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

Warming and disturbances affect Arctic-boreal vegetation resilience across northwestern North America

Rapid warming and increasing disturbances in high-latitude regions have caused extensive vegetation shifts and uncertainty in future carbon budgets. Better predictions of vegetation dynamics and functions require characterizing resilience, which indicates the capability of an ecosystem to recover from perturbations. Here, using temporal autocorrelation of remotely sensed greenness, we quantify time-varying vegetation resilience during 2000-2019 across northwestern North American Arctic-boreal ecosystems. We find that vegetation resilience significantly decreased in southern boreal forests, including forests showing greening trends, while it increased in most of the Arctic tundra. Warm and dry areas with high elevation and dense vegetation cover were among the hotspots of reduced resilience. Resilience further declined both before and after forest losses and fires, especially in southern boreal forests. These findings indicate that warming and disturbance have been altering vegetation resilience, potentially undermining the expected long-term increase of high-latitude carbon uptake under future climate.

Resilience Indicators for Tropical Rainforests in a Dynamic Vegetation Model

Tropical forests and particularly the Amazon rainforest have been identified as potential tipping elements in the Earth system. According to a dynamical systems theory, a decline in forest resilience preceding a potential shift to a savanna-like biome could manifest as increasing autocorrelation of biomass time series. Recent satellite records indeed exhibit such a trend and also show larger autocorrelation, indicative of reduced resilience, in drier forest regions. However, it is unclear which processes underlie these observational findings and on which scales they operate. Here, we investigate which processes determine tropical forest resilience in the stand-alone, state-of-the-art dynamic global vegetation model LPJmL. We find that autocorrelation is higher in dry climates than wet climates (approx. 0.75 vs. 0.2, for a lag of 10 years), which qualitatively agrees with observations. By constructing a reduced version of LPJmL and by disabling and enabling certain processes in the model, we show that (i) this pattern is associated with population dynamics operating on different time scales in different climates and (ii) that the pattern is sensitive to the allocation of carbon to different pools, especially in years of stress. Both processes are highly uncertain, oversimplified or even lacking in most Earth system models. Our results indicate that the observed spatial variations and trends in vegetation resilience indicators may be explained by local physiological and ecological mechanisms alone, without climate-vegetation feedbacks. In principle, this is consistent with the view that the Amazon rainforest is responding to climate change locally and does not necessarily need to approach one large-scale tipping point, although the latter cannot be ruled out based on our findings.

The impacts of rising vapour pressure deficit in natural and managed ecosystems

An exponential rise in the atmospheric vapour pressure deficit (VPD) is among the most consequential impacts of climate change in terrestrial ecosystems. Rising VPD has negative and cascading effects on nearly all aspects of plant function including photosynthesis, water status, growth and survival. These responses are exacerbated by land-atmosphere interactions that couple VPD to soil water and govern the evolution of drought, affecting a range of ecosystem services including carbon uptake, biodiversity, the provisioning of water resources and crop yields. However, despite the global nature of this phenomenon, research on how to incorporate these impacts into resilient management regimes is largely in its infancy, due in part to the entanglement of VPD trends with those of other co-evolving climate drivers. Here, we review the mechanistic bases of VPD impacts at a range of spatial scales, paying particular attention to the independent and interactive influence of VPD in the context of other environmental changes. We then evaluate the consequences of these impacts within key management contexts, including water resources, croplands, wildfire risk mitigation and management of natural grasslands and forests. We conclude with recommendations describing how management regimes could be altered to mitigate the otherwise highly deleterious consequences of rising VPD.

Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening

Vegetation greening has been suggested to be a dominant trend over recent decades, but severe pulses of tree mortality in forests after droughts and heatwaves have also been extensively reported. These observations raise the question of to what extent the observed severe pulses of tree mortality induced by climate could affect overall vegetation greenness across spatial grains and temporal extents. To address this issue, here we analyse three satellite-based datasets of detrended growing-season normalized difference vegetation index (NDVIGS) with spatial resolutions ranging from 30 m to 8 km for 1,303 field-documented sites experiencing severe drought- or heat-induced tree-mortality events around the globe. We find that severe tree-mortality events have distinctive but localized imprints on vegetation greenness over annual timescales, which are obscured by broad-scale and long-term greening. Specifically, although anomalies in NDVIGS (ΔNDVI) are negative during tree-mortality years, this reduction diminishes at coarser spatial resolutions (that is, 250 m and 8 km). Notably, tree-mortality-induced reductions in NDVIGS (|ΔNDVI|) at 30-m resolution are negatively related to native plant species richness and forest height, whereas topographic heterogeneity is the major factor affecting ΔNDVI differences across various spatial grain sizes. Over time periods of a decade or longer, greening consistently dominates all spatial resolutions. The findings underscore the fundamental importance of spatio-temporal scales for cohesively understanding the effects of climate change on forest productivity and tree mortality under both gradual and abrupt changes.

Enhanced growth resistance but no decline in growth resilience under long-term extreme droughts

The frequency, intensity, and duration of extreme droughts, with devastating impacts on tree growth and survival, have increased with climate change over the past decades. Assessing growth resistance and resilience to drought is a crucial prerequisite for understanding the responses of forest functioning to drought events. However, the responses of growth resistance and resilience to extreme droughts with different durations across different climatic zones remain unclear. Here, we investigated the spatiotemporal patterns in growth resistance and resilience in response to extreme droughts with different durations during 1901-2015, relying on tree-ring chronologies from 2389 forest stands over the mid- and high-latitudinal Northern Hemisphere, species-specific plant functional traits, and diverse climatic factors. The findings revealed that growth resistance and resilience under 1-year droughts were higher in humid regions than in arid regions. Significant higher growth resistance was observed under 2-year droughts than under 1-year droughts in both arid and humid regions, while growth resilience did not show a significant difference. Temporally, tree growth became less resistant and resilient to 1-year droughts in 1980-2015 than in 1901-1979 in both arid and humid regions. As drought duration lengthened, the predominant impacts of climatic factors on growth resistance and resilience weakened and instead foliar economic traits, plant hydraulic traits, and soil properties became much more important in both climatic regions; in addition, such trends were also observed temporally. Finally, we found that most of the Earth system models (ESMs) used in this study overestimated growth resistance and underestimated growth resilience under both 1-year and 2-year droughts. A comprehensive ecophysiological understanding of tree growth responses to longer and intensified drought events is urgently needed, and a specific emphasis should be placed on improving the performance of ESMs.

Early warning signals have limited applicability to empirical lake data

Research aimed at identifying indicators of persistent abrupt shifts in ecological communities, a.k.a regime shifts, has led to the development of a suite of early warning signals (EWSs). As these often perform inaccurately when applied to real-world observational data, it remains unclear whether critical transitions are the dominant mechanism of regime shifts and, if so, which EWS methods can predict them. Here, using multi-trophic planktonic data on multiple lakes from around the world, we classify both lake dynamics and the reliability of classic and second generation EWSs methods to predict whole-ecosystem change. We find few instances of critical transitions, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly processing dependant, with most indicators not performing better than chance, multivariate EWSs being weakly superior to univariate, and a recent machine learning model performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions, developing methods suitable for predicting resilience loss not limited to the strict bounds of bifurcation theory.

Shifting taxonomic and functional community composition of rivers under land use change

Land use intensification has led to conspicuous changes in plant and animal communities across the world. Shifts in trait-based functional composition have recently been hypothesized to manifest at lower levels of environmental change when compared to species-based taxonomic composition; however, little is known about the commonalities in these responses across taxonomic groups and geographic regions. We investigated this hypothesis by testing for taxonomic and geographic similarities in the composition of riverine fish and insect communities across gradients of land use in major hydrological regions of the conterminous United States. We analyzed an extensive data set representing 556 species and 33 functional trait modalities from 8023 fish communities and 1434 taxa and 50 trait modalities from 5197 aquatic insect communities. Our results demonstrate abrupt threshold changes in both taxonomic and functional community composition due to land use conversion. Functional composition consistently demonstrated lower land use threshold responses compared to taxonomic composition for both fish (urban p = 0.069; agriculture p = 0.029) and insect (urban p = 0.095; agriculture p = 0.043) communities according to gradient forest models. We found significantly lower thresholds for urban versus agricultural land use for fishes (taxonomic and functional p < 0.001) and insects (taxonomic p = 0.001; functional p = 0.033). We further revealed that threshold responses in functional composition were more geographically consistent than for taxonomic composition to both urban and agricultural land use change. Traits contributing the most to overall functional composition change differed along urban and agricultural land gradients and conformed to predicted ecological mechanisms underpinning community change. This study points to reliable early-warning thresholds that accurately forecast compositional shifts in riverine communities to land use conversion, and highlight the importance of considering trait-based indicators of community change to inform large-scale land use management strategies and policies.

Declining tree growth resilience mediates subsequent forest mortality in the US Mountain West

Climate change-triggered forest die-off is an increasing threat to global forests and carbon sequestration but remains extremely challenging to predict. Tree growth resilience metrics have been proposed as measurable proxies of tree susceptibility to mortality. However, it remains unclear whether tree growth resilience can improve predictions of stand-level mortality. Here, we use an extensive tree-ring dataset collected at ~3000 permanent forest inventory plots, spanning 13 dominant species across the US Mountain West, where forests have experienced strong drought and extensive die-off has been observed in the past two decades, to test the hypothesis that tree growth resilience to drought can explain and improve predictions of observed stand-level mortality. We found substantial increases in growth variability and temporal autocorrelation as well declining drought resistance and resilience for a number of species over the second half of the 20th century. Declining resilience and low tree growth were strongly associated with cross- and within-species patterns of mortality. Resilience metrics had similar explicative power compared to climate and stand structure, but the covariance structure among predictors implied that the effect of tree resilience on mortality could partially be explained by stand and climate variables. We conclude that tree growth resilience offers highly valuable insights on tree physiology by integrating the effect of stressors on forest mortality but may have only moderate potential to improve large-scale projections of forest die-off under climate change.

Carbon uptake in Eurasian boreal forests dominates the high-latitude net ecosystem carbon budget

Arctic-boreal landscapes are experiencing profound warming, along with changes in ecosystem moisture status and disturbance from fire. This region is of global importance in terms of carbon feedbacks to climate, yet the sign (sink or source) and magnitude of the Arctic-boreal carbon budget within recent years remains highly uncertain. Here, we provide new estimates of recent (2003-2015) vegetation gross primary productivity (GPP), ecosystem respiration (R_eco ), net ecosystem CO₂ exchange (NEE; R_eco  - GPP), and terrestrial methane (CH₄ ) emissions for the Arctic-boreal zone using a satellite data-driven process-model for northern ecosystems (TCFM-Arctic), calibrated and evaluated using measurements from >60 tower eddy covariance (EC) sites. We used TCFM-Arctic to obtain daily 1-km² flux estimates and annual carbon budgets for the pan-Arctic-boreal region. Across the domain, the model indicated an overall average NEE sink of -850 Tg CO₂ -C year^-1 . Eurasian boreal zones, especially those in Siberia, contributed to a majority of the net sink. In contrast, the tundra biome was relatively carbon neutral (ranging from small sink to source). Regional CH₄ emissions from tundra and boreal wetlands (not accounting for aquatic CH₄ ) were estimated at 35 Tg CH₄ -C year^-1 . Accounting for additional emissions from open water aquatic bodies and from fire, using available estimates from the literature, reduced the total regional NEE sink by 21% and shifted many far northern tundra landscapes, and some boreal forests, to a net carbon source. This assessment, based on in situ observations and models, improves our understanding of the high-latitude carbon status and also indicates a continued need for integrated site-to-regional assessments to monitor the vulnerability of these ecosystems to climate change.

Linking remotely sensed ecosystem resilience with forest mortality across the continental United States

Episodes of forest mortality have been observed worldwide associated with climate change, impacting species composition and ecosystem services such as water resources and carbon sequestration. Yet our ability to predict forest mortality remains limited, especially across large scales. Time series of satellite imagery has been used to document ecosystem resilience globally, but it is not clear how well remotely sensed resilience can inform the prediction of forest mortality across continental, multi-biome scales. Here, we leverage forest inventories across the continental United States to systematically assess the potential of ecosystem resilience derived using different data sets and methods to predict forest mortality. We found high resilience was associated with low mortality in eastern forests but was associated with high mortality in western regions. The unexpected resilience-mortality relation in western United States may be due to several factors including plant trait acclimation, insect population dynamics, or resource competition. Overall, our results not only supported the opportunity to use remotely sensed ecosystem resilience to predict forest mortality but also highlighted that ecological factors may have crucial influences because they can reverse the sign of the resilience-mortality relationships.

The Interplay of the Tree and Stand-Level Processes Mediate Drought-Induced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches

Drought-induced forest dieback can lead to a tipping point in community dominance, but the coupled response at the tree and stand-level response has not been properly addressed. New spatially and temporally integrated monitoring approaches that target different biological organization levels are needed. Here, we compared the temporal responses of dendrochronological and spectral indices from 1984 to 2020 at both tree and stand levels, respectively, of a drought-prone Mediterranean Pinus pinea forest currently suffering strong dieback. We test the influence of climate on temporal patterns of tree radial growth, greenness and wetness spectral indices; and we address the influence of major drought episodes on resilience metrics. Tree-ring data and spectral indices followed different spatio-temporal patterns over the study period (1984–2020). Combined information from tree growth and spectral trajectories suggests that a reduction in tree density during the mid-1990s could have promoted tree growth and reduced dieback risk. Additionally, over the last decade, extreme and recurrent droughts have resulted in crown defoliation greater than 40% in most plots since 2019. We found that tree growth and the greenness spectral index were positively related to annual precipitation, while the wetness index was positively related to mean annual temperature. The response to drought, however, was stronger for tree growth than for spectral indices. Our study demonstrates the value of long-term retrospective multiscale analyses including tree and stand-level scales to disentangle mechanisms triggering and driving forest dieback.

Strategic roadmap to assess forest vulnerability under air pollution and climate change

Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long-term monitoring programs.

Transcriptome and association mapping revealed functional genes respond to drought stress in Populus

Drought frequency and severity are exacerbated by global climate change, which could compromise forest ecosystems. However, there have been minimal efforts to systematically investigate the genetic basis of the response to drought stress in perennial trees. Here, we implemented a systems genetics approach that combines co-expression analysis, association genetics, and expression quantitative trait nucleotide (eQTN) mapping to construct an allelic genetic regulatory network comprising four key regulators (PtoeIF-2B, PtoABF3, PtoPSB33, and PtoLHCA4) under drought stress conditions. Furthermore, Hap_01PtoeIF-2B, a superior haplotype associated with the net photosynthesis, was revealed through allelic frequency and haplotype analysis. In total, 75 candidate genes related to drought stress were identified through transcriptome analyses of five Populus cultivars (P. tremula × P. alba, P. nigra, P. simonii, P. trichocarpa, and P. tomentosa). Through association mapping, we detected 92 unique SNPs from 38 genes and 104 epistatic gene pairs that were associated with six drought-related traits by association mapping. eQTN mapping unravels drought stress-related gene loci that were significantly associated with the expression levels of candidate genes for drought stress. In summary, we have developed an integrated strategy for dissecting a complex genetic network, which facilitates an integrated population genomics approach that can assess the effects of environmental threats.

LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data

Vegetation index time-series analysis of multitemporal satellite data is widely used to study vegetation dynamics in the present climate change era. This paper proposes a systematic methodology to predict the Normalized Difference Vegetation Index (NDVI) using time-series data extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS). The key idea is to obtain accurate NDVI predictions by combining the merits of two effective computational intelligence techniques; namely, fuzzy clustering and long short-term memory (LSTM) neural networks under the framework of dynamic time warping (DTW) similarity measure. The study area is the Lesvos Island, located in the Aegean Sea, Greece, which is an insular environment in the Mediterranean coastal region. The algorithmic steps and the main contributions of the current work are described as follows. (1) A data reduction mechanism was applied to obtain a set of representative time series. (2) Since DTW is a similarity measure and not a distance, a multidimensional scaling approach was applied to transform the representative time series into points in a low-dimensional space, thus enabling the use of the Euclidean distance. (3) An efficient optimal fuzzy clustering scheme was implemented to obtain the optimal number of clusters that better described the underline distribution of the low-dimensional points. (4) The center of each cluster was mapped into time series, which were the mean of all representative time series that corresponded to the points belonging to that cluster. (5) Finally, the time series obtained in the last step were further processed in terms of LSTM neural networks. In particular, development and evaluation of the LSTM models was carried out considering a one-year period, i.e., 12 monthly time steps. The results indicate that the method identified unique time-series patterns of NDVI among different CORINE land-use/land-cover (LULC) types. The LSTM networks predicted the NDVI with root mean squared error (RMSE) ranging from 0.017 to 0.079. For the validation year of 2020, the difference between forecasted and actual NDVI was less than 0.1 in most of the study area. This study indicates that the synergy of the optimal fuzzy clustering based on DTW similarity of NDVI time-series data and the use of LSTM networks with clustered data can provide useful results for monitoring vegetation dynamics in fragmented Mediterranean ecosystems.

Predicting Tree Mortality Using Spectral Indices Derived from Multispectral UAV Imagery

Past research has shown that remotely sensed spectral information can be used to predict tree health and vitality. Recent developments in unmanned aerial vehicles (UAVs) have now made it possible to derive such information at the tree and stand scale from high-resolution imagery. We used visible and multispectral bands from UAV imagery to calculate a set of spectral indices for 52,845 individual tree crowns within 38 forest stands in western Canada. We then used those indices to predict the mortality of these canopy trees over the following year. We evaluated whether including multispectral indices leads to more accurate predictions than indices derived from visible wavelengths alone and how the performance varies among three different tree species (Picea glauca, Pinus contorta, Populus tremuloides). Our results show that spectral information can be effectively used to predict tree mortality, with a random forest model producing a mean area under the receiver operating characteristic curve (AUC) of 89.8% and a balanced accuracy of 83.3%. The exclusion of multispectral indices worsened the model performance, but only slightly (AUC = 87.9%, balanced accuracy = 81.8%). We found variation in model performance among species, with higher accuracy for the broadleaf species (balanced accuracy = 85.2%) than the two conifer species (balanced accuracy = 73.3% and 77.8%). However, all models overpredicted tree mortality by a major degree, which limits the use for tree mortality predictions on an individual level. Further improvements such as long-term monitoring, the use of hyperspectral data and cost-sensitive learning algorithms, and training the model with a larger and more balanced data set are necessary. Nevertheless, our results demonstrate that imagery from UAVs has strong potential for predicting annual mortality for individual canopy trees.

Satellite observations document trends consistent with a boreal forest biome shift

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.

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought

Extreme events such as the summer drought of 2018 in Central Europe are projected to occur more frequently in the future and may cause major damages including increased tree mortality and negative impacts on forest ecosystem services. Here, we quantify the response of >1 million forest pixels of 10 × 10 m across Switzerland to the 2018 drought in terms of resistance, recovery, and resilience. We used the Normalized Difference Water Index (NDWI) derived from Sentinel-2 satellite data as a proxy for canopy water content and analyzed its relative change. We calculated NDWI change between the 2017 pre-drought and 2018 drought years (indicating resistance), 2018 and the 2019 post-drought (indicating recovery), and between 2017-2019 (indicating resilience). Analyzing the data from this large natural experiment, we found that for 4.3% of the Swiss forest the NDWI declined between 2017 and 2018, indicating areas with low resistance of the forest canopy to drought effects. While roughly 50% of this area recovered, in 2.7% of the forested area NDWI continued to decline from 2018 to 2019, suggesting prolonged negative effects or delayed damage. We found differential forest responses to drought associated with site topographic characteristics and forest stand characteristics, and to a lesser extent with climatic conditions and interactions between these drivers. Low drought resistance and high recovery were most prominent at forest edges, but also on south-facing slopes and lower elevations. Tree functional type was the most important driver of drought resilience, with most of the damage in stands with high conifer abundance. Our results demonstrate the suitability of satellite-based quantification of drought-induced forest damage at high spatial resolution across large areas. Such information is important to predict how local site characteristics may impact forest vulnerability to future extreme events and help in the search for appropriate adaptation strategies.

Reduced ecosystem resilience quantifies fine-scale heterogeneity in tropical forest mortality responses to drought

Sensitivity of forest mortality to drought in carbon-dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, the potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site-year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave-one-out cross-validation R²  = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine-scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high-resolution remote sensing to "fingerprint" forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought.

Changes in tree drought sensitivity provided early warning signals to the California drought and forest mortality event

Climate warming in recent decades has negatively impacted forest health in the western United States. Here, we report on potential early warning signals (EWS) for drought-related mortality derived from measurements of tree-ring growth (ring width index; RWI) and carbon isotope discrimination (∆¹³ C), primarily focused on ponderosa pine (Pinus ponderosa). Sampling was conducted in the southern Sierra Nevada Mountains, near the epicenter of drought severity and mortality associated with the 2012-2015 California drought and concurrent outbreak of western pine beetle (Dendroctonus brevicomis). At this site, we found that widespread mortality was presaged by five decades of increasing sensitivity (i.e., increased explained variation) of both tree growth and ∆¹³ C to Palmer Drought Severity Index (PDSI). We hypothesized that increasing sensitivity of tree growth and ∆¹³ C to hydroclimate constitute EWS that indicate an increased likelihood of widespread forest mortality caused by direct and indirect effects of drought. We then tested these EWS in additional ponderosa pine-dominated forests that experienced varying mortality rates associated with the same California drought event. In general, drier sites showed increasing sensitivity of RWI to PDSI over the last century, as well as higher mortality following the California drought event compared to wetter sites. Two sites displayed evidence that thinning or fire events that reduced stand basal area effectively reversed the trend of increasing hydroclimate sensitivity. These comparisons indicate that reducing competition for soil water and/or decreasing bark beetle host tree density via forest management-particularly in drier regions-may buffer these forests against drought stress and associated mortality risk. EWS such as these could provide land managers more time to mitigate the extent or severity of forest mortality in advance of droughts. Substantial efforts at deploying additional dendrochronological research in concert with remote sensing and forest modeling will aid in forecasting of forest responses to continued climate warming.

Using spectral indices as early warning signals of forest dieback: The case of drought-prone Pinus pinaster forests

Forest dieback processes linked to drought are expected to increase due to climate warming. Remotely sensed data offer several advantages over common field monitoring methods such as the ability to observe large areas on a systematic basis and monitoring their changes, making them increasingly used to assess changes in forest health. Here we aim to use a combined approximation of fieldwork and remote sensing to explore possible links between forest dieback and land surface phenological and trend variables derived from long Landsat time series. Forest dieback was evaluated in the field over 31 plots in a Mediterranean, xeric Pinus pinaster forest. Landsat 31-year time series of three greenness (EVI, NDVI, SAVI) and two wetness spectral indices (NMDI and TCW) were derived covering the period 1990-2020. Spectral indices from time series were decomposed into trend and seasonality using a Bayesian estimator while the relationships of the phenological and trend variables among levels of damage were assessed using linear and additive mixed models. We have not found any statistical pieces of evidence of extension or shortening patterns for the length of the phenological season over the examined 31-year period. Our results indicate that the dieback process was mainly related to the trend component of the spectral indices series whereas the phenological metrics were not related to forest dieback. We also found that plots with more dying or damaged trees displayed lower spectral indices trends after a severe drought event in the middle of the 1990s, which confirms the Landsat-derived spectral indices as indicators of early-warning signals. Drops in trends occurred earlier for wetness indices rather than for greenness indices which suggests that the former could be more appropriate for dieback detection, i.e. they could be used as early warning signals of impending loss of tree vigor.

The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis

Forests are increasingly subject to a number of disturbances that can adversely influence their health. Remote sensing offers an efficient alternative for assessing and monitoring forest health. A myriad of methods based upon remotely sensed data have been developed, tailored to the different definitions of forest health considered, and covering a broad range of spatial and temporal scales. The purpose of this review paper is to identify and analyse studies that addressed forest health issues applying remote sensing techniques, in addition to studying the methodological wealth present in these papers. For this matter, we applied the PRISMA protocol to seek and select studies of our interest and subsequently analyse the information contained within them. A final set of 107 journal papers published between 2015 and 2020 was selected for evaluation according to our filter criteria and 20 selected variables. Subsequently, we pair-wise exhaustively read the journal articles and extracted and analysed the information on the variables. We found that (1) the number of papers addressing this issue have consistently increased, (2) that most of the studies placed their study area in North America and Europe and (3) that satellite-borne multispectral sensors are the most commonly used technology, especially from Landsat mission. Finally, most of the studies focused on evaluating the impact of a specific stress or disturbance factor, whereas only a small number of studies approached forest health from an early warning perspective.

Old-growth forests show low canopy resilience to droughts at the southern edge of the taiga

Forest mortality and resilience driven by drought disturbances have attracted tons of attention. However, the acquisition of continuous spatial-temporal data is generally enslaved to the conventional field investigations. In this study, the resilience of semiarid forest was characterized with canopy dynamics from remote sensing observations, combining the variations in canopy greenness and water content. We integrated dense normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) time series from Landsat datasets, intending to assess the canopy resilience in 24 conifer patches along a climatic aridity gradient at the southern edge of the taiga in northern Mongolia and southern Siberia of Russia. The results exhibited four patterns of coordinated NDVI-NDII variation trends, indicating that the canopy water content of coniferous forests may decrease at first during a drought period, and sustained water loss may, in turn, induce an accompanying reduction in canopy greenness. Meanwhile, the patches with canopy recovery growth after initial declines were considered to have resilience to climate change. We further observed the combined effects of aridity degree and tree age on canopy resilience, and all seven patches with no resilience corresponded to the old-tree group (the oldest trees reached or exceeded the age of 90). The observations indicated that the old-growth forests in semiarid regions were less likely to show canopy resilience, which corresponded to a higher risk of sustained decline.

Early warning signals of failures in building management systems

In the context of sensor data generated by Building Management Systems (BMS), early warning signals are still an unexplored topic. The early detection of anomalies can help preventing malfunctions of key parts of a heating, cooling and air conditioning (HVAC) system that may lead to a range of BMS problems, from important energy waste to fatal errors in the worst case. We analyse early warning signals in BMS sensor data for early failure detection. In this paper, the studied failure is a malfunction of one specific Air Handling Unit (AHU) control system that causes temperature spikes of up to 30 degrees Celsius due to overreaction of the heating and cooling valves in response to an anomalous temperature change caused by the pre-heat coil in winter period in a specific area of a manufacturing facility. For such purpose, variance, lag-1 autocorrelation function (ACF1), power spectrum (PS) and variational autoencoder (VAE) techniques are applied to both univariate and multivariate scenarios. The univariate scenario considers the application of these techniques to the control variable only (the one that displays the failure), whereas the multivariate analysis considers the variables affecting the control variable for the same purpose. Results show that anomalies can be detected up to 32 hours prior to failure, which gives sufficient time to BMS engineers to prevent a failure and therefore, an proactive approach to BMS failures is adopted instead of a reactive one.

Widespread mortality of trembling aspen (Populus tremuloides) throughout interior Alaskan boreal forests resulting from a novel canker disease

Over the past several decades, growth declines and mortality of trembling aspen throughout western Canada and the United States have been linked to drought, often interacting with outbreaks of insects and fungal pathogens, resulting in a “sudden aspen decline” throughout much of aspen’s range. In 2015, we noticed an aggressive fungal canker causing widespread mortality of aspen throughout interior Alaska and initiated a study to quantify potential drivers for the incidence, virulence, and distribution of the disease. Stand-level infection rates among 88 study sites distributed across 6 Alaska ecoregions ranged from <1 to 69%, with the proportion of trees with canker that were dead averaging 70% across all sites. The disease is most prevalent north of the Alaska Range within the Tanana Kuskokwim ecoregion. Modeling canker probability as a function of ecoregion, stand structure, landscape position, and climate revealed that smaller-diameter trees in older stands with greater aspen basal area have the highest canker incidence and mortality, while younger trees in younger stands appear virtually immune to the disease. Sites with higher summer vapor pressure deficits had significantly higher levels of canker infection and mortality. We believe the combined effects of this novel fungal canker pathogen, drought, and the persistent aspen leaf miner outbreak are triggering feedbacks between carbon starvation and hydraulic failure that are ultimately driving widespread mortality. Warmer early-season temperatures and prolonged late summer drought are leading to larger and more severe wildfires throughout interior Alaska that are favoring a shift from black spruce to forests dominated by Alaska paper birch and aspen. Widespread aspen mortality fostered by this rapidly spreading pathogen has significant implications for successional dynamics, ecosystem function, and feedbacks to disturbance regimes, particularly on sites too dry for Alaska paper birch.

The European Forest Condition Monitor: Using Remotely Sensed Forest Greenness to Identify Hot Spots of Forest Decline

Forest decline, in course of climate change, has become a frequently observed phenomenon. Much of the observed decline has been associated with an increasing frequency of climate change induced hotter droughts while decline induced by flooding, late-frost, and storms also play an important role. As a consequence, tree mortality rates have increased across the globe. Despite numerous studies that have assessed forest decline and predisposing factors for tree mortality, we still lack an in-depth understanding of (I) underlying eco-physiological mechanisms, (II) the influence of varying environmental conditions related to soil, competition, and micro-climate, and (III) species-specific strategies to cope with prolonged environmental stress. To deepen our knowledge within this context, studying tree performance within larger networks seems a promising research avenue. Ideally such networks are already established during the actual period of environmental stress. One approach for identifying stressed forests suitable for such monitoring networks is to assess measures related to tree vitality in near real-time across large regions by means of satellite-borne remote sensing. Within this context, we introduce the European Forest Condition monitor (EFCM)-a remote-sensing based, freely available, interactive web information tool. The EFCM depicts forest greenness (as approximated using NDVI from MODIS at a spatial resolution of roughly 5.3 hectares) for the pixel-specific growing season across Europe and consequently allows for guiding research within the context of concurrent forest performance. To allow for inter-temporal comparability and account for pixel-specific features, all observations are set in relation to normalized difference vegetation index (NDVI) records over the monitoring period beginning in 2001. The EFCM provides both a quantile-based and a proportion-based product, thereby allowing for both relative and absolute comparison of forest greenness over the observational record. Based on six specific examples related to spring phenology, drought, late-frost, tree die-back on water-logged soils, an ice storm, and windthrow we exemplify how the EFCM may help identifying hotspots of extraordinary forest greenness. We discuss advantages and limitations when monitoring forest condition at large scales on the basis of moderate resolution remote sensing products to guide users toward an appropriate interpretation.

Forest carbon sink neutralized by pervasive growth-lifespan trade-offs

Land vegetation is currently taking up large amounts of atmospheric CO₂, possibly due to tree growth stimulation. Extant models predict that this growth stimulation will continue to cause a net carbon uptake this century. However, there are indications that increased growth rates may shorten trees' lifespan and thus recent increases in forest carbon stocks may be transient due to lagged increases in mortality. Here we show that growth-lifespan trade-offs are indeed near universal, occurring across almost all species and climates. This trade-off is directly linked to faster growth reducing tree lifespan, and not due to covariance with climate or environment. Thus, current tree growth stimulation will, inevitably, result in a lagged increase in canopy tree mortality, as is indeed widely observed, and eventually neutralise carbon gains due to growth stimulation. Results from a strongly data-based forest simulator confirm these expectations. Extant Earth system model projections of global forest carbon sink persistence are likely too optimistic, increasing the need to curb greenhouse gas emissions.

Siberian and temperate ecosystems shape Northern Hemisphere atmospheric CO2 seasonal amplification

The amplitude of the atmospheric CO2 seasonal cycle has increased by 30 to 50% in the Northern Hemisphere (NH) since the 1960s, suggesting widespread ecological changes in the northern extratropics. However, substantial uncertainty remains in the continental and regional drivers of this prominent amplitude increase. Here we present a quantitative regional attribution of CO2 seasonal amplification over the past 4 decades, using a tagged atmospheric transport model prescribed with observationally constrained fluxes. We find that seasonal flux changes in Siberian and temperate ecosystems together shape the observed amplitude increases in the NH. At the surface of northern high latitudes, enhanced seasonal carbon exchange in Siberia is the dominant contributor (followed by temperate ecosystems). Arctic-boreal North America shows much smaller changes in flux seasonality and has only localized impacts. These continental contrasts, based on an atmospheric approach, corroborate heterogeneous vegetation greening and browning trends from field and remote-sensing observations, providing independent evidence for regionally divergent ecological responses and carbon dynamics to global change drivers. Over surface midlatitudes and throughout the midtroposphere, increased seasonal carbon exchange in temperate ecosystems is the dominant contributor to CO2 amplification, albeit with considerable contributions from Siberia. Representing the mechanisms that control the high-latitude asymmetry in flux amplification found in this study should be an important goal for mechanistic land surface models moving forward.

Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

The Threat of the Combined Effect of Biotic and Abiotic Stress Factors in Forestry Under a Changing Climate

Plants encounter several biotic and abiotic stresses, usually in combination. This results in major economic losses in agriculture and forestry every year. Climate change aggravates the adverse effects of combined stresses and increases such losses. Trees suffer even more from the recurrence of biotic and abiotic stress combinations owing to their long lifecycle. Despite the effort to study the damage from individual stress factors, less attention has been given to the effect of the complex interactions between multiple biotic and abiotic stresses. In this review, we assess the importance, impact, and mitigation strategies of climate change driven interactions between biotic and abiotic stresses in forestry. The ecological and economic importance of biotic and abiotic stresses under different combinations is highlighted by their contribution to the decline of the global forest area through their direct and indirect roles in forest loss and to the decline of biodiversity resulting from local extinction of endangered species of trees, emission of biogenic volatile organic compounds, and reduction in the productivity and quality of forest products and services. The abiotic stress factors such as high temperature and drought increase forest disease and insect pest outbreaks, decrease the growth of trees, and cause tree mortality. Reports of massive tree mortality events caused by "hotter droughts" are increasing all over the world, affecting several genera of trees including some of the most important genera in plantation forests, such as Pine, Poplar, and Eucalyptus. While the biotic stress factors such as insect pests, pathogens, and parasitic plants have been reported to be associated with many of these mortality events, a considerable number of the reports have not taken into account the contribution of such biotic factors. The available mitigation strategies also tend to undermine the interactive effect under combined stresses. Thus, this discussion centers on mitigation strategies based on research and innovation, which build on models previously used to curb individual stresses.

Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species

Extreme climate events (ECEs) such as severe droughts, heat waves, and late spring frosts are rare but exert a paramount role in shaping tree species distributions. The frequency of such ECEs is expected to increase with climate warming, threatening the sustainability of temperate forests. Here, we analyzed 2,844 tree-ring width series of five dominant European tree species from 104 Swiss sites ranging from 400 to 2,200 m a.s.l. for the period 1930-2016. We found that (a) the broadleaved oak and beech are sensitive to late frosts that strongly reduce current year growth; however, tree growth is highly resilient and fully recovers within 2 years; (b) radial growth of the conifers larch and spruce is strongly and enduringly reduced by spring droughts-these species are the least resistant and resilient to droughts; (c) oak, silver fir, and to a lower extent beech, show higher resistance and resilience to spring droughts and seem therefore better adapted to the future climate. Our results allow a robust comparison of the tree growth responses to drought and spring frost across large climatic gradients and provide striking evidence that the growth of some of the most abundant and economically important European tree species will be increasingly limited by climate warming. These results could serve for supporting species selection to maintain the sustainability of forest ecosystem services under the expected increase in ECEs.

Testing early warning metrics for drought-induced tree physiological stress and mortality

Climate change-driven drought stress has triggered numerous large-scale tree mortality events in recent decades. Advances in mechanistic understanding and prediction are greatly limited by an inability to detect in situ where trees are likely to die in order to take timely measurements and actions. Thus, algorithms of early warning and detection of drought-induced tree stress and mortality could have major scientific and societal benefits. Here, we leverage two consecutive droughts in the southwestern United States to develop and test a set of early warning metrics. Using Landsat satellite data, we constructed early warning metrics from the first drought event. We then tested these metrics' ability to predict spatial patterns in tree physiological stress and mortality from the second drought. To test the broader applicability of these metrics, we also examined a separate drought in the Amazon rainforest. The early warning metrics successfully explained subsequent tree mortality in the second drought in the southwestern US, as well as mortality in the independent drought in tropical forests. The metrics also strongly correlated with spatial patterns in tree hydraulic stress underlying mortality, which provides a strong link between tree physiological stress and remote sensing during the severe drought and indicates that the loss of hydraulic function during drought likely mediated subsequent mortality. Thus, early warning metrics provide a critical foundation for elucidating the physiological mechanisms underpinning tree mortality in mature forests and guiding management responses to these climate-induced disturbances.

Spatial early warning signals for impending regime shifts: A practical framework for application in real-world landscapes

Prediction of ecosystem response to global environmental change is a pressing scientific challenge of major societal relevance. Many ecosystems display nonlinear responses to environmental change, and may even undergo practically irreversible 'regime shifts' that initiate ecosystem collapse. Recently, early warning signals based on spatiotemporal metrics have been proposed for the identification of impending regime shifts. The rapidly increasing availability of remotely sensed data provides excellent opportunities to apply such model-based spatial early warning signals in the real world, to assess ecosystem resilience and identify impending regime shifts induced by global change. Such information would allow land-managers and policy makers to interfere and avoid catastrophic shifts, but also to induce regime shifts that move ecosystems to a desired state. Here, we show that the application of spatial early warning signals in real-world landscapes presents unique and unexpected challenges, and may result in misleading conclusions when employed without careful consideration of the spatial data and processes at hand. We identify key practical and theoretical issues and provide guidelines for applying spatial early warning signals in heterogeneous, real-world landscapes based on literature review and examples from real-world data. Major identified issues include (1) spatial heterogeneity in real-world landscapes may enhance reversibility of regime shifts and boost landscape-level resilience to environmental change (2) ecosystem states are often difficult to define, while these definitions have great impact on spatial early warning signals and (3) spatial environmental variability and socio-economic factors may affect spatial patterns, spatial early warning signals and associated regime shift predictions. We propose a novel framework, shifting from an ecosystem perspective towards a landscape approach. The framework can be used to identify conditions under which resilience assessment with spatial remotely sensed data may be successful, to support well-informed application of spatial early warning signals, and to improve predictions of ecosystem responses to global environmental change.

One Century of Forest Monitoring Data in Switzerland Reveals Species- and Site-Specific Trends of Climate-Induced Tree Mortality

Climate-induced tree mortality became a global phenomenon during the last century and it is expected to increase in many regions in the future along with a further increase in the frequency of drought and heat events. However, tree mortality at the ecosystem level remains challenging to quantify since long-term, tree-individual, reliable observations are scarce. Here, we present a unique data set of monitoring records from 276 permanent plots located in 95 forest stands across Switzerland, which include five major European tree species (Norway spruce, Scots pine, silver fir, European beech, and sessile and common oak) and cover a time span of over one century (1898-2013), with inventory periods of 5-10 years. The long-term average annual mortality rate of the investigated forest stands was 1.5%. In general, species-specific annual mortality rates did not consistently increase over the last decades, except for Scots pine forests at lower altitudes, which exhibited a clear increase of mortality since the 1960s. Temporal trends of tree mortality varied also depending on diameter at breast height (DBH), with large trees generally experiencing an increase in mortality, while mortality of small trees tended to decrease. Normalized mortality rates were remarkably similar between species and a modest, but a consistent and steady increasing trend was apparent throughout the study period. Mixed effects models revealed that gradually changing stand parameters (stand basal area and stand age) had the strongest impact on mortality rates, modulated by climate, which had increasing importance during the last decades. Hereby, recent climatic changes had highly variable effects on tree mortality rates, depending on the species in combination with abiotic and biotic stand and site conditions. This suggests that forest species composition and species ranges may change under future climate conditions. Our data set highlights the complexity of forest dynamical processes such as long-term, gradual changes of forest structure, demography and species composition, which together with climate determine mortality rates.

Drought-Affected Populus simonii Carr. Show Lower Growth and Long-Term Increases in Intrinsic Water-Use Efficiency Prior to Tree Mortality

The Three-North Shelter Forest (TNSF) is a critical ecological barrier against sandstorms in northern China, but has shown extensive decline and death in Populus simonii Carr. in the last decade. We investigated the characteristics—tree-ring width, basal area increment (BAI), carbon isotope signature (13Ccor), and intrinsic water-use efficiency (iWUE)—of now-dead, dieback, and non-dieback trees in TNSF shelterbelts of Zhangbei County. Results from the three groups were compared to understand the long-term process of preceding drought-induced death and to identify potential early-warning proxies of drought-triggered damage. The diameter at breast height (DBH) was found to decrease with the severity of dieback, showing an inverse relationship. In all three groups, both tree-ring width and BAI showed quadratic relationships with age, and peaks earlier in the now-dead and dieback groups than in the non-dieback group. The tree-ring width and BAI became significantly lower in the now-dead and dieback groups than in the non-dieback group from 17 to 26 years before death, thus, these parameters can serve as early-warning signals for future drought-induced death. The now-dead and dieback groups had significantly higher δ13Ccor and iWUEs than the non-dieback group at 7–16 years prior to the mortality, indicating a more conservative water-use strategy under drought stress compared with non-dieback trees, possibly at the cost of canopy defoliation and long-term shoot dieback. The iWUE became significantly higher in the now-dead group than in the dieback group at 0–7 years before death, about 10 years later than the divergence of BAI. After the iWUE became significantly different among the groups, the now-dead trees showed lower growth and died over the next few years. This indicates that, for the TNSF shelterbelts studied, an abrupt iWUE increase can be used as a warning signal for acceleration of impending drought-induced tree death. In general, we found that long-term drought decreased growth and increased iWUE of poplar tree. Successive droughts could drive dieback and now-dead trees to their physiological limits of drought tolerance, potentially leading to decline and mortality episodes.

Early-Warning Signals of Individual Tree Mortality Based on Annual Radial Growth

Tree mortality is a key driver of forest dynamics and its occurrence is projected to increase in the future due to climate change. Despite recent advances in our understanding of the physiological mechanisms leading to death, we still lack robust indicators of mortality risk that could be applied at the individual tree scale. Here, we build on a previous contribution exploring the differences in growth level between trees that died and survived a given mortality event to assess whether changes in temporal autocorrelation, variance, and synchrony in time-series of annual radial growth data can be used as early warning signals of mortality risk. Taking advantage of a unique global ring-width database of 3065 dead trees and 4389 living trees growing together at 198 sites (belonging to 36 gymnosperm and angiosperm species), we analyzed temporal changes in autocorrelation, variance, and synchrony before tree death (diachronic analysis), and also compared these metrics between trees that died and trees that survived a given mortality event (synchronic analysis). Changes in autocorrelation were a poor indicator of mortality risk. However, we found a gradual increase in inter-annual growth variability and a decrease in growth synchrony in the last ∼20 years before mortality of gymnosperms, irrespective of the cause of mortality. These changes could be associated with drought-induced alterations in carbon economy and allocation patterns. In angiosperms, we did not find any consistent changes in any metric. Such lack of any signal might be explained by the relatively high capacity of angiosperms to recover after a stress-induced growth decline. Our analysis provides a robust method for estimating early-warning signals of tree mortality based on annual growth data. In addition to the frequently reported decrease in growth rates, an increase in inter-annual growth variability and a decrease in growth synchrony may be powerful predictors of gymnosperm mortality risk, but not necessarily so for angiosperms.