Ross K. Meentemeyer

Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems

A central challenge to studying emerging infectious diseases (EIDs) is a landscape dilemma: Our best empirical understanding of disease dynamics occurs at local scales, whereas pathogen invasions and management occur over broad spatial extents. The burgeoning field of landscape epidemiology integrates concepts and approaches from disease ecology with the macroscale lens of landscape ecology, enabling examination of disease across spatiotemporal scales in complex environmental settings. We review the state of the field and describe analytical frontiers that show promise for advancement, focusing on natural and human-altered ecosystems. Concepts fundamental to practicing landscape epidemiology are discussed, including spatial scale, static versus dynamic modeling, spatially implicit versus explicit approaches, selection of ecologically meaningful variables, and inference versus prediction. We highlight studies that have advanced the field by incorporating multiscale analyses, landscape connectivity, and dynamic modeling. Future research directions include understanding disease as a component of interacting ecological disturbances, scaling up the ecological impacts of disease, and examining disease dynamics as a coupled human-natural system.

Epidemiological modeling of invasion in heterogeneous landscapes: spread of sudden oak death in California (1990–2030)

The spread of emerging infectious diseases (EIDs) in natural environments poses substantial risks to biodiversity and ecosystem function. As EIDs and their impacts grow, landscape- to regional-scale models of disease dynamics are increasingly needed for quantitative prediction of epidemic outcomes and design of practicable strategies for control. Here we use spatio-temporal, stochastic epidemiological modeling in combination with realistic geographical modeling to predict the spread of the sudden oak death pathogen (Phytophthora ramorum) through heterogeneous host populations in wildland forests, subject to fluctuating weather conditions. The model considers three stochastic processes: (1) the production of inoculum at a given site; (2) the chance that inoculum is dispersed within and among sites; and (3) the probability of infection following transmission to susceptible host vegetation. We parameterized the model using Markov chain Monte Carlo (MCMC) estimation from snapshots of local- and regional-scale data on disease spread, taking account of landscape heterogeneity and the principal scales of spread. Our application of the model to Californian landscapes over a 40-year period (1990–2030), since the approximate time of pathogen introduction, revealed key parameters driving the spatial spread of disease and the magnitude of stochastic variability in epidemic outcomes. Results show that most disease spread occurs via local dispersal (<250 m) but infrequent long-distance dispersal events can substantially accelerate epidemic spread in regions with high host availability and suitable weather conditions. In the absence of extensive control, we predict a ten-fold increase in disease spread between 2010 and 2030 with most infection concentrated along the north coast between San Francisco and Oregon. Long-range dispersal of inoculum to susceptible host communities in the Sierra Nevada foothills and coastal southern California leads to little secondary infection due to lower host availability and less suitable weather conditions. However, a shift to wetter and milder conditions in future years would double the amount of disease spread in California through 2030. This research illustrates how stochastic epidemiological models can be applied to realistic geographies and used to increase predictive understanding of disease dynamics in large, heterogeneous regions.

Early detection of emerging forest disease using dispersal estimation and ecological niche modeling

Distinguishing the manner in which dispersal limitation and niche requirements control the spread of invasive pathogens is important for prediction and early detection of disease outbreaks. Here, we use niche modeling augmented by dispersal estimation to examine the degree to which local habitat conditions vs. force of infection predict invasion of Phytophthora ramorum, the causal agent of the emerging infectious tree disease sudden oak death. We sampled 890 field plots for the presence of P. ramorum over a three-year period (2003-2005) across a range of host and abiotic conditions with variable proximities to known infections in California, USA. We developed and validated generalized linear models of invasion probability to analyze the relative predictive power of 12 niche variables and a negative exponential dispersal kernel estimated by likelihood profiling. Models were developed incrementally each year (2003, 2003-2004, 2003-2005) to examine annual variability in model parameters and to create realistic scenarios for using models to predict future infections and to guide early-detection sampling. Overall, 78 new infections were observed up to 33.5 km from the nearest known site of infection, with slightly increasing rates of prevalence across time windows (2003, 6.5%; 2003-2004, 7.1%; 2003-2005, 9.6%). The pathogen was not detected in many field plots that contained susceptible host vegetation. The generalized linear modeling indicated that the probability of invasion is limited by both dispersal and niche constraints. Probability of invasion was positively related to precipitation and temperature in the wet season and the presence of the inoculum-producing foliar host Umbellularia californica and decreased exponentially with distance to inoculum sources. Models that incorporated niche and dispersal parameters best predicted the locations of new infections, with accuracies ranging from 0.86 to 0.90, suggesting that the modeling approach can be used to forecast locations of disease spread. Application of the combined niche plus dispersal models in a geographic information system predicted the presence of P. ramorum across approximately 8228 km2 of Californias 84785 km2 (9.7%) of land area with susceptible host species. This research illustrates how probabilistic modeling can be used to analyze the relative roles of niche and dispersal limitation in controlling the distribution of invasive pathogens.

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.

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 pathogens 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 pathogens 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.

Climatologically Aided Mapping of Daily Precipitation and Temperature

Abstract Accurately mapped meteorological data are an essential component for hydrologic and ecological research conducted at broad scales. A simple yet effective method for mapping daily weather conditions across heterogeneous landscapes is described and assessed. Daily weather data recorded at point locations are integrated with long-term-average climate maps to reconstruct spatially explicit estimates of daily precipitation and temperature extrema. The method uses ordinary kriging to interpolate base station data spatially into fields of approximately 2-km grain size. The fields are subsequently adjusted by 30-yr-average climate maps [Parameter-Elevation Regression on Independent Slopes Model (PRISM)], which incorporate adiabatic lapse rates, orographic effects, coastal proximity, and other environmental factors. The accuracy assessment evaluated an interpolation-only approach and the new method by comparing predicted and observed values from an independent validation dataset. The results of the accura...

INFLUENCE OF LAND‐COVER CHANGE ON THE SPREAD OF AN INVASIVE FOREST PATHOGEN

Human-caused changes in land use and land cover have dramatically altered ecosystems worldwide and may facilitate the spread of infectious diseases. To address this issue, we examined the influence of land-cover changes between 1942 and 2000 on the establishment of an invasive pathogen, Phytophthora ramorum, which causes the forest disease known as Sudden Oak Death. We assessed effects of land-cover change, forest structure, and understory microclimate on measures of inoculum load and disease prevalence in 102 15 x 15 m plots within a 275-km2 region in northern California. Within a 150 m radius area around each plot, we mapped types of land cover (oak woodland, chaparral, grassland, vineyard, and development) in 1942 and 2000 using detailed aerial photos. During this 58-year period, oak woodlands significantly increased in area by 25%, while grassland and chaparral decreased by 34% and 51%, respectively. Analysis of covariance revealed that vegetation type in 1942 and woodland expansion were significant predictors of pathogen inoculum load in bay laurel (Umbellularia californica), the primary inoculum-producing host for P. ramorum in mixed evergreen forests. Path analysis showed that woodland expansion resulted in larger forests with higher densities of the primary host trees (U. californica, Quercus agrifolia, Q. kelloggii) and cooler understory temperatures. Together, the positive effects of woodland size and negative effects of understory temperature explained significant variation in inoculum load and disease prevalence in bay laurel; host stem density had additional positive effects on inoculum load. We conclude that enlargement of woodlands and closure of canopy gaps, likely due largely to years of fire suppression, facilitated establishment of P. ramorum by increasing the area occupied by inoculum-production foliar hosts and enhancing forest microclimate conditions. Epidemiological studies that incorporate land-use change are rare but may increase understanding of disease dynamics and improve our ability to manage invasive forest pathogens.

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.

FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm

We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes.

Automated mapping of conformity between topographic and geological surfaces

We present a technique to produce spatially distributed fields of geometric alignment between topography and the orientation of geologic bedding planes (topographic/bedding-plane intersection angle). Computation and digital mapping of the topographic/bedding-plane intersection angle (TOBIA) requires the derivation of four spatially distributed variables: topographic slope, slope aspect, bedding dip, and dip azimuth. Slope and slope aspect surfaces are derived from a high resolution (10 m) digital elevation model. Ordinary kriging is used to interpolate spatially continuous fields of dip azimuth and dip from point measurements of strike and dip. Using these variables, TOBIA can be mapped either categorically as slope types, or as a continuous index. Categorical mapping requires two steps. First, slopes are classified into three functional types based on the alignment between the dip azimuth and slope aspect. Slopes are then further partitioned based on the alignment between slope angle and dip angle. Continuous computations of TOBIA rely on a geometric equation using all four variables. The methods provide an eAcient means for estimating topographic/bedding plane intersection angles over large areas. Resulting surfaces are useful for a variety of landscape-scale modeling applications, such as the prediction of potential hillslope failure, hydrologic flow paths, and vegetation patterns. 7 2000 Elsevier Science Ltd. All rights reserved.