Partial asynchrony of coniferous forest carbon sources and sinks at the intra-annual time scale.

As major terrestrial carbon sinks, forests play an important role in mitigating climate change. The relationship between the seasonal uptake of carbon and its allocation to woody biomass remains poorly understood, leaving a significant gap in our capacity to predict carbon sequestration by forests. Here, we compare the intra-annual dynamics of carbon fluxes and wood formation across the Northern hemisphere, from carbon assimilation and the formation of non-structural carbon compounds to their incorporation in woody tissues. We show temporally coupled seasonal peaks of carbon assimilation (GPP) and wood cell differentiation, while the two processes are substantially decoupled during off-peak periods. Peaks of cambial activity occur substantially earlier compared to GPP, suggesting the buffer role of non-structural carbohydrates between the processes of carbon assimilation and allocation to wood. Our findings suggest that high-resolution seasonal data of ecosystem carbon fluxes, wood formation and the associated physiological processes may reduce uncertainties in carbon source-sink relationships at different spatial scales, from stand to ecosystem levels.

Twenty years of irrigation acclimation is driven by denser canopies and not by plasticity in twig- and needle-level hydraulics in a Pinus sylvestris forest.

Climate change is predicted to increase atmospheric vapor pressure deficit, exacerbating soil drought, and thus enhancing tree evaporative demand and mortality. Yet, few studies have addressed the longer-term drought acclimation strategy of trees, particularly the importance of morphological versus hydraulic plasticity. Using a long-term (20 years) irrigation experiment in a natural forest, we investigated the acclimation of Scots pine (Pinus sylvestris) morpho-anatomical traits (stomatal anatomy and crown density) and hydraulic traits (leaf water potential, vulnerability to cavitation (Ψ50), specific hydraulic conductivity (Ks), and tree water deficit) to prolonged changes in soil moisture. We found that low water availability reduced twig water potential and increased tree water deficit during the growing season. Still, the trees showed limited adjustments in most branch-level hydraulic traits (Ψ50 and Ks) and needle anatomy. In contrast, trees acclimated to prolonged irrigation by increasing their crown density and hence the canopy water demand. This study demonstrates that despite substantial canopy adjustments, P. sylvestris may be vulnerable to extreme droughts because of limited adjustment potential in its hydraulic system. While sparser canopies reduce water demand, such shifts take decades to occur under chronic water deficits and might not mitigate short-term extreme drought events.

Drought alters aboveground biomass production efficiency: Insights from two European beech forests.

The fraction of photosynthetically assimilated carbon that trees allocate to long-lasting woody biomass pools (biomass production efficiency - BPE), is a key metric of the forest carbon balance. Its apparent simplicity belies the complex interplay between underlying processes of photosynthesis, respiration, litter and fruit production, and tree growth that respond differently to climate variability. Whereas the magnitude of BPE has been routinely quantified in ecological studies, its temporal dynamics and responses to extreme events such as drought remain less well understood. Here, we combine long-term records of aboveground carbon increment (ACI) obtained from tree rings with stand-level gross primary productivity (GPP) from eddy covariance (EC) records to empirically quantify aboveground BPE (= ACI/GPP) and its interannual variability in two European beech forests (Hainich, DE-Hai, Germany; Sorø, DK-Sor, Denmark). We found significant negative correlations between BPE and a daily-resolved drought index at both sites, indicating that woody growth is de-prioritized under water limitation. During identified extreme years, early-season drought reduced same-year BPE by 29 % (Hainich, 2011), 31 % (Sorø, 2006), and 14 % (Sorø, 2013). By contrast, the 2003 late-summer drought resulted in a 17 % reduction of post-drought year BPE at Hainich. Across the entire EC period, the daily-to-seasonal drought response of BPE resembled that of ACI, rather than that of GPP. This indicates that BPE follows sink dynamics more closely than source dynamics, which appear to be decoupled given the distinctive climate response patterns of GPP and ACI. Based on our observations, we caution against estimating the magnitude and variability of the carbon sink in European beech (and likely other temperate forests) based on carbon fluxes alone. We also encourage comparable studies at other long-term EC measurement sites from different ecosystems to further constrain the BPE response to rare climatic events.

High preseason temperature variability drives convergence of xylem phenology in the Northern Hemisphere conifers.

Wood growth is key to understanding the feedback of forest ecosystems to the ongoing climate warming. An increase in spatial synchrony (i.e., coincident changes in distant populations) of spring phenology is one of the most prominent climate responses of forest trees. However, whether temperature variability contributes to an increase in the spatial synchrony of spring phenology and its underlying mechanisms remains largely unknown. Here, we analyzed an extensive dataset of xylem phenology observations of 20 conifer species from 75 sites over the Northern Hemisphere. Along the gradient of increase in temperature variability in the 75 sites, we observed a convergence in the onset of cell enlargement roughly toward the 5th of June, with a convergence in the onset of cell wall thickening toward the summer solstice. The increase in rainfall since the 5th of June is favorable for cell division and expansion, and as the most hours of sunlight are received around the summer solstice, it allows the optimization of carbon assimilation for cell wall thickening. Hence, the convergences can be considered as the result of matching xylem phenological activities to favorable conditions in regions with high temperature variability. Yet, forest trees relying on such consistent seasonal cues for xylem growth could constrain their ability to respond to climate warming, with consequences for the potential growing season length and, ultimately, forest productivity and survival in the future.

Inpatient Costs of Treating Patients With COVID-19.

Importance: With more than 6.2 million hospitalizations due to COVID-19 in the US, recognition of the average hospital costs to provide inpatient care during the pandemic is necessary to understanding the national medical resource use and improving public health readiness and related policies. Objective: To examine the mean cost to provide inpatient care to treat COVID-19 and how it varied through the pandemic waves and by important sociodemographic patient characteristics. Design, Setting, and Participants: This cross-sectional study used inpatient-level data from March 1, 2020, to March 31, 2022, extracted from a repository of clinical, administrative, and financial information covering 97% of academic medical centers across the US. Main Outcomes and Measures: Cost to produce care for each stay was calculated using direct hospital costs to provide care adjusted for geographic differences in labor costs using area wage indices. Results: The sample included 1 333 404 stays with a primary or secondary COVID-19 diagnosis from 841 hospitals. The cohort included 692 550 (52%) men, with mean (SD) age of 59.2 (17.5) years. The adjusted mean cost of an inpatient stay was $11 275 (95% CI, $11 252-$11 297) overall, increasing from $10 394 (95% CI, $10 228-$10 559) at the end of March 2020 to $13 072 (95% CI, $12 528-$13 617) by the end of March 2022. Patients with specific comorbidities had significantly higher mean costs than their counterparts: those with obesity incurred an additional $2924 in inpatient stay costs, and those with coagulation deficiency incurred an additional $3017 in inpatient stay costs. Stays during which the patient required extracorporeal membrane oxygenation (ECMO) had an adjusted mean cost of $36 484 (95% CI, $34 685-$38 284). Conclusions and Relevance: In this cross-sectional study, an adjusted mean hospital cost to provide care for patients with COVID-19 increased more than 5 times the rate of medical inflation overall. This appeared to be explained partly by changes in the use of ECMO, which increased over time.

Improving paramedic responses for patients dying at home: a theory of change-based approach.

BACKGROUND: Paramedics are increasingly being called to attend patients dying from advanced incurable conditions. However, confidence to deal with such calls varies, with many feeling relatively unskilled in this aspect of their role. A number of interventions have been piloted to improve their skills in end-of-life care (EoLC) but without a fully specified theoretical model. Theory of Change models can provide theoretical and testable links from intervention activities to proposed long-term outcomes and indicate the areas for assessment of effectiveness. This study aimed to develop an intervention for improving paramedic EoLC for patients in the community. METHODS: A Theory of Change approach was used as the overarching theoretical framework for developing an intervention to improve paramedic end-of-life skills. Nine stakeholders - including specialist community paramedics, ambulance call handlers and palliative care specialists - were recruited to five consecutive online workshops, ranging between 60 and 90 min. Each workshop had 2-3 facilitators. Over multiple workshops, stakeholders decided on the desired impact, short- and long-term outcomes, and possible interventions. During and between these workshops a Theory of Change model was created, with the components shared with stakeholders. RESULTS: The stakeholders agreed the desired impact was to provide consistent, holistic, patient-centred, and effective EoLC. Four potential long-term outcomes were suggested: (1) increased use of anticipatory and regular end-of-life medications; (2) reduced end-of-life clinical and medication errors; (3) reduced unnecessary hospitalisations; (4) increased concordance between patient preferred and actual place of death. Key interventions focused on providing immediate information on what to do in such situations including: appraising the situation, developing an algorithm for a treatment plan (including whether or not to convey to hospital) and how to identify ongoing support in the community. CONCLUSIONS: A Theory of Change approach was effective at identifying impact, outcomes, and the important features of an end-of-life intervention for paramedics. This study identified the need for paramedics to have immediate access to information and resources to support EoLC, which the workshop stakeholders are now seeking to develop as an intervention.

High vapour pressure deficit enhances turgor limitation of stem growth in an Asian tropical rainforest tree.

Tropical forests are experiencing increases in vapour pressure deficit (D), with possible negative impacts on tree growth. Tree-growth reduction due to rising D is commonly attributed to carbon limitation, thus overlooking the potentially important mechanism of D-induced impairment of wood formation due to an increase in turgor limitation. Here we calibrate a mechanistic tree-growth model to simulate turgor limitation of radial stem growth in mature Toona cilitata trees in an Asian tropical forest. Hourly sap flow and dendrometer measurements were collected to simulate turgor-driven growth during the growing season. Simulated seasonal patterns of radial stem growth matched well with growth observations. Growth mainly occurred at night and its pre-dawn build-up appeared to be limited under higher D. Across seasons, the night-time turgor pressure required for growth was negatively related to previous midday D, possibly due to a relatively high canopy conductance at high D, relative to stem rehydration. These findings provide the first evidence that tropical trees grow at night and that turgor pressure limits tree growth. We suggest including turgor limitation of tree stem growth in models also for tropical forest carbon dynamics, in particular, if these models simulate effects of warming and increased frequency of droughts.

Daytime stomatal regulation in mature temperate trees prioritizes stem rehydration at night.

Trees remain sufficiently hydrated during drought by closing stomata and reducing canopy conductance (Gc ) in response to variations in atmospheric water demand and soil water availability. Thresholds that control the reduction of Gc are proposed to optimize hydraulic safety against carbon assimilation efficiency. However, the link between Gc and the ability of stem tissues to rehydrate at night remains unclear. We investigated whether species-specific Gc responses aim to prevent branch embolisms, or enable night-time stem rehydration, which is critical for turgor-dependent growth. For this, we used a unique combination of concurrent dendrometer, sap flow and leaf water potential measurements and collected branch-vulnerability curves of six common European tree species. Species-specific Gc reduction was weakly related to the water potentials at which 50% of branch xylem conductivity is lost (P50 ). Instead, we found a stronger relationship with stem rehydration. Species with a stronger Gc control were less effective at refilling stem-water storage as the soil dries, which appeared related to their xylem architecture. Our findings highlight the importance of stem rehydration for water-use regulation in mature trees, which likely relates to the maintenance of adequate stem turgor. We thus conclude that stem rehydration must complement the widely accepted safety-efficiency stomatal control paradigm.

Semi-Supervised Behavior Labeling Using Multimodal Data during Virtual Teamwork-Based Collaborative Activities.

Adaptive human-computer systems require the recognition of human behavior states to provide real-time feedback to scaffold skill learning. These systems are being researched extensively for intervention and training in individuals with autism spectrum disorder (ASD). Autistic individuals are prone to social communication and behavioral differences that contribute to their high rate of unemployment. Teamwork training, which is beneficial for all people, can be a pivotal step in securing employment for these individuals. To broaden the reach of the training, virtual reality is a good option. However, adaptive virtual reality systems require real-time detection of behavior. Manual labeling of data is time-consuming and resource-intensive, making automated data annotation essential. In this paper, we propose a semi-supervised machine learning method to supplement manual data labeling of multimodal data in a collaborative virtual environment (CVE) used to train teamwork skills. With as little as 2.5% of the data manually labeled, the proposed semi-supervised learning model predicted labels for the remaining unlabeled data with an average accuracy of 81.3%, validating the use of semi-supervised learning to predict human behavior.

Tree-ring isotopes from the Swiss Alps reveal non-climatic fingerprints of cyclic insect population outbreaks over the past 700 years.

Recent experiments have underlined the potential of δ2H in tree-ring cellulose as a physiological indicator of shifts in autotrophic versus heterotrophic processes (i.e., the use of fresh versus stored non-structural carbohydrates). However, the impact of these processes has not yet been quantified under natural conditions. Defoliator outbreaks disrupt tree functioning and carbon assimilation, stimulating remobilization, therefore providing a unique opportunity to improve our understanding of changes in δ2H. By exploring a 700-year tree-ring isotope chronology from Switzerland, we assessed the impact of 79 larch budmoth (LBM, Zeiraphera griseana [Hübner]) outbreaks on the growth of its host tree species, Larix decidua [Mill]. The LBM outbreaks significantly altered the tree-ring isotopic signature, creating a 2H-enrichment and an 18O- and 13C-depletion. Changes in tree physiological functioning in outbreak years are shown by the decoupling of δ2H and δ18O (O-H relationship), in contrast to the positive correlation in non-outbreak years. Across the centuries, the O-H relationship in outbreak years was not significantly affected by temperature, indicating that non-climatic physiological processes dominate over climate in determining δ2H. We conclude that the combination of these isotopic parameters can serve as a metric for assessing changes in physiological mechanisms over time.

Networking the forest infrastructure towards near real-time monitoring - A white paper.

Forests account for nearly 90 % of the world's terrestrial biomass in the form of carbon and they support 80 % of the global biodiversity. To understand the underlying forest dynamics, we need a long-term but also relatively high-frequency, networked monitoring system, as traditionally used in meteorology or hydrology. While there are numerous existing forest monitoring sites, particularly in temperate regions, the resulting data streams are rarely connected and do not provide information promptly, which hampers real-time assessments of forest responses to extreme climate events. The technology to build a better global forest monitoring network now exists. This white paper addresses the key structural components needed to achieve a novel meta-network. We propose to complement - rather than replace or unify - the existing heterogeneous infrastructure with standardized, quality-assured linking methods and interacting data processing centers to create an integrated forest monitoring network. These automated (research topic-dependent) linking methods in atmosphere, biosphere, and pedosphere play a key role in scaling site-specific results and processing them in a timely manner. To ensure broad participation from existing monitoring sites and to establish new sites, these linking methods must be as informative, reliable, affordable, and maintainable as possible, and should be supplemented by near real-time remote sensing data. The proposed novel meta-network will enable the detection of emergent patterns that would not be visible from isolated analyses of individual sites. In addition, the near real-time availability of data will facilitate predictions of current forest conditions (nowcasts), which are urgently needed for research and decision making in the face of rapid climate change. We call for international and interdisciplinary efforts in this direction.

A critical thermal transition driving spring phenology of Northern Hemisphere conifers.

Despite growing interest in predicting plant phenological shifts, advanced spring phenology by global climate change remains debated. Evidence documenting either small or large advancement of spring phenology to rising temperature over the spatio-temporal scales implies a potential existence of a thermal threshold in the responses of forests to global warming. We collected a unique data set of xylem cell-wall-thickening onset dates in 20 coniferous species covering a broad mean annual temperature (MAT) gradient (-3.05 to 22.9°C) across the Northern Hemisphere (latitudes 23°-66° N). Along the MAT gradient, we identified a threshold temperature (using segmented regression) of 4.9 ± 1.1°C, above which the response of xylem phenology to rising temperatures significantly decline. This threshold separates the Northern Hemisphere conifers into cold and warm thermal niches, with MAT and spring forcing being the primary drivers for the onset dates (estimated by linear and Bayesian mixed-effect models), respectively. The identified thermal threshold should be integrated into the Earth-System-Models for a better understanding of spring phenology in response to global warming and an improved prediction of global climate-carbon feedbacks.

Surge in Incidence and Coronavirus Disease 2019 Hospital Risk of Death, United States, September 2020 to March 2021.

Background: Studies of the early months of the coronavirus disease 2019 (COVID-19) pandemic indicate that patient outcomes may be adversely affected by surges. However, the impact on in-hospital mortality during the largest surge to date, September 2020-March 2021, has not been studied. This study aimed to determine whether in-hospital mortality was impacted by the community surge of COVID-19. Methods: This is a retrospective cohort study of 416 962 adult COVID-19 patients admitted immediately before or during the surge at 229 US academic and 432 community hospitals in the Vizient Clinical Database. The odds ratios (ORs) of death among hospitalized patients during each phase of the surge was compared with the corresponding odds before the surge and adjusted for demographic, comorbidity, hospital characteristic, length of stay, and complication variables. Results: The unadjusted proportion of deaths among discharged patients was 9% in both the presurge and rising surge stages but rose to 12% during both the peak and declining surge intervals. With the presurge phase defined as the referent, the risk-adjusted ORs (aORs) for the surge periods were rising, 1.14 (1.10-1.19), peak 1.37 (1.32-1.43), and declining, 1.30 (1.25-1.35). The surge rise in-hospital mortality was present in 7 of 9 geographic divisions and greater for community hospitals than for academic centers. Conclusions: These data support public policies aimed at containing pandemic surges and supporting healthcare delivery during surges.

Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R package.

Ecological research, just as all Earth System Sciences, is becoming increasingly data-rich. Tools for processing of "big data" are continuously developed to meet corresponding technical and logistical challenges. However, even at smaller scales, data sets may be challenging when best practices in data exploration, quality control and reproducibility are to be met. This can occur when conventional methods, such as generating and assessing diagnostic visualizations or tables, become unfeasible due to time and practicality constraints. Interactive processing can alleviate this issue, and is increasingly utilized to ensure that large data sets are diligently handled. However, recent interactive tools rarely enable data manipulation, may not generate reproducible outputs, or are typically data/domain-specific. We developed datacleanr, an interactive tool that facilitates best practices in data exploration, quality control (e.g., outlier assessment) and flexible processing for multiple tabular data types, including time series and georeferenced data. The package is open-source, and based on the R programming language. A key functionality of datacleanr is the "reproducible recipe"-a translation of all interactive actions into R code, which can be integrated into existing analyses pipelines. This enables researchers experienced with script-based workflows to utilize the strengths of interactive processing without sacrificing their usual work style or functionalities from other (R) packages. We demonstrate the package's utility by addressing two common issues during data analyses, namely 1) identifying problematic structures and artefacts in hierarchically nested data, and 2) preventing excessive loss of data from 'coarse,' code-based filtering of time series. Ultimately, with datacleanr we aim to improve researchers' workflows and increase confidence in and reproducibility of their results.

Properties of an attention-grabbing motion signal: a comparison of tail and body movements in a lizard.

Animals signals must be detected by receiver sensory systems, and overcome a variety of local ecological factors that could otherwise affect their transmission and reception. Habitat structure, competition, avoidance of unintended receivers and varying environmental conditions have all been shown to influence how animals signal. Environmental noise is also crucial, and animals modify their behavior in response to it. Animals generating movement-based visual signals have to contend with wind-blown plants that generate motion noise and can affect the detection of salient movements. The lizard Amphibolurus muricatus uses tail flicking at the start of displays to attract attention, and we hypothesized that tail movements are ideally suited to this function. We compared visual amplitudes generated by tail movements with push-ups, which are a key component of the rest of the display. We show that tail movement amplitudes are highly variable over the course of the display but consistently greater than amplitudes generated by push-ups and not constrained by viewing position. We suggest that these features, combined with the tail being a light structure that does not compromise other activities, provide an ideal introductory component for attracting attention in the ecological setting in which they are generated.

The 2018 European heatwave led to stem dehydration but not to consistent growth reductions in forests.

Heatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.

Number of growth days and not length of the growth period determines radial stem growth of temperate trees.

Radial stem growth dynamics at seasonal resolution are essential to understand how forests respond to climate change. We studied daily radial growth of 160 individuals of seven temperate tree species at 47 sites across Switzerland over 8 years. Growth of all species peaked in the early part of the growth season and commenced shortly before the summer solstice, but with species-specific seasonal patterns. Day length set a window of opportunity for radial growth. Within this window, the probability of daily growth was constrained particularly by air and soil moisture, resulting in intermittent growth to occur only on 29 to 77 days (30% to 80%) within the growth period. The number of days with growth largely determined annual growth, whereas the growth period length contributed less. We call for accounting for these non-linear intra-annual and species-specific growth dynamics in tree and forest models to reduce uncertainties in predictions under climate change.

Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy.

The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder (Alnus glutinosa L.); a diffuse to semi-diffuse-porous species, European beech (Fagus sylvatica L.); and a ring-porous species, sessile oak (Quercus petraea Liebl.). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the "learning process" defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms' performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining.