abundance as permafrost thaw progresses and will likely have a substantial influence on future NDVI trends. Our results emphasize that NDVI trends in boreal peatlands can be positive even when the forest as a whole is in decline, and that the magnitude of trends can be strongly influenced by the demographics of uncommon species.

Tracking rates of postfire conifer regeneration vs. deciduous vegetation recovery across the western United States

Postfire shifts in vegetation composition will have broad ecological impacts. However, information characterizing postfire recovery patterns and their drivers are lacking over large spatial extents. In this analysis, we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of postfire, dual-season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally specific drivers of postfire rates of NDVI recovery. Rates of postfire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n = 230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in postfire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track postfire forest recovery across the western United States. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Allometric Equations for Shrub and Short-Stature Tree Aboveground Biomass within Boreal Ecosystems of Northwestern Canada

Aboveground biomass (AGB) of short-stature shrubs and trees contain a substantial part of the total carbon pool within boreal ecosystems. These ecosystems, however, are changing rapidly due to climate-mediated atmospheric changes, with overall observed decline in woody plant AGB in boreal northwestern Canada. Allometric equations provide a means to quantify woody plant AGB and are useful to understand aboveground carbon stocks as well as changes through time in unmanaged boreal ecosystems. In this paper, we provide allometric equations, regression coefficients, and error statistics to quantify total AGB of shrubs and short-stature trees. We provide species- and genus-specific as well as multispecies allometric models for shrub and tree species commonly found in northwestern boreal forest and peatland ecosystems. We found that the three-dimensional field variable (volume) provided the most accurate prediction of shrub multispecies AGB (R2 = 0.79, p < 0.001), as opposed to the commonly used one-dimensional variable (basal diameter) measured on the longest and thickest stem (R2 = 0.23, p < 0.001). Short-stature tree AGB was most accurately predicted by stem diameter measured at 0.3 m along the stem length (R2 = 0.99, p < 0.001) rather than stem length (R2 = 0.29, p < 0.001). Via the two-dimensional variable cross-sectional area, small-stature shrub AGB was combined with small-stature tree AGB within one single allometric model (R2 = 0.78, p < 0.001). The AGB models provided in this paper will improve our understanding of shrub and tree AGB within rapidly changing boreal environments.

Excessive positive response of model-simulated land net primary production to climate changes over circumboreal forests

Land carbon cycle components in an Earth system model (ESM) play a crucial role in the projections of forest ecosystem responses to climate/environmental changes. Evaluating models from the viewpoint of observations is essential for an improved understanding of model performance and for identifying uncertainties in their outputs. Herein, we evaluated the land net primary production (NPP) for circumboreal forests simulated with 10 ESMs in Phase 5 of the Coupled Model Intercomparison Project by comparisons with observation-based indexes for forest productivity, namely, the composite version 3G of the normalized difference vegetation index (NDVI3g) and tree-ring width index (RWI). These indexes show similar patterns in response to past climate change over the forests, i.e., a one-year time lag response and smaller positive responses to past climate changes in comparison with the land NPP simulated by the ESMs. The latter showed overly positive responses to past temperature and/or precipitation changes in comparison with the NDVI3g and RWI. These results indicate that ESMs may overestimate the future forest NPP of circumboreal forests (particularly for inland dry regions, such as inner Alaska and Canada, and eastern Siberia, and for hotter, southern regions, such as central Europe) under the expected increases in both average global temperature and precipitation, which are common to all current ESMs.

Long-Term Wood Micro-Density Variation in Alpine Forests at Central México and Their Spatial Links with Remotely Sensed Information

Ongoing climate variability strongly affects high-elevation forests, influencing the wood formation process (e.g., xylogenesis). Furthermore, spatio-temporal studies to establish links of wood properties and tree performance are needed. Using linear mixed-effects models, empirical cumulative distribution functions, and spatial analysis, we explore time trends and space connections of wood density of Pinus hartwegii Lindl. to remotely sensed variables (Moderate Resolution Imaging Spectro-radiometer MODIS-derived) in two high-elevation forests in México, Tláloc (TLA) and Jocotitlán (JOC) Mountains. Results indicated that elevation and cambial age effects are important factors explaining wood density variation. Minimum earlywood—MID, average—AVE, and maximum latewood density—MXD were statistically similar between mountains (p > 0.05), but TLA showed a significant increase in MID over time with higher values after 1950. Wood density values and spatial correlations were site-dependent with TLA exhibiting the highest correlations between MXD and the Normalized Difference Vegetation Index (NDVI) of the spring season (r = 0.59, p < 0.05). Overall, correlations to remotely sensed information were positive with MXD, negative for MID and divergent for AVE. Historical temperature defines MID along the elevation gradient, while MXD was related to soil moisture only at low-elevation sites where soils are deeper. We found that two high-elevation forests, 115 km away from each other, with similar climate, soil, and vegetation, behaved differently regarding their xylogenesis, indicating the potential of using the link between wood micro-density and remotely sensed information to understand forest response to climate change effects.

Tree-ring evidence of the impacts of climate change and agricultural cultivation on vegetation coverage in the upper reaches of the Weihe River, northwest China

Comprehending the characteristics and causes of vegetation coverage in history is of practical significance for studying ecological and environmental changes. As a typical region of the semi-arid and semi-humid climatic zone in northwest China, the upper reaches of the Weihe River have relatively fragile ecological environment. Based on tree-ring width chronologies, the vegetation coverage represented by the normalized difference vegetation index was reconstructed from 1630 to 2006 using a regression model. There were 64 years with high vegetation coverage and 56 years with low vegetation coverage over the past 377 years. At low frequencies, the coverage was relatively higher in the 1650s and from the 1880s to 1890s, while the coverage was lower in the 1720s and from the 1760s to 1770s. While precipitation and temperature had positive and negative influences on the changes of vegetation coverage, respectively, during the past several centuries, the agricultural cultivation played an important role on coverage changes. Along with the land reclamation expansion in history, the forest cover gradually declined, and vegetation coverage decreased. The vegetation coverage was lower when there were more arable lands reclaimed from woodlands. Regardless of the land reclamation policy during the historical period or the current conversion project of cropland to forest, they affected the vegetation coverage by influencing cover ranges of woodland and farmland.

Spatial Upscaling of Tree-Ring-Based Forest Response to Drought with Satellite Data

We have integrated the observational capability of satellite remote sensing with plot-scale tree-ring data to upscale the evaluation of forest responses to drought. Satellite data, such as the normalized difference vegetation index (NDVI), can provide a spatially continuous measure with limited temporal coverage, while tree-ring width index (RWI) provides an accurate assessment with a much longer time series at local scales. Here, we explored the relationship between RWI and NDVI of three dominant species in the Southwestern United States (SWUS) and predicted RWI spatial distribution from 2001 to 2017 based on Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km resolution NDVI data with stringent quality control. We detected the optimum time windows (around June–August) during which the RWI and NDVI were most closely correlated for each species, when the canopy growth had the greatest effect on growth of tree trunks. Then, using our upscaling algorithm of NDVI-based RWI, we were able to detect the significant impact of droughts in 2002 and in 2011–2014, which supported the validity of this algorithm in quantifying forest response to drought on a large scale.

Does Earlier and Increased Spring Plant Growth Lead to Reduced Summer Soil Moisture and Plant Growth on Landscapes Typical of Tundra-Taiga Interface?

Over the past four decades, satellite observations have shown intensified global greening. At the same time, widespread browning and reversal of or stalled greening have been reported at high latitudes. One of the main reasons for this browning/lack of greening is thought to be warming-induced water stress, i.e., soil moisture depletion caused by earlier spring growth and increased summer evapotranspiration. To investigate these phenomena, we use MODIS collection 6, Global Inventory Modeling and Mapping Studies third-generation (GIMMS) normalized difference vegetation index (NDVI3g), and Global Land Evaporation Amsterdam Model (GLEAM) satellite-based root-zone soil moisture data. The study area was the Far North of Ontario (FNO), 453,788 km2 of heterogeneous landscape typical of the tundra-taiga interface, consisting of unmanaged boreal forests growing on mineral and peat soils, wetlands, and the most southerly area of tundra. The results indicate that the increased plant growth in spring leads to decreased summer growth. Lower summer soil moisture is related to increased spring plant growth in areas with lower soil moisture content. We also found that earlier start of growing season leads to decreased summer and peak season maximum plant growth. In conclusion, increased spring plant growth and earlier start of growing season deplete summer soil moisture and decrease the overall summer plant growth even in temperature-limited high latitude ecosystems. Our findings contribute to evolving understanding of changes in vegetation dynamics in relation to climate in northern high latitude terrestrial ecosystems.

Spatial and Temporal Variation of Drought Based on Satellite Derived Vegetation Condition Index in Nepal from 1982⁻2015

Identification of drought is essential for many environmental and agricultural applications. To further understand drought, this study presented spatial and temporal variations of drought based on satellite derived Vegetation Condition Index (VCI) on annual (Jan⁻Dec), seasonal monsoon (Jun⁻Nov) and pre-monsoon (Mar⁻May) scales from 1982⁻2015 in Nepal. The Vegetation Condition Index (VCI) obtained from NOAA, AVHRR (National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer) and climate data from meteorological stations were used. VCI was used to grade the drought, and the Mann⁻Kendall test and linear trend analysis were conducted to examine drought trends and the Pearson correlation between VCI and climatic factors (i.e., temperature and precipitation) was also acquired. The results identified that severe drought was identified in 1982, 1984, 1985 and 2000 on all time scales. However, VCI has increased at the rate of 1.14 yr^-1 (p = 0.04), 1.31 yr^-1 (p = 0.03) and 0.77 yr^-1 (p = 0.77) on the annual, seasonal monsoon and pre-monsoon scales, respectively. These increased VCIs indicated decreases in drought. However, spatially, increased trends of drought were also found in some regions in Nepal. For instance, northern areas mainly in the Trans-Himalayan regions identified severe drought. The foothills and the lowlands of Terai (southern Nepal) experienced normal VCI, i.e., no drought. Similarly, the Anomaly Vegetation Condition Index (AVCI) was mostly negative before 2000 which indicated deficient soil moisture. The exceedance probability analysis results on the annual time scale showed that there was a 20% chance of occurring severe drought (VCI ≤ 35%) and a 35% chance of occurring normal drought (35% ≤ VCI ≤ 50%) in Nepal. Drought was also linked with climates in which temperature on the annual and seasonal monsoon scales was significant and positively correlated with VCI. Drought occurrence and trends in Nepal need to be further studied for comprehensive information and understanding.

Time lag and negative responses of forest greenness and tree growth to warming over circumboreal forests

The terrestrial forest ecosystems in the northern high latitude region have been experiencing significant warming rates over several decades. These forests are considered crucial to the climate system and global carbon cycle and are particularly vulnerable to climate change. To obtain an improved estimate of the response of vegetation activity, e.g., forest greenness and tree growth, to climate change, we investigated spatiotemporal variations in two independent data sets containing the dendroecological information for this region over the past 30 years. These indices are the normalized difference vegetation index (NDVI3g) and the tree-ring width index (RWI), both of which showed significant spatial variability in past trends and responses to climate changes. These trends and responses to climate change differed significantly in the ecosystems of the circumarctic (latitude higher than 67°N) and the circumboreal forests (latitude higher and lower than 50°N and 67°N, respectively), but the way in which they differed was relatively similar in the NDVI3g and the RWI. In the circumarctic ecosystem, the climate variables of the current summer were the main climatic drivers for the positive response to the increase in temperatures showed by both the NDVI3g and the RWI indices. On the other hand, in the circumboreal forest ecosystem, the climate variables of the previous year (from summer to winter) were also important climatic drivers for both the NDVI3g and the RWI. Importantly, both indices showed that the temperatures in the previous year negatively affected the ecosystem. Although such negative responses to warming did not necessarily lead to a past negative linear trend in the NDVI3g and the RWI over the past 30 years, future climate warming could potentially cause severe reduction in forest greenness and tree growth in the circumboreal forest ecosystem.

Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data

Increasing tree mortality from global change drivers such as drought and biotic infestations is a widespread phenomenon, including in the boreal zone where climate changes and feedbacks to the Earth system are relatively large. Despite the importance for science and management communities, our ability to forecast tree mortality at landscape to continental scales is limited. However, two independent information streams have the potential to inform and improve mortality forecasts: repeat forest inventories and satellite remote sensing. Time series of tree-level growth patterns indicate that productivity declines and related temporal dynamics often precede mortality years to decades before death. Plot-level productivity, in turn, has been related to satellite-based indices such as the Normalized difference vegetation index (NDVI). Here we link these two data sources to show that early warning signals of mortality are evident in several NDVI-based metrics up to 24 years before death. We focus on two repeat forest inventories and three NDVI products across western boreal North America where productivity and mortality dynamics are influenced by periodic drought. These data sources capture a range of forest conditions and spatial resolution to highlight the sensitivity and limitations of our approach. Overall, results indicate potential to use satellite NDVI for early warning signals of mortality. Relationships are broadly consistent across inventories, species, and spatial resolutions, although the utility of coarse-scale imagery in the heterogeneous aspen parkland was limited. Longer-term NDVI data and annually remeasured sites with high mortality levels generate the strongest signals, although we still found robust relationships at sites remeasured at a typical 5 year frequency. The approach and relationships developed here can be used as a basis for improving forest mortality models and monitoring systems.

Tree-ring analysis and modeling approaches yield contrary response of circumboreal forest productivity to climate change

Circumboreal forest ecosystems are exposed to a larger magnitude of warming in comparison with the global average, as a result of warming-induced environmental changes. However, it is not clear how tree growth in these ecosystems responds to these changes. In this study, we investigated the sensitivity of forest productivity to climate change using ring width indices (RWI) from a tree-ring width dataset accessed from the International Tree-Ring Data Bank and gridded climate datasets from the Climate Research Unit. A negative relationship of RWI with summer temperature and recent reductions in RWI were typically observed in continental dry regions, such as inner Alaska and Canada, southern Europe, and the southern part of eastern Siberia. We then developed a multiple regression model with regional meteorological parameters to predict RWI, and then applied to these models to predict how tree growth will respond to twenty-first-century climate change (RCP8.5 scenario). The projections showed a spatial variation and future continuous reduction in tree growth in those continental dry regions. The spatial variation, however, could not be reproduced by a dynamic global vegetation model (DGVM). The DGVM projected a generally positive trend in future tree growth all over the circumboreal region. These results indicate that DGVMs may overestimate future wood net primary productivity (NPP) in continental dry regions such as these; this seems to be common feature of current DGVMs. DGVMs should be able to express the negative effect of warming on tree growth, so that they simulate the observed recent reduction in tree growth in continental dry regions.

Shrubline but not treeline advance matches climate velocity in montane ecosystems of south-central Alaska

Tall shrubs and trees are advancing into many tundra and wetland ecosystems but at a rate that often falls short of that predicted due to climate change. For forest, tall shrub, and tundra ecosystems in two pristine mountain ranges of Alaska, we apply a Bayesian, error-propagated calculation of expected elevational rise (climate velocity), observed rise (biotic velocity), and their difference (biotic inertia). We show a sensitive dependence of climate velocity on lapse rate and derive biotic velocity as a rigid elevational shift. Ecosystem presence identified from recent and historic orthophotos ~50 years apart was regressed on elevation. Biotic velocity was estimated as the difference between critical point elevations of recent and historic logistic fits divided by time between imagery. For both mountain ranges, the 95% highest posterior density of climate velocity enclosed the posterior distributions of all biotic velocities. In the Kenai Mountains, mean tall shrub and climate velocities were both 2.8 m y(-1). In the better sampled Chugach Mountains, mean tundra retreat was 1.2 m y(-1) and climate velocity 1.3 m y(-1). In each mountain range, the posterior mode of tall woody vegetation velocity (the complement of tundra) matched climate velocity better than either forest or tall shrub alone, suggesting competitive compensation can be important. Forest velocity was consistently low at 0.1-1.1 m y(-1), indicating treeline is advancing slowly. We hypothesize that the high biotic inertia of forest ecosystems in south-central Alaska may be due to competition with tall shrubs and/or more complex climate controls on the elevational limits of trees than tall shrubs. Among tall shrubs, those that disperse farthest had lowest inertia. Finally, the rapid upward advance of woody vegetation may be contributing to regional declines in Dall's sheep (Ovis dalli), a poorly dispersing alpine specialist herbivore with substantial biotic inertia due to dispersal reluctance.

What determines tree mortality in dry environments? A multi-perspective approach

Forest ecosystems function under increasing pressure due to global climate changes, while factors determining when and where mortality events will take place within the wider landscape are poorly understood. Observational studies are essential for documenting forest decline events, understanding their determinants, and developing sustainable management plans. A central obstacle towards achieving this goal is that mortality is often patchy across a range of spatial scales, and characterized by long-term temporal dynamics. Research must therefore integrate different methods, from several scientific disciplines, to capture as many relevant informative patterns as possible. We performed a landscape-scale assessment of mortality and its determinants in two representative Pinus halepensis planted forests from a dry environment (~300 mm), recently experiencing an unprecedented sequence of two severe drought periods. Three data sources were integrated to analyze the spatiotemporal variation in forest performance: (1) Normalized Difference Vegetation Index (NDVI) time-series, from 18 Landsat satellite images; (2) individual dead trees point-pattern, based on a high-resolution aerial photograph; and (3) Basal Area Increment (BAI) time-series, from dendrochronological sampling in three sites. Mortality risk was higher in older-aged sparse stands, on southern aspects, and on deeper soils. However, mortality was patchy across all spatial scales, and the locations of patches within "high-risk" areas could not be fully explained by the examined environmental factors. Moreover, the analysis of past forest performance based on NDVI and tree rings has indicated that the areas affected by each of the two recent droughts do not coincide. The association of mortality with lower tree densities did not support the notion that thinning semiarid forests will increase survival probability of the remaining trees when facing extreme drought. Unique information was obtained when merging dendrochronological and remotely sensed performance indicators, in contrast to potential bias when using a single approach. For example, dendrochronological data suggested highly resilient tree growth, since it was based only on the "surviving" portion of the population, thus failing to identify past demographic changes evident through remote sensing. We therefore suggest that evaluation of forest resilience should be based on several metrics, each suited for detecting transitions at a different level of organization.

Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment

Satellite-derived indices of photosynthetic activity are the primary data source used to study changes in global vegetation productivity over recent decades. Creating coherent, long-term records of vegetation activity from legacy satellite data sets requires addressing many factors that introduce uncertainties into vegetation index time series. We compared long-term changes in vegetation productivity at high northern latitudes (>50°N), estimated as trends in growing season NDVI derived from the most widely used global NDVI data sets. The comparison included the AVHRR-based GIMMS-NDVI version G (GIMMSg ) series, and its recent successor version 3g (GIMMS3g ), as well as the shorter NDVI records generated from the more modern sensors, SeaWiFS, SPOT-VGT, and MODIS. The data sets from the latter two sensors were provided in a form that reduces the effects of surface reflectance associated with solar and view angles. Our analysis revealed large geographic areas, totaling 40% of the study area, where all data sets indicated similar changes in vegetation productivity over their common temporal record, as well as areas where data sets showed conflicting patterns. The newer, GIMMS3g data set showed statistically significant (α = 0.05) increases in vegetation productivity (greening) in over 15% of the study area, not seen in its predecessor (GIMMSg ), whereas the reverse was rare (<3%). The latter has implications for earlier reports on changes in vegetation activity based on GIMMSg , particularly in Eurasia where greening is especially pronounced in the GIMMS3g data. Our findings highlight both critical uncertainties and areas of confidence in the assessment of ecosystem-response to climate change using satellite-derived indices of photosynthetic activity. Broader efforts are required to evaluate NDVI time series against field measurements of vegetation growth, primary productivity, recruitment, mortality, and other biological processes in order to better understand ecosystem responses to environmental change over large areas.

A tree-ring perspective on the terrestrial carbon cycle

Tree-ring records can provide valuable information to advance our understanding of contemporary terrestrial carbon cycling and to reconstruct key metrics in the decades preceding monitoring data. The growing use of tree rings in carbon-cycle research is being facilitated by increasing recognition of reciprocal benefits among research communities. Yet, basic questions persist regarding what tree rings represent at the ecosystem level, how to optimally integrate them with other data streams, and what related challenges need to be overcome. It is also apparent that considerable unexplored potential exists for tree rings to refine assessments of terrestrial carbon cycling across a range of temporal and spatial domains. Here, we summarize recent advances and highlight promising paths of investigation with respect to (1) growth phenology, (2) forest productivity trends and variability, (3) CO2 fertilization and water-use efficiency, (4) forest disturbances, and (5) comparisons between observational and computational forest productivity estimates. We encourage the integration of tree-ring data: with eddy-covariance measurements to investigate carbon allocation patterns and water-use efficiency; with remotely sensed observations to distinguish the timing of cambial growth and leaf phenology; and with forest inventories to develop continuous, annually-resolved and long-term carbon budgets. In addition, we note the potential of tree-ring records and derivatives thereof to help evaluate the performance of earth system models regarding the simulated magnitude and dynamics of forest carbon uptake, and inform these models about growth responses to (non-)climatic drivers. Such efforts are expected to improve our understanding of forest carbon cycling and place current developments into a long-term perspective.

Unusual forest growth decline in boreal North America covaries with the retreat of Arctic sea ice

The 20th century was a pivotal period at high northern latitudes as it marked the onset of rapid climatic warming brought on by major anthropogenic changes in global atmospheric composition. In parallel, Arctic sea ice extent has been decreasing over the period of available satellite data records. Here, we document how these changes influenced vegetation productivity in adjacent eastern boreal North America. To do this, we used normalized difference vegetation index (NDVI) data, model simulations of net primary productivity (NPP) and tree-ring width measurements covering the last 300 years. Climatic and proxy-climatic data sets were used to explore the relationships between vegetation productivity and Arctic sea ice concentration and extent, and temperatures. Results indicate that an unusually large number of black spruce (Picea mariana) trees entered into a period of growth decline during the late-20th century (62% of sampled trees; n = 724 cross sections of age >70 years). This finding is coherent with evidence encoded in NDVI and simulated NPP data. Analyses of climatic and vegetation productivity relationships indicate that the influence of recent climatic changes in the studied forests has been via the enhanced moisture stress (i.e. greater water demands) and autotrophic respiration amplified by the declining sea ice concentration in Hudson Bay and Hudson Strait. The recent decline strongly contrasts with other growth reduction events that occurred during the 19th century, which were associated with cooling and high sea ice severity. The recent decline of vegetation productivity is the first one to occur under circumstances related to excess heat in a 300-year period, and further culminates with an intensifying wildfire regime in the region. Our results concur with observations from other forest ecosystems about intensifying temperature-driven drought stress and tree mortality with ongoing climatic changes.

Consistent response of vegetation dynamics to recent climate change in tropical mountain regions

Global climate change has emerged as a major driver of ecosystem change. Here, we present evidence for globally consistent responses in vegetation dynamics to recent climate change in the world's mountain ecosystems located in the pan-tropical belt (30°N-30°S). We analyzed decadal-scale trends and seasonal cycles of vegetation greenness using monthly time series of satellite greenness (Normalized Difference Vegetation Index) and climate data for the period 1982-2006 for 47 mountain protected areas in five biodiversity hotspots. The time series of annual maximum NDVI for each of five continental regions shows mild greening trends followed by reversal to stronger browning trends around the mid-1990s. During the same period we found increasing trends in temperature but only marginal change in precipitation. The amplitude of the annual greenness cycle increased with time, and was strongly associated with the observed increase in temperature amplitude. We applied dynamic models with time-dependent regression parameters to study the time evolution of NDVI-climate relationships. We found that the relationship between vegetation greenness and temperature weakened over time or was negative. Such loss of positive temperature sensitivity has been documented in other regions as a response to temperature-induced moisture stress. We also used dynamic models to extract the trends in vegetation greenness that remain after accounting for the effects of temperature and precipitation. We found residual browning and greening trends in all regions, which indicate that factors other than temperature and precipitation also influence vegetation dynamics. Browning rates became progressively weaker with increase in elevation as indicated by quantile regression models. Tropical mountain vegetation is considered sensitive to climatic changes, so these consistent vegetation responses across widespread regions indicate persistent global-scale effects of climate warming and associated moisture stresses.

Plant response to climate change along the forest-tundra ecotone in northeastern Siberia

Russia's boreal (taiga) biome will likely contract sharply and shift northward in response to 21st century climatic change, yet few studies have examined plant response to climatic variability along the northern margin. We quantified climate dynamics, trends in plant growth, and growth-climate relationships across the tundra shrublands and Cajander larch (Larix cajanderi Mayr.) woodlands of the Kolyma river basin (657 000 km(2) ) in northeastern Siberia using satellite-derived normalized difference vegetation indices (NDVI), tree ring-width measurements, and climate data. Mean summer temperatures (Ts ) increased 1.0 °C from 1938 to 2009, though there was no trend (P > 0.05) in growing year precipitation or climate moisture index (CMIgy ). Mean summer NDVI (NDVIs ) increased significantly from 1982 to 2010 across 20% of the watershed, primarily in cold, shrub-dominated areas. NDVIs positively correlated (P < 0.05) with Ts across 56% of the watershed (r = 0.52 ± 0.09, mean ± SD), principally in cold areas, and with CMIgy across 9% of the watershed (r = 0.45 ± 0.06), largely in warm areas. Larch ring-width measurements from nine sites revealed that year-to-year (i.e., high-frequency) variation in growth positively correlated (P < 0.05) with June temperature (r = 0.40) and prior summer CMI (r = 0.40) from 1938 to 2007. An unexplained multi-decadal (i.e., low-frequency) decline in annual basal area increment (BAI) occurred following the mid-20th century, but over the NDVI record there was no trend in mean BAI (P > 0.05), which significantly correlated with NDVIs (r = 0.44, P < 0.05, 1982-2007). Both satellite and tree-ring analyses indicated that plant growth was constrained by both low temperatures and limited moisture availability and, furthermore, that warming enhanced growth. Impacts of future climatic change on forests near treeline in Arctic Russia will likely be influenced by shifts in both temperature and moisture, which implies that projections of future forest distribution and productivity in this area should take into account the interactions of energy and moisture limitations.

Estimating tree growth from complex forest monitoring data

Understanding tree growth as a function of tree size is important for a multitude of ecological and management applications. Determining what limits growth is of central interest, and forest inventory permanent plots are an abundant source of long-term information but are highly complex. Observation error and multiple sources of shared variation (spatial plot effects, temporal repeated measures, and a mosaic of sampling intervals) make these data challenging to use for growth estimation. We account for these complexities and incorporate potential limiting factors (tree size, competition, and resource supply) into a hierarchical state-space model. We estimate the diameter growth of white fir (Abies concolor) in the Sierra Nevada of California from forest inventory data, showing that estimating such a model is feasible in a Bayesian framework using readily available modeling tools. In this forest, white fir growth depends strongly on tree size, total plot basal area, and unexplained variation between individual trees. Plot-level resource supply variables (representing light, water, and nutrient availability) do not have a strong impact on inventory-size trees. This approach can be applied to other networks of permanent forest plots, leading to greater ecological insights on tree growth.

Multi-year lags between forest browning and soil respiration at high northern latitudes

High-latitude northern ecosystems are experiencing rapid climate changes, and represent a large potential climate feedback because of their high soil carbon densities and shifting disturbance regimes. A significant carbon flow from these ecosystems is soil respiration (R_S, the flow of carbon dioxide, generated by plant roots and soil fauna, from the soil surface to atmosphere), and any change in the high-latitude carbon cycle might thus be reflected in R_S observed in the field. This study used two variants of a machine-learning algorithm and least squares regression to examine how remotely-sensed canopy greenness (NDVI), climate, and other variables are coupled to annual R_S based on 105 observations from 64 circumpolar sites in a global database. The addition of NDVI roughly doubled model performance, with the best-performing models explaining ∼62% of observed R_S variability. We show that early-summer NDVI from previous years is generally the best single predictor of R_S, and is better than current-year temperature or moisture. This implies significant temporal lags between these variables, with multi-year carbon pools exerting large-scale effects. Areas of decreasing R_S are spatially correlated with browning boreal forests and warmer temperatures, particularly in western North America. We suggest that total circumpolar R_S may have slowed by ∼5% over the last decade, depressed by forest stress and mortality, which in turn decrease R_S. Arctic tundra may exhibit a significantly different response, but few data are available with which to test this. Combining large-scale remote observations and small-scale field measurements, as done here, has the potential to allow inferences about the temporal and spatial complexity of the large-scale response of northern ecosystems to changing climate.