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Browsing by Author "Dean, Daniel, author"

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    Combined effects of warming and drying on a temperate-to-boreal forest ecotone exert additive changes on soil microbiome structure and diversity
    (Colorado State University. Libraries, 2020) Dean, Daniel, author; Trivedi, Pankaj, advisor; Leach, Jan E., committee member; Wrighton, Kelly, committee member; Reich, Peter B., committee member
    The soil microbial community is an important mediator of many ecosystem functions, so understanding dynamics under climate change. These responses could be more robust in transitional zones such as the temperate-to-boreal forest ecotones, which are poised to experience substantial changes under projected climate change over the next century and beyond. Because these systems are projected to move towards a warmer, drier climate, it is important to understand how the soil microbiome's structure and interactions shift under such conditions. Here, we examined the response of microbial communities to simulated warming and drought conditions using the B4WarmED (Boreal Forest Warming in an Ecotone in Danger) experiment in Minnesota, USA. B4WarmED is a fully factorial blocking experiment which uses in situ experimental 3.4°C warming and precipitation reduction to simulate the projected regional late-21st century climate. Using Shannon-Weaver Diversity and Canonical Analysis of Principled Coordinates, we found that combined warming and drying effects exerted significant effects on the diversity and structure of microbial communities after 8 years of warming, and 5 of drought treatments. Specifically, warming and drying effects appeared to combine additively, rather than exhibiting nonlinear interactive effects, at the community level. Per-taxon linear models revealed a sizeable portion of individual microbes exhibit a significant abundance response to one or both of warming and drying effects. However, co-occurrence network analysis and Dufrene-Legrende Indicator Value characterization revealed a smaller portion of bacterial sub-communities with persistent taxonomical makeup and response profiles across treatments. Within the microbial communities our analysis identified three types of taxon-specific responses to climate change stressors: resistant, opportunistic, and sensitive, with most taxa being resistant to warming and drying effects. However, our results provide strong evidence that combined warming and drought influences will impact soil microbial communities of temperate-to-boreal ecotone forests ("boreal ecotone" hereafter), with potential implications for ecosystem functioning.
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    Quantitative health impact assessments as a tool for exploring public health dimensions of environmental exposures
    (Colorado State University. Libraries, 2024) Dean, Daniel, author; Rojas-Rueda, David, advisor; Anderson, G. Brooke, advisor; Peel, Jennifer, committee member; Hurrell, James, committee member
    Public health is influenced by a population's built and natural environment in both negative (e.g., natural disasters or ongoing stress from heat) and positive (for instance, heat-moderating effects of vegetation) ways, as well as interactively with behavioral and social dynamics. One framing of policy priorities and urban resilience is a "triad" consisting of exposure reduction (limiting the extent to which community members are exposed to environmental hazards—including "ambient" ones like stressful temperatures), vulnerability reduction (mitigating the impacts of sustained hazards), and hazard reduction (actively reducing the frequency or intensity of hazards) (Hoegh-Guldberg et al. 2018). Because any such measures carry tradeoffs in financial and other resources, it is important that policymakers and other stakeholders weigh comparative benefits of potential environmental hazards or interventions with consistent, quantifiable metrics. In this body of work, we applied quantitative health impact assessments, an epidemiology framework that provides a valuable tool here, allowing researchers to project health outcome changes for a population of interest given predicted changes in a relevant exposure and using epidemiological evidence, including exposure-response functions (exposure-response functions), which link exposure and health outcomes. In this body of work, we use HIAs to explore three different resilience-relevant systems spanning a range of intervention types, environmental systems, and spatiotemporal scales: Project 1: Health Impacts of Future Tropical Cyclones in the Eastern United States: While tropical cyclones are among the most damaging natural disasters faced by the United States, the temporal and spatial rarity of these events impedes traditional frequency-based estimates for public health and related risk projections, leading to potential oversights in risk characterization. In addition, mortality associated with tropical cyclones may not be readily apparent between delayed onset and indirect causes (e.g. stress, disrupted medical care, infections), meaning that immediate mortality counts often underestimate full attributable mortality. In this project, we performed a pilot quantitative health impact assessment designed to address aspects of these limitations. First, we tested extending the historical tropical storm dataset using a pool of 10,000 simulated, or "synthetic" tropical cyclone seasons from the widely used and open-source STORM algorithm, trained from and intended to represent the "gold standard" of historical International Best Tracks Archive for Climate Stewardship (IBTrACS) data. To the extent that STORM represents real-world conditions, this vastly expanded 'sample population provided information on potential tropical cyclone exposure risk than would be possible from historical data alone. For the second challenge of accounting for delayed and indirect attributable mortality, we combined the synthetic data with a recently-developed exposure-response function: an integrated Bayesian causal-predictive model trained on Medicare Claims data (simplified to Americans aged 65 and older), featuring an integrated model approach to combine a whole-population causal inference model for central trends with county-specific predictive models for give county-specific estimates. This model also tracked up to 21 days of "lag time" in health outcomes after a TC exposure to capture delayed mortality. This combination of methodologies promises a comprehensive, county level picture of tropical cyclone-associated all-cause mortality risk among older adults. This approach provided insights including regions of the country at the greatest risk for tropical cyclone-related exposures among older adults. However, as our study represented a new application of the STORM algorithm (in particular, our emphasis focusing on post-landfall behavior of tropical cyclones), we also assessed the level of agreement between STORM and the historical dataset, finding some discrepancies including lower overall frequency, and considerably 'smoother' spatial distribution in exposures; some discrepancies were in line with previously noted limitations. This project used recent innovations in atmospheric science and epidemiology modeling to explore the utility of a quantitative health impact assessment framework for present-day risk and could inform policy and planning decisions in terms of tropical cyclone preparedness and response measures. Project 2: Health impacts of Urban Tree Canopy policy scenarios in Denver and Phoenix: We explored potential health impacts (in terms of all-cause mortality, stroke, and dementia) of standing policy goals in Denver, Colorado and Phoenix, Arizona, for increasing the urban tree canopy coverage in these relatively arid cities. We projected health benefits (in terms of reduced attributable all-cause mortality, stroke, and dementia incidence) at a census block group level using several existing exposure-response functions based on the widely used Normalized Difference Vegetation Index (NDVI). Because the cities expressed policy goals in terms of percentage urban tree canopy, we generated predictive models to "translate" between this metric used in policy goals and the NDVI metric. We modeled the public health impacts of proposed real-world policies for near-future policy interventions in the form of increasing urban tree canopy, using current populations, and modeling an "overnight" change in exposure, with policy scenario benefits modeled for populations in year 2020, rather than demographic projections for the 2030 (Phoenix) and 2050 (Denver) dates in the policy goal timelines. We also considered socioeconomic dimensions by using the census-based Social Vulnerability Index to trace the equity of current UTC and NDVI exposures, as well as of potential benefits. We determined that each city could, by reaching its standing policy goals, could avert hundreds of all-cause mortality cases, with even a partial attainment scenario (halfway between current and desired UTC levels) having appreciable benefits, with roughly half the captured mortality prevention; with respect to equity of UTC access, more-vulnerable communities in the cities saw lower access to current canopy cover, and consequently greater potential per-capita benefits under successful intervention scenarios. Project 3: Health Impacts of Future Temperature Extremes Under a Solar Climate Intervention Scenario. In this project, we explored potential all-cause mortality implications of a proposed climate intervention effort intended to counteract anthropogenic warming, modeling the years 2050-2060 under alternate climate scenarios. Specifically, we projected temperature-associated mortality under a stratospheric aerosol injection (SAI) intervention scenario, as well as a corresponding scenario of "middle-of-the-road" climate change. We used a study population of 65-and-older Americans in eight major US cities (Seattle, Chicago, New York, Philadelphia, Los Angeles, Phoenix, Houston, and Miami) spanning a range of local climates. We built our analysis on widely used models and the shared socioeconomic pathway platform, allowing our two scenarios to be compatible, differing only in the SAI intervention itself. We focused on two age groups (65-75, and 75+) to reflect elevated heat- and cold-associated mortality risks among this population, finding broadly similar trends between age groups. We explored city-specific exposure-response functions for the temperature-mortality association, using a widely used modeling, comparing the anticipated number of cold- and heat-related deaths under each scenario, and highlighted tradeoffs for either policy scenario, finding considerable heterogeneity in trends between these cities. To make our analysis more specific to the mid-21st century, we incorporated existing estimates for population growth and mortality rate changes based on the same climate modeling scenarios as the SAU exposure scenarios. We observed dramatic variability in minimum mortality temperatures and temperature-attributable mortality between cities and found that SAI was not associated with decisive reductions in all-cause mortality among either age group. While SAI did effectively reduce heat-attributable mortality, lower cold-attributable mortality under the warmer, non-SAI scenario counterbalanced this effect, yielding a weak net impact in central tendencies. This observation could help inform planning and resilience efforts as far as types of temperature-related stress under each scenario, as well as provide insights for larger cost-benefit analyses for the overall proposition of SAI. Together, these projects demonstrated how quantitative health impact assessments can help form a methodological foundation for exploring epidemiology and resilience-relevant systems. The variety of projects covered demonstrated the utility of this methodology in a variety of spatial scales, ranging from census block groups (comparable to neighborhoods) in Project 2 to county-level characterizations of tropical cyclone-associated risk for much of the eastern United States in Project 1. We also explored a range of time periods, ranging from Project 1's focus on characterizing tropical cyclone risk representative of the past several decades (as represented by the STORM resampling algorithm), through our attempts to explicitly model mid-21st century populations and temperature-related mortality trends using both climate and demographic projections. The modularity of the quantitative health impact assessment framework enabled our projects to leverage of existing research and datasets for low-cost, comparatively rapid assessments, as well as to lay infrastructure for future research and introduce several specific innovations in their respective designs.