Neighbor Tree Research Articles

Use of UAV photogrammetric data in forest genetic trials: measuring tree height, growth, and phenology in Norway spruce (Picea abies L. Karst.)

ABSTRACT Phenotyping of genetic resources that are used for development of new improved and adapted forest reproductive materials is costly. Therefore, high-throughput-phenotyping from a standard equipped unmanned aerial vehicle (UAV) with a RGB (red, green, blue) camera was used to develop a canopy height model (CHM) of a dense field trial of 15-year-old trees of Norway spruce. The site represents a typical situation of genetic testing on abandoned farm field land, where spacing between trees often are around 1-1.5 m. The CHM was used to estimate total height, intra-annual height growth and phenology of individual trees. The estimates were based on individual tree grid cells assigned according to the original planting grid. Intra-annual growth and phenology could not be estimated accurately, whereas total tree height to the uppermost whorl was accurately estimated provided that the trees were correctly identified. However, the apices of suppressed trees were often misidentified due to overhanging branches from more dominating neighboring trees. The method represents a cost and time efficient alternative to manual height measurements of the intermediate and dominating trees in closed stands. The accuracy is sufficiently high for most breeding purposes when total tree height is the target trait.

Household Perceptions and Practices of Recycling Tree Debris from Residential Properties

Thousands of trees are lost in urbanizing areas of Virginia each year to land development, storms, and pests. As a result, large amounts of tree debris, much of which could be suitable for high-value wood products, are flowing from Virginia’s urban forests annually. Finding cost-effective, sustainable strategies for recycling this debris, particularly into durable wood products that keep carbon stored, could benefit the local economy and the local environment throughout the state. To inform outreach and technical assistance efforts of multiple groups across the state, a survey study was conducted in the City of Harrisonburg to determine household perceptions and practices of tree debris recycling. A random sample of owner-occupied, single-family dwellings was contacted using a mixed-mode survey approach to determine why and how trees were removed from the properties in the past and how the debris was disposed of or recycled. Survey responses were received from 189 households, with survey responses pointing toward a strong community sentiment for trees and their care. Nearly all respondents agreed that wood from street trees, park trees, and other neighborhood trees should be recycled into products rather than disposed of in a landfill; however, the majority of households do not currently recycle woody debris from trees removed on these properties. The three most important factors that would facilitate future participation in tree recycling include timely removal of the wood, free curbside pick-up of the wood, and knowledge of who to contact to handle the wood. Overall, these results point to household interest and willingness to participate in wood recycling programs given appropriate information to guide their decisions and local services to facilitate transfer of wood to the municipality or commercial woodworkers. This suggests a need for greater availability of neighborhood or municipal wood recycling programs, ideally coupled with greater education and outreach about the economic and environmental benefits of recovering and utilizing wood from felled trees.

Network science applied to forest megaplots: tropical tree species coexist in small-world networks

Network analysis is an important tool to analyze the structure of complex systems such as tropical forests. Here, we infer spatial proximity networks in tropical forests by using network science. First, we focus on tree neighborhoods to derive spatial tree networks from forest inventory data. In a second step, we construct species networks to describe the potential for interactions between species. We find remarkably similar tree and species networks among tropical forests in Panama, Sri Lanka and Taiwan. Across these sites only 32 to 51% of all possible connections between species pairs were realized in the species networks. The species networks show the common small-world property and constant node degree distributions not yet described and explained by network science. Our application of network analysis to forest ecology provides a new approach in biodiversity research to quantify spatial neighborhood structures for better understanding interactions between tree species. Our analyses show that details of tree positions and sizes have no important influence on the detected network structures. This suggests existence of simple principles underlying the complex interactions in tropical forests.

Neighborhood context mediates probability of host tree mortality in a severe bark beetle outbreak

AbstractUnderstanding drivers of disturbances across scales is critical as environmental constraints change in a warming climate. Outbreaks of native bark beetles (Curculionidae: Scolytinae) are key natural disturbances that shape the structure and function of conifer forests across the northern hemisphere. While drivers of bark beetle outbreaks have been studied extensively at spatial scales ranging from stands to continents, within‐stand processes governing individual tree mortality in an outbreak are less well understood. Here, we use a spatially explicit long‐term monitoring dataset of a lodgepole pine (Pinus contorta var. latifolia) forest (>9000 individually mapped trees in three 2‐ha plots) impacted by a severe mountain pine beetle (Dendroctonus ponderosae) outbreak to explore interactions among fine scale drivers of beetle‐caused tree mortality. Using a Bayesian spatial modeling approach, we evaluated how tree scale and tree neighborhood scale characteristics interact with tree size to mediate host tree susceptibility to mountain pine beetle outbreak in the Southern Rocky Mountains (USA). We found evidence that both tree growth rate preceding the outbreak and neighborhood structure (within a 10 meter radius of the host tree) mediate the effect of tree size, and that the direction and magnitude of these mediating effects vary with tree size. Tree scale mortality probability increased with pre‐outbreak growth rate for small to medium sized host trees (~10–25 cm diameter), but that same effect was not detected for large trees. Conversely, tree scale mortality probability increased with greater neighborhood density, with the most pronounced effects for medium to large sized host trees (~15–30 cm diameter). Within‐stand topographic variability was not an important predictor of mortality probability; among stands, however, the stand in the driest topographic position experienced the greatest overall mortality. By explicitly considering how within‐stand heterogeneity mediates individual tree scale susceptibility to bark beetle outbreak, our findings bridge an important gap in understanding multi‐scale drivers of disturbance dynamics.

Multi-aged micro-neighborhood patches challenge the forest cycle model in primeval European beech

As currently framed, the forest cycle model that underlies close-to-nature management in temperate beech forests throughout the globe specifies an orderly sequence of temporal development within even-aged patches comprising the forest mosaic. Although this model has been widely applied to European beech (Fagus sylvatica L.) forests, the underlying assumptions of disturbance-induced even-agedness (i.e., within-patch age homogeneity) and competition-induced size differentiation (i.e., within-patch size heterogeneity) have not been tested in natural beech forests due to prohibitions on tree coring in primeval forest reserves. In a rare and unprecedented test dataset of spatially explicit tree ages in an old-growth European beech forest, we employed triangulated irregular networks of Delaunay triangles to objectively identify natural tree neighborhoods to determine if neighboring (i.e., within-patch) trees were even- or, at most, two-aged. Age differences among neighboring trees (summarized in 25-yr age classes) were rarely <25 yrs and mostly >50 yrs, while the few "even-aged" patches were very small (100 m2) and relatively young (<150 yrs). In this first assessment of the assumptions underlying the forest cycle model in European beech, we observed neither the even-aged cohorts expected for disturbance-induced patches in different phases of development, nor the size differentiation among similarly aged trees that should arise from the neighborhood dynamics of competition, self-thinning, and growth. The lack of patches indicating demographic turnover is fundamentally inconsistent with the forest cycle model as it is currently framed. We call for further exploration of spatially-explicit tree age datasets to determine the generality of these observations.

Automatic migraine classification using artificial neural networks.

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients' health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient's symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

Community and neighbourhood tree species richness effects on fungal species in leaf litter

Abstract Investigating the effects of individual tree species on fungal species in leaf litter allows a mechanistic understanding of how tree diversity affects the diversity and composition of fungal species at the community level. We collected freshly-fallen leaves of eight focal tree species at four tree species richness levels in a large-scale subtropical forest diversity experiment to estimate tree species richness effects on fungal species diversity and community composition at two spatial scales: at the local tree neighbourhood and at the tree community level. The identity of focal tree species affected both the diversity and composition of the fungal community in freshly-fallen leaves, particularly structuring the composition of both the pathogenic and non-pathogenic fungal community. Furthermore, we found that the effects of community tree species richness on fungal OTU composition were tree species-specific. Besides community tree species richness, the neighbour tree community had significant effects on the structure of the entire fungal community and of functional groups in freshly-fallen leaves. These findings highlight that the response of fungal species assemblages to tree species richness depends on fungal-associated tree species identity, and suggest that heterospecificity of local tree neighbours is an important driver of tree richness effects on litter fungal community.

The scale dependency of trait‐based tree neighborhood models

AbstractQuestionsWe asked: (a) whether the strength of conspecific and heterospecific neighborhood crowding effects on focal tree survival and growth vary with neighborhood radii; and (b) if the relative strength of the effect of neighborhood interactions on tree growth and survival varies with neighborhood scale.LocationLuquillo Forest Dynamics Plot, Puerto Rico.MethodsWe used tree survival and growth data and included information on species‐mean trait values related to several leaf traits, maximum height, seed mass and wood density. We incorporated a tree neighborhood modeling approach that uses an area around a focal tree with a specified radius, to describe the interactions between a focal tree and its neighbors. We constructed survival and growth models for each functional trait using a Bayesian approach, and varied the size of the radius from 5 m to 30 m, at 5‐m intervals.ResultsThe results suggested that the estimated effects of conspecific and heterospecific neighbors on tree performance do not vary based on the size of the neighborhood (5–30 m), suggesting that the effects of conspecific and heterospecific neighbors on the performance of a focal tree likely do not vary substantially beyond a neighborhood radius of 5 m in the Luquillo forest. In contrast, the estimated strength of the functional neighborhood (effect of neighbors based on their functional trait values) on tree performance was dependent on the neighborhood range. Our results also suggested that the effects of trait distances and trait hierarchies on tree survival and growth are acting simultaneously and at the same spatial scales.ConclusionFindings from this study highlight the importance of spatial scale in community assembly processes, and specifically, call for increased attention when selecting the radius that defines the neighborhood around a focal tree as the selected neighborhood radius influences the community patterns discovered, and affects the conclusions about the drivers that control community assembly.

Activity patterns of arboreal mammals in a tropical rain forest in México

Studying activity patterns of canopy mammals provides information on the biology of these species. However, the challenges of reaching and sampling the forest canopy restrict the information available on activity patterns. This study aimed to describe the activity patterns of canopy mammals, and analyze their temporal overlap, in the Santa Gertrudis Ecological Reserve, State of Veracruz, Mexico. Camera trap records were obtained from February 2016 to February 2017. Nine camera traps were set in the study area, separated between 300 and 1500 m from each other, along a straight line in the forest canopy (~ 8 to 12 m above the ground). Cameras were affixed to a trunk above a branch that was perpendicular to the line and overlapping a neighboring tree. A total of 12 mammal species that use the tree stratum were recorded, including two diurnal and ten nocturnal species. The five species for which at least ten records were obtained were included in the activity pattern analyses. Four of these are nocturnal species and showed temporal overlap values (Δ1) ranging from 0.69 to 0.9. Four of the 12 species recorded are listed in an extinctionrisk category as per the Norma Oficial Mexicana NOM. This is one of the first studies providing ecological information on activity patterns of canopy mammals in Mexico.

Connectedness With Nearby Nature and Well-Being

Trees are an integral and salient feature of the natural environment with multiple benefits for environmental and human health. Little is understood, however, about how connectedness with trees or other features of nature (e.g., wildlife) are associated with human health perceptions and well-being. Similarly, research on links between neighbourhood trees and nature connectedness is lacking. Community participants (n = 102 older adults, Mage = 61.56, SD = 9.71) in Peterborough, Canada completed questionnaires related to general health (perceived), subjective well-being, interconnectedness with nature, and other demographics. A subsample of participants (n = 36) provided postal codes that allowed for the tree canopy sampling of four neighbourhoods. People living near trees reported better mental health perceptions (GHQ-12) and a greater sense of connectedness to the natural world around them. Connectedness with trees, wildlife, and nature was associated with better psychological well-being and less mental distress. This sense of connection was still related to better mental health - more positive moods and feelings of vitality - when controlling for age, income, and neighbourhood connectedness. Trees are an integral part of communities and are a cost-effective way of enhancing health as well as mitigating the effects of climate change. Cultivating connectedness with specific elements of the natural environment may help to promote greater environmental concern and behaviour, while providing a positively-framed motivation for such action.

0390 Associations of the Neighborhood Built Environment with Adolescent Sleep Outcomes

Abstract Introduction Over 75% of U.S. high school students obtain insufficient amounts of sleep. Identification of modifiable environmental determinants of adolescent sleep is needed to inform interventions and public health strategies, yet little is known about the influence of the built environment on adolescent sleep. We examined associations of the built environment with objectively-measured adolescent sleep outcomes. Methods In this longitudinal, prospective study, we used actigraphy to assess sleep outcomes for 14 days each in 8th grade and 9th grade: duration (hours/night), onset (hours from 00:00), offset (hours from 00:00), and sleeping &amp;gt;8 hours. Home addresses were linked to built environment exposures based on half-mile Euclidian buffers (overall/human-made sound levels, percent tree canopy cover, street density, intersection density) and census block group (population density, housing density). Mixed-effects linear (sleep duration, onset, offset) and logistic (&amp;gt;8 hours) regression estimated associations of each built environment measure with sleep outcomes, adjusting for sex, race, parent education, household income, grade and weeknight status (school or non-school night). Results Among 108 adolescents - 53% female and 25% Black - providing 2,388 nights of sleep data across 8th and 9th grades, a 1-standard deviation increase in neighborhood sound (overall and human-made) associated with 11 minutes later sleep onset (β=0.19; 95% CI: 0.01, 0.38) and 20% lower odds of sleeping for &amp;gt;8 hours (OR=0.80, 95% CI: 0.62, 1.02). A 1-standard deviation increase in neighborhood tree canopy cover associated with 11 minutes earlier sleep onset (β= -0.19, 95% CI: -0.35, -0.03) and 7 minutes earlier sleep offset (β= -0.12, 95% CI: -0.23, -0.02). No associations were observed for “density based” exposures. Conclusion Higher tree canopy cover associated with more favorable sleep timing while higher neighborhood sound level associated with later timing of sleep onset. These modifiable neighborhood built environment factors should be considered when intervening to support healthier sleep among adolescents. Support NIH/NHLBI K01HL123612 (JM) and Sleep Research Society Foundation and K23HD094905 (AAW)

Associational resistance to both insect and pathogen damage in mixed forests is modulated by tree neighbour identity and drought

Abstract Tree health declines can be caused by interactions between pests and pathogens and many studies have shown a reduction in their damage in mixed species forests compared to monocultures. Yet few authors have considered tree diversity effects on both groups simultaneously. Moreover, it is unclear whether diversity effects on tree pests and pathogens are robust to changes in abiotic conditions, such as drought. We addressed tree diversity effects on foliar insect herbivory, oak powdery mildew and their interaction under contrasting water regimes in a large-scale tree diversity experiment in SW France. Using an irrigation treatment that alleviated drought conditions, we were able to experimentally assess the effects of tree diversity under contrasting abiotic environments. We surveyed plots along a richness gradient from one to four tree species, in which a focal study species of oak (Quercus robur) was mixed with other oak species (Q. pyrenaica and Q. ilex) and a taller, broadleaved species (Betula pendula). Increasing tree species richness lowered leaf miner abundance, leaf chewer damage and oak powdery mildew infection, consistent with a protective effect of resource dilution. However, richness effects on leaf miners were stronger in irrigated compared to non-irrigated blocks, indicating that environmental conditions can modulate diversity effects. Separate from the effect of tree species richness, the presence of birch in a plot increased damage by leaf chewers and powdery mildew, but lowered leaf miner damage, suggesting additional tree neighbour identity effects potentially linked to modulation of microclimate. We found a negative association between leaf miner abundance and oak powdery mildew, consistent with antagonism between oak damage agents. Synthesis. Overall, our study illustrates the importance of considering both tree diversity and composition (neighbour identity) in designing forests more resistant to pest and pathogen damage.

Application of Machine Learning for Diagnosis of Head and Neck Cancer in Primary Healthcare Organisation

Head and neck cancers (HNC) are indicated when cells grow abnormally. The disturbing rate of morbidity and mortality of patients with HNC due to late presentation is on the increase especially in Africa (developing countries). There is need to diagnose head and neck cancer early if patients present so that prompt referral could be facilitated. The collected data consists of 1473 instances with 18 features. The dataset was divided into training and test data. Two supervised learning algorithms were deployed for the study namely: Decision Tree (C4.5) and k-Nearest Neighbors (KNN). It showed that Decision Tree outperformed with accuracy of 91.40% while KNN had accuracy of 88.24%. Hence, machine learning algorithm like Decision Tree can be used for diagnosis of HNC in healthcare organisations.

Application of Machine Learning for Diagnosis of Head and Neck Cancer in Primary Healthcare Organisation

Head and neck cancers (HNC) are indicated when cells grow abnormally. The disturbing rate of morbidity and mortality of patients with HNC due to late presentation is on the increase especially in Africa (developing countries). There is need to diagnose head and neck cancer early if patients present so that prompt referral could be facilitated. The collected data consists of 1473 instances with 18 features. The dataset was divided into training and test data. Two supervised learning algorithms were deployed for the study namely: Decision Tree (C4.5) and k-Nearest Neighbors (KNN). It showed that Decision Tree outperformed with accuracy of 91.40% while KNN had accuracy of 88.24%. Hence, machine learning algorithm like Decision Tree can be used for diagnosis of HNC in healthcare organisations.

Neighboring trees regulate the root-associated pathogenic fungi on the host plant in a subtropical forest.

Root‐associated fungi and host‐specific pathogens are major determinants of species coexistence in forests. Phylogenetically related neighboring trees can strongly affect the fungal community structure of the host plant, which, in turn, will affect the ecological processes. Unfortunately, our understanding of the factors influencing fungal community composition in forests is still limited. In particular, investigation of the relationship between the phytopathogenic fungal community and neighboring trees is incomplete. In the current study, we tested the host specificity of members of the root‐associated fungal community collected from seven tree species and determined the influence of neighboring trees and habitat variation on the composition of the phytopathogenic fungal community of the focal plant in a subtropical evergreen forest. Using high‐throughput sequencing data with respect to the internal transcribed spacer (ITS) region, we characterized the community composition of the root‐associated fungi and found significant differences with respect to fungal groups among the seven tree species. The density of conspecific neighboring trees had a significantly positive influence on the relative abundance of phytopathogens, especially host‐specific pathogens, while the heterospecific neighbor density had a significant negative impact on the species richness of host‐specific pathogens, as well as phytopathogens. Our work provides evidence that the root‐associated phytopathogenic fungi of a host plant depend greatly on the tree neighbors of the host plant.

Tree Neighbourhood Diversity Has Negligible Effects on Drought Resilience of European Beech, Silver Fir and Norway Spruce

Promoting tree species diversity is commonly advocated in the anticipation of predicted increases in drought frequency and severity. However, mixing effects on drought responses vary substantially with site conditions and species identity. We combined annually resolved tree-ring data and repeated forest inventory data spanning the last 90 years to examine the effect of species-specific neighbourhood competition on the drought response (resistance, recovery and resilience) of European beech (Fagus sylvatica), silver fir (Abies alba) and Norway spruce (Picea abies) for six drought events that occurred since the 1970s at three sites in Switzerland. We found predominantly weak neighbourhood competition and tree species diversity effects, with significant interspecific influences only for resistance and recovery of beech. These minor neighbourhood effects were outweighed by tree age and size effects. Although age effects depended on species identity and components of resilience, tree size consistently negatively affected all species. Our results emphasize that diversity effects may vary for each given species combination which makes broader conclusions challenging. This is because species interact through their specific set of traits and interactions vary in space and time. Adaptive management strategies are likely to be more effective when they promote more drought-tolerant species and reductions in stand density. Despite the absence of an unequivocal advantage of tree diversity on drought resilience, striving towards species-rich forests nonetheless allows for a risk spreading among multiple species and the reinforced provision of numerous ecosystem services.

Leaf my neighbourhood alone! predicting the influence of densification on residential tree canopy cover in Perth

Trees provide myriad ecosystem services of benefit to urban populations; however, urban development is pressuring existing urban tree coverage. Thus, a pertinent challenge for planners is identifying development scenarios that find synergies between urban growth and the preservation or enhancement of tree canopy coverage. This paper presents the training and validation of a model that predicts changes in neighbourhood-level urban tree canopy cover associated with different socio-economic and physical urban form variables. Neighbourhoods across Perth, the capital city of Western Australia, were used as a case study. A Random Forests model was trained using a suite of socio-economic and urban form variables and neighbourhood percentage tree canopy cover derived from very high resolution multispectral remote sensing images and digital surface models. This model was validated using independent test data with a mean absolute error of 1.78% and a root mean square error of 2.42%. An application of this model was demonstrated using the City of Nedlands, Perth, where a new planning scheme allowing denser urban development has been approved by the State Government. The magnitude and spatial variation in the change of neighbourhood tree canopy cover in the City of Nedlands in 2050 associated with three urban development scenarios was predicted using the model.

A permutation test and spatial cross-validation approach to assess models of interspecific competition between trees.

Measuring species-specific competitive interactions is key to understanding plant communities. Repeat censused large forest dynamics plots offer an ideal setting to measure these interactions by estimating the species-specific competitive effect on neighboring tree growth. Estimating these interaction values can be difficult, however, because the number of them grows with the square of the number of species. Furthermore, confidence in the estimates can be overestimated if any spatial structure of model errors is not considered. Here we measured these interactions in a forest dynamics plot in a transitional oak-hickory forest. We analytically fit Bayesian linear regression models of annual tree radial growth as a function of that tree's species, its size, and its neighboring trees. We then compared these models to test whether the identity of a tree's neighbors matters and if so at what level: based on trait grouping, based on phylogenetic family, or based on species. We used a spatial cross-validation scheme to better estimate model errors while avoiding potentially over-fitting our models. Since our model is analytically solvable we can rapidly evaluate it, which allows our proposed cross-validation scheme to be computationally feasible. We found that the identity of the focal and competitor trees mattered for competitive interactions, but surprisingly, identity mattered at the family rather than species-level.

Soil-borne pathogens as determinants of regeneration patterns at community level in Mediterranean forests.

Emergent diseases are an increasing problem in forests worldwide. Exotic pathogens are now threatening forests where pathogens have not traditionally been considered to be major ecological drivers of tree demography, such as water-limited Mediterranean forests. However, how pathogens might limit regeneration in invaded forests is largely unknown. Here we used fungicide to analyse the impact of soil-borne oomycete pathogens on seedling establishment at community level in Mediterranean forests invaded by the exotic oomycete Phytophthora cinnamomi. Fungicide effects were modelled as a function of the tree neighbourhood composition, the seed mass of the target species, and the abiotic environment. Fungicide application had positive effects on seedling performance that varied in magnitude and spatial structure among coexisting species. Seed mass predicted fungicide effects on seedling emergence, but not on survival or growth. Positive fungicide effects were modulated by levels of abiotic resources, mainly water, increasing with soil moisture. Our results support a novel role for soil-borne oomycete pathogens as one more axis of the regeneration niche of woody species in water-limited forests. Given the increasing numbers of exotic oomycete pathogens worldwide, more research is needed to understand the role of this relevant microbial group as a factor shaping seedling establishment.

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.