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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Understanding human--coyote encounters in urban ecosystems using citizen science data: what do socioeconomics tell us?

The coyote (Canis latrans) has dramatically expanded its range to include the cities and suburbs of the western US and those of the Eastern Seaboard. Highly adaptable, this newcomer's success causes conflicts with residents, necessitating research to understand the distribution of coyotes in urban landscapes. Citizen science can be a powerful approach toward this aim. However, to date, the few studies that have used publicly reported coyote sighting data have lacked an in-depth consideration of human socioeconomic variables, which we suggest are an important source of overlooked variation in data that describe the simultaneous occurrence of coyotes and humans. We explored the relative importance of socioeconomic variables compared to those describing coyote habitat in predicting human-coyote encounters in highly-urbanized Mecklenburg County, North Carolina, USA using 707 public reports of coyote sightings, high-resolution land cover, US Census data, and an autologistic multi-model inference approach. Three of the four socioeconomic variables which we hypothesized would have an important influence on encounter probability, namely building density, household income, and occupation, had effects at least as large as or larger than coyote habitat variables. Our results indicate that the consideration of readily available socioeconomic variables in the analysis of citizen science data improves the prediction of species distributions by providing insight into the effects of important factors for which data are often lacking, such as resource availability for coyotes on private property and observer experience. Managers should take advantage of citizen scientists in human-dominated landscapes to monitor coyotes in order to understand their interactions with humans.

Unexpected redwood mortality from synergies between wildfire and an emerging infectious disease.

An under-examined component of global change is the alteration of disturbance regimes due to warming climates, continued species invasions, and accelerated land-use change. These drivers of global change are themselves novel ecosystem disturbances that may interact with historically occurring disturbances in complex ways. Here we use the natural experiment presented by wildfires in redwood forests impacted by an emerging infectious disease to demonstrate unexpected synergies of novel disturbance interactions. The dominant tree, coast redwood (fire resistant without negative disease impacts), experienced unexpected synergistic increases in mortality when fire and disease co-occurred. The increased mortality risk, more than fourfold at the peak of the effect, was not predictable from impacts of either disturbance alone. Changes in fire behavior associated with changes to forest fuels that occurred through disease progression overwhelmed redwood's usual resilience to wildfire. Our results demonstrate the potential for interacting disturbances to initiate novel successional trajectories and compromise ecosystem resilience.

Landscape epidemiology and control of pathogens with cryptic and long-distance dispersal: sudden oak death in northern Californian forests.

Exotic pathogens and pests threaten ecosystem service, biodiversity, and crop security globally. If an invasive agent can disperse asymptomatically over long distances, multiple spatial and temporal scales interplay, making identification of effective strategies to regulate, monitor, and control disease extremely difficult. The management of outbreaks is also challenged by limited data on the actual area infested and the dynamics of spatial spread, due to financial, technological, or social constraints. We examine principles of landscape epidemiology important in designing policy to prevent or slow invasion by such organisms, and use Phytophthora ramorum, the cause of sudden oak death, to illustrate how shortfalls in their understanding can render management applications inappropriate. This pathogen has invaded forests in coastal California, USA, and an isolated but fast-growing epidemic focus in northern California (Humboldt County) has the potential for extensive spread. The risk of spread is enhanced by the pathogen's generalist nature and survival. Additionally, the extent of cryptic infection is unknown due to limited surveying resources and access to private land. Here, we use an epidemiological model for transmission in heterogeneous landscapes and Bayesian Markov-chain-Monte-Carlo inference to estimate dispersal and life-cycle parameters of P. ramorum and forecast the distribution of infection and speed of the epidemic front in Humboldt County. We assess the viability of management options for containing the pathogen's northern spread and local impacts. Implementing a stand-alone host-free "barrier" had limited efficacy due to long-distance dispersal, but combining curative with preventive treatments ahead of the front reduced local damage and contained spread. While the large size of this focus makes effective control expensive, early synchronous treatment in newly-identified disease foci should be more cost-effective. We show how the successful management of forest ecosystems depends on estimating the spatial scales of invasion and treatment of pathogens and pests with cryptic long-distance dispersal.

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.

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.

Forest species diversity reduces disease risk in a generalist plant pathogen invasion.

Empirical evidence suggests that biodiversity loss can increase disease transmission, yet our understanding of the 'diversity-disease hypothesis' for generalist pathogens in natural ecosystems is limited. We used a landscape epidemiological approach to examine two scenarios regarding diversity effects on the emerging plant pathogen Phytophthora ramorum across a broad, heterogeneous ecoregion: (1) an amplification effect exists where disease risk is greater in areas with higher plant diversity due to the pathogen's wide host range, or (2) a dilution effect where risk is reduced with increasing diversity due to lower competency of alternative hosts. We found evidence for pathogen dilution, whereby disease risk was lower in sites with higher species diversity, after accounting for potentially confounding effects of host density and landscape heterogeneity. Our results suggest that although nearly all plants in the ecosystem are hosts, alternative hosts may dilute disease transmission by competent hosts, thereby buffering forest health from infectious disease.

Interacting disturbances: wildfire severity affected by stage of forest disease invasion.

Sudden oak death (SOD) is an emerging forest disease causing extensive tree mortality in coastal California forests. Recent California wildfires provided an opportunity to test a major assumption underlying discussions of SOD and land management: SOD mortality will increase fire severity. We examined prefire fuels from host species in a forest monitoring plot network in Big Sur, California (USA), to understand the interactions between disease-caused mortality and wildfire severity during the 2008 Basin Complex wildfire. Detailed measurements of standing dead woody stems and downed woody debris 1-2 years prior to the Basin fire provided a rare picture of the increased fuels attributable to SOD mortality. Despite great differences in host fuel abundance, we found no significant difference in burn severity between infested and uninfested plots. Instead, the relationship between SOD and fire reflected the changing nature of the disease impacts over time. Increased SOD mortality contributed to overstory burn severity only in areas where the pathogen had recently invaded. Where longer-term disease establishment allowed dead material to fall and accumulate, increasing log volumes led to increased substrate burn severity. These patterns help inform forest management decisions regarding fire, both in Big Sur and in other areas of California as the pathogen continues to expand throughout coastal forests.

Predicting the economic costs and property value losses attributed to sudden oak death damage in California (2010-2020).

Phytophthora ramorum, cause of sudden oak death, is a quarantined, non-native, invasive forest pathogen resulting in substantial mortality in coastal live oak (Quercus agrifolia) and several other related tree species on the Pacific Coast of the United States. We estimate the discounted cost of oak treatment, removal, and replacement on developed land in California communities using simulations of P. ramorum spread and infection risk over the next decade (2010-2020). An estimated 734 thousand oak trees occur on developed land in communities in the analysis area. The simulations predict an expanding sudden oak death (SOD) infestation that will likely encompass most of northwestern California and warrant treatment, removal, and replacement of more than 10 thousand oak trees with discounted cost of $7.5 million. In addition, we estimate the discounted property losses to single family homes of $135 million. Expanding the land base to include developed land outside as well as inside communities doubles the estimates of the number of oak trees killed and the associated costs and losses. The predicted costs and property value losses are substantial, but many of the damages in urban areas (e.g. potential losses from increased fire and safety risks of the dead trees and the loss of ecosystem service values) are not included.

Apparent competition in canopy trees determined by pathogen transmission rather than susceptibility.

Epidemiological theory predicts that asymmetric transmission, susceptibility, and mortality within a community will drive pathogen and disease dynamics. These epidemiological asymmetries can result in apparent competition, where a highly infectious host reduces the abundance of less infectious or more susceptible members in a community via a shared pathogen. We show that the exotic pathogen Phytophthora ramorum and resulting disease, sudden oak death, cause apparent competition among canopy trees and that transmission differences among canopy trees drives patterns of disease severity in California coast redwood forests. P. ramorum ranges in its ability to infect, sporulate on, and cause mortality of infected hosts. A path analysis showed that the most prolific inoculum producer, California bay laurel (Umbellularia californica), had a greater impact on the mortality rate of tanoak (Lithocarpus densiflorus) than did other inoculum-supporting species. In stands experiencing high tanoak mortality, lack of negative impacts by P. ramorum on bay laurel may increase bay laurel density and subsequently result in positive feedback on pathogen populations. This study demonstrates the degree to which invasive, generalist pathogens can cause rapid changes in forest canopy composition and that differences in transmission can be more important than susceptibility in driving patterns of apparent competition.