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deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics
Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.
Who is benefiting from the dramatic decline in U.S. cancer mortality? Place-based evidence of disparities in rates of improvement
After decades of increasing cancer mortality, U.S. rates declined from 1991 to 2019, a 32% decrease. we investigated rates of cancer mortality improvement across 2954 counties and selected characteristics associated with mortality improvements. Data was 21,381,009 county-level neoplasm deaths gleaned from death certificates via CDC WONDER. Analytical techniques included GIS and Moran’s I, OLS, GWR models, and trend comparisons. Counties with the greatest improvement (reduction) in cancer mortality tended to be coastal, higher-income, metropolitan locations. OLS model (R 2 = 0.65) indicated that greatest improvements were observed in counties with higher initial mortality ($\beta =.32$) closely followed by percent urban ($\beta =.31$) and median household income ($\beta =.16$). Whereas percent Black residents ($\beta =-.06$), and percent with education beyond high school ($\beta =-.10$) was less associated on outcomes. Highest income counties were the first to experience improvement in cancer mortality, the highest rates of mortality decline, and the greatest reduction in excess deaths. Even though there was significant improvement in cancer mortality nationally, there were variations in the degree of improvement linked to county location, income, and urbanisation. These results underlie the need to expand place-based initiatives designed to advance cancer health and more equitable improvements in cancer mortality outcomes.
Mortality correlates with tree functional traits across a wood density gradient in the Central Amazon
Introduction: Understanding the mechanisms of tree mortality in tropical ecosystems remains challenging, in part due to the high diversity of tree species and the inherently stochastic nature of mortality. Plant functional traits offer a mechanistic link between plant physiology and performance, yet their ability to predict growth and mortality remains poorly understood. Given recent increases in tree mortality rates in the Amazon forest following extreme drought and wind events, we tested if lower wood density and acquisitive plant functional traits were associated with increased growth and mortality for common co-occurring trees in the Central Amazon. Methods: Seventeen trees of different species with similar sizes but a range in wood density (WD) and wood traits were felled, then assessed for 27 different individual functional parameters, including whole tree architecture, stem xylem anatomical and hydraulic traits and leaf traits. Traits of the individual trees were related to stand-level growth and mortality rates collected periodically over 30 years from nearby permanent inventory plots. Results: Higher wood density was associated with smaller leaf size, lower foliar base cations, lower stem water content and sapwood fraction, in agreement with the fast-slow plant economics spectrum. Lower wood density was associated with more acquisitive characteristics with greater hydraulic capacity and foliar nutrient concentrations, correlating with greater growth and mortality rates. Discussion: Our results show that lower wood density is part of a coordinated suite of traits linked to high resource acquisition, fast growth, and increased mortality risk, providing a functional framework for predicting species performance and forest vulnerability under future climate stress.
Risk of Mortality in Family Members of Men Seeking Fertility Assessment
Objective: To assess mortality in family members of men seeking fertility assessment. Subfertility serves as a biomarker for overall somatic health, and poor semen quality is associated with increased risk of hospitalization and mortality from chronic conditions. However, it is unclear if these risks extend to family members of men with low sperm count. Design: Retrospective cohort study. Subjects: Family members, up to third-degree relatives, of men in the Subfertility, Health and Assisted Reproduction and the Environment cohort who underwent a semen analysis as part of a fertility assessment 1996–2017. Relatives of men with a recorded total sperm count who lived in Utah for ≥1 year 1904–2017 were included in the analysis (N = 22,280 families). Exposure: Individuals were classified by family membership. Families were classified as relatives of azoospermic (0M), oligozoospermic (<39M), or normozoospermic (≥39M) men. The average total sperm count of the proband (male relative) with fertility assessment was also included as a continuous exposure measure. Main Outcome Measures: The main outcomes were all-cause and cause-specific mortality risk by sex, age, and degree of relation: first-, second-, and third-degree. Cox proportional hazard models were used to test the association between fertility classification and mortality, controlling for sex, race/ethnicity, and birth year. Results: A total of 666,437 relatives of men with fertility assessment (N deaths = 183,974) were included in the analysis. Relative to normozoospermia families, all-cause mortality risk increased in oligozoospermia families (hazard ratio [HR] oligozoospermia , 1.03; 95% confidence interval [CI], 1.01–1.05). Close relatives, first- (HR oligozoospermia , 1.17; 95% CI, 1.07–1.28) and second-degree relatives (HR azoospermia , 1.11; 95% CI, 1.04–1.20; HR oligozoospermia , 1.05; 95% CI,1.01–1.09), of azoospermic and oligozoospermic men had the highest all-cause and cause-specific mortality risk, including death attributed to cardiovascular disease or congenital birth conditions. Conclusion: Our results suggest that familial all-cause and cause-specific mortality risk differ by fertility phenotype. Families of azoospermic and oligozoospermic men showed significantly increased risk, particularly for close relatives. This study provides further evidence that shared genetic and/or environmental factors could influence both fertility and somatic health.
Investigating the opioid epidemic across the United States: Associations between county-level characteristics and overdose mortality
The opioid crisis remains a critical public health challenge in the United States. Despite national efforts that reduced opioid prescribing by nearly 44% between 2011 and 2021, opioid overdose deaths more than tripled during the same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although supply-side factors fueling this transition have been widely studied, the structural and community-level conditions that shape overdose mortality are less well understood. To help address this gap, this study has three primary objectives: (1) overcome structural gaps in national data to construct a complete nationwide county-level dataset from 2010 to 2022; (2) using data analysis, identify and investigate spatiotemporal anomalies in overdose mortality; and (3) using two machine-learning models, quantify the importance of thirteen social vulnerability variables in predicting overdose mortality. Our results identify unemployment and limited vehicle access as key county-level predictors of overdose mortality. Higher levels of these vulnerabilities are associated with elevated mortality, whereas lower levels are associated with reduced mortality. These findings highlight factors that may be relevant for public health planning and policy prioritization within the context of the opioid crisis.
Impact of solar geoengineering on temperature-attributable mortality
Decisions about solar geoengineering (SG) entail risk–risk tradeoffs between the direct risks of SG and SG’s ability to reduce climate risks. Quantitative comparisons between these risks are needed to inform public policy. We evaluate idealized SG’s effectiveness in reducing deaths from warming using two climate models and an econometric analysis of temperature-attributable mortality. We find SG’s impact on temperature-attributable mortality is uneven with decreases for hotter, poorer regions and increases in cooler, richer regions. Relative to no SG, global mortality is reduced by over 400,000 deaths annually [90% CI: (−1.2 million,2.7 million)] for cooling of 1 °C from 2.5 °C above preindustrial in 2080. We find no evidence that mortality reduction achieved by SG is smaller than the reduction from equivalent cooling by emissions reductions. Combining our estimates with existing estimates of sulphate aerosol injection direct mortality risk from air quality and UV-attributable cancer enables the first quantitative risk-risk comparison of SG. We estimate with 61% probability that the mortality benefits of cooling outweigh these direct SG risks. We find the benefits outweigh these risks by 13 times for our central estimates, or 4 deaths per 100,000 per 1 °C per year [90% CI: (−11,23)]. This is not a comprehensive evaluation of the risk–risk tradeoffs around SG, yet by comparing some of the most consequential impacts on human welfare it is a useful first step. While these findings are robust to a variety of alternative assumptions, considerable uncertainties remain and require further investigation.
Attributing human mortality from fire PM 2.5 to climate change
Climate change intensifies fire smoke, emitting hazardous air pollutants that impact human health. However, the global influence of climate change on fire-induced health impacts remains unquantified. Here, in this study, we used three well-tested fire-vegetation models in combination with a chemical transport model and health risk assessment framework to attribute global human mortality from fire fine particulate matter (PM 2.5 ) emissions to climate change. Of the 46401 (1960s) –to 98748 (2010s) annual fire PM 2.5 mortalities, 669 (1.2%, 1960s) –to 12566 (12.8%, 2010s) were attributed to climate change. The most substantial influence of climate change on fire mortality occurred in South America, Australia, and Europe, coinciding with decreased relative humidity, and in boreal forests with increased air temperature. Increasing relative humidity lowered fire mortality in other regions, like South Asia. Our study highlights the role of climate change in fire mortality, aiding public health authorities in spatial targeting adaptation measures for sensitive fire-prone areas.
Geographical Insights into Suicide Mortality Through Spatial Machine Learning
Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.
Soil fertility controls on tropical forest productivity and mortality: synthesis and roadmap
Tropical forests are highly diverse and productive ecosystems and store nearly 60% of vegetation biomass. Across the tropics, forests span large gradients of soil fertility, from vast regions situated on highly weathered, ancient geological formations to others on young, nutrient-rich landscapes. Tropical forest productivity, mortality, and biomass all vary systematically across these gradients in soil fertility. Aboveground forest productivity tends to increase with higher soil fertility, while counterintuitively, aboveground biomass does not increase proportionally. This disconnect is likely due to coinciding increases in mortality with higher soil fertility. However, we know relatively little about the mechanisms underlying how soil fertility regulates productivity or mortality – two critical determinants of forest biomass – and even less about how these relationships will shape tropical forest responses to global change. Here, we present a mechanistic framework for understanding how soil fertility affects productivity through photosynthesis, carbon allocation, and carbon-use efficiency; how it modulates mortality via direct and indirect interactions with various drivers of mortality; and how global change may alter these relationships. Based on this synthesis, we outline a roadmap to advance our understanding of tropical forests and improve predictions of their responses to global change.
Mortality among workers at the Rocky Flats Plant, 1951–2017
The Rocky Flats (RFs) Plant operated from 1951–1989 as part of the U.S. Department of Energy (DOE) nuclear complex. Its primary mission was weapons component fabrication, whereby workers were potentially exposed to radioactive and non-radioactive hazards. RF worker mortality was compared to the general population, and dose-response relationships between mortality and radiation organ doses were examined. RF workers first employed between 1951 and 1979 for ⩾30 d were identified (n = 9397). Vital status was determined using national and state death records up to 2017. Organ doses from external photons and neutrons irritation and internalised plutonium (Pu), americium (Am), and uranium (U) were modelled as cumulative lagged total doses per year. Beryllium exposure was evaluated as an effect modifier using data from the DOE Nationwide Beryllium Medical Program. Statistical analyses included standardised mortality ratios (SMRs), Cox proportional hazard models, and excess relative risk (ERR) models. Approximately 53.2% of workers were deceased by the end of the study. Nearly 90% were monitored for radiation exposure, with a mean weighted absorbed dose of 59.0 mGy for the lungs. Nearly 45% of workers had intakes of alpha-particle emitting radionuclides, and 46.7% were monitored for neutrons. Leading causes of death included ischemic heart disease (n = 999) and lung cancer (n = 361). The highest SMRs were observed for berylliosis (SMR: 176.9; 95% CI: 76.2, 348.7; n < 10) and asbestosis (SMR: 4.65; 95% CI: 2.23, 8.55; n = 10). Dose-response analyses showed no statistical increase in risk from low-dose radiation including lung cancer (ERR per 100 mGy: −0.02; 95% CI: −0.11, 0.08; n = 361) and Parkinson’s disease (ERR per 100 mGy: 0.13; 95% CI: −0.26, 0.31; n = 57). Approximately 45% of workers were monitored for beryllium, with a weak non-significant indication of effect modification for lung cancer risk. The RF cohort showed no evidence of a statistically significant increase in mortality from occupational radiation exposure. However, this study was limited by low statistical power, which inhibits the ability to detect effects. Future pooling of Million Person Study (MPS) cohorts will provide further insights, particularly regarding Pu as a carcinogen.
Growth–Mortality Coordination Differs Among Xerophytic Versus Mesophytic Tree Species During Severe Drought
ABSTRACT Forest composition is changing, yet the consequences for terrestrial carbon cycling are unclear. In the eastern United States, water‐demanding “mesophytic” tree species are replacing “xerophytic” oaks (Quercusspp.) and hickories (Caryaspp.), raising concerns that forest productivity will become increasingly sensitive to more frequent and severe drought conditions predicted for the region. However, we have a limited understanding of the extent to which the mortality risk of xerophytes versus mesophytes is coordinated with their growth sensitivity during drought. Here, we evaluated growth and mortality dynamics for 20 abundant eastern United States tree species following a severe drought in the summer of 2012. We synthesized data from ~4500 forest inventory plots and used an approach that quantified relative drought responses between co‐located trees to minimize impacts from environmental heterogeneity. We found that mesophytes were just as likely to perish as co‐occurring xerophytes but were more sensitive to drought in terms of diminished growth. These findings suggest that xerophytic decline is likely to lead to reduced carbon uptake during drought and that management efforts to conserve oak‐hickory stands will be decisive to sustain the carbon mitigation potential of these forests. However, we also found that growth‐mortality relationships differed between functional groups. Among xerophytes, growth and survival during drought were decoupled. Among mesophytes, there was a high degree of coordination, where species that experienced greater mortality also experienced greater growth reductions. Therefore, mesophytes with high growth sensitivity to water deficits are likely to be the most vulnerable to drought‐driven die‐off events moving forward.
Mortality attributable to PM 2.5 from wildland fires in California from 2008 to 2018
In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM 2.5 ), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM 2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM 2.5 estimates. We used a concentration-response function for PM 2.5 , applying ZIP code–level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM 2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $\$432$ to $\$456$ billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.
Estimating the Benefits of Sustainable Aviation Fuel Usage at Chicago O’Hare International Airport on Ultrafine Particle Exposure and Mortalities Reductions
The expanding commercial aviation sector necessitates diverse energy sources, and sustainable aviation fuels (SAFs) have emerged as a promising option. Widespread SAF adoption can help meet transportation fuel demand and offer health benefits for people residing near airports or along airport landing and takeoff (LTO) pathways, where elevated levels of aircraft-derived air pollution often exist. Blending SAF with traditional jet fuels can reduce ultrafine particle (UFP) emissions, which may improve health of near airport population. We analyzed a population of about 8 million people in 1925 census tracts around the Chicago O’Hare International Airport (ORD). We conducted a risk assessment to estimate anticipated UFP reductions for three adoption scenarios using blends of traditional jet fuels with 5, 25, and 50% SAF across all flights landing and taking off from ORD. We calculated baseline estimates of UFP emissions using ORD flight data, a dispersion model, and a calibration function derived from mobile monitoring data. We used this baseline UFP emission profile across the study area to estimate population-weighted UFP, as well as the attributable case reductions (ACRs) and attributable mortality rate reductions (AMRRs) across the demographic distribution around the airport, based on the SAF blending scenarios. We found a positive association of SAF blending with UFP reductions, particularly near the airport and along LTO flight pathways. Our study showed that the population-weighted UFP across different demographics was similar. ACRs were largely dependent on individual demographic populations, while AMRRs for all populations were relatively similar, with an estimated 0.3 (95% range: 0.2−0.3), 1.1 (0.9−1.4), and 1.8 (1.5−2.2) fewer mortalities per 100,000 people per year expected with the adoption of 5, 25, and 50% SAF blends, respectively. This study indicates that communities near ORD, across a range of demographics, may benefit similarly from SAF adoption, thus highlighting how SAF adoption may offer an opportunity to improve health outcomes like aviation UFP-related mortalities around airports.
The impact of urban configuration types on urban heat islands, air pollution, CO 2 emissions, and mortality in Europe: a data science approach
The world is becoming increasingly urbanized. As cities around the world continue to grow, it is important for urban planners and policymakers to understand how different urban configuration patterns affect the environment and human health. We aimed at identifying European urban configuration types, based on the Local Climate Zones categories and street design variables from Open Street Map, and evaluating their association with motorized traffic flows, Surface Urban Heat Island (SUHI) intensities, tropospheric nitrogen dioxide (NO 2 ), CO 2 per capita emissions and age-standardized mortality. We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal–Wallis test followed by a post-hoc Dunn's test. We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low-density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low-density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO 2 exposure were significantly higher in compact high density and open low rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low-density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO 2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types. Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health.
Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality
Background: The opioid crisis remains one of the most daunting and complex public health problems in the United States. This study investigates the national epidemic by analyzing vulnerability profiles of three key factors: opioid-related mortality rates, opioid prescription dispensing rates, and disability rank ordered rates. Methods: This study utilizes county level data, spanning the years 2014 through 2020, on the rates of opioid-related mortality, opioid prescription dispensing, and disability. To successfully estimate and predict trends in these opioid-related factors, we augment the Kalman Filter with a novel spatial component. To define opioid vulnerability profiles, we create heat maps of our filter’s predicted rates across the nation’s counties and identify the hotspots. In this context, hotspots are defined on a year-by-year basis as counties with rates in the top 5% nationally. Results: Our spatial Kalman filter demonstrates strong predictive performance. From 2014 to 2018, these predictions highlight consistent spatiotemporal patterns across all three factors, with Appalachia distinguished as the nation’s most vulnerable region. Starting in 2019 however, the dispensing rate profiles undergo a dramatic and chaotic shift. Conclusions: The initial primary drivers of opioid abuse in the Appalachian region were likely prescription opioids; however, it now appears that abuse is sustained by illegal drugs. Additionally, we find that the disabled subpopulation may be more at risk of opioid-related mortality than the general population. Public health initiatives must extend beyond controlling prescription practices to address the transition to and impact of illicit drug use.
Ultrasonic Deterrents To Reduce Bat Mortality at Wind Turbines - Short Science Summary
In North America, several bat species commonly collide with operating turbines. Though the ultimate driver of collisions is unknown, studies suggest that bats may be attracted to turbines because they often approach and spend extended periods of time (seconds to minutes) interacting with turbine structures. To reduce bat mortality at wind farm operators, seek to reduce mortality rates by reducing the amount of time that bats interact with spinning turbine blades (or exposure). Current approaches to reduce exposure include curtailment, whereby turbine operators stop turbine rotors from spinning during periods when bats are expected to be at the highest risk of collision, and deterrent technologies that seek to discourage bats from entering/ limit the amount of time spent within the rotor swept area. We briefly describe the state of the science on ultrasonic deterrent technology, highlight potential drivers of variable efficacy for bat mortality reduction, and suggest next steps focus on identifying the driver(s) of such variability.
Rapid climate action is needed: comparing heat vs. COVID-19-related mortality
Abstract The impacts of climate change on human health are often underestimated or perceived to be in a distant future. Here, we present the projected impacts of climate change in the context of COVID-19, a recent human health catastrophe. We compared projected heat mortality with COVID-19 deaths in 38 cities worldwide and found that in half of these cities, heat-related deaths could exceed annual COVID-19 deaths in less than ten years (at + 3.0 °C increase in global warming relative to preindustrial). In seven of these cities, heat mortality could exceed COVID-19 deaths in less than five years. Our results underscore the crucial need for climate action and for the integration of climate change into public health discourse and policy.