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

Systematic reduction of natural enemies and competition across variable precipitation approximates buffelgrass invasiveness (Cenchrus ciliaris) in its native range

Invasive grasses cause devastating losses to biodiversity and ecosystem function directly and indirectly by altering ecosystem processes. Escape from natural enemies, plant-plant competition, and variable resource availability provide frameworks for understanding invasion. However, we lack a clear understanding of how natural stressors interact in their native range to regulate invasiveness. In this study, we reduced diverse guilds of natural enemies and plant competitors of the highly invasive buffelgrass across a precipitation gradient throughout major climatic shifts in Laikipia, Kenya. To do this, we used a long-term ungulate exclosure experiment design across a precipitation gradient with nested treatments that (1) reduced plant competition through clipping, (2) reduced insects through systemic insecticide, and (3) reduced fungal associates through fungicide application. Additionally, we measured the interaction of ungulates on two stem-boring insect species feeding on buffelgrass. Finally, we measured a multiyear smut fungus outbreak. Our findings suggest that buffelgrass exhibits invasive qualities when released from a diverse group of natural stressors in its native range. We show natural enemies interact with precipitation to alter buffelgrass productivity patterns. In addition, interspecific plant competition decreased the basal area of buffelgrass, suggesting that biotic resistance mediates buffelgrass dominance in the home range. Surprisingly, systemic insecticides and fungicides did not impact buffelgrass production or reproduction, perhaps because other guilds filled the niche space in these highly diverse systems. For example, in the absence of ungulates, we showed an increase in host-specific stem-galling insects, where these insects compensated for reduced ungulate use. Finally, we documented a smut outbreak in 2020 and 2021, corresponding to highly variable precipitation patterns caused by a shifting Indian Ocean Dipole. In conclusion, we observed how reducing natural enemies and competitors and certain interactions increased properties related to buffelgrass invasiveness.

SWECO25: a cross-thematic raster database for ecological research in Switzerland

Standard and easily accessible cross-thematic spatial databases are key resources in ecological research. In Switzerland, as in many other countries, available data are scattered across computer servers of research institutions and are rarely provided in standard formats (e.g., different extents or projections systems, inconsistent naming conventions). Consequently, their joint use can require heavy data management and geomatic operations. Here, we introduce SWECO25, a Swiss-wide raster database at 25-meter resolution gathering 5,265 layers. The 10 environmental categories included in SWECO25 are: geologic, topographic, bioclimatic, hydrologic, edaphic, land use and cover, population, transportation, vegetation, and remote sensing. SWECO25 layers were standardized to a common grid sharing the same resolution, extent, and geographic coordinate system. SWECO25 includes the standardized source data and newly calculated layers, such as those obtained by computing focal or distance statistics. SWECO25 layers were validated by a data integrity check, and we verified that the standardization procedure had a negligible effect on the output values. SWECO25 is available on Zenodo and is intended to be updated and extended regularly.

Toward One Health: a spatial indicator system to model the facilitation of the spread of zoonotic diseases

Recurrent outbreaks of zoonotic infectious diseases highlight the importance of considering the interconnections between human, animal, and environmental health in disease prevention and control. This has given rise to the concept of One Health, which recognizes the interconnectedness of between human and animal health within their ecosystems. As a contribution to the One Health approach, this study aims to develop an indicator system to model the facilitation of the spread of zoonotic diseases. Initially, a literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to identify relevant indicators related to One Health. The selected indicators focused on demographics, socioeconomic aspects, interactions between animal and human populations and water bodies, as well as environmental conditions related to air quality and climate. These indicators were characterized using values obtained from the literature or calculated through distance analysis, geoprocessing tasks, and other methods. Subsequently, Multi-Criteria Decision-Making (MCDM) techniques, specifically the Entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, were utilized to combine the indicators and create a composite metric for assessing the spread of zoonotic diseases. The final indicators selected were then tested against recorded zoonoses in the Valencian Community (Spain) for 2021, and a strong positive correlation was identified. Therefore, the proposed indicator system can be valuable in guiding the development of planning strategies that align with the One Health principles. Based on the results achieved, such strategies may prioritize the preservation of natural landscape features to mitigate habitat encroachment, protect land and water resources, and attenuate extreme atmospheric conditions.

Plant disease risk is modified by multiple global change drivers

Plant diseases are strongly influenced by host biodiversity, spatial structure, and abiotic conditions. All of these are undergoing rapid change, as the climate is warming, habitats are being lost, and nitrogen deposition is changing nutrient dynamics of ecosystems with ensuing consequences for biodiversity. Here, I review examples of plant-pathogen associations to demonstrate how our ability to understand, model and predict disease dynamics is becoming increasingly difficult, as both plant and pathogen populations and communities are undergoing extensive change. The extent of this change is influenced via both direct and combined effects of global change drivers, and especially the latter are still poorly understood. Change at one trophic level is expected to drive change also at the other, and hence feedback loops between plants and their pathogens are expected to drive changes in disease risk both through ecological as well as evolutionary mechanisms. Many of the examples discussed here demonstrate an increase in disease risk as a result of ongoing change, suggesting that unless we successfully mitigate global environmental change, plant disease is going to become an increasingly heavy burden on our societies with far-reaching consequences for food security and functioning of ecosystems.

A Survey of the Landscape Visibility Analysis Tools and Technical Improvements

Visual perception of the urban landscape in a city is complex and dynamic, and it is largely influenced by human vision and the dynamic spatial layout of the attractions. In return, landscape visibility not only affects how people interact with the environment but also promotes regional values and urban resilience. The development of visibility has evolved, and the digital landscape visibility analysis method allows urban researchers to redefine visible space and better quantify human perceptions and observations of the landscape space. In this paper, we first reviewed and compared the theoretical results and measurement tools for spatial visual perception and compared the value of the analytical methods and tools for landscape visualization in multiple dimensions on the principal of urban planning (e.g., complex environment, computational scalability, and interactive intervention between computation and built environment). We found that most of the research was examined in a static environment using simple viewpoints, which can hardly explain the actual complexity and dynamic superposition of the landscape perceptual effect in an urban environment. Thus, those methods cannot effectively solve actual urban planning issues. Aiming at this demand, we proposed a workflow optimization and developed a responsive cross-scale and multilandscape object 3D visibility analysis method, forming our analysis model for testing on the study case. By combining the multilandscape batch scanning method with a refined voxel model, it can be adapted for large-scale complex dynamic urban visual problems. As a result, we obtained accurate spatial visibility calculations that can be conducted across scales from the macro to micro, with large external mountain landscapes and small internal open spaces. Our verified approach not only has a good performance in the analysis of complex visibility problems (e.g., we defined the two most influential spatial variables to maintain good street-based landscape visibility) but also the high efficiency of spatial interventions (e.g., where the four recommended interventions were the most valuable), realizing the improvement of intelligent landscape evaluations and interventions for urban spatial quality and resilience.

Habitat connectivity and host relatedness influence virus spread across an urbanising landscape in a fragmentation-sensitive carnivore

Spatially heterogeneous landscape factors such as urbanisation can have substantial effects on the severity and spread of wildlife diseases. However, research linking patterns of pathogen transmission to landscape features remains rare. Using a combination of phylogeographic and machine learning approaches, we tested the influence of landscape and host factors on feline immunodeficiency virus (FIV_Lru) genetic variation and spread among bobcats (Lynx rufus) sampled from coastal southern California. We found evidence for increased rates of FIV_Lru lineage spread through areas of higher vegetation density. Furthermore, single-nucleotide polymorphism (SNP) variation among FIV_Lru sequences was associated with host genetic distances and geographic location, with FIV_Lru genetic discontinuities precisely correlating with known urban barriers to host dispersal. An effect of forest land cover on FIV_Lru SNP variation was likely attributable to host population structure and differences in forest land cover between different populations. Taken together, these results suggest that the spread of FIV_Lru is constrained by large-scale urban barriers to host movement. Although urbanisation at fine spatial scales did not appear to directly influence virus transmission or spread, we found evidence that viruses transmit and spread more quickly through areas containing higher proportions of natural habitat. These multiple lines of evidence demonstrate how urbanisation can change patterns of contact-dependent pathogen transmission and provide insights into how continued urban development may influence the incidence and management of wildlife disease.

Field and Landscape Risk Factors Impacting Flavescence Dorée Infection: Insights from Spatial Bayesian Modeling in the Bordeaux Vineyards

Flavescence dorée (FD) is a quarantine disease threatening European vineyards. Its management is based on mandatory insecticide treatments and the uprooting of infected plants identified during annual surveys. Field surveys are currently not optimized because the drivers affecting FD spread in vineyard landscapes remain poorly understood. We collated a georeferenced dataset of FD detection, collected from 34,581 vineyard plots over 5 years in the South West France wine region. Spatial models fitted with integrated nested Laplace approximation were used to identify local and landscape factors affecting FD detection and infection. Our analysis highlights the importance of sampling period on FD detection and of local practices and landscape context on FD infection. At field scale, altitude and cultivar choice were the main factors affecting FD infection. In particular, the odds ratio of FD infection in fields planted with the susceptible Cabernet Sauvignon, Cabernet Franc, or Muscadelle varieties were approximately twice those in fields planted with the less susceptible Merlot. Field infection was also affected by the field's immediate surroundings (within a circle with a radius of 150 to 200 m), corresponding to landscapes of 7 to 12 ha. In particular, the probability of FD infection increased with the proportions of forest and urban land and with the proportion of susceptible cultivars, demonstrating that the cultivar composition impacts FD epidemiology at landscape scale. The satisfactory predictive performance of the model for identifying districts with a prevalence of FD detection >10% of the fields suggests that it could be used to target areas in which future surveys would be most valuable.

Human-Altered Landscapes and Climate to Predict Human Infectious Disease Hotspots

Background: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. Methods: We modelled presence–absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. Results: For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications. Conclusions: Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.

An Integrated, Tentative Remote-Sensing Approach Based on NDVI Entropy to Model Canine Distemper Virus in Wildlife and to Prompt Science-Based Management Policies

Simple Summary

Canine distemper virus (CDV) is a pathogen that affects wildlife with particular regard to Canidae family such as red foxes, wolves, etc. In this study, we focus on CDV outbreaks in the Aosta Valley territory, an alpine region in the NW of Italy which was affected by important waves of this disease during the years 2015–2020 (hereinafter called τ). Ground data are collected on the entire territory at a municipality level. The detection of the canine distemper virus is performed by means of real-time PCR. By adopting satellite remote-sensing data, we notice that CDV trends are strongly related to anomalies in the NDVI entropy changes through (τ). A tentative local model is developed concerning on-the-ground data, helping veterinarians, foresters, and wildlife ecologists enforce management health policies in a One Health perspective.

Abstract

Changes in land use and land cover as well as feedback on the climate deeply affect the landscape worldwide. This phenomenon has also enlarged the human–wildlife interface and amplified the risk of potential new zoonoses. The expansion of the human settlement is supposed to affect the spread and distribution of wildlife diseases such as canine distemper virus (CDV), by shaping the distribution, density, and movements of wildlife. Nevertheless, there is very little evidence in the scientific literature on how remote sensing and GIS tools may help the veterinary sector to better monitor the spread of CDV in wildlife and to enforce ecological studies and new management policies in the near future. Thus, we perform a study in Northwestern Italy (Aosta Valley Autonomous Region), focusing on the relative epidemic waves of CDV that cause a virulent disease infecting different animal species with high host mortality. CDV has been detected in several mammalian from Canidae, Mustelidae, Procyonidae, Ursidae, and Viverridae families. In this study, the prevalence is determined at 60% in red fox (Vulpes vulpes, n = 296), 14% in wolf (Canis lupus, n = 157), 47% in badger (Meles meles, n = 103), and 51% in beech marten (Martes foina, n = 51). The detection of CDV is performed by means of real-time PCR. All the analyses are done using the TaqMan approach, targeting the chromosomal gene for phosphoprotein, gene P, that is involved in the transcription and replication of the virus. By adopting Earth Observation Data, we notice that CDV trends are strongly related to an altitude gradient and NDVI entropy changes through the years. A tentative model is developed concerning the ground data collected in the Aosta Valley region. According to our preliminary study, entropy computed from remote-sensing data can represent a valuable tool to monitor CDV spread as a proxy data predictor of the intensity of fragmentation of a given landscape and therefore also to monitor CDV. In conclusion, the evaluation from space of the landscape variations regarding the wildlife ecological corridors due to anthropic or natural disturbances may assist veterinarians and wildlife ecologists to enforce management health policies in a One Health perspective by pointing out the time and spatial conditions of interaction between wildlife. Surveillance and disease control actions are supposed to be carried out to strengthen the usage of geospatial analysis tools and techniques. These tools and techniques can deeply assist in better understanding and monitoring diseases affecting wildlife thanks to an integrated management approach.

The role of abiotic variables in an emerging global amphibian fungal disease in mountains

The emergence of the chytridiomycete fungal pathogen Batrachochytrium dendrobatidis (Bd), causing the disease chytridiomycosis, has caused collapse of amphibian communities in numerous mountain systems. The health of amphibians and of mountain freshwater habitats they inhabit is also threatened by ongoing changes in environmental and anthropogenic factors such as climate, hydrology, and pollution. Climate change is causing more extreme climatic events, shifts in ice occurrence, and changes in the timing of snowmelt and pollutant deposition cycles. All of these factors impact both pathogen and host, and disease dynamics. Here we review abiotic variables, known to control Bd occurrence and chytridiomycosis severity, and discuss how climate change may modify them. We propose two main categories of abiotic variables that may alter Bd distribution, persistence, and physiology: 1) climate and hydrology (temperature, precipitation, hydrology, ultraviolet radiation (UVR); and, 2) water chemistry (pH, salinity, pollution). For both categories, we identify topics for further research. More studies on the relationship between global change, pollution and pathogens in complex landscapes, such as mountains, are needed to allow for accurate risk assessments for freshwater ecosystems and resulting impacts on wildlife and human health. Our review emphasizes the importance of using data of higher spatiotemporal resolution and uniform abiotic metrics in order to better compare study outcomes. Fine-scale temperature variability, especially of water temperature, variability of moisture conditions and water levels, snow, ice and runoff dynamics should be assessed as abiotic variables shaping the mountain habitat of pathogen and host. A better understanding of hydroclimate and water chemistry variables, as co-factors in disease, will increase our understanding of chytridiomycosis dynamics.

Delays in Epidemic Outbreak Control Cost Disproportionately Large Treatment Footprints to Offset

Epidemic outbreak control often involves a spatially explicit treatment area (quarantine, inoculation, ring cull) that covers the outbreak area and adjacent regions where hosts are thought to be latently infected. Emphasis on space however neglects the influence of treatment timing on outbreak control. We conducted field and in silico experiments with wheat stripe rust (WSR), a long-distance dispersed plant disease, to understand interactions between treatment timing and area interact to suppress an outbreak. Full-factorial field experiments with three different ring culls (outbreak area only to a 25-fold increase in treatment area) at three different disease control timings (1.125, 1.25, and 1.5 latent periods after initial disease expression) indicated that earlier treatment timing had a conspicuously greater suppressive effect than the area treated. Disease spread computer simulations over a broad range of influential epidemic parameter values (R0, outbreak disease prevalence, epidemic duration) suggested that potentially unrealistically large increases in treatment area would be required to compensate for even small delays in treatment timing. Although disease surveillance programs are costly, our results suggest that treatments early in an epidemic disease outbreak require smaller areas to be effective, which may ultimately compensate for the upfront costs of proactive disease surveillance programs.

Zooscape ecology: a conceptual analysis of zoos and landscape ecology

CONTEXT

Zoos are a unique landscape with fascinating connections to the principles of landscape ecology. These 'zooscapes' have a focus on managing wild species.

OBJECTIVES

This article examines the multiple scales of zoos as urban green spaces, exhibit landscapes, and resources for resilience. I identify that landscape ecology can inform zoo evolution and note how zoos may provide a novel research site for landscape ecology.

METHODS

I provide a brief history of American zoos and insight into lingering questions within zoos, including their representations of animals and humans. Additionally, I note conceptual overlap between zoo design/function and landscape ecology literature.

RESULTS

Zoos provide habitat for native species and valued cultural ecosystem services. Zoo exhibits developed a landscape focus as modern landscape ecology emerged in the 1980s. Patches, corridors, and matrices exist within a zoo, and these facilities have value for the genetic support of fragmented populations. Zoos' strategies for disease management are increasingly relevant for global health. Simultaneously, zoos must exhibit sustainable landscapes, not just ecological simulacrums for threatened species.

CONCLUSIONS

Zoos must promote humanity's continued coexistence with other species. A landscape view is essential to achieving this goal. Zoos need to model sustainable landscapes of our present and future.

Optimal surveillance against bioinvasions: a sample average approximation method applied to an agent-based spread model

Trade-offs exist between the point of early detection and the future cost of controlling any invasive species. Finding optimal levels of early detection, with post-border active surveillance, where time, space and randomness are explicitly considered, is computationally challenging. We use a stochastic programming model to find the optimal level of surveillance and predict damages, easing the computational challenge by combining a sample average approximation (SAA) approach and parallel processing techniques. The model is applied to the case of Asian Papaya Fruit Fly (PFF), a highly destructive pest, in Queensland, Australia. To capture the non-linearity in PFF spread, we use an agent-based model (ABM), which is calibrated to a highly detailed land-use raster map (50 m × 50 m) and weather-related data, validated against a historical outbreak. The combination of SAA and ABM sets our work apart from the existing literature. Indeed, despite its increasing popularity as a powerful analytical tool, given its granularity and capability to model the system of interest adequately, the complexity of ABM limits its application in optimizing frameworks due to considerable uncertainty about solution quality. In this light, the use of SAA ensures quality in the optimal solution (with a measured optimality gap) while still being able to handle large-scale decision-making problems. With this combination, our application suggests that the optimal (economic) trap grid size for PFF in Queensland is ˜0.7 km, much smaller than the currently implemented level of 5 km. Although the current policy implies a much lower surveillance cost per year, compared with the $2.08 million under our optimal policy, the expected total cost of an outbreak is $23.92 million, much higher than the optimal policy of roughly $7.74 million.

Spatio-temporal changes in chronic wasting disease risk in wild deer during 14 years of surveillance in Alberta, Canada

Disease risk modeling is a key first step to understand the spatio-temporal dynamics of wildlife disease and to direct cost-effective surveillance and management. In Alberta, active surveillance for chronic wasting disease (CWD) in wild cervids began in 1998 with the first case detected in free-ranging cervids in 2005. Following the detection, a herd reduction program was implemented during 2005-2008 and in 2006 the ongoing hunter-based CWD Surveillance Program became mandatory in high-risk Wildlife Management Units (WMU). We used data collected during the CWD surveillance program to 1) document growth in sex-specific CWD prevalence (proportion of deer in sample that is CWD-positive) in hunter-harvest deer in 6 WMUs consistently monitored from 2006 to 2018, 2) document landscape features associated with where CWD-positive compared to CWD-negative deer were removed during hunter harvest and herd reduction in an early (2005-2012) and in a late period (2013-2017), and 3) to map the spatial risk of harvesting a deer infected with CWD in the prairie parklands of Alberta. In the 6 continuously monitored WMUs, risk of a harvested deer being CWD positive increased from 2006 to 2018 with CWD prevalence remaining highest in male mule deer whereas overall growth rate in CWD prevalence was greater in female mule deer, but similar to male white-tailed deer. We found no evidence that the 3-year herd reduction program conducted immediately after CWD was first detected affected the rate at which CWD grew over the course of the invasion. Risk of deer being CWD-positive was the highest in animals taken near small stream drainages and on soils with low organic carbon content in the early period, whereas risk became highest in areas of agriculture especially when far from large river drainages where deer often concentrate in isolated woody patches. The change in the influence of proximity to known CWD-positive cases suggested the disease was initially patchy but became more spatially homogeneous over time. Our results indicate that a targeted-removal program will remove more CWD positive animals compared to hunter harvest. However, the discontinuation of targeted removals during our research program, restricted our ability to assess its long term impact on CWD prevalence.

Landscape Ecological Analysis of Green Network in Urban Area Using Circuit Theory and Least-Cost Path

Quantitative securing of green space in already developed cities has many practical limitations due to socio-economic limitations. Currently, South Korea is planning a green network to secure and inject effective green space, but it is difficult to reflect it in the actual space plan due to the abstract plan. This study utilizes circuit theory and least-cost path methods for presenting a green network that is objectified and applicable to spatial planning. First, an analysis of the Least-cost Path revealed 69 least-cost paths between 43 core green areas of the study site. Most least-cost paths have been identified as passing through small green areas and streams in the city. Using the circuit theory, it was also possible to distinguish areas other than least-cost paths from areas with high potential for development, areas where target species are concentrated within corridors. In particular, areas with relatively high green network improvement effects were derived within and around corridors. This study is most significant in establishing and evaluating existing urban green networks, overcoming the limitations discussed at the linear level and expanding to the area level. To increase the utilization of this study in the future, field surveys and monitoring studies on target species need to be supplemented.

Landscape-scale approaches for enhancing biological pest control in agricultural systems

Over the last decades, land management options have been investigated that aim at enhancing services to agriculture delivered by biodiversity and its associated biotic interactions. Such services can be promoted through land management strategies ranging from in-field single agricultural practices, long-term strategies compiling these agricultural practices at the crop rotation scale, to management strategies at the landscape scale. In this paper, we provide an overview of the land management options that can be implemented at multiple scales, with a specific focus on the provision of one service that is key in agriculture, i.e. pest control. We present existing knowledge and highlight current gaps and limitations in our understanding of pest control response to land management. Based on this analysis, we propose two promising and complementary research approaches that could help filling existing knowledge gaps and provide guidelines for designing landscapes for agroecological services: (1) landscape monitoring networks (LMN), based on long-term monitoring of ecological and managerial processes within sets of landscapes located in contrasted production contexts; (2) agroecological system experiments (ASE), which design and assess combinations of land management options at multiple embedded spatial scales.

Movement can mediate temporal mismatches between resource availability and biological events in host-pathogen interactions

Global change is shifting the timing of biological events, leading to temporal mismatches between biological events and resource availability. These temporal mismatches can threaten species' populations. Importantly, temporal mismatches not only exert strong pressures on the population dynamics of the focal species, but can also lead to substantial changes in pairwise species interactions such as host-pathogen systems. We adapted an established individual-based model of host-pathogen dynamics. The model describes a viral agent in a social host, while accounting for the host's explicit movement decisions. We aimed to investigate how temporal mismatches between seasonal resource availability and host life-history events affect host-pathogen coexistence, that is, disease persistence. Seasonal resource fluctuations only increased coexistence probability when in synchrony with the hosts' biological events. However, a temporal mismatch reduced host-pathogen coexistence, but only marginally. In tandem with an increasing temporal mismatch, our model showed a shift in the spatial distribution of infected hosts. It shifted from an even distribution under synchronous conditions toward the formation of disease hotspots, when host life history and resource availability mismatched completely. The spatial restriction of infected hosts to small hotspots in the landscape initially suggested a lower coexistence probability due to the critical loss of susceptible host individuals within those hotspots. However, the surrounding landscape facilitated demographic rescue through habitat-dependent movement. Our work demonstrates that the negative effects of temporal mismatches between host resource availability and host life history on host-pathogen coexistence can be reduced through the formation of temporary disease hotspots and host movement decisions, with implications for disease management under disturbances and global change.

Sources and Assembly of Microbial Communities in Vineyards as a Functional Component of Winegrowing

Microbiomes are integral to viticulture and winemaking – collectively termed winegrowing – where diverse fungi and bacteria can exert positive and negative effects on grape health and wine quality. Wine is a fermented natural product, and the vineyard serves as a key point of entry for quality-modulating microbiota, particularly in wine fermentations that are conducted without the addition of exogenous yeasts. Thus, the sources and persistence of wine-relevant microbiota in vineyards critically impact its quality. Site-specific variations in microbiota within and between vineyards may contribute to regional wine characteristics. This includes distinctions in microbiomes and microbiota at the strain level, which can contribute to wine flavor and aroma, supporting the role of microbes in the accepted notion of terroir as a biological phenomenon. Little is known about the factors driving microbial biodiversity within and between vineyards, or those that influence annual assembly of the fruit microbiome. Fruit is a seasonally ephemeral, yet annually recurrent product of vineyards, and as such, understanding the sources of microbiota in vineyards is critical to the assessment of whether or not microbial terroir persists with inter-annual stability, and is a key factor in regional wine character, as stable as the geographic distances between vineyards. This review examines the potential sources and vectors of microbiota within vineyards, general rules governing plant microbiome assembly, and how these factors combine to influence plant-microbe interactions relevant to winemaking.

Effects of Scale on Modeling West Nile Virus Disease Risk

Modeling vector-borne diseases is best conducted when heterogeneity among interacting biotic and abiotic processes is captured. However, the successful integration of these complex processes is difficult, hindered by a lack of understanding of how these relationships influence disease transmission across varying scales. West Nile virus (WNV) is the most important mosquito-borne disease in the United States. Vectored by Culex mosquitoes and maintained in the environment by avian hosts, the virus can spill over into humans and horses, sometimes causing severe neuroinvasive illness. Several modeling studies have evaluated drivers of WNV disease risk, but nearly all have done so at broad scales and have reported mixed results of the effects of common explanatory variables. As a result, fine-scale relationships with common explanatory variables, particularly climatic, socioeconomic, and human demographic, remain uncertain across varying spatial extents. Using an interdisciplinary approach and an ongoing 12-year study of the Chicago region, this study evaluated the factors explaining WNV disease risk at high spatiotemporal resolution, comparing the human WNV model and covariate performance across three increasing spatial extents: ultrafine, local, and county scales. Our results demonstrate that as spatial extent increased, model performance increased. In addition, only six of the 23 assessed covariates were included in best-fit models of at least two scales. These results suggest that the mechanisms driving WNV ecology are scale-dependent and covariate importance increases as extent decreases. These tools may be particularly helpful for public health, mosquito, and disease control personnel in predicting and preventing disease within local and fine-scale jurisdictions, before spillover occurs.

Planting period is the main factor for controlling maize rough dwarf disease

Maize rough dwarf virus (MRDV) is one of the main yield-limiting factors of maize in the Mediterranean. However, knowledge about the interactions between the agroecosystem and the virus-vector-host relationship continues to be limited. We used multi-model inference to test a landscape-scale approach together with variables measured in the field, and we estimated the effects of early and late planting on MRDV incidence. The results revealed that the virus incidence increased by 3% when the planting was delayed, and this increase was coincident with the first peak of the vector population. The variables at the field and landscape scales with a strong effect on virus incidence were the proportions of grasses in adjacent crops, in uncultivated areas, and in edges close to maize plants. Grass plant cover in the edges also affected virus incidence, but these effects varied with the planting period. These findings provide new insights into the causes of MRDV incidence and may provide some guidance to growers to reduce losses caused by the virus. Among the recommendations to be prioritized are early planting, management of grasses at field edges, and non-overlapping cultivation of maize and winter cereals in the same area.

Dispersal and Land Cover Contribute to Pseudorabies Virus Exposure in Invasive Wild Pigs

We investigated the landscape epidemiology of a globally distributed mammal, the wild pig (Sus scrofa), in Florida (U.S.), where it is considered an invasive species and reservoir to pathogens that impact the health of people, domestic animals, and wildlife. Specifically, we tested the hypothesis that two commonly cited factors in disease transmission, connectivity among populations and abundant resources, would increase the likelihood of exposure to both pseudorabies virus (PrV) and Brucella spp. (bacterial agent of brucellosis) in wild pigs across the Kissimmee Valley of Florida. Using DNA from 348 wild pigs and sera from 320 individuals at 24 sites, we employed population genetic techniques to infer individual dispersal, and an Akaike information criterion framework to compare candidate logistic regression models that incorporated both dispersal and land cover composition. Our findings suggested that recent dispersal conferred higher odds of exposure to PrV, but not Brucella spp., among wild pigs throughout the Kissimmee Valley region. Odds of exposure also increased in association with agriculture and open canopy pine, prairie, and scrub habitats, likely because of highly localized resources within those land cover types. Because the effect of open canopy on PrV exposure reversed when agricultural cover was available, we suggest that small-scale resource distribution may be more important than overall resource abundance. Our results underscore the importance of studying and managing disease dynamics through multiple processes and spatial scales, particularly for non-native pathogens that threaten wildlife conservation, economy, and public health.

Harmonising the fields of invasion science and forest pathology

Invasive alien species are widely recognised as significant drivers of global environmental change, with far reaching ecological and socio-economic impacts. The trend of continuous increases in first records, with no apparent sign of saturation, is consistent across all taxonomic groups. However, taxonomic biases exist in the extent to which invasion processes have been studied. Invasive forest pathogens have caused, and they continue to result in dramatic damage to natural forests and woody ecosystems, yet their impacts are substantially underrepresented in the invasion science literature. Conversely, most studies of forest pathogens have been undertaken in the absence of a connection to the frameworks developed and used to study biological invasions. We believe this is, in part, a consequence of the mechanistic approach of the discipline of forest pathology; one that has been inherited from the broader discipline of plant pathology. Rather than investigating the origins of, and the processes driving the arrival of invasive microorganisms, the focus of pathologists is generally to investigate specific interactions between hosts and pathogens, with an emphasis on controlling the resulting disease problems. In contrast, central to the field of invasion science, which finds its roots in ecology, is the development and testing of general concepts and frameworks. The lack of knowledge of microbial biodiversity and ecology, speciation and geographic origin present challenges in understanding invasive forest pathogens under existing frameworks, and there is a need to address this shortfall. Advances in molecular technologies such as gene and genome sequencing and metagenomics studies have increased the “visibility” of microorganisms. We consider whether these technologies are being adequately applied to address the gaps between forest pathology and invasion science. We also interrogate the extent to which the two fields stand to gain by becoming more closely linked.

Landscape epidemiology of neglected tick-borne pathogens in central Europe

Studies of tick-borne diseases (TBDs) in Europe focus on pathogens with principal medical importance (e.g. Lyme disease and tick-borne encephalitis), but we have limited epidemiological information on the neglected pathogens, such as the members of the genera Anaplasma, Rickettsia, Babesia and Candidatus Neoehrlichia mikurensis. Here, we integrated an extensive field sampling, laboratory analysis and GIS models to provide first publicly available information on pathogen diversity, prevalence and infection risk for four overlooked zoonotic TBDs in the Czech Republic. In addition, we assessed the effect of landscape variables on the abundance of questing ticks at different spatial scales and examined whether pathogen prevalence increased with tick density. Our data from 13,340 ticks collected in 142 municipalities showed that A. phagocytophilum (MIR = 3.5%) and Ca. Neoehrlichia mikurensis (MIR = 4.0%) pose geographically uneven risks with localized hotspots, while Rickettsia (MIR = 4.9%) and Babesia (MIR = 1.1%) had relatively homogeneous spatial distribution. Landscape variables had significant effect on tick abundance up to the scale of 1 km around the sampling sites. Questing ticks responded positively to landscape diversity and configuration, especially to forest patch density that strongly correlates with the amount of woodland-grassland ecotones. For all four pathogens, we found higher prevalence in places with higher densities of ticks, confirming the hypothesis that tick abundance amplifies the risk of TB infection. Our findings highlight the importance of landscape parameters for tick vectors, likely due to their effect on small vertebrates as reservoir hosts. Future studies should explicitly investigate the combined effect of landscape parameters and the composition and population dynamics of hosts on the host-vector-pathogen system.

The environmental drivers of bacterial meningitis epidemics in the Democratic Republic of Congo, central Africa

INTRODUCTION

Bacterial meningitis still constitutes an important threat in Africa. In the meningitis belt, a clear seasonal pattern in the incidence of meningococcal disease during the dry season has been previously correlated with several environmental parameters like dust and sand particles as well as the Harmattan winds. In parallel, the evidence of seasonality in meningitis dynamics and its environmental variables remain poorly studied outside the meningitis belt. This study explores several environmental factors associated with meningitis cases in the Democratic Republic of Congo (DRC), central Africa, outside the meningitis belt area.

METHODS

Non-parametric Kruskal-Wallis' tests were used to establish the difference between the different health zones, climate and vegetation types in relation to both the number of cases and attack rates for the period 2000-2018. The relationships between the number of meningitis cases for the different health zones and environmental and socio-economical parameters collected were modeled using different generalized linear (GLMs) and generalized linear mixed models (GLMMs), and different error structure in the different models, i.e., Poisson, binomial negative, zero-inflated binomial negative and more elaborated multi-hierarchical zero-inflated binomial negative models, with randomization of certain parameters or factors (health zones, vegetation and climate types). Comparing the different statistical models, the model with the smallest Akaike's information criterion (AIC) were selected as the best ones. 515 different health zones from 26 distinct provinces were considered for the construction of the different GLM and GLMM models.

RESULTS

Non-parametric bivariate statistics showed that there were more meningitis cases in urban health zones than in rural conditions (χ2 = 6.910, p-value = 0.009), in areas dominated by savannah landscape than in areas with dense forest or forest in mountainous areas (χ2 = 15.185, p-value = 0.001), and with no significant difference between climate types (χ2 = 1.211, p-value = 0,449). Additionally, no significant difference was observed for attack rate between the two types of heath zones (χ2 = 0.982, p-value = 0.322). Conversely, strong differences in attack rate values were obtained for vegetation types (χ2 = 13.627, p-value = 0,001) and climate types (χ2 = 13.627, p-value = 0,001). This work demonstrates that, all other parameters kept constant, an urban health zone located at high latitude and longitude eastwards, located at low-altitude like in valley ecosystems predominantly covered by savannah biome, with a humid tropical climate are at higher risk for the development of meningitis. In addition, the regions with mean range temperature and a population with a low index of economic well-being (IEW) constitute the perfect conditions for the development of meningitis in DRC.

CONCLUSION

In a context of global environmental change, particularly climate change, our findings tend to show that an interplay of different environmental and socio-economic drivers are important to consider in the epidemiology of bacterial meningitis epidemics in DRC. This information is important to help improving meningitis control strategies in a large country located outside of the so-called meningitis belt.

Global Cropland Connectivity: A Risk Factor for Invasion and Saturation by Emerging Pathogens and Pests

The geographic pattern of cropland is an important risk factor for invasion and saturation by crop-specific pathogens and arthropods. Understanding cropland networks supports smart pest sampling and mitigation strategies. We evaluate global networks of cropland connectivity for key vegetatively propagated crops (banana and plantain, cassava, potato, sweet potato, and yam) important for food security in the tropics. For each crop, potential movement between geographic location pairs was evaluated using a gravity model, with associated uncertainty quantification. The highly linked hub and bridge locations in cropland connectivity risk maps are likely priorities for surveillance and management, and for tracing intraregion movement of pathogens and pests. Important locations are identified beyond those locations that simply have high crop density. Cropland connectivity risk maps provide a new risk component for integration with other factors-such as climatic suitability, genetic resistance, and global trade routes-to inform pest risk assessment and mitigation.

Genomic and transcriptomic insights into Raffaelea lauricola pathogenesis

BACKGROUND

Laurel wilt caused by Raffaelea lauricola is a lethal vascular disease of North American members of the Lauraceae plant family. This fungus and its primary ambrosia beetle vector Xyleborus glabratus originated from Asia; however, there is no report of laurel wilt causing widespread mortality on native Lauraceae trees in Asia. To gain insight into why R. lauricola is a tree-killing plant pathogen in North America, we generated and compared high quality draft genome assemblies of R. lauricola and its closely related non-pathogenic species R. aguacate.

RESULTS

Relative to R. aguacate, the R. lauricola genome uniquely encodes several small-secreted proteins that are associated with virulence in other pathogens and is enriched in secondary metabolite biosynthetic clusters, particularly polyketide synthase (PKS), non-ribosomal peptide synthetase (NRPS) and PKS-NRPS anchored gene clusters. The two species also exhibit significant differences in secreted proteins including CAZymes that are associated with polysaccharide binding including the chitin binding CBM50 (LysM) domain. Transcriptomic comparisons of inoculated redbay trees and in vitro-grown fungal cultures further revealed a number of secreted protein genes, secondary metabolite clusters and alternative sulfur uptake and assimilation pathways that are coordinately up-regulated during infection.

CONCLUSIONS

Through these comparative analyses we have identified potential adaptations of R. lauricola that may enable it to colonize and cause disease on susceptible hosts. How these adaptations have interacted with co-evolved hosts in Asia, where little to no disease occurs, and non-co-evolved hosts in North America, where lethal wilt occurs, requires additional functional analysis of genes and pathways.

Comparing different spatio-temporal modeling methods in dengue fever data analysis in Colombia during 2012-2015

In this paper, we compare a variety of spatio-temporal conditional autoregressive models to a dengue fever dataset in Colombia, and incorporate an innovative data transformation method in the data analysis. In order to gain a better understanding on the effects of different niche variables in the epidemiological process, we explore Poisson-lognormal and binomial models with different Bayesian spatio-temporal modeling methods in this paper. Our results show that the selected model can well capture the variations of the data. The population density, elevation, daytime and night land surface temperatures are among the contributory variables to identify potential dengue outbreak regions; precipitation and vegetation variables are not significant in the selected spatio-temporal mixed effects model. The generated dengue fever probability maps from the model show a geographic distribution of risk that apparently coincides with the elevation gradient. The results in the paper provide the most benefits for future work in dengue studies.

Optimal surveillance against foot-and-mouth disease: A sample average approximation approach

Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty. This paper focuses on the case of finding the optimal level of surveillance against a highly infectious animal disease where time, space and randomness are fully considered. We apply the Sample Average Approximation approach to solve our problem, and to control model dimension, we propose the use of an infection tree model, in combination with sensible ‘tree-pruning’ and parallel processing techniques. Our proposed model and techniques are generally applicable to a number of disease types, but we demonstrate the approach by solving for optimal surveillance levels against foot-and-mouth disease using bulk milk testing as an active surveillance protocol, during an epidemic, among 42,279 farms, fully characterised by their location, livestock type and size, in the state of Victoria, Australia.

Threats to global food security from emerging fungal and oomycete crop pathogens

Emerging fungal and oomycete pathogens infect staple calorie crops and economically important commodity crops, thereby posing a significant risk to global food security. Our current agricultural systems - with emphasis on intensive monoculture practices - and globalized markets drive the emergence and spread of new pathogens and problematic traits, such as fungicide resistance. Climate change further promotes the emergence of pathogens on new crops and in new places. Here we review the factors affecting the introduction and spread of pathogens and current disease control strategies, illustrating these with the historic example of the Irish potato famine and contemporary examples of soybean rust, wheat blast and blotch, banana wilt and cassava root rot. Our Review looks to the future, summarizing what we see as the main challenges and knowledge gaps, and highlighting the direction that research must take to face the challenge of emerging crop pathogens.

Variation in host home range size decreases rabies vaccination effectiveness by increasing the spatial spread of rabies virus

Animal movement influences the spatial spread of directly transmitted wildlife disease through host-host contact structure. Wildlife disease hosts vary in home range-associated foraging and social behaviours, which may increase the spread and intensity of disease outbreaks. The consequences of variation in host home range movement and space use on wildlife disease dynamics are poorly understood, but could help to predict disease spread and determine more effective disease management strategies. We developed a spatially explicit individual-based model to examine the effect of spatiotemporal variation in host home range size on the spatial spread rate, persistence and incidence of rabies virus (RABV) in raccoons (Procyon lotor). We tested the hypothesis that variation in home range size increases RABV spread and decreases vaccination effectiveness in host populations following pathogen invasion into a vaccination zone. We simulated raccoon demography and RABV dynamics across a range of magnitudes and variances in weekly home range size for raccoons. We examined how variable home range size influenced the relative effectiveness of three components of oral rabies vaccination (ORV) programmes targeting raccoons-timing and frequency of bait delivery, width of the ORV zone and proportion of hosts immunized. Variability in weekly home range size increased RABV spread rates by 1.2-fold to 5.2-fold compared to simulations that assumed a fixed home range size. More variable host home range sizes decreased relative vaccination effectiveness by 71% compared to less variable host home range sizes under conventional vaccination conditions. We found that vaccination timing was more influential for vaccination effectiveness than vaccination frequency or vaccination zone width. Our results suggest that variation in wildlife home range movement behaviour increases the spatial spread and incidence of RABV. Our vaccination results underscore the importance of prioritizing individual-level space use and movement data collection to understand wildlife disease dynamics and plan their effective control and elimination.

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’.

Amplifier Hosts May Play an Essential Role in Tomato Begomovirus Epidemics in Brazil

Current control of tomato golden mosaic disease, caused in Brazil predominantly by tomato severe rugose virus (ToSRV), is dependent on both, planting resistant/tolerant hybrids and intensive insecticide sprays (two to three per week) for controlling Bemisia tabaci, the vector of ToSRV. Resistant hybrids only confer moderate resistance to infection by ToSRV and some tolerance to the disease. Insecticide sprays, although widely used, have failed in most tomato production areas in Brazil, as they are unable to reduce primary spread, i.e., infection caused by the influx of viruliferous whiteflies coming from external sources of inoculum. Severe epidemics are recurrently observed in some tomato fields in several Brazilian regions, which prompted us to postulate the existence in the agroecosystem, in some places and time, of amplifier hosts that provide the necessary force of infection for epidemics to occur, even in the absence of secondary spread in the target crop. Amplifier hosts are ideally asymptomatic, occur in high density near the target crop, and support growth of both virus and vector. Soybean and common bean are potential amplifier hosts for begomovirus in tomato crops. Our results support the hypothesis that soybean plants may play an important role as an amplifier host of ToSRV for tomato crops in the field, although this does not seem to be a frequent phenomenon. Successful amplification will depend on several factors, including the soybean cultivar, the soybean stage of development at the moment of infection, the ToSRV isolate, and the perfect synchrony between the beginning of a soybean field and the end of a ToSRV-infected crop, and, later, between the senescence of the ToSRV-infected soybean plants and the new tomato crop. The concept of amplifier hosts has been widely used in ecology of zoonoses but, to our knowledge, has never been used in botanical epidemiology.

Spatial and Temporal Dynamics of Mal Secco Disease Spread in Lemon Orchards in Israel

Mal Secco is a severe disease of citrus in which the fungus Plenodomus tracheiphilus (formerly Phoma tracheiphila) penetrates the vascular system of the host. In this study, we characterized the spatial dynamics of the disease in seven lemon orchards. A representative block of trees from each orchard was evaluated monthly during 3 consecutive years. In addition, scouts assessed disease severity in 75 orchards from three different geographical regions and tested for association between disease severity and measures of orchard management, environmental factors, cultural practices, and cultivar type. We assessed disease incidence and characteristics of spatial patterns using Ripley's K function and fitted logistic regression models for different neighboring tree structures followed by model selection methods to provide insight into the spatial and temporal dynamics of disease progress. We found different rates of disease spread in different orchards, which are most likely the result of differences in orchard management practices or less likely the result of differences in climatic conditions. There was an indication that agricultural tools contribute to spread of the disease within rows of trees. The results confirm that the lemon cultivar Interdonato is less susceptible compared with other citrus cultivars, and they suggest that the density of urban terrain surrounding each orchard is positively correlated with the severity of the disease. In contrast to our expectations, no correlation was found between the density of lemon orchards surrounding an orchard and the severity of the disease within it, which corroborates previous findings regarding the limited distribution of the disease.

Estimating epidemiological parameters from experiments in vector access to host plants, the method of matching gradients

Estimation of pathogenic life-history values, for instance the duration a pathogen is retained in an insect vector (i.e., retention period) is of particular importance for understanding plant disease epidemiology. How can we extract values for these epidemiological parameters from conventional small-scale laboratory experiments in which transmission success is measured in relation to durations of vector access to host plants? We provide a solution to this problem by deriving formulae for the empirical curves that these experiments produce, called access period response curves (i.e., transmission success vs access period). We do this by writing simple equations for the fundamental life-cycle components of insect vectors in the laboratory. We then infer values of epidemiological parameters by matching the theoretical and empirical gradients of access period response curves. Using the example of Cassava brown streak virus (CBSV), which has emerged in sub-Saharan Africa and now threatens regional food security, we illustrate the method of matching gradients. We show how applying the method to published data produces a new understanding of CBSV through the inference of retention period, acquisition period and inoculation period parameters. We found that CBSV is retained for a far shorter duration in its insect vector (Bemisia tabaci whitefly) than had previously been assumed. Our results shed light on a number of critical factors that may be responsible for the transition of CBSV from sub- to super-threshold R₀ in sub-Saharan Africa. The method is applicable to plant pathogens in general, to supply epidemiological parameter estimates that are crucial for practical management of epidemics and prediction of pandemic risk.

Cassava brown streak virus (CBSV), which was confined to coastal East Africa and the shores of Lake Malawi, has rapidly expanded its geographic range and now threatens regional food security. A recent laboratory experiment, in which vector access to plants infected with CBSV is varied, has provided plausible ranges for epidemiological parameters such as pathogen retention period (i.e., the duration that the pathogen is retained by the vector). In this work we introduce a new computational and mathematical framework for estimating epidemiological parameter values, instead of plausible ranges, from experiments in vector access to host plants. Since long distance carriage of insect vectors occurs within atmospheric air flows, the duration that the vector retains the pathogen (i.e., retention period) indirectly limits the scale of epidemics. Using our methods, we found that CBSV is retained for a far shorter duration than had previously been assumed. Our methods can be applied to the many experiments in vector access to host plants that have been conducted for numerous arthropod-transmitted plant pathogens across a range of genera. The ensuing epidemiological parameter estimates can be used in landscape computer simulations to predict epidemic risk and the prospects for control.

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.

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.

Interacting Effects of Global Change on Forest Pest and Pathogen Dynamics

Pathogens and insect pests are important drivers of tree mortality and forest dynamics, but global change has rapidly altered or intensified their impacts. Predictive understanding of changing disease and outbreak occurrence has been limited by two factors: ( a) tree mortality and morbidity are emergent phenomena determined by interactions between plant hosts, biotic agents (insects or pathogens), and the environment; and ( b) disparate global change drivers co-occur, obscuring net impacts on each of these components. To expand our understanding of changing forest diseases, declines, and outbreaks, we adopt a framework that identifies and organizes observed impacts of diverse global change drivers on the primary mechanisms underlying agent virulence and host susceptibility. We then discuss insights from ecological theory that may advance prediction of forest epidemics and outbreaks. This approach highlights key drivers of changing pest and pathogen dynamics, which may inform forest management aimed at mitigating accelerating rates of tree mortality globally.

Coordination as Management Response to the Spread of a Global Plant Disease: A Case Study in a Major Philippine Banana Production Area

An integrative management approach to the spread and emergence of global plant diseases, such as the soil-borne fungus Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4), entails a combination of technical measures and the responsiveness and awareness of area-specific constellations supporting conditions conducive to interactions and coordination among organizations and actors with different resources and diverse interests. Responses to banana diseases are mostly studied through technical and epidemiological lenses and reflect a bias to the export industry. Some authors, however, indicate that cross-sector collaboration is crucial in responding to a disease outbreak. Earlier studies on the outbreak of diseases and natural disasters suggest that shared cognition and effective partnerships increased the success rate of response. Hence, it is important not to focus exclusively on the impacts of a pathogen at farm or field level and to shift attention to how tasks and knowledge are coordinated and shared. This paper aims to detect whether and how the emergence of Foc TR4 is a driver of coordination. The case study focuses on the interactions between a variety of banana producers and among a range of public and private actors in southern Philippines. The analysis identifies distinct forms of coordination emerging in the context of three organizational fields responding to Foc TR4, which underlie shared capacity to handle and understand the spread of a global plant disease. The research is based on qualitative key informant interviews and document analysis and on observations of instructive events in 2014-2017. Analysis of the composition and actions developed in three organizational fields leads to distinguishing three theory-driven forms of coordination: rule-based, cognition-based, and skill-based. The combination of these three forms constitutes the possibility of a collaborative community, which conditions the implementation of an integrative management approach to mitigate Foc TR4.

Prediction of Spread and Regional Development of Hop Powdery Mildew: A Network Analysis

Dispersal is a fundamental aspect of epidemic development at multiple spatial scales, including those that extend beyond the borders of individual fields and to the landscape level. In this research, we used the powdery mildew of the hop pathosystem (caused by Podosphaera macularis) to formulate a model of pathogen dispersal during spring (May to June) and early summer (June to July) at the intermediate scale between synoptic weather systems and microclimate (mesoscale) based on a census of commercial hop yards during 2014 to 2017 in a production region in western Oregon. This pathosystem is characterized by a low level of overwintering of the pathogen as a result of absence of the ascigerious stage of the fungus and consequent annual cycles of localized survival via bud perennation and pathogen spread by windborne dispersal. An individual hop yard was considered a node in the model, whose disease status in a given month was expressed as a nonlinear function of disease incidence in the preceding month, susceptibility to two races of the fungus, and disease spread from other nodes as influenced by their disease incidence, area, distance away, and wind run and direction in the preceding month. Parameters were estimated by maximum likelihood over all 4 years but were allowed to vary for time transition periods from May to June and from June to July. The model accounted for 34 to 90% of the observed variation in disease incidence at the field level, depending on the year and season. Network graphs and analyses suggest that dispersal was dominated by relatively localized dispersal events (<2 km) among the network of fields, being mostly restricted to the same or adjacent farms. When formed, predicted disease attributable to dispersal from other hop yards (edges) associated with longer distance dispersal was more frequent in the June to July time transition. Edges with a high probability of disease transmission were formed in instances where yards were in close proximity or where disease incidence was relatively high in large hop yards, as moderated by wind run. The modeling approach provides a flexible and generalizable framework for understanding and predicting pathogen dispersal at the regional level as well as the implications of network connectivity on epidemic development.

Top 10 Research Priorities in Spatial Lifecourse Epidemiology

The International Initiative on Spatial Lifecourse Epidemiology (ISLE) convened its first International Symposium on Lifecourse Epidemiology and Spatial Science at the Lorentz Center in Leiden, Netherlands, 16–20 July 2018. Its aim was to further an emerging transdisciplinary field: Spatial Lifecourse Epidemiology. This field draws from a broad perspective of scientific disciplines including lifecourse epidemiology, environmental epidemiology, community health, spatial science, health geography, biostatistics, spatial statistics, environmental science, climate change, exposure science, health economics, evidence-based public health, and landscape ecology. The participants, spanning 30 institutions in 10 countries, sought to identify the key issues and research priorities in spatial lifecourse epidemiology. The results published here are a synthesis of the top 10 list that emerged out of the discussion by a panel of leading experts, reflecting a set of grand challenges for spatial lifecourse epidemiology in the coming years. https://doi.org/10.1289/EHP4868.

Pathogens in space: Advancing understanding of pathogen dynamics and disease ecology through landscape genetics

Landscape genetics has provided many insights into how heterogeneous landscape features drive processes influencing spatial genetic variation in free-living organisms. This rapidly developing field has focused heavily on vertebrates, and expansion of this scope to the study of infectious diseases holds great potential for landscape geneticists and disease ecologists alike. The potential application of landscape genetics to infectious agents has garnered attention at formative stages in the development of landscape genetics, but systematic examination is lacking. We comprehensively review how landscape genetics is being used to better understand pathogen dynamics. We characterize the field and evaluate the types of questions addressed, approaches used and systems studied. We also review the now established landscape genetic methods and their realized and potential applications to disease ecology. Lastly, we identify emerging frontiers in the landscape genetic study of infectious agents, including recent phylogeographic approaches and frameworks for studying complex multihost and host-vector systems. Our review emphasizes the expanding utility of landscape genetic methods available for elucidating key pathogen dynamics (particularly transmission and spread) and also how landscape genetic studies of pathogens can provide insight into host population dynamics. Through this review, we convey how increasing awareness of the complementarity of landscape genetics and disease ecology among practitioners of each field promises to drive important cross-disciplinary advances.

Predicting the origins of next forest-based emerging infectious disease

Land use change near dense forests is the single major cause of emergence of forest-based emerging infectious diseases (EIDs) among humans. In an attempt to predict where the next EID would originate from, we are hypothesizing that future EIDs would originate from a region having high population density, excessive poverty, and is located near dense vegetation. Using ArcGIS, we identified forest regions in ten countries across the globe that meet all the three conditions identified in the hypothesis. We further narrowed down the locations using Global Forest Watch data, which eliminates locations next to protected forests and fragmented forests. Our results indicate that there is high likelihood of next infectious disease originating from the southern and eastern forests around Freetown in Sierra Leone, the forest region around Douala in Cameroon, or the southern forest region in Nigeria. Concerted efforts need to be made to identify any new disease in the areas as soon as it emerges in the human population and contain the spread within the population.

Topographic effects on dispersal patterns of Phytophthora cinnamomi at a stand scale in a Spanish heathland

Phytophthora cinnamomi is one of the most important plant pathogens in the world, causing root rot in more than a thousand plant species. This observational study was carried out on a P. cinnamomi infected heathland of Erica umbellata used as goat pasture. The patterns and shapes of disease foci and their distribution were described in a spatial and temporal context using an aerial photograph record. A set of topographic traits was selected on the basis of a disease dynamic hypothesis and their effects on observed spatial disease patterns were analyzed. Incipient infections situated in flat terrain expanded as compact circular front patterns with a low growth rate. On slopes, disease patches developed more rapidly down slope, forming parabolic shapes. The axis direction of the parabolas was highly correlated with terrain aspect, while the parabolic amplitude was associated with land curvature and slope. New secondary foci appeared over the years producing an accelerated increase of the affected surface. These new foci were observed in sites where disease density was higher or near sites more frequently visited by animals such as the stable or the forage crop. In contrast, a smaller number of disease foci occur in areas which animals are reluctant to visit, such as where they have a short range of vision. Our results suggest that 1) the growth of existing P. cinnamomi foci is controlled by a combination of root-to-root contact and water flows, 2) the increase in the diseased area arises mainly from the multiplication of patches, 3) the formation of new foci is mediated by long-distance transport due to the movement of animals and humans along certain preferential pathways, and 4) geomorphology and topography traits are associated with the epidemiology of this soil-borne pathogen.