Medication patterns and dosing guidance in pediatric patients supported with intermittent hemodialysis or continuous kidney replacement therapy.

BACKGROUND: Hemodialysis is a life-saving technology used during periods of acute or chronic kidney failure to remove toxins, and maintain fluid, electrolyte and metabolic balance. While this technology plays an important role for pediatric patients with kidney dysfunction, it can alter the pharmacokinetic behavior of medications placing patients at risk for suboptimal dosing and drug toxicity. The ability to directly translate pharmacokinetic alterations into dosing recommendations has thus far been limited and dosing guidance specific to pediatric hemodialysis patients is rare. Despite differences in dialysis prescription and patient populations, intermittent (iHD) and continuous kidney replacement therapy (CKRT) patients are often pooled together. In order to develop evidence-based dosing guidelines, it is important to first prioritize drugs for study in each modality. METHODS: Here we aim to identify priority drugs in two hemodialysis modalities, through: 1) Identification of hospitalized, pediatric patients who received CKRT or intermittent hemodialysis (iHD) using a machine learning-based predictive model based on medications; 2) Identification of medication administration patterns in these patient cohorts; and 3) Identification of the most commonly prescribed drugs that lack published dosing guidance. RESULTS: Notable differences were found in the pattern of medications and drug dosing guidance between iHD and CKRT patients. Antibiotics, diuretics and sedatives were more common in CKRT patients. Out of the 50 most commonly administered medications in the two modalities, only 34% and 28% had dosing guidance present for iHD and CKRT, respectively. CONCLUSIONS: Our results add to the understanding of the differences between iHD and CKRT patient populations by identifying commonly used medications that lack dosing guidance for each hemodialysis modality, helping to pinpoint priority medications for further study. Overall, this study provides an overview of the current limitations in medication use in this at-risk population, and provides a framework for future studies by identifying commonly used medications in pediatric CKRT and iHD patients.

Reconciling divergent trends and millennial variations in Holocene temperatures.

Cooling during most of the past two millennia has been widely recognized and has been inferred to be the dominant global temperature trend of the past 11,700 years (the Holocene epoch). However, long-term cooling has been difficult to reconcile with global forcing, and climate models consistently simulate long-term warming. The divergence between simulations and reconstructions emerges primarily for northern mid-latitudes, for which pronounced cooling has been inferred from marine and coastal records using multiple approaches. Here we show that temperatures reconstructed from sub-fossil pollen from 642 sites across North America and Europe closely match simulations, and that long-term warming, not cooling, defined the Holocene until around 2,000 years ago. The reconstructions indicate that evidence of long-term cooling was limited to North Atlantic records. Early Holocene temperatures on the continents were more than two degrees Celsius below those of the past two millennia, consistent with the simulated effects of remnant ice sheets in the climate model Community Climate System Model 3 (CCSM3). CCSM3 simulates increases in 'growing degree days'-a measure of the accumulated warmth above five degrees Celsius per year-of more than 300 kelvin days over the Holocene, consistent with inferences from the pollen data. It also simulates a decrease in mean summer temperatures of more than two degrees Celsius, which correlates with reconstructed marine trends and highlights the potential importance of the different subseasonal sensitivities of the records. Despite the differing trends, pollen- and marine-based reconstructions are correlated at millennial-to-centennial scales, probably in response to ice-sheet and meltwater dynamics, and to stochastic dynamics similar to the temperature variations produced by CCSM3. Although our results depend on a single source of palaeoclimatic data (pollen) and a single climate-model simulation, they reinforce the notion that climate models can adequately simulate climates for periods other than the present-day. They also demonstrate that amplified warming in recent decades increased temperatures above the mean of any century during the past 11,000 years.

Pedestrians and the Built Environment during the COVID-19 Pandemic: Changing Relationships by the Pandemic Phases in Salt Lake County, Utah, U.S.A.

The COVID-19 pandemic has dramatically altered people's travel behavior, in particular outdoor activities, including walking. Their behavior changes may have prolonged effects after the pandemic, and such changes vary by the context and are related to the characteristics of the built environment. But empirical studies about the relationships between pedestrians and the built environment during the pandemic are lacking. This study explores how COVID-19 and related travel restrictions have affected the relationship between pedestrian traffic volume and the built environment. We estimate daily pedestrian volumes for all signalized intersections in Salt Lake County, Utah, U.S.A., from pedestrian push-button log data between January 2019 and October 2020. Multilevel spatial filtering models show that the COVID-19 pandemic has altered the relationship between pedestrian traffic volume and the built environment. During the pandemic, the higher the number of COVID-19 cases, the less (or more negative) the effects of density, street connectivity, and destination accessibility on pedestrian volume being observed. The exception is access to urban parks, as it became more significant in increasing pedestrian activities during the pandemic. The models also highlight the negative impacts of the pandemic in economically disadvantaged areas. Our findings could help urban and transportation planners find effective interventions to promote active transportation and physical activity amid the global pandemic.

Evidence of transgenerational effects on autism spectrum disorder using multigenerational space-time cluster detection.

BACKGROUND: Transgenerational epigenetic risks associated with complex health outcomes, such as autism spectrum disorder (ASD), have attracted increasing attention. Transgenerational environmental risk exposures with potential for epigenetic effects can be effectively identified using space-time clustering. Specifically applied to ancestors of individuals with disease outcomes, space-time clustering characterized for vulnerable developmental stages of growth can provide a measure of relative risk for disease outcomes in descendants. OBJECTIVES: (1) Identify space-time clusters of ancestors with a descendent with a clinical ASD diagnosis and matched controls. (2) Identify developmental windows of ancestors with the highest relative risk for ASD in descendants. (3) Identify how the relative risk may vary through the maternal or paternal line. METHODS: Family pedigrees linked to residential locations of ASD cases in Utah have been used to identify space-time clusters of ancestors. Control family pedigrees of none-cases based on age and sex have been matched to cases 2:1. The data have been categorized by maternal or paternal lineage at birth, childhood, and adolescence. A total of 3957 children, both parents, and maternal and paternal grandparents were identified. Bernoulli space-time binomial relative risk (RR) scan statistic was used to identify clusters. Monte Carlo simulation was used for statistical significance testing. RESULTS: Twenty statistically significant clusters were identified. Thirteen increased RR (> 1.0) space-time clusters were identified from the maternal and paternal lines at a p-value < 0.05. The paternal grandparents carry the greatest RR (2.86-2.96) during birth and childhood in the 1950's-1960, which represent the smallest size clusters, and occur in urban areas. Additionally, seven statistically significant clusters with RR < 1 were relatively large in area, covering more rural areas of the state. CONCLUSION: This study has identified statistically significant space-time clusters during critical developmental windows that are associated with ASD risk in descendants. The geographic space and time clusters family pedigrees with over 3 + generations, which we refer to as a person's geographic legacy, is a powerful tool for studying transgenerational effects that may be epigenetic in nature. Our novel use of space-time clustering can be applied to any disease where family pedigree data is available.

Stable or seral? Fire-driven alternative states in aspen forests of western North America.

As important centres for biological diversity, aspen forests are essential to the function and aesthetics of montane ecosystems in western North America. Aspen stands are maintained by a nuanced relationship with wildfire, although in recent decades aspen mortality has increased. The need to understand the baseline environmental conditions that favour aspen is clear; however, long-term fire history reconstructions are rare due to the scarcity of natural archives in dry montane settings. Here, we analyse a high-resolution lake sediment record from southwestern, Utah, USA to quantify the compositional and burning conditions that promote stable (or seral) aspen forests. Our results show that aspen presence is negatively correlated with subalpine fir and that severe fires tend to promote persistent and diverse aspen ecosystems over centennial timescales. This information improves our understanding of aspen disturbance ecology and identifies the circumstances where critical transitions in montane forests may occur.

Emergence patterns of novelty in European vegetation assemblages over the past 15 000 years.

Plant communities are not stable over time and biological novelty is predicted to emerge due to climate change, the introduction of exotic species and land-use change. However, the rate at which this novelty may arise over longer time periods has so far received little attention. We reconstruct the emergence of novelty in Europe for a set of baseline conditions over the past 15 000 years to assess past rates of emergence and investigate underlying causes. The emergence of novelty is baseline specific and, during the early-Holocene, was mitigated by the rapid spread of plant taxa. Although novelty generally increases as a function of time, climate and human-induced landscape changes contributed to a non-linear post-glacial trajectory of novelty with jumps corresponding to periods of rapid changes. Emergence of novelty accelerated during the past 1000 years. Historical cultural landscapes experienced a faster novelty development due to the contribution from anthropogenic land-cover changes.

Global climate evolution during the last deglaciation.

Deciphering the evolution of global climate from the end of the Last Glacial Maximum approximately 19 ka to the early Holocene 11 ka presents an outstanding opportunity for understanding the transient response of Earth's climate system to external and internal forcings. During this interval of global warming, the decay of ice sheets caused global mean sea level to rise by approximately 80 m; terrestrial and marine ecosystems experienced large disturbances and range shifts; perturbations to the carbon cycle resulted in a net release of the greenhouse gases CO(2) and CH(4) to the atmosphere; and changes in atmosphere and ocean circulation affected the global distribution and fluxes of water and heat. Here we summarize a major effort by the paleoclimate research community to characterize these changes through the development of well-dated, high-resolution records of the deep and intermediate ocean as well as surface climate. Our synthesis indicates that the superposition of two modes explains much of the variability in regional and global climate during the last deglaciation, with a strong association between the first mode and variations in greenhouse gases, and between the second mode and variations in the Atlantic meridional overturning circulation.

Exploring Pattern of Relapse in Pediatric Patients with Acute Lymphocytic Leukemia and Acute Myeloid Leukemia Undergoing Stem Cell Transplant Using Machine Learning Methods.

Background. Leukemic relapse remains the primary cause of treatment failure and death after allogeneic hematopoietic stem cell transplant. Changes in post-transplant donor chimerism have been identified as a predictor of relapse. A better predictive model of relapse incorporating donor chimerism has the potential to improve leukemia-free survival by allowing earlier initiation of post-transplant treatment on individual patients. We explored the use of machine learning, a suite of analytical methods focusing on pattern recognition, to improve post-transplant relapse prediction. Methods. Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pre-transplant and post-transplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool was used to confirm our random forest classification result. Results. Our analysis showed that a random forest model using these hyperparameter values achieved 85% accuracy, 85% sensitivity, 89% specificity for ALL, while for AML 81% accuracy, 75% sensitivity, and 100% specificity at predicting relapses within 24 months post-HSCT in cross validation. The Local Interpretable Model-Agnostic Explanations tool was able to confirm many variables that the random forest classifier identified as important for the relapse prediction. Conclusions. Machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and robust predictions can be obtained even with a modest clinical dataset. The random forest classifier distinguished different important predictive factors between ALL and AML in our relapse models, consistent with previous knowledge, lending increased confidence to adopting machine learning prediction to clinical management.

Utah Latina/o/x suicide decedents less likely to die by firearm, even in rural areas: examining population-wide data from the Utah Office of the Medical Examiner.

Introduction: Suicide death remains a significantly rarer event among Latina/o/x populations compared to non-Latina/o/x populations. However, the reasons why Latina/o/x communities experience relatively lower suicide rates are not fully understood. Critical gaps exist in the examination of Latina/o/x suicide death, especially in rural settings, where suicide death by firearm is historically more common within non-Latina/o/x populations. Method: We tested whether the prevalence of Latina/o/x firearm suicide was meaningfully different in urban and rural environments and from non-Latino/a/x decedents when controlling for age, sex, and a social deprivation metric, the Area Deprivation Index. Suicide death data used in this analysis encompasses 2,989 suicide decedents ascertained in Utah from 2016 to 2019. This included death certificate data from the Utah Office of the Medical Examiner on all Utah suicide deaths linked to information by staff at the Utah Population Database. Results: Compared to non-Latina/o/x suicide decedents, Latina/o/x suicide decedents had 34.7% lower adjusted odds of dying by firearm. Additionally, among the firearm suicide decedents living only in rural counties, Latina/o/x decedents had 40.5% lower adjusted odds of dying by firearm compared to non-Latina/o/x suicide decedents. Discussion: The likelihood of firearm suicide death in Utah differed by ethnicity, even in rural populations. Our findings may suggest underlying factors contributing to lower firearm suicide rates within Latina/o/x populations, e.g., aversion to firearms or less access to firearms, especially in rural areas, though additional research on these phenomena is needed.

Observational methods for human studies of transgenerational effects.

There are substantial challenges in studying human transgenerational epigenetic outcomes resulting from environmental conditions. The task requires specialized methods and tools that incorporate specific knowledge of multigenerational relationship combinations of probands and their ancestors, phenotype data for individuals, environmental information of ancestors and their descendants, which can span historical to present datasets, and informative environmental data that chronologically aligns with ancestors and descendants over space and time. As a result, there are few epidemiologic studies of potential transgenerational effects in human populations, thus limiting the knowledge of ancestral environmental conditions and the potential impacts we face with modern human health outcomes. In an effort to overcome some of the challenges in studying human transgenerational effects, we present two transgenerational study designs: transgenerational space-time cluster detection and transgenerational case-control study design. Like other epidemiological methods, these methods determine whether there are statistical associations between phenotypic outcomes (e.g., adverse health outcomes) among probands and the shared environments and environmental factors facing their ancestors. When the ancestor is a paternal grandparent, a statistically significant association provides some evidence that a transgenerational inheritable factor may be involved. Such results may generate useful hypotheses that can be explored using epigenomic data to establish conclusive evidence of transgenerational heritable effects. Both methods are proband-centric: They are designed around the phenotype of interest in the proband generation for case selection and family pedigree creation. In the examples provided, we incorporate at least three generations of paternal lineage in both methods to observe a potential transgenerational effect.

Dry growing seasons predicted Central American migration to the US from 2012 to 2018.

Controlling for factors such as criminal violence and poverty, we tested if drier than usual growing season weather was a predictor of emigration from El Salvador, Guatemala, and Honduras to the US between 2012 and 2018. We focus on growing season weather because agriculture is a primary transmission pathway from the effects of climate change upon migration. We secured the migration apprehensions data for our analysis through a FOIA request to US Customs and Border Protection. Border Patrol intake interviews recorded the original home location of families that arrived at the southern US border. We used this geographic information to measure recent weather patterns and social circumstances in the area that each family departed. We found 70.7% more emigration to the US when local growing seasons in Central America were recently drier than the historical average since 1901.

The impacts of climate change, energy policy and traditional ecological practices on future firewood availability for Diné (Navajo) People.

Local-scale human-environment relationships are fundamental to energy sovereignty, and in many contexts, Indigenous ecological knowledge (IEK) is integral to such relationships. For example, Tribal leaders in southwestern USA identify firewood harvested from local woodlands as vital. For Diné people, firewood is central to cultural and physical survival and offers a reliable fuel for energy embedded in local ecological systems. However, there are two acute problems: first, climate change-induced drought will diminish local sources of firewood; second, policies aimed at reducing reliance on greenhouse-gas-emitting energy sources may limit alternatives like coal for home use, thereby increasing firewood demand to unsustainable levels. We develop an agent-based model trained with ecological and community-generated ethnographic data to assess the future of firewood availability under varying climate, demand and IEK scenarios. We find that the long-term sustainability of Indigenous firewood harvesting is maximized under low-emissions and low-to-moderate demand scenarios when harvesters adhere to IEK guidance. Results show how Indigenous ecological practices and resulting ecological legacies maintain resilient socio-environmental systems. Insights offered focus on creating energy equity for Indigenous people and broad lessons about how Indigenous knowledge is integral for adapting to climate change. This article is part of the theme issue 'Climate change adaptation needs a science of culture'.

Decomposing the mid-Holocene Tsuga decline in eastern North America.

The mid-Holocene decline of Tsuga canadensis (hereafter Tsuga) populations across eastern North America is widely perceived as a synchronous event, driven by pests/pathogens, rapid climate change, or both. Pattern identification and causal attribution are hampered by low stratigraphic density of pollen-sampling and radiometric dates at most sites, and by absence of highly resolved, paired pollen and paleoclimate records from single sediment cores, where chronological order of climatic and vegetational changes can be assessed. We present an intensely sampled (contiguous 1-cm intervals) record of pollen and water table depth (inferred from testate amoebae) from a single core spanning the Tsuga decline at Irwin Smith Bog in Lower Michigan, with high-precision chronology. We also present an intensively sampled pollen record from Tower Lake in Upper Michigan. Both sites show high-magnitude fluctuations in Tsuga pollen percentages during the pre-decline maximum. The terminal decline is dated at both sites ca. 5000 cal yr BP, some 400 years later than estimates from other sites and data compilations. The terminal Tsuga decline was evidently heterochronous across its range. A transient decline ca. 5350 cal yr BP at both sites may correspond to the terminal decline at other sites in eastern North America. At Irwin Smith Bog, the terminal Tsuga decline preceded an abrupt and persistent decline in water table depths by approximately 200 years, suggesting the decline was not directly driven by abrupt climate change. The Tsuga decline may best be viewed as comprising at least three phases: a long-duration pre-decline maximum with high-magnitude and high-frequency fluctuations, followed by a terminal decline at individual sites, followed in turn by two millennia of persistently low Tsuga populations. These phases may not be causally linked, and may represent dynamics taking place at multiple temporal and spatial scales. Further progress toward understanding the phenomenon requires an expanded network of high-resolution pollen and paleoclimate chronologies.

Medication based machine learning to identify subpopulations of pediatric hemodialysis patients in an electronic health record database.

Electronic health records (EHRs) have given rise to large and complex databases of medical information that have the potential to become powerful tools for clinical research. However, differences in coding systems and the detail and accuracy of the information within EHRs can vary across institutions. This makes it challenging to identify subpopulations of patients and limits the widespread use of multi-institutional databases. In this study, we leveraged machine learning to identify patterns in medication usage among hospitalized pediatric patients receiving renal replacement therapy and created a predictive model that successfully differentiated between intermittent (iHD) and continuous renal replacement therapy (CRRT) hemodialysis patients. We trained six machine learning algorithms (logistical regression, Naïve Bayes, k-nearest neighbor, support vector machine, random forest, and gradient boosted trees) using patient records from a multi-center database (n = 533) and prescribed medication ingredients (n = 228) as features to discriminate between the two hemodialysis types. Predictive skill was assessed using a 5-fold cross-validation, and the algorithms showed a range of performance from 0.7 balanced accuracy (logistical regression) to 0.86 (random forest). The two best performing models were further tested using an independent single-center dataset and achieved 84-87% balanced accuracy. This model overcomes issues inherent within large databases and will allow us to utilize and combine historical records, significantly increasing population size and diversity within both iHD and CRRT populations for future clinical studies. Our work demonstrates the utility of using medications alone to accurately differentiate subpopulations of patients in large datasets, allowing codes to be transferred between different coding systems. This framework has the potential to be used to distinguish other subpopulations of patients where discriminatory ICD codes are not available, permitting more detailed insights and new lines of research.

Climate and demography drive 7000 years of dietary change in the Central Andes.

Explaining the factors that influence past dietary variation is critically important for understanding changes in subsistence, health, and status in past societies; yet systematic studies comparing possible driving factors remain scarce. Here we compile the largest dataset of past diet derived from stable isotope δ13C‰ and δ15N‰ values in the Americas to quantitatively evaluate the impact of 7000 years of climatic and demographic change on dietary variation in the Central Andes. Specifically, we couple paleoclimatic data from a general circulation model with estimates of relative past population inferred from archaeologically derived radiocarbon dates to assess the influence of climate and population on spatiotemporal dietary variation using an ensemble machine learning model capable of accounting for interactions among predictors. Results reveal that climate and population strongly predict diet (80% of δ15N‰ and 66% of Î´13C‰) and that Central Andean diets correlate much more strongly with local climatic conditions than regional population size, indicating that the past 7000 years of dietary change was influenced more by climatic than socio-demographic processes. Visually, the temporal pattern suggests decreasing dietary variation across elevation zones during the Late Horizon, raising the possibility that sociopolitical factors overrode the influence of local climatic conditions on diet during that time. The overall findings and approach establish a general framework for understanding the influence of local climate and demography on dietary change across human history.

Large historical carbon emissions from cultivated northern peatlands.

When a peatland is drained and cultivated, it behaves as a notable source of CO2 However, we lack temporally and spatially explicit estimates of carbon losses from cultivated peatlands. Using a process-based land surface model that explicitly includes representation of peatland processes, we estimate that northern peatlands converted to croplands emitted 72 Pg C over 850-2010, with 45% of this source having occurred before 1750. This source surpassed the carbon accumulation by high-latitude undisturbed peatlands (36 to 47 Pg C). Carbon losses from the cultivation of northern peatlands are omitted in previous land-use emission assessments. Adding this ignored historical land-use emission implies an 18% larger terrestrial carbon storage since 1750 to close the historical global carbon budget. We also show that carbon emission per unit area decrease with time since drainage, suggesting that time since drainage should be accounted for in inventories to refine land-use emissions from cultivated peatlands.

Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument.

Predictive models are central to both archaeological research and cultural resource management. Yet, archaeological applications of predictive models are often insufficient due to small training data sets, inadequate statistical techniques, and a lack of theoretical insight to explain the responses of past land use to predictor variables. Here we address these critiques and evaluate the predictive power of four statistical approaches widely used in ecological modeling-generalized linear models, generalized additive models, maximum entropy, and random forests-to predict the locations of Formative Period (2100-650 BP) archaeological sites in the Grand Staircase-Escalante National Monument. We assess each modeling approach using a threshold-independent measure, the area under the curve (AUC), and threshold-dependent measures, like the true skill statistic. We find that the majority of the modeling approaches struggle with archaeological datasets due to the frequent lack of true-absence locations, which violates model assumptions of generalized linear models, generalized additive models, and random forests, as well as measures of their predictive power (AUC). Maximum entropy is the only method tested here which is capable of utilizing pseudo-absence points (inferred absence data based on known presence data) and controlling for a non-representative sampling of the landscape, thus making maximum entropy the best modeling approach for common archaeological data when the goal is prediction. Regression-based approaches may be more applicable when prediction is not the goal, given their grounding in well-established statistical theory. Random forests, while the most powerful, is not applicable to archaeological data except in the rare case where true-absence data exist. Our results have significant implications for the application of predictive models by archaeologists for research and conservation purposes and highlight the importance of understanding model assumptions.

Differing climatic mechanisms control transient and accumulated vegetation novelty in Europe and eastern North America.

Understanding the mechanisms of climate that produce novel ecosystems is of joint interest to conservation biologists and palaeoecologists. Here, we define and differentiate transient from accumulated novelty and evaluate four climatic mechanisms proposed to cause species to reshuffle into novel assemblages: high climatic novelty, high spatial rates of change (displacement), high variance among displacement rates for individual climate variables, and divergence among displacement vector bearings. We use climate simulations to quantify climate novelty, displacement and divergence across Europe and eastern North America from the last glacial maximum to the present, and fossil pollen records to quantify vegetation novelty. Transient climate novelty is consistently the strongest predictor of transient vegetation novelty, while displacement rates (mean and variance) are equally important in Europe. However, transient vegetation novelty is lower in Europe and its relationship to climatic predictors is the opposite of expectation. For both continents, accumulated novelty is greater than transient novelty, and climate novelty is the strongest predictor of accumulated ecological novelty. These results suggest that controls on novel ecosystems vary with timescale and among continents, and that the twenty-first century emergence of novelty will be driven by both rapid rates of climate change and the emergence of novel climate states. This article is part of a discussion meeting issue 'The past is a foreign country: how much can the fossil record actually inform conservation?'

Postglacial change of the floristic diversity gradient in Europe.

Climate warming is expected to cause a poleward spread of species, resulting in increased richness at mid to high latitudes and weakening the latitudinal diversity gradient. We used pollen data to test if such a change in the latitudinal diversity gradient occurred during the last major poleward shift of plant species in Europe following the end of the last glacial period. In contrast to expectations, the slope of the gradient strengthened during the Holocene. The increase in temperatures around 10 ka ago reduced diversity at mid to high latitude sites due to the gradual closure of forests. Deforestation and the introduction of agriculture during the last 5 ky had a greater impact on richness in central Europe than the earlier climate warming. These results do not support the current view that global warming alone will lead to a loss in biodiversity, and demonstrate that non-climatic human impacts on the latitudinal diversity gradient is of a greater magnitude than climate change.

European Forest Cover During the Past 12,000 Years: A Palynological Reconstruction Based on Modern Analogs and Remote Sensing.

Characterization of land cover change in the past is fundamental to understand the evolution and present state of the Earth system, the amount of carbon and nutrient stocks in terrestrial ecosystems, and the role played by land-atmosphere interactions in influencing climate. The estimation of land cover changes using palynology is a mature field, as thousands of sites in Europe have been investigated over the last century. Nonetheless, a quantitative land cover reconstruction at a continental scale has been largely missing. Here, we present a series of maps detailing the evolution of European forest cover during last 12,000 years. Our reconstructions are based on the Modern Analog Technique (MAT): a calibration dataset is built by coupling modern pollen samples with the corresponding satellite-based forest-cover data. Fossil reconstructions are then performed by assigning to every fossil sample the average forest cover of its closest modern analogs. The occurrence of fossil pollen assemblages with no counterparts in modern vegetation represents a known limit of analog-based methods. To lessen the influence of no-analog situations, pollen taxa were converted into plant functional types prior to running the MAT algorithm. We then interpolate site-specific reconstructions for each timeslice using a four-dimensional gridding procedure to create continuous gridded maps at a continental scale. The performance of the MAT is compared against methodologically independent forest-cover reconstructions produced using the REVEALS method. MAT and REVEALS estimates are most of the time in good agreement at a trend level, yet MAT regularly underestimates the occurrence of densely forested situations, requiring the application of a bias correction procedure. The calibrated MAT-based maps draw a coherent picture of the establishment of forests in Europe in the Early Holocene with the greatest forest-cover fractions reconstructed between ∼8,500 and 6,000 calibrated years BP. This forest maximum is followed by a general decline in all parts of the continent, likely as a result of anthropogenic deforestation. The continuous spatial and temporal nature of our reconstruction, its continental coverage, and gridded format make it suitable for climate, hydrological, and biogeochemical modeling, among other uses.