Nitrogen isotope enrichment predicts growth response of Pinus radiata in New Zealand to nitrogen fertiliser addition

The growth and survival of urban trees and maintenance of urban forest canopy are important considerations in adaptation of urban regions to climate change, especially in relation to increasing frequency of extreme climatic events such as drought. However, urban forest growth and drought response may vary considerably within large urban landscapes across gradients in land use, urbanization, forest composition and structure, and environmental factors. We quantified urban forest growth and resilience and resistance to extreme drought in the greater Chicago metropolitan region based on patterns of annual basal area production from increment core analysis. We evaluated variation in growth and drought response in relation to a broad urban to rural gradient, land-use categories, local-scale environmental predictors, and forest community characteristics. Urban forest growth varied greatly among land-use classes and major genera. Plot-level variation in productivity was predicted most strongly (R2 = 0.53) by total plot-level basal area, canopy height, species composition, soil and ground-cover characteristics, and position within the urban-rural gradient. Urban forest growth was strongly related to regional meteorological drought. In periods of extreme drought conditions growth declined in the year of the drought (i.e., was not resistant to drought effects), but was highly resilient to drought in the subsequent 5 year period. Drought response did not vary consistently across land-use classes or among major genera, and site or community characteristics had little explanatory power in predicting drought response. Improved understanding of factors driving variation in urban forest growth and drought response could help inform adaptation-focused urban forest management strategies.

BackgroundEffectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis.ObjectiveIn this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease.MethodsFor this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types.ResultsUsing clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy.ConclusionsOur findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch–spruce–fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.

To what extent are stand structure and tree species composition affected by the nature of stand-initiating disturbances and other disturbances that cause significant tree mortality? I documented recent disturbance history and tree species composition, density, spatial pattern, and age structure in 48 stands dominated by Pinuscontorta Dougl. ex Loud. ssp. latifolia (Engelm.) Critchfield in western Montana. Stand modal ages ranged from 8 to 267 years, and sites were sampled across a range of elevations and aspects. Disturbance histories included stand-replacing fires (N = 25), partial burns (N = 8), clear-cutting (N = 7), and other disturbances (N = 8). All young stands (modal tree age < 23 years) had unimodal age structures; however, within-stand ranges in tree ages were greater following cutting than burning. Young fire-origin stands were more strongly dominated by P. contorta than young clearcut-origin stands. For older stands, within-stand variability in tree ages was greatest after nonfire disturbances and lowest after stand-replacing burns, while tree density was highest after such bums, and tree species diversity did not differ among disturbance types. The primary axis of variation in age structures across all stands, as revealed by principal components analysis, related to within-stand variability in tree ages. Compositional and structural stand features were not correlated with this axis.

BackgroundStunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for early intervention and prevention of morbidity. The aim of this study was to investigate patterns of growth in infants up through 48 months of age to assess whether the growth of infants with stunting eventually improved as well as the potential predictors of growth.MethodsHeight-for-age z-scores (HAZ) of children from Matiari (rural site, Pakistan) at birth, 18 months, and 48 months were obtained. Results of serum-based biomarkers collected at 6 and 9 months were recorded. A descriptive analysis of the population was followed by assessment of growth predictors via traditional machine learning random forest models.ResultsOf the 107 children who were followed up till 48 months of age, 51% were stunted (HAZ < − 2) at birth which increased to 54% by 48 months of age. Stunting status for the majority of children at 48 months was found to be the same as at 18 months. Most children with large gains started off stunted or severely stunted, while all of those with notably large losses were not stunted at birth. Random forest models identified HAZ at birth as the most important feature in predicting HAZ at 18 months. Of the biomarkers, AGP (Alpha- 1-acid Glycoprotein), CRP (C-Reactive Protein), and IL1 (interleukin-1) were identified as strong subsequent growth predictors across both the classification and regressor models.ConclusionWe demonstrated that children most children with stunting at birth remained stunted at 48 months of age. Value was added for predicting growth outcomes with the use of traditional machine learning random forest models. HAZ at birth was found to be a strong predictor of subsequent growth in infants up through 48 months of age. Biomarkers of systemic inflammation, AGP, CRP, IL1, were also strong predictors of growth outcomes. These findings provide support for continued focus on interventions prenatally, at birth, and early infancy in children at risk for stunting who live in resource-constrained regions of the world.

Machine learning has the potential to help researchers better understand and close the gap in HIV care delivery in large metropolitan regions such as Mecklenburg County, North Carolina, USA. We aim to identify important risk factors associated with delayed linkage to care for HIV patients with novel machine learning models and identify high-risk regions of the delay. Deidentified 2013-2017 Mecklenburg County surveillance data in eHARS format were requested. Both univariate analyses and machine learning random forest model (developed in R 3.5.0) were applied to quantify associations between delayed linkage to care (>30 days after diagnosis) and various risk factors for individual HIV patients. We also aggregated linkage to care by zip codes to identify high-risk communities within the county. Types of HIV-diagnosing facility significantly influenced time to linkage; first diagnosis in hospital was associated with the shortest time to linkage. HIV patients with lower CD4+ cell counts (<200/ml) were twice as likely to link to care within 30 days than those with higher CD4+ cell count. Random forest model achieved high accuracy (>80% without CD4+ cell count data and >95% with CD4+ cell count data) to predict risk of delay in linkage to care. In addition, we also identified top high-risk zip codes of delayed linkage. The findings helped public health teams identify high-risk communities of delayed HIV care continuum across Mecklenburg County. The methodology framework can be applied to other regions with HIV epidemic and challenge of delayed linkage to care.

Modeling drying shrinkage presents significant challenges due to the complexity and multitude of contributing parameters. This study provides detailed insights into the input requirements and predictive capabilities of established models by leveraging various datasets from the NU-ITI database. Initially, the performance of a shrinkage model was evaluated. The data for a machine learning random forest model included eight variables, interpreted through SHapley Additive exPlanations (SHAP), which elucidates the most influential inputs. However, the partial dependency graphs yielded minimal information on their relative impacts. This research demonstrates that enhancements in the random forest model’s predictive accuracy improved shrinkage predictions by 25%. This advancement significantly mitigates potential deviations in anticipated strains and stresses. The findings from this comprehensive analysis facilitate the selection and prediction of drying shrinkage, focusing on the most critical factors to ensure the highest accuracy.

Multiple myeloma is an incurable and fatal cancer of immunoglobulin-secreting plasma cells. Most conventional therapies aim to induce apoptosis in myeloma cells but resistance to these drugs often arises and drives relapse. In this study, we sought to identify the best adjunct targets to kill myeloma cells resistant to conventional therapies using deep profiling by mass cytometry (CyTOF). We validated probes to simultaneously detect 26 regulators of cell death, mitosis, cell signaling, and cancer-related pathways at the single-cell level following treatment of myeloma cells with dexamethasone or bortezomib. Time-resolved visualization algorithms and machine learning random forest models (RFMs) delineated putative cell death trajectories and a hierarchy of parameters that specified myeloma cell survival versus apoptosis following treatment. Among these parameters, increased amounts of phosphorylated cAMP response element-binding protein (CREB) and the pro-survival protein, MCL-1, were defining features of cells surviving drug treatment. Importantly, the RFM prediction that the combination of an MCL-1 inhibitor with dexamethasone would elicit potent, synergistic killing of myeloma cells was validated in other cell lines, in vivo preclinical models and primary myeloma samples from patients. Furthermore, CyTOF analysis of patient bone marrow cells clearly identified myeloma cells and their key cell survival features. This study demonstrates the utility of CyTOF profiling at the single-cell level to identify clinically relevant drug combinations and tracking of patient responses for future clinical trials.

Background Climate change has become one of the significant global challenges confronting all the people in the world. Climate change, particularly rising atmospheric carbon dioxide (CO2) concentration (ca) and temperature, and changes in water availability have also affected tree growth of forest ecosystems worldwide. Several studies about the effects of climate change on tree growth and physiological responses have been reported. However, these are mostly based only on experiments with isolated trees or seedlings grown under intensive and short-term exposure to one or two climatic factors. Since long-term and gradual impacts of climate change on tree growth and physiological responses could be different from the short-term effects, there is an urgent need to investigate how tree species respond to elevated atmospheric CO2 and temperature and water availability changes at larger scales and over more extended periods. This study is complicated because tree growth rates vary among genotypes and change as trees age and because climatic conditions, such as temperature, precipitation and humidity, and atmospheric CO2, have been changing over time. There are also indications that tree responses to climate change may change with time as the trees grow. Moreover, since tree growth (biomass) is a product of physiological processes, the physiological processes are affected by the structure of the organs or tissues where the processes occur. In turn, the structures of the organs are determined by the products of the previous physiological processes. To understand the mechanisms on how long-term climate change affects tree physiological processes and growth, we have to understand the relationships among the climate, tree growth and physiological responses, and how long tree growth trend will remain alongside the elevating CO2 concentration before it declines, considering that the forest ecosystems are one of the most important contributors to the global CO2 assimilation, which can effectively counteract the global warming. Hypothesis and objective My research has been focused on studying the long-term tree growth and water use efficiency in response to rising atmospheric CO2 concentration, in combining with other environmental factors, and their influences on climate change in the future. I hypothesised that tree growth is affected by biological effects, such as tree species and ages, and non-biological effects, namely locations, temperature, precipitation and humidity. Thus, all my experiments' main objectives were to confirm my hypothesis and quantify how those biological and non-biological factors would influence tree growth and water use efficiency in the context of both spatial and temporal scales. The goals and objectives of this research were: To determine the effect of long-term climatic conditions on tree growth and physiological processes. The objectives were to determine how climatic factors would influence tree growth and physiological responses; to determine the key climatic controls of the change in tree growth and physiological responses, and determine how each of the key climatic factors and their interactions affect tree growth and physiological responses. To determine the variation of the climate-tree relationship among tree species. The objective was to determine the phenotypic and genotypic variation of the effects of climate change on tree growth and physiological responses among tree species, To determine the acclimatization of tree species in response to climate change. The objectives were to determine the responses of tree species to climate change over time and determine tree species' responses to climate change before and after they are exposed to specific climatic conditions. Materials and methods To achieve the objectives, tree ring technologies were adopted. Trees record relevant information in their annual rings, represent important natural archives of climate changes, and provide archives of tree growth responses to the past climate variation. With tree ring width growth, information from tree-ring stable isotope compositions were used to better understand the dynamic relationships among the climate, tree growth, and physiological responses. My current research was commenced with seven tree species sampled from five different forests in China, covering both subtropical and boreal climatic conditions. The long-term tree-ring chronology was established by applying tree ring width measurement and cross-dating verified by COFECHA program; therefore, the basal area increments (BAI) were calculated sequentially. Meanwhile, the intrinsic water-use efficiency (iWUE) was calculated by measuring carbon isotope composition (δ13C) in tree ring samples. Tree ring δ13C relationships with BAI and atmospheric CO2 concentration were also quantified. Results and discussion From this study, we have gained further understanding of the relationships among long-term climate change, tree growth and physiology, as a basis for future projection of silvicultural manipulations under different climate change scenarios. In Chapter 2, both BAI values of the two tree species (Pseudolarix amabilis and Cryptomeria japonica, sampled from a subtropical monsoon forest located in eastern China), continuously increased with the rising of CO2 concentration until the atmospheric CO2 concentration tipping points were reached (the tipping points of Pseudolarix amabilis and Cryptomeria japonica were in year 1997 and 1996 when atmospheric CO2 concentration reached 365.1 ppm and 636.0 ppm respectively), after which tree growth started to decline with the rising CO2, while iWUE exerted a continuous increase trend with the increasing CO2 concentration. In Chapter 3, the results showed a decreasing trend in relative humidity over the past 70 years in a subtropical forest of south-east China with rising CO2 concentrations and temperature and the initial increasing tree growth for both Pinus massoniana and Cryptomeria japonica from the rising CO2, which peaked when CO2 concentration reached 330 ppm and 385 ppm in year 1974 and 2008 respectively, but decreased thereafter with increasing water limitation. Tree iWUE showed the same continuing increase trend as the two species in Chapter 2. In Chapter 4, three tree species (Cinnamomum micranthum, Pinus massoniana and Cunninghamia lanceolata) were sampled in two nearby subtropical forests of south-east China. The tree-growth also initially increased with the rising Ca, then decreased with the increasing Ca. The tipping points among the three species slightly varied but all happened between year 1995 and 1999. In addition, iWUE continuously increased with the rising Ca regardless the tipping points of BAI with the Ca. In Chapter 5, two species (Larix gmelinii Rupr and Betula platyphylla) were sampled in a boreal forest of north-east China, the results were similar to the previous chapters, while iWUE showed consistent increase during the entire growth period for both species, BAI reached the tipping points when Ca reached 366 ppm in year 1998 for Larix gmelinii Rupr, and 353.5 ppm in year 1989 for Betula platyphylla. In summary, the experimental results demonstrated that tree growth of BAI showed a continuous increase among all sampled tree species with the rising CO2 concentration until the CO2 concentration tipping points were passed. The trees’ responses were both species and site dependent. After reaching the critical points, tree growth started to decline even with the rising CO2 concentration, while iWUE exerted a continuously increasing trend with the increasing CO2 concentration, which biologically proofed that the decreased BAI was not dominated by tree age, but due to the rise of Ca and warming induced water limitation. The series of tree ring studies reported in this thesis has highlighted that there would be non-linear tree growth responses to the increasing Ca of the tree species in both subtropical and boreal forests, with the initial increases in tree growth detected as the atmospheric CO2 increased, but the tree growth peaked when the critical tipping points of Ca were reached and then declined thereafter. However, tree WUE continued to increase with the rising Ca, initially due to the increasing photosynthesis and tree growth, then later due to the warming induced water limitation. Unfortunately, the tipping points of Ca for tree species in both subtropical and boreal forests were reached between 1974 and 2008, and tree growth decreased with the rising Ca once the CO2 tipping points were passed, leading to a positive feedback to climate change.

Background: Machine learning (ML) in translational medicine has led to prediction of clinical outcomes and identification of new biomarkers. We employ ML in prediction of pathologic complete response (pCR) in high-risk breast cancer patients in the neoadjuvant I-SPY2 TRIAL where not all novel agents have strong predictive biomarkers. Leveraging a ML approach using progressively expanded candidate genes, we explore the limitations of using only known mechanisms of action in predicting pCR, and the extent to which biology outside known drug action improves response prediction in the first 10 arms of the trial. Methods: ML random forest models were developed in I-SPY2 patients (n=982) with pre-treatment gene expression and pCR data across 10 treatment arms (PMID: 35623341), including inhibitors of HER2: neratinib (N), pertuzumab (P), TDM1/P; AKT (MK-2206); IGF1R (ganitumab); HSP90 (ganetespib); PARP/DNA repair (veliparib/carboplatin, VC); ANG1/2 (trebananib, T); immune checkpoints (PD1-inh); and Control (Ctr). Each HR/HER2 receptor/treatment arm subset (m=27) was evaluated independently. We employed a three-pronged feature-selection approach using (1) genes restricted to known mechanism of action of individual I-SPY2 agents (k=10 to 88 genes); (2) genes expanded to include targeted pathways for all 10 agents/combinations (k=282); and (3) an unbiased whole genome approach (k=17,990). Samples were partitioned with 75% used for training and cross-validation, and 25% held out as test sets. Predictive ML models were defined as those with performance ≥ 0.90 based on different performance metrics (e.g., AUC, sensitivity, specificity). Results: For each of the 27 subtype-treatment subsets, at least one high performing model was identified. In 6 subtype-treatment subsets, mechanism of action genes were sufficient to predict pCR: AKT/PI3K/HER genes in HR+HER2- N and HR-HER2+ P; DNA repair genes in HR+HER2- VC; angiogenesis-associated genes in HR+HER2+ T; and immune-associated genes in both HR+HER2- and HR-HER2- PD1-inh subsets. Expanded targeted pathway models were required to identify predictive models in 8 additional subtype-treatment pairs from the N, T-DM1/P, MK-2206, VC, T, and HER2+ Ctr arms, with significant contribution of DNA repair, immune, and HSP90 genes for multiple arms. A genome-wide approach was required for the remaining 13 subtype-treatment pairs with no previous models from the N, P, MK-2206, ganitumab, ganetespib, T, and HER2- Ctr arms. Even for subtype-treatment pairs where mechanism of action gene sets was sufficient for reasonable models, expanded gene sets resulted in improved performance. For instance, metabolism genes improved model performance for HR-HER2+ in N and Ctr, and for HR+HER2- in the PD1-inh arm; and mitochondrial and protein folding dysfunction genes improved response prediction in HR-HER2- in the ganetespib arm. Conclusion: Our study identifies mechanism of action biomarkers associated with response to each drug and elucidates possible off-target effects contributing to observed drug sensitivity and resistance. Citation Format: Rosalyn W. Sayaman, Denise M. Wolf, Christina Yau, Julia Wulfkhule, Emanuel F. Petricoin, Lamorna Brown-Swigart, Tam Binh Bui, Gillian L. Hirst, Diane Heditsian, W. Fraser Symmans, Angela DeMichele, Mark LaBarge, Laura J. Esserman, Laura van ‘t Veer. Machine learning elucidates biology of response within and outside the mechanisms of action of therapeutic agents in the I-SPY2 breast cancer TRIAL [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A066.

This study analyzed spatiotemporal variation and long-term trends in water quality indicators and trophic state conditions in an Asian temperate reservoir, Juam Reservoir (JR), and developed models that forecast algal chlorophyll (CHL-a) over a period of 30 years, 1993–2022. The analysis revealed that there were longitudinal gradients in water quality indicators along the reservoir, with notable influences from tributaries and seasonal variations in nutrient regimes and suspended solids. The empirical model showed phosphorus was found to be the key determinant of algal biomass, while suspended solids played a significant role in regulating water transparency. The trophic state indices indicated varying levels of trophic status, ranging from mesotrophic to eutrophic. Eutrophic states were particularly observed in zones after the summer monsoons, indicating a heightened risk of algal blooms, which were more prevalent in flood years. The analysis of trophic state index deviation suggested that phosphorus availability strongly influences the reservoir trophic status, with several episodes of non-algal turbidity at each site during Mon. Increases in non-algal turbidity were more prevalent during the monsoon in flood years. This study also highlighted overall long-term trends in certain water quality parameters, albeit with indications of shifting pollution sources towards non-biodegradable organic matter. According to the machine learning tests, a random forest (RF) model strongly predicted CHL-a (R2 = 0.72, p < 0.01), except for algal biomass peaks (>60 μg/L), compared to all other models. Overall, our research suggests that CHL-a and trophic variation are primarily regulated by the monsoon intensity and predicted well by the machine learning RF model.

Objective Declines in Cisco Coregonus artedi populations in some inland lakes have prompted assessments of Cisco occurrence and extirpation risk in relation to various stressors to identify refuge lakes and factors that promote Cisco persistence. However, most previous assessments have focused on presence–absence of Cisco rather than examining how population characteristics, such as relative abundance or growth, might change in relation to lake- and landscape-level environmental factors. Consequently, our specific objectives were to identify important environmental factors explaining variation in Cisco relative abundance and growth and to determine whether population metrics describing size and age distributions were related to relative abundance in Wisconsin inland lakes. Methods Cisco were collected from 48 inland Wisconsin lakes during 2011–2015 using vertical monofilament gill nets and population-specific relative abundance estimates (catch per unit effort [CPUE]) were quantified as the number of individuals per gill-net night. Sagittal otoliths were removed from a subsample of Cisco for age estimation and growth was indexed as mean total length (TL; mm) at age 2. Length and age data were used to develop a suite of metrics describing size and age distributions of each population. Random forest models were used to evaluate relationships between 10 biologically relevant predictor variables representing variation in physical, climatic, catchment, and limnological characteristics and Cisco CPUE and growth. Pearson correlations were used to determine whether population characteristics were related to CPUE. Result Cisco populations exhibited large variation in relative abundance, growth, and size and age distributions. Best-fit random forest models explained approximately 25% of the variation in Cisco CPUE and 46% of the variation in growth. Growing degree-days and variables associated with availability, quality, and quantity of suitable oxythermal conditions were identified as important predictors of both Cisco CPUE and growth; CPUE was also identified as an important predictor of growth. Mean TL and mean TL at age 2 were negatively related to Cisco CPUE, whereas mean age, number of age-classes present, and maximum observed age were positively related to CPUE. Conclusion Our results suggest that maintenance of suitable oxythermal habitat conditions may be critical to conserving abundant Cisco populations. Our assessment also provides insights on how Cisco populations may respond to environmental and anthropogenic stressors, which could aid ongoing and future conservation and management efforts in Wisconsin and elsewhere.

Dendroecological techniques were used to describe the variation in growth response of subalpine fir (Abies lasiocarpa)to climate across a range of elevations (1350–1850 m) and annual precipitation (125–350 cm y−1) in the Olympic Mountains, Washington. Correlation analysis is used to describe individual growth‐climate relationships. Growth response is quantified in years with unusually warmer, colder, wetter, and drier climates during the period 1895–1990. Combinations of climatic variables that result in unusually fast or slow growth years are also described. Differences in growth‐climate relationships among sites, and among individuals from the same site, emphasize within‐species variability in response to climate. Growth was not significantly faster or slower on the majority of sites for extreme climate years examined. Few climate variables are correlated with growth of the majority of individuals on most sites, suggesting that some individuals are relatively unresponsive to climate. Individual growth‐climate correlations also indicate an increase in the percentage of individuals whose growth is significantly correlated with a climate variable, as the value of the mean site growth correlation increases for that climate variable. Individual differences in growth‐climate relationships probably result from microsite variation (soil depth, soil moisture, wind, insolation) and from individual genetic differences. Descriptions of tree species response to climate change need to incorporate both individual and site variation in growth response to climate in order to accurately represent existing environmental heterogeneity.

Anthropogenic nitrogen deposition alters growth responses of European beech (Fagus sylvativa L.) to climate change.

Amazonian forest productivity is related to gradients in climate and soil fertility, and impacted by extreme climate events such as drought. However, interactions between soil fertility and drought in influencing regional and interannual variations in tree diameter growth are still poorly explored. To fill this gap, we used radiocarbon measurements to evaluate the variation in tree growth rates over the past decades for 30 individual trees from an important hyperdominant species, Eschweilera coriacea (Lecythidaceae). Trees were sampled from six sites in the state of Amazonas, Brazil, spanning a range of soil properties and climate. Using a linear mixed model, we show that temporal variations in mean annual diameter increment for a specific time period reflects interactions between soil fertility and SPEI drought index (Standardized Precipitation and Evapotranspiration Index). Overall differences between sites in mean tree growth, wood density and biomass production primarily reflected soil fertility, while temporal variations in growth response to drought also strongly dependence on soil fertility. Whereas drought strongly limited tree growth in fertile environments, its impact on tree growth was attenuated in poorer soils. Our results suggest that the growth response of trees to drought is strongly dependent on soil conditions, a facet of Amazon forest productivity that is still underexplored. As the Eschweilera coriacea is a hyperdominant species in the Amazon and is ranked second for highest biomass production in the basin, the pattern of tree growth in response to soil-climate interactions influences the carbon balance of the entire Amazon basin. This result has a large potential to improve predictions of how tropical tree growth affect the global carbon cycle in the face of climate change.