Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk.

Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%-24%, depending on flood hazard zone (50%-0.2% annual probability). We simulated various "what-if" scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.

Point Density Variations in Airborne Lidar Point Clouds.

In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit point density variations. Some of these density variations indicate issues with point clouds, potentially leading to errors in derived products. To highlight these issues, we provide an overview of point density variations and show examples in six airborne lidar point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, homogenizations, and their applicability.

Integrating principles and tools of decision science into value-driven watershed planning for compensatory mitigation.

Several environmental policies strive to restore impaired ecosystems and could benefit from a consistent and transparent process-codeveloped with key stakeholders-to prioritize impaired ecosystems for restoration activities. The Clean Water Act, for example, establishes reallocation mechanisms to transfer ecosystem services from sites of disturbance to compensation sites to offset aquatic resource functions that are unavoidably lost through land development. However, planning for the prioritization of compensatory mitigation areas is often hampered by decision-making processes that fall into a myopic decision frame because they are not coproduced with stakeholders. In this study, we partnered with domain experts from the North Carolina Division of Mitigation Services to codevelop a real-world decision framework to prioritize catchments by potential for the development of mitigation projects following principles of a structured decision-making process and knowledge coproduction. Following an iterative decision analysis cycle, domain experts revised foundational components of the decision framework and progressively added complexity and realism as they gained additional insights or more information became available. Through the course of facilitated in-person and remote interactions, the codevelopment of a decision framework produced three main "breakthroughs" from the perspective of the stakeholder group: (a) recognition of the problem as a multiobjective decision driven by several values in addition to biogeophysical goals (e.g., functional uplift, restoring or enhancing lost functionality of ecosystems); (b) that the decision comprises a linked and sequential planning-to-implementation process; and (c) future risk associated with land-use and climate change must be considered. We also present an interactive tool for "on-the-fly" assessment of alternatives and tradeoff analysis, allowing domain experts to quickly test, react to, and revise prioritization strategies. The decision framework described in this study is not limited to the prioritization of compensatory mitigation activities across North Carolina but rather serves as a framework to prioritize a wide range of restoration, conservation, and resource allocation activities in similar environmental contexts across the nation.

Spotted lanternfly predicted to establish in California by 2033 without preventative management.

Models that are both spatially and temporally dynamic are needed to forecast where and when non-native pests and pathogens are likely to spread, to provide advance information for natural resource managers. The potential US range of the invasive spotted lanternfly (SLF, Lycorma delicatula) has been modeled, but until now, when it could reach the West Coast's multi-billion-dollar fruit industry has been unknown. We used process-based modeling to forecast the spread of SLF assuming no treatments to control populations occur. We found that SLF has a low probability of first reaching the grape-producing counties of California by 2027 and a high probability by 2033. Our study demonstrates the importance of spatio-temporal modeling for predicting the spread of invasive species to serve as an early alert for growers and other decision makers to prepare for impending risks of SLF invasion. It also provides a baseline for comparing future control options.

Iteratively forecasting biological invasions with PoPS and a little help from our friends.

Ecological forecasting has vast potential to support environmental decision making with repeated, testable predictions across management-relevant timescales and locations. Yet resource managers rarely use co-designed forecasting systems or embed them in decision making. Although prediction of planned management outcomes is particularly important for biological invasions to optimize when and where resources should be allocated, spatial-temporal models of spread typically have not been openly shared, iteratively updated, or interactive to facilitate exploration of management actions. We describe a species-agnostic, open-source framework - called the Pest or Pathogen Spread (PoPS) Forecasting Platform - for co-designing near-term iterative forecasts of biological invasions. Two case studies are presented to demonstrate that iterative calibration yields higher forecast skill than using only the earliest-available data to predict future spread. The PoPS framework is a primary example of an ecological forecasting system that has been both scientifically improved and optimized for real-world decision making through sustained participation and use by management stakeholders.

Evaluating online and tangible interfaces for engaging stakeholders in forecasting and control of biological invasions.

Ecological forecasts will be best suited to inform intervention strategies if they are accessible to a diversity of decision-makers. Researchers are developing intuitive forecasting interfaces to guide stakeholders through the development of intervention strategies and visualization of results. Yet, few studies to date have evaluated how user interface design facilitates the coordinated, cross-boundary management required for controlling biological invasions. We used a participatory approach to develop complementary tangible and online interfaces for collaboratively forecasting biological invasions and devising control strategies. A diverse group of stakeholders evaluated both systems in the real-world context of controlling sudden oak death, an emerging forest disease killing millions of trees in California and Oregon. Our findings suggest that while both interfaces encouraged adaptive experimentation, tangible interfaces are particularly well suited to support collaborative decision-making. Reflecting on the strengths of both systems, we suggest workbench-style interfaces that support simultaneous interactions and dynamic geospatial visualizations.

Forecasting water demand across a rapidly urbanizing region.

Urban growth and climate change together complicate planning efforts meant to adapt to increasingly scarce water supplies. Several studies have independently examined the impacts of urban planning and climate change on water demand, but little attention has been given to their combined impact. Here we forecast urban water demand using a Geographically Weighted Regression model informed by socio-economic, environmental and landscape pattern metrics. The purpose of our study is to evaluate how future scenarios of population densities and climate warming will jointly affect water demand across two rapidly growing U.S. states (North Carolina and South Carolina). Our forecasts indicate that regional water demand by 2065 will increase by 37%-383% relative to the baseline in 2010, across all scenarios of change. Our results show future water demand will increase under rising temperatures, but could be ameliorated by policies that promote higher density development and urban infill. These water-efficient land use policies show a 5% regional reduction in water demand and up to 25% reduction locally for counties with the highest expected population growth by 2065. For rural counties experiencing depopulation, the land use policies we considered are insufficient to significantly reduce water demand. For expanding communities seeking to increase their adaptive capacity to changing socio-environmental conditions, our framework can assist in developing sustainable solutions.

Spatiotemporal patterns and drivers of soil contamination with heavy metals during an intensive urbanization period (1989-2018) in southern China.

This three-decade long study was conducted in the Pearl River Delta (PRD), a rapidly urbanizing region in southern China. Extensive soil samples for a diverse land uses were collected in 1989 (113), 2005 (1384), 2009 (521), and 2018 (421) for heavy metals of As, Cr, Cd, Cu, Hg, Ni, Pb and Zn. Multiple pollution indices and Structural Equation Models (SEMs) were used in attribution analysis and comprehensive assessments. Data showed that majority of the sampling sites was contaminated by one or more heavy metals, but pollutant concentrations had not reached levels of concerns for food security or human health. There was an increasing trend in heavy metal contamination over time and the variations of soil contamination were site-, time- and pollutant-dependent. Areas with high concentrations of heavy metals overlapped with highly industrialized and populated areas in western part of the study region. A dozen SEMs path analyses were used to compare the relative influences of key environmental factors on soil contamination across space and time. The high or elevated soil contaminations by As, Cr, Ni, Cu and Zn were primarily affected by soil properties during the study period, except 1989-2005, followed by land use patterns. Parent materials had a significant effect on elevated soil contamination of Cd, Cr, Ni, Pb and overall soil pollution during 1989-2005. We hypothesized that other factors not considered in the present study, such as atmospheric deposition, sewage irrigation, and agrochemical uses, may be also important to explain the variability of soil contamination. This study implied that strategies to improve soil physiochemical properties and optimize landscape structures are viable methods to mitigate soil contamination. Future studies should monitor pollutant sources identified by this study to fully understand the causes of heavy metal contamination in rapidly industrialized regions in southern China.

Forecasting and control of emerging infectious forest disease through participatory modelling.

Epidemiological models are powerful tools for evaluating scenarios and visualizing patterns of disease spread, especially when comparing intervention strategies. However, the technical skill required to synthesize and operate computational models frequently renders them beyond the command of the stakeholders who are most impacted by the results. Participatory modelling (PM) strives to restructure the power relationship between modellers and the stakeholders who rely on model insights by involving these stakeholders directly in model development and application; yet, a systematic literature review indicates little adoption of these techniques in epidemiology, especially plant epidemiology. We investigate the potential for PM to integrate stakeholder and researcher knowledge, using Phytophthora ramorum and the resulting sudden oak death disease as a case study. Recent introduction of a novel strain (European 1 or EU1) in southwestern Oregon has prompted significant concern and presents an opportunity for coordinated management to minimize regional pathogen impacts. Using a PM framework, we worked with local stakeholders to develop an interactive forecasting tool for evaluating landscape-scale control strategies. We find that model co-development has great potential to empower stakeholders in the design, development and application of epidemiological models for disease control. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Direct and indirect effects of forest microclimate on pathogen spillover.

Disease dynamics are governed by variation of individuals, species, and environmental conditions across space and time. In some cases, an alternate reservoir host amplifies pathogen loads and drives disease transmission to less competent hosts in a process called pathogen spillover. Spillover is frequently associated with multi-host disease systems where a single species is more tolerant of infection and more competent in pathogen transmission compared to other hosts. Pathogen spillover must be driven by biotic factors, including host and community characteristics, yet biotic factors interact with the abiotic environment (e.g., temperature) to create disease. Despite its fundamental role in disease dynamics, the influence of the abiotic environment on pathogen spillover has seldom been examined. Improving our understanding of disease processes such as pathogen spillover hinges on disentangling the effects of interrelated biotic and abiotic factors over space and time. We applied 10 yr of fine-scale microclimate, disease, and tree community data in a path analysis to investigate the relative influence of biotic and abiotic factors on pathogen spillover for the emerging infectious forest disease sudden oak death (SOD). Disease transmission in SOD is primarily driven by the reservoir host California bay laurel, which supports high foliar pathogen loads that spillover onto neighboring oak trees and create lethal canker infections. The foliar pathogen load and susceptibility of oaks is expected to be sensitive to forest microclimate conditions. We found that biotic factors of pathogen load and tree diversity had relatively stronger effects on pathogen spillover compared to abiotic microclimate factors, with pathogen load increasing oak infection and tree diversity reducing oak infection. Abiotic factors still had significant effects, with greater heat exposure during summer months reducing pathogen loads and optimal pathogen conditions during the wet season increasing oak infection. Our results offer clues to possible disease dynamics under future climate change where hotter and drier or warmer and wetter conditions could have opposing effects on pathogen spillover in the SOD system. Disentangling direct and indirect effects of biotic and abiotic factors affecting disease processes can provide key insights into disease dynamics including potential avenues for reducing disease spread and predicting future epidemics.

Novel disturbance interactions between fire and an emerging disease impact survival and growth of resprouting trees.

Human-altered ecological disturbances may challenge system resilience and disrupt biological legacies maintaining ecosystem recovery. Yet, the extent to which novel regimes challenge these legacies varies. This may be partially explained by differences in the vulnerability of life history strategies to disturbance characteristics. In the fire-prone, resprouter-dominated coast redwood forests of California, the introduced disease sudden oak death (SOD) alters fuel profiles, fire behavior, and aboveground tree mortality; however, this system is dominated by resprouting trees that are well-adapted to aboveground damage, and belowground survival of individuals may represent the principal biological legacy connecting pre- and post-fire communities. Much of the research exploring altered disturbances and forest recovery has focused on legacies determined by seed dispersal and aboveground survival of adults. In this work, we use pre- and post-fire data from a long-term monitoring network to assess the impacts of novel disturbance interactions between wildfire and SOD on the belowground survival and vegetative reproduction of resprouters. We found that increasing accumulation of coarse woody surface fuels from SOD-killed hosts decreased the likelihood of belowground survival for resprouting tanoak trees, but not for redwoods. Tanoaks' belowground survival was negatively related to substrate burn severity, which increased with the volume of surface fuels from hosts, suggesting heat damage as a possible mechanism influencing altered patterns of resprouter mortality. These impacts increased with decreasing tree size. By contrast, redwood and tanoak trees that survived both disturbances resprouted more vigorously, regardless of post-fire infection by P. ramorum, and generated similar recruitment at the stand level. Our results demonstrate that disease-fire interactions can narrow recruitment filters for resprouters, which could impact long-term population and demographic structure; yet, compounded disturbance may also reduce stand density and disease pressure, allowing competitive release of survivors. Resprouters displayed vulnerabilities to altered disturbance, but our research suggests that legacies maintained by resprouting may be more resilient to certain compounded disturbances, compared to seed-obligate species, because of high rates of individual survival under increasingly severe events. These trends have important implications for conservation of declining tree species in SOD-impacted forests, as well as predictions of human impacts in other disturbance-prone systems where resprouters are present.

Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR.

Landscapes are increasingly recognized for providing valuable cultural ecosystem services with numerous non-material benefits by serving as places of rest, relaxation, and inspiration that ultimately improve overall mental health and physical well-being. Maintaining and enhancing these valuable benefits through targeted management and conservation measures requires understanding the spatial and temporal determinants of perceived landscape values. Content contributed through mobile technologies and the web are emerging globally, providing a promising data source for localizing and assessing these landscape benefits. These georeferenced data offer rich in situ qualitative information through photos and comments that capture valued and special locations across large geographic areas. We present a novel method for mapping and modeling landscape values and perceptions that leverages viewshed analysis of georeferenced social media data. Using a high resolution LiDAR (Light Detection and Ranging) derived digital surface model, we are able to evaluate landscape characteristics associated with the visual-sensory qualities of outdoor recreationalists. Our results show the importance of historical monuments and attractions in addition to specific environmental features which are appreciated by the public. Evaluation of photo-image content highlights the opportunity of including temporally and spatially variable visual-sensory qualities in cultural ecosystem services (CES) evaluation like the sights, sounds and smells of wildlife and weather phenomena.

The managed clearing: An overlooked land-cover type in urbanizing regions?

Urban ecosystem assessments increasingly rely on widely available map products, such as the U.S. Geological Service (USGS) National Land Cover Database (NLCD), and datasets that use generic classification schemes to detect and model large-scale impacts of land-cover change. However, utilizing existing map products or schemes without identifying relevant urban class types such as semi-natural, yet managed land areas that account for differences in ecological functions due to their pervious surfaces may severely constrain assessments. To address this gap, we introduce the managed clearings land-cover type-semi-natural, vegetated land surfaces with varying degrees of management practices-for urbanizing landscapes. We explore the extent to which managed clearings are common and spatially distributed in three rapidly urbanizing areas of the Charlanta megaregion, USA. We visually interpreted and mapped fine-scale land cover with special attention to managed clearings using 2012 U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) images within 150 randomly selected 1-km2 blocks in the cities of Atlanta, Charlotte, and Raleigh, and compared our maps with National Land Cover Database (NLCD) data. We estimated the abundance of managed clearings relative to other land use and land cover types, and the proportion of land-cover types in the NLCD that are similar to managed clearings. Our study reveals that managed clearings are the most common land cover type in these cities, covering 28% of the total sampled land area- 6.2% higher than the total area of impervious surfaces. Managed clearings, when combined with forest cover, constitutes 69% of pervious surfaces in the sampled region. We observed variability in area estimates of managed clearings between the NAIP-derived and NLCD data. This suggests using high-resolution remote sensing imagery (e.g., NAIP) instead of modifying NLCD data for improved representation of spatial heterogeneity and mapping of managed clearings in urbanizing landscapes. Our findings also demonstrate the need to more carefully consider managed clearings and their critical ecological functions in landscape- to regional-scale studies of urbanizing ecosystems.

Uncertainties in mapping forest carbon in urban ecosystems.

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m2, aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.

Continental-scale quantification of landscape values using social media data.

Individuals, communities, and societies ascribe a diverse array of values to landscapes. These values are shaped by the aesthetic, cultural, and recreational benefits and services provided by those landscapes. However, across the globe, processes such as urbanization, agricultural intensification, and abandonment are threatening landscape integrity, altering the personally meaningful connections people have toward specific places. Existing methods used to study landscape values, such as social surveys, are poorly suited to capture dynamic landscape-scale processes across large geographic extents. Social media data, by comparison, can be used to indirectly measure and identify valuable features of landscapes at a regional, continental, and perhaps even worldwide scale. We evaluate the usefulness of different social media platforms-Panoramio, Flickr, and Instagram-and quantify landscape values at a continental scale. We find Panoramio, Flickr, and Instagram data can be used to quantify landscape values, with features of Instagram being especially suitable due to its relatively large population of users and its functional ability of allowing users to attach personally meaningful comments and hashtags to their uploaded images. Although Panoramio, Flickr, and Instagram have different user profiles, our analysis revealed similar patterns of landscape values across Europe across the three platforms. We also found variables describing accessibility, population density, income, mountainous terrain, or proximity to water explained a significant portion of observed variation across data from the different platforms. Social media data can be used to extend our understanding of how and where individuals ascribe value to landscapes across diverse social, political, and ecological boundaries.

Wildfire and forest disease interaction lead to greater loss of soil nutrients and carbon.

Fire and forest disease have significant ecological impacts, but the interactions of these two disturbances are rarely studied. We measured soil C, N, Ca, P, and pH in forests of the Big Sur region of California impacted by the exotic pathogen Phytophthora ramorum, cause of sudden oak death, and the 2008 Basin wildfire complex. In Big Sur, overstory tree mortality following P. ramorum invasion has been extensive in redwood and mixed evergreen forests, where the pathogen kills true oaks and tanoak (Notholithocarpus densiflorus). Sampling was conducted across a full-factorial combination of disease/no disease and burned/unburned conditions in both forest types. Forest floor organic matter and associated nutrients were greater in unburned redwood compared to unburned mixed evergreen forests. Post-fire element pools were similar between forest types, but lower in burned-invaded compared to burned-uninvaded plots. We found evidence disease-generated fuels led to increased loss of forest floor C, N, Ca, and P. The same effects were associated with lower %C and higher PO4-P in the mineral soil. Fire-disease interactions were linear functions of pre-fire host mortality which was similar between the forest types. Our analysis suggests that these effects increased forest floor C loss by as much as 24.4 and 21.3 % in redwood and mixed evergreen forests, respectively, with similar maximum losses for the other forest floor elements. Accumulation of sudden oak death generated fuels has potential to increase fire-related loss of soil nutrients at the region-scale of this disease and similar patterns are likely in other forests, where fire and disease overlap.

Modeling when, where, and how to manage a forest epidemic, motivated by sudden oak death in California.

Sudden oak death, caused by Phytophthora ramorum, has killed millions of oak and tanoak in California since its first detection in 1995. Despite some localized small-scale management, there has been no large-scale attempt to slow the spread of the pathogen in California. Here we use a stochastic spatially explicit model parameterized using data on the spread of P. ramorum to investigate whether and how the epidemic can be controlled. We find that slowing the spread of P. ramorum is now not possible, and has been impossible for a number of years. However, despite extensive cryptic (i.e., presymptomatic) infection and frequent long-range transmission, effective exclusion of the pathogen from large parts of the state could, in principle, have been possible were it to have been started by 2002. This is the approximate date by which sufficient knowledge of P. ramorum epidemiology had accumulated for large-scale management to be realistic. The necessary expenditure would have been very large, but could have been greatly reduced by optimizing the radius within which infected sites are treated and careful selection of sites to treat. In particular, we find that a dynamic strategy treating sites on the epidemic wave front leads to optimal performance. We also find that "front loading" the budget, that is, treating very heavily at the start of the management program, would greatly improve control. Our work introduces a framework for quantifying the likelihood of success and risks of failure of management that can be applied to invading pests and pathogens threatening forests worldwide.

Effects of individual, community, and landscape drivers on the dynamics of a wildland forest epidemic.

The challenges posed by observing host-pathogen-environment interactions across large geographic extents and over meaningful time scales limit our ability to understand and manage wildland epidemics. We conducted a landscape-scale, longitudinal study designed to analyze the dynamics of sudden oak death (an emerging forest disease caused by Phytophthora ramorum) across hierarchical levels of ecological interactions, from individual hosts up to the community and across the broader landscape. From 2004 to 2011, we annually assessed disease status of 732 coast live oak, 271 black oak, and 122 canyon live oak trees in 202 plots across a 275-km2 landscape in central California. The number of infected oak stems steadily increased during the eight-year study period. A survival analysis modeling framework was used to examine which level of ecological heterogeneity best predicted infection risk of susceptible oak species, considering variability at the level of individuals (species identity, stem size), the community (host density, inoculum load, and species richness), and the landscape (seasonal climate variability, habitat connectivity, and topographic gradients). After accounting for unobserved risk shared among oaks in the same plot, survival models incorporating heterogeneity across all three levels better predicted oak infection than did models focusing on only one level. We show that larger oak trees (especially coast live oak) were more susceptible, and that interannual variability in inoculum production by the highly infectious reservoir host, California bay laurel, more strongly influenced disease risk than simply the density of this important host. Concurrently, warmer and wetter rainy-season conditions in consecutive years intensified infection risk, presumably by creating a longer period of inoculum build-up and increased probability of pathogen spillover from bay laurel to oaks. Despite the presence of many alternate host species, we found evidence of pathogen dilution, where less competent hosts in species-rich communities reduce pathogen transmission and overall risk of oak infection. These results identify key parameters driving the dynamics of emerging infectious disease in California woodlands, while demonstrating how multiple levels of ecological heterogeneity jointly determine epidemic trajectories in wildland settings.

Host Phenology and Leaf Effects on Susceptibility of California Bay Laurel to Phytophthora ramorum.

Spread of the plant pathogen Phytophthora ramorum, causal agent of the forest disease sudden oak death, is driven by a few competent hosts that support spore production from foliar lesions. The relationship between traits of a principal foliar host, California bay laurel (Umbellularia californica), and susceptibility to P. ramorum infection were investigated with multiple P. ramorum isolates and leaves collected from multiple trees in leaf-droplet assays. We examined whether susceptibility varies with season, leaf age, or inoculum position. Bay laurel susceptibility was highest during spring and summer and lowest in winter. Older leaves (>1 year) were more susceptible than younger ones (8 to 11 months). Susceptibility was greater at leaf tips and edges than the middle of the leaf. Leaf surfaces wiped with 70% ethanol were more susceptible to P. ramorum infection than untreated leaf surfaces. Our results indicate that seasonal changes in susceptibility of U. californica significantly influence P. ramorum infection levels. Thus, in addition to environmental variables such as temperature and moisture, variability in host plant susceptibility contributes to disease establishment of P. ramorum.

Geographic clustering of elevated blood heavy metal levels in pregnant women.

BACKGROUND: Cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) exposure is ubiquitous and has been associated with higher risk of growth restriction and cardiometabolic and neurodevelopmental disorders. However, cost-efficient strategies to identify at-risk populations and potential sources of exposure to inform mitigation efforts are limited. The objective of this study was to describe the spatial distribution and identify factors associated with Cd, Pb, Hg, and As concentrations in peripheral blood of pregnant women. METHODS: Heavy metals were measured in whole peripheral blood of 310 pregnant women obtained at gestational age ~12 weeks. Prenatal residential addresses were geocoded and geospatial analysis (Getis-Ord Gi* statistics) was used to determine if elevated blood concentrations were geographically clustered. Logistic regression models were used to identify factors associated with elevated blood metal levels and cluster membership. RESULTS: Geospatial clusters for Cd and Pb were identified with high confidence (p-value for Gi* statistic <0.01). The Cd and Pb clusters comprised 10.5 and 9.2 % of Durham County residents, respectively. Medians and interquartile ranges of blood concentrations (µg/dL) for all participants were Cd 0.02 (0.01-0.04), Hg 0.03 (0.01-0.07), Pb 0.34 (0.16-0.83), and As 0.04 (0.04-0.05). In the Cd cluster, medians and interquartile ranges of blood concentrations (µg/dL) were Cd 0.06 (0.02-0.16), Hg 0.02 (0.00-0.05), Pb 0.54 (0.23-1.23), and As 0.05 (0.04-0.05). In the Pb cluster, medians and interquartile ranges of blood concentrations (µg/dL) were Cd 0.03 (0.02-0.15), Hg 0.01 (0.01-0.05), Pb 0.39 (0.24-0.74), and As 0.04 (0.04-0.05). Co-exposure with Pb and Cd was also clustered, the p-values for the Gi* statistic for Pb and Cd was <0.01. Cluster membership was associated with lower education levels and higher pre-pregnancy BMI. CONCLUSIONS: Our data support that elevated blood concentrations of Cd and Pb are spatially clustered in this urban environment compared to the surrounding areas. Spatial analysis of metals concentrations in peripheral blood or urine obtained routinely during prenatal care can be useful in surveillance of heavy metal exposure.