Consequences of Local Conspecific Density Effects for Plant Diversity and Community Dynamics.

Conspecific density dependence (CDD) in plant populations is widespread, most likely caused by local-scale biotic interactions, and has potentially important implications for biodiversity, community composition, and ecosystem processes. However, progress in this important area of ecology has been hindered by differing viewpoints on CDD across subfields in ecology, lack of synthesis across CDD-related frameworks, and misunderstandings about how empirical measurements of local CDD fit within the context of broader ecological theories on community assembly and diversity maintenance. Here, we propose a conceptual synthesis of local-scale CDD and its causes, including species-specific antagonistic and mutualistic interactions. First, we compare and clarify different uses of CDD and related concepts across subfields within ecology. We suggest the use of local stabilizing/destabilizing CDD to refer to the scenario where local conspecific density effects are more negative/positive than heterospecific effects. Second, we discuss different mechanisms for local stabilizing and destabilizing CDD, how those mechanisms are interrelated, and how they cut across several fields of study within ecology. Third, we place local stabilizing/destabilizing CDD within the context of broader ecological theories and discuss implications and challenges related to scaling up the effects of local CDD on populations, communities, and metacommunities. The ultimate goal of this synthesis is to provide a conceptual roadmap for researchers studying local CDD and its implications for population and community dynamics.

gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity.

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species' abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, ß, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth's Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth's biodiversity.

Fitting individual-based models of spatial population dynamics to long-term monitoring data.

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Climate-driven, but dynamic and complex? A reconciliation of competing hypotheses for species' distributions.

Estimates of the percentage of species "committed to extinction" by climate change range from 15% to 37%. The question is whether factors other than climate need to be included in models predicting species' range change. We created demographic range models that include climate vs. climate-plus-competition, evaluating their influence on the geographic distribution of Pinus edulis, a pine endemic to the semiarid southwestern U.S. Analyses of data on 23,426 trees in 1941 forest inventory plots support the inclusion of competition in range models. However, climate and competition together only partially explain this species' distribution. Instead, the evidence suggests that climate affects other range-limiting processes, including landscape-scale, spatial processes such as disturbances and antagonistic biotic interactions. Complex effects of climate on species distributions-through indirect effects, interactions, and feedbacks-are likely to cause sudden changes in abundance and distribution that are not predictable from a climate-only perspective.

Accuracy, realism and general applicability of European forest models.

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.

Towards robust statistical inference for complex computer models.

Ecologists increasingly rely on complex computer simulations to forecast ecological systems. To make such forecasts precise, uncertainties in model parameters and structure must be reduced and correctly propagated to model outputs. Naively using standard statistical techniques for this task, however, can lead to bias and underestimation of uncertainties in parameters and predictions. Here, we explain why these problems occur and propose a framework for robust inference with complex computer simulations. After having identified that model error is more consequential in complex computer simulations, due to their more pronounced nonlinearity and interconnectedness, we discuss as possible solutions data rebalancing and adding bias corrections on model outputs or processes during or after the calibration procedure. We illustrate the methods in a case study, using a dynamic vegetation model. We conclude that developing better methods for robust inference of complex computer simulations is vital for generating reliable predictions of ecosystem responses.

Siberian plants shift their phenology in response to climate change.

Siberia has undergone dramatic climatic changes due to global warming in recent decades. Yet, the ecological responses to these climatic changes are still poorly understood due to a lack of data. Here, we use a unique data set from the Russian 'Chronicles of Nature' network to analyse the long-term (1976-2018) phenological shifts in leaf out, flowering, fruiting and senescence of 67 common Siberian plant species. We find that Siberian boreal forest plants advanced their early season (leaf out and flowering) and mid-season (fruiting) phenology by -2.2, -0.7 and -1.6 days/decade, and delayed the onset of senescence by 1.6 days/decade during this period. These mean values, however, are subject to substantial intraspecific variability, which is partly explained by the plants' growth forms. Trees and shrubs advanced leaf out and flowering (-3.1 and -3.3. days/decade) faster than herbs (-1 day/decade), presumably due to the more direct exposure of leaf and flower buds to ambient air for the woody vegetation. For senescence, we detected a reverse pattern: stronger delays in herbs (2.1 days/decade) than in woody plants (1.0-1.2 days/decade), presumably due to the stronger effects of autumn frosts on the leaves of herbs. Interestingly, the timing of fruiting in all four growth forms advanced at similar paces, from 1.4 days/decade in shrubs to 1.7 days/decade in trees and herbs. Our findings point to a strong, yet heterogeneous, response of Siberian plant phenology to recent global warming. Furthermore, the results highlight that species- and growth form-specific differences among study species could be used to identify plants particularly at risk of decline due to their low adaptive capacity or a loss of synchronization with important interaction partners.

Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion.

The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960-2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha-1  year-1  km-1 for P. abies and 0.93 ± 0.010 Mg C ha-1  year-1  km-1 for F. sylvatica). During warm-dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm-dry extremes. Importantly, cold-dry extremes had negative impacts on regional forest NPP comparable to warm-dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.

Bayesian calibration of a growth-dependent tree mortality model to simulate the dynamics of European temperate forests.

Dynamic vegetation models (DVMs) are important tools to understand and predict the functioning and dynamics of terrestrial ecosystems under changing environmental conditions. In these models, uncertainty in the description of demographic processes, in particular tree mortality, is a persistent problem. Current mortality formulations lack realism and are insufficiently constrained by empirical evidence. It has been suggested that empirically estimated mortality submodels would enhance DVM performance, but due to the many processes and interactions within a DVM, the claim has rarely been tested. Here, we compare the performance of three alternative growth-dependent tree mortality submodels in the DVM ForClim: (1) a mortality function with theoretical foundation (ForClim v3.3); (2) a mortality function with parameters directly estimated based on forest inventory data; and (3) the same function, but with parameters estimated using an inverse approach through Bayesian calibration (BC). Time series of inventory data from 30 ecologically distinct Swiss natural forest reserves collected over 35+ yr, including the main tree species of Central Europe, were used for the calibration and subsequent validation of the mortality functions and the DVM. The recalibration resulted in mortality parameters that differed from the direct empirical estimates, particularly for the relationship between tree size and mortality. The calibrated parameters outperformed the direct estimates, and to a lesser extent the original mortality function, for predicting decadal-scale forest dynamics at both calibration and validation sites. The same pattern was observed regarding the plausibility of their long-term projections under contrasting environmental conditions. Our results demonstrate that inverse calibration may be useful even when direct empirical estimates of DVM parameters are available, as structural model deficiencies or data problems can result in discrepancies between direct and inverse estimates. Thus, we interpret the good performance of the inversely calibrated model for long-term projections (which were not a calibration target) as evidence that the calibration did not compensate for model errors. Rather, we surmise that the discrepancy was mainly caused by a lack of representativeness of the mortality data. Our results underline the potential for learning more about elusive processes, such as tree mortality or recruitment, through data integration in DVMs.

Monitoring of low dabigatran concentrations: diagnostic performance at clinically relevant decision thresholds.

The direct oral anticoagulant dabigatran does not require therapeutic drug monitoring, however emergency measurements are gaining importance. Current assays feature good performance at intermediate and high dabigatran concentrations but show limited accuracy at low concentrations. This area requires more attention as clinical decision threshold values currently lie at 30 and 50 ng/ml. The objective of the study was to evaluate and compare diagnostic performance of dabigatran assays at these thresholds. Dabigatran concentrations of 293 plasma samples taken from 50 patients were measured with the INNOVANCE direct thrombin inhibitor assay (DTI) from Siemens, the Biophen direct thrombin inhibitor assay (BDTI), the BDTI using a low range calibrator (BDTI-low), the Hemoclot direct thrombin inhibitor assay (HTI) and an ecarin clotting time assay (ECT). Assay results were compared to ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and test characteristics were calculated for thresholds of 30 and 50 ng/ml. DTI, BDTI-low and ECT showed very strong correlation and high agreement with UPLC-MS/MS and an improved determination of low dabigatran concentrations. ROC curve analyses revealed very high accuracy at the 30/50 ng/ml thresholds for DTI (AUC = 0.989/0.995), BDTI-low (AUC = 0.980/0.991) and ECT (AUC = 0.990/0.996) measurements. Sensitivity and specificity in detecting were calculated for DTI (98/92%), BDTI-low (87/95%), ECT (97/96%), BDTI (99/82%) and HTI (86/89%) measurements. Compared to the previously available HTI and BDTI, both novel assays, DTI and BDTI-low, reliably determine low dabigatran plasma concentrations around the clinical decision thresholds with very high sensitivity and specificity.

Point-of-care testing of coagulation in patients treated with edoxaban.

Edoxaban, alongside other direct oral anticoagulants (DOAC), is increasingly used for prevention of thromboembolism, including stroke. Despite DOAC therapy, however, annual stroke rate in patients with atrial fibrillation remains 1-2%. Rapid exclusion of relevant anticoagulation is necessary to guide thrombolysis or reversal therapy but, so far, no data exists on the effect of edoxaban on available point-of-care test systems (POCT). To complete our previous investigation on global coagulation-POCT for the detection of DOAC, we evaluated whether CoaguChek®-INR (CC-INR) is capable of safely ruling out edoxaban concentrations above the current treatment thresholds of 30/50 ng/mL in a blood sample. We studied patients receiving a first dose of edoxaban; excluding subjects receiving other anticoagulants. Six blood samples were collected from each patient: before drug intake, 0.5, 1, 2 and 8 h after intake, and at trough (24 h). CC-INR and mass spectrometry for edoxaban concentrations were performed for each time-point. One hundred and twenty blood samples from 20 patients contained 0-302 ng/mL of edoxaban. CC-INR ranged from 0.9 to 2.3. Pearson's correlation coefficient showed strong correlation between CC-INR and edoxaban concentrations (r = 0.73, p < 0.001). Edoxaban concentrations > 30 and > 50 ng/mL were ruled out by CC-INR ≤ 1.0 and ≤ 1.1, respectively, with high specificity (> 95%), and a sensitivity of 44% (95%-confidence interval: 30-59%) and 86% (74-93%), respectively. Our study represents the first evaluation of coagulation-POCT in edoxaban-treated patients. CC-POCT is suitable to safely exclude clinically relevant edoxaban concentrations prior to thrombolysis, or guide reversal therapy in stroke patients.

Plant species richness increases with light availability, but not variability, in temperate forests understorey.

BACKGROUND: Temperate forest understorey vegetation poses an excellent study system to investigate whether increases in resource availability lead to an increase in plant species richness. Most sunlight is absorbed by the species-poor tree canopy, making the much more species-rich understorey species inhabit a severely resource-limited habitat. Additionally, the heterogeneity of light availability, resulting from management-moderated tree composition and age structure, may contribute to species coexistence. One would therefore expect that the diversity in the herb layer correlates positively with either the overall light availability, or the light heterogeneity, depending on whether resource availability or heterogeneity are more important drivers of diversity. To test this idea, we assessed variability of light conditions in 75 forest plots across three ecoregions with four different methods. RESULTS: We correlated these data with vegetation relevés and found light availability to be strongly positively correlated with understorey plant species richness, as well as with understorey cover. Light variability (assessed with two approaches) within plots was positively correlated with transmittance, but did not improve the relationship further, suggesting that the main driver of species richness in this system is the overall resource availability. Two of the three beech-dominated regions exhibited near-identical effects of light transmittance, while the third, featuring pine alongside beech and thus with the longest gradient of transmittance and lowest species richness, displayed a weaker light response. CONCLUSIONS: While site conditions are certainly responsible for the trees selected by foresters, for the resulting forest structure, and for the differences in plant species pools, our results suggest that light transmittance is a strong mediating factor of understorey plant species richness.

A Minimal Model for the Latitudinal Diversity Gradient Suggests a Dominant Role for Ecological Limits.

The latitudinal diversity gradient (LDG) is one of Earth's most iconic biodiversity patterns and still one of the most debated. Explanations for the LDG are often categorized into three broad pathways in which the diversity gradient is created by (1) differential diversification rates, (2) differential carrying capacities (ecological limits), or (3) differential time to accumulate species across latitude. Support for these pathways has, however, been mostly verbally expressed. Here, we present a minimal model to clarify the essential assumptions of the three pathways and explore the sensitivity of diversity dynamics to these pathways. We find that an LDG arises most easily from a gradient in ecological limits compared with a gradient in the time for species accumulation or diversification rate in most modeled scenarios. Differential diversification rates create a stronger LDG than ecological limits only when speciation and dispersal rates are low, but then the predicted LDG seems weaker than the observed LDG. Moreover, range dynamics may reduce an LDG created by a gradient in diversification rates or time for species accumulation, but they cannot reduce an LDG induced by differential ecological limits. We conclude that our simple model provides a null prediction for the effectiveness of the three LDG pathways and can thus aid discussions about the causal mechanisms underlying the LDG or motivate more complex models to confirm or falsify our findings.

The multi-dimensional nature of information drives prioritization of private over social information in ants.

When personally gathered and socially acquired information conflict, animals often prioritize private information. We propose that this is because private information often contains details that social information lacks. We test this idea in an ant model. Ants using a food source learn its location and quality rapidly (private information), whereas pheromone trails (social information) provide good directional information, but lack reliable information about food quality. If this lack is indeed responsible for the choice of memory over pheromone trails, adding information that better food is available should cause foragers to switch their priority to social information. We show it does: while ants follow memory rather than pheromones when they conflict, adding unambiguous information about a better potential food source (a 2 µl droplet of good food) reverses this pattern, from 60% of ants following their memory to 75% following the pheromone trail. Using fluorescence microscopy, we demonstrate that food (and thus information) flows from fed workers to outgoing foragers, explaining the frequent contacts of ants on trails. Ants trained to poor food that contact nest-mates fed with good food are more likely to follow a trail than ants which received information about poor food. We conclude that social information may often be ignored because it lacks certain crucial dimensions, suggesting that information content is crucial for understanding how and when animals prioritize social and private information.

Chilling and forcing temperatures interact to predict the onset of wood formation in Northern Hemisphere conifers.

The phenology of wood formation is a critical process to consider for predicting how trees from the temperate and boreal zones may react to climate change. Compared to leaf phenology, however, the determinism of wood phenology is still poorly known. Here, we compared for the first time three alternative ecophysiological model classes (threshold models, heat-sum models and chilling-influenced heat-sum models) and an empirical model in their ability to predict the starting date of xylem cell enlargement in spring, for four major Northern Hemisphere conifers (Larix decidua, Pinus sylvestris, Picea abies and Picea mariana). We fitted models with Bayesian inference to wood phenological data collected for 220 site-years over Europe and Canada. The chilling-influenced heat-sum model received most support for all the four studied species, predicting validation data with a 7.7-day error, which is within one day of the observed data resolution. We conclude that both chilling and forcing temperatures determine the onset of wood formation in Northern Hemisphere conifers. Importantly, the chilling-influenced heat-sum model showed virtually no spatial bias whichever the species, despite the large environmental gradients considered. This suggests that the spring onset of wood formation is far less affected by local adaptation than by environmentally driven plasticity. In a context of climate change, we therefore expect rising winter-spring temperature to exert ambivalent effects on the spring onset of wood formation, tending to hasten it through the accumulation of forcing temperature, but imposing a higher forcing temperature requirement through the lower accumulation of chilling.

Calibrating an individual-based movement model to predict functional connectivity for little owls.

Dispersal is crucial for population viability and thus a popular target for conservation measures. However, the ability of individuals to move between habitat patches is notoriously difficult to estimate. One solution is to quantify functional connectivity via realistic individual-based movement models. Such simulation models, however, are difficult to build and even more difficult to parameterize. Here, we use the example of natal little owl (Athene noctua) dispersal to develop a new analysis chain for the calibration of individual-based dispersal models using a hybrid of statistical parameter estimation and Approximate Bayesian Computation (ABC). Specifically, we use locations of 126 radio-tracked juveniles to first estimate habitat utilization by generalized additive models (GAMs) and the biased random bridges (BRB) method. We then include the estimated parameters in a spatially explicit individual-based model (IBM) of little owl dispersal and calibrate further movement parameters using ABC. To derive efficient summary statistics, we use a new dimension reduction method based on random forest (RF) regression. Finally, we use the calibrated IBM to predict the dispersal potential of little owls from local populations in southwestern Germany to suitable habitat patches in northern Switzerland. We show that pre-calibrating habitat preference parameters while inferring movement behavioral parameters via ABC is a computationally efficient solution to obtain a plausible IBM parameterization. We also find that dimension reduction via RF regression outperforms the widely used least squares regression, which we applied as a benchmark approach. Estimated movement parameters for the individuals reveal plausible inter-individual and inter-sexual differences in movement behavior during natal dispersal. In agreement with a sex-biased dispersal distance in little owls, females show longer individual flights and higher directional persistence. Simulations from the fitted model indicate that a (re)colonization of northern Switzerland is generally possible, albeit restricted. We conclude that the presented analysis chain is a sensible work-flow to assess dispersal connectivity across species and ecosystems. It embraces species- and individual-specific behavioral responses to the landscape and allows likelihood-based calibration, despite an irregular sampling design. Our study highlights existing, yet narrow dispersal corridors, which may require enhancements to facilitate a recolonization of little owl habitat patches in northern Switzerland.

Last-century forest productivity in a managed dry-edge Scots pine population: the two sides of climate warming.

Climate change in the Mediterranean, associated with warmer temperatures and more frequent droughts, is expected to impact forest productivity and the functioning of forests ecosystems as carbon reservoirs in the region. Climate warming can positively affect forest growth by extending the growing season, whereas increasing summer drought generally reduces forest productivity and may cause growth decline, trigger dieback, hamper regeneration, and increase mortality. Forest management could potentially counteract such negative effects by reducing stand density and thereby competition for water. The effectiveness of such interventions, however, has so far mostly been evaluated for short time periods at the tree and stand levels, which limits our confidence regarding the efficacy of thinning interventions over longer time scales under the complex interplay between climate, stand structure, and forest management. In this study, we use a century-long historical data set to assess the effects of climate and management on forest productivity. We consider rear-edge Scots pine (Pinus sylvestris) populations covering continental and Mediterranean conditions along an altitudinal gradient in Central Spain. We use linear mixed-effects models to disentangle the effects of altitude, climate, and stand volume on forest growth and ingrowth (recruitment and young trees' growth). We find that warming tends to benefit these tree populations, warmer winter temperature has a significant positive effect on both forest growth and ingrowth, and the effect is more pronounced at low elevations. However, drought conditions severely reduce growth and ingrowth, in particular when competition (stand volume) is high. We conclude that summer droughts are the main threat to Scots pine populations in the region, and that a reduction of stand volume can partially mitigate the negative impacts of more arid conditions. Mitigation and adaptation measures could therefore manage stand structure to adopt for the anticipated impacts of climate change in Mediterranean forest ecosystems.

Emergency Coagulation Assessment During Treatment With Direct Oral Anticoagulants: Limitations and Solutions.

BACKGROUND AND PURPOSE: In patients receiving direct oral anticoagulants (DOACs), emergency treatment like thrombolysis for acute ischemic stroke is complicated by insufficient availability of DOAC-specific coagulation tests. Conflicting recommendations have been published concerning the use of global coagulation assays for ruling out relevant DOAC-induced anticoagulation. METHODS: Four hundred eighty-one samples from 96 DOAC-treated patients were tested using prothrombin time (PT), activated partial thromboplastin time (aPTT) and thrombin time (TT), DOAC-specific assays (anti-Xa activity, diluted TT), and liquid chromatography-tandem mass spectrometry. Sensitivity and specificity of test results to identify DOAC concentrations <30 ng/mL were calculated. Receiver operating characteristic analyses were used to define reagent-specific cutoff values. RESULTS: Normal PT and aPTT provide insufficient specificity to safely identify DOAC concentrations <30 ng/mL (rivaroxaban/PT: specificity, 77%/sensitivity, 94%; apixaban/PT: specificity, 13%/sensitivity, 94%, dabigatran/aPTT: specificity, 49%/sensitivity, 91%). Normal TT was 100% specific for dabigatran, but sensitivity was 26%. In contrast, reagent-specific PT and aPTT cutoffs provided >95% specificity and a specific TT cutoff enhanced sensitivity for dabigatran to 84%. For apixaban, no cutoffs could be established. CONCLUSIONS: Even if highly DOAC-reactive reagents are used, normal results of global coagulation tests are not suited to guide emergency treatment: whereas normal PT and aPTT lack specificity to rule out DOAC-induced anticoagulation, the low sensitivity of normal TT excludes the majority of eligible patients from treatment. However, reagent-specific cutoffs for global coagulation tests ensure high specificity and optimize sensitivity for safe emergency decision making in rivaroxaban- and dabigatran-treated patients. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifiers: NCT02371044 and NCT02371070.

Point-of-care testing for emergency assessment of coagulation in patients treated with direct oral anticoagulants.

BACKGROUND: Point-of-care testing (POCT) of coagulation has been proven to be of great value in accelerating emergency treatment. Specific POCT for direct oral anticoagulants (DOAC) is not available, but the effects of DOAC on established POCT have been described. We aimed to determine the diagnostic accuracy of Hemochron® Signature coagulation POCT to qualitatively rule out relevant concentrations of apixaban, rivaroxaban, and dabigatran in real-life patients. METHODS: We enrolled 68 patients receiving apixaban, rivaroxaban, or dabigatran and obtained blood samples at six pre-specified time points. Coagulation testing was performed using prothrombin time/international normalized ratio (PT/INR), activated partial thromboplastin time (aPTT), and activated clotting time (ACT+ and ACT-low range) POCT cards. For comparison, laboratory-based assays of diluted thrombin time (Hemoclot) and anti-Xa activity were conducted. DOAC concentrations were determined by liquid chromatography-tandem mass spectrometry. RESULTS: Four hundred and three samples were collected. POCT results of PT/INR and ACT+ correlated with both rivaroxaban and dabigatran concentrations. Insufficient correlation was found for apixaban. Rivaroxaban concentrations at <30 and <100 ng/mL were detected with >95% specificity at PT/INR POCT ≤1.0 and ≤1.1 and ACT+ POCT ≤120 and ≤130 s. Dabigatran concentrations at <30 and <50 ng/mL were detected with >95% specificity at PT/INR POCT ≤1.1 and ≤1.2 and ACT+ POCT ≤100 s. CONCLUSIONS: Hemochron® Signature POCT can be a fast and reliable alternative for guiding emergency treatment during rivaroxaban and dabigatran therapy. It allows the rapid identification of a relevant fraction of patients that can be treated immediately without the need to await the results of much slower laboratory-based coagulation tests. TRIAL REGISTRATION: Unique identifier, NCT02371070 . Retrospectively registered on 18 February 2015.

Intraspecific trait variation across scales: implications for understanding global change responses.

Recognition of the importance of intraspecific variation in ecological processes has been growing, but empirical studies and models of global change have only begun to address this issue in detail. This review discusses sources and patterns of intraspecific trait variation and their consequences for understanding how ecological processes and patterns will respond to global change. We examine how current ecological models and theories incorporate intraspecific variation, review existing data sources that could help parameterize models that account for intraspecific variation in global change predictions, and discuss new data that may be needed. We provide guidelines on when it is most important to consider intraspecific variation, such as when trait variation is heritable or when nonlinear relationships are involved. We also highlight benefits and limitations of different model types and argue that many common modeling approaches such as matrix population models or global dynamic vegetation models can allow a stronger consideration of intraspecific trait variation if the necessary data are available. We recommend that existing data need to be made more accessible, though in some cases, new experiments are needed to disentangle causes of variation.