1206. Effects of Climatic Variability on Lyme Disease Outbreaks in California

Background Climate change has increased the risk of tick borne infections. The life cycle and prevalence of deer ticks are strongly influenced by temperature. Warmer temperatures associated with climate change are projected to increase the range of suitable tick habitats driving the spread of Lyme disease (LD). Short winters could also increase tick activity increasing the risk of exposure. This study examines the relationship between LD incidence and temperature-precipitation and their anomalies in CA counties. Methods Trends and relationships of Lyme Disease (LD) cases and climatic factors were analyzed among the California counties from 2000 to 2019. Lyme disease tabulate data and climatic data were obtained from Centers for Disease Control, and NOAA, and Climate Data Guide respectively. Canonical correspondence analysis (CCA) was performed using variables: (i) LD cases, (ii) precipitation & anomaly, and temperature & anomaly. The CCA ordination explained the variability between LD cases and climatic variables. Biplots were used to visualize the associations between LD cases and climatic anomalies. Results We compared the countywide LD cases in relation to climatic factors in California from 2000 to 2019. A total of 96 cases in 2000, 117 cases in 2009, and 144 cases in 2019 were reported in the 55 counties of California. Santa Clara reported the highest LD cases in 2003 (23 cases; 16%), followed by Los Angeles in 2013 (20 cases; 18%) and Santa Cruz in 2017 (19 cases; 13%). CCA ordination showed distinguishable clustering patterns between southern California counties (Santa Clara, Santa Cruz, Alameda, and San Diego) and northern coast and Klamath mountains range (Humboldt, Trinity, Shasta, and Siskiyou) regions (Fig. 1). Moderate mean annual temperature (56.5 °F - 62.5 °F) and temperature anomaly (3.8 °F - 5.5 °F) were the most important variable predictor for high LD outbreak. The CCA ordination shows the relationships between Lyme Disease and climatic variables for the 55 Counties of California. The bottom right circle represents Lyme cases positively correlated with temperature anomaly (3.8 °F - 5.5 °F) and moderate annual mean temperature (56.5 °F - 62.5 °F). The upper left circle represents Lyme cases negatively correlated with mean annual precipitation. Conclusion Moderate temperature with low moist spell anomalies in the south neighboring CA counties showed a positive influence on LD outbreak. The climatic conditions in those areas suitable for Oak trees and masting acorn resulting in the establishment of tick and host (deer) populations. We recommend robust surveillance and lab testing for patients with a history of tick bites in these regions. Disclosures All Authors: No reported disclosures

Refining Land-Cover Maps Based on Probabilistic Re-Classification in CCA Ordination Space

Due to spatial inhomogeneity of land-cover types and spectral confusions among them, land-cover maps suffer from misclassification errors. While much research has focused on improving image classification by re-processing source images with more advanced algorithms and/or using images of finer resolution, there is rarely any systematic work on re-processing existing maps to increase their accuracy. We propose refining existing maps to achieve accuracy gains by exploring and utilizing relationships between reference data, which are often already available or can be collected, and map data. For this, we make novel use of canonical correspondence analysis (CCA) to analyze reference-map class co-occurrences to facilitate probabilistic re-classification of map classes in CCA ordination space, a synthesized feature space constrained by map class occurrence patterns. Experiments using GlobeLand30 land-cover (2010) over Wuhan, China were carried out using reference sample data collected previously for accuracy assessment in the same area. Reference sample data were stratified by map classes and their spatial heterogeneity. To examine effects of model-training sample size on refinements, three subset samples (360, 720, and 1480 pixels) were selected from a pool of 3000 sample pixels (the full training sample). Logistic regression modeling was employed as a baseline method for comparisons. Performance evaluation was based on a test sample of 1020 pixels using a strict and relaxed definitions of agreement between reference classification and map classification, resulting in measures of types I and II, respectively. It was found that the CCA-based method is more accurate than logistic regression in general. With increasing sample sizes, refinements generally lead to greater accuracy gains. Heterogeneous sub-strata usually see greater accuracy gains than in homogeneous sub-strata. It was also revealed that accuracy gains in specific strata (map classes and sub-strata) are related to strata refinability. Regarding CCA-based refinements, a relatively small sample of 360 pixels achieved a 3% gain in both overall accuracy (OA) and F0.01 score (II). By using a selective strategy in which only refinable strata of cultivated land and forest are included in refinement, accuracy gains are further increased, with 5–11% gains in users’ accuracies (UAs) (II) and 4–10% gains in F0.01 scores (II). In conclusion, on condition of refinability, map refinement is well worth pursuing, as it increases accuracy of existing maps, extends utility of reference data, facilitates uncertainty-informed map representation, and enhances our understanding about relationships between reference data and map data and about their synthesis.

692. Effects of Regional Climatic Variability on West Nile Virus Outbreaks in the United States

Background Transmission of WNV to humans in the United States typically occurs between June and September since warm temperatures accelerate mosquito life cycle. Precipitation can cause increase in aquatic breeding but outbreaks often depends upon human water management. We examine epidemiology, patterns of WNV disease transmission, and identification of high-risk areas in the United States from 2003 to 2014. Methods Trends and relationships of WNV cases and climatic factors were analyzed among the regions of the United States from 2003 to 2014. Human WNV tabulate data and climatic data were obtained from Centers for Disease Control, and NOAA and Climate Data Guide, respectively. Canonical correspondence analysis (CCA) was performed using variables: (i) neuroinvasive disease cases, non-neuroinvasive disease cases, deaths, presumptiveviremic blood donors, (ii) precipitation, temperature, Palmer Drought Severity Index (PDSI) and population density. The CCA ordination was explained the variability between WNV disease cases andclimatic variables. Biplots were used to visualize the associations between WNV cases and climatic anomalies. Results We compared the state wise WNV disease cases in relation to climatic and population density in the United States from 2003 to 2014. A total of 4,064 cases in 2006, 956 cases in 2010 and, 2,141 cases in 2014 were reported in the 32 states of the United States. Colorado state reported the highest WNV cases in 2003 (2,947 cases; 33%), followed by Texas in 2012 (1,868 cases; 35%) and California in 2014 (801 case; 37%). CCA ordination showed distinguishable clustering patterns between south central (Texas, Louisiana, Mississippi, Arkansas, and Oklahoma) and northern Great Plains (North Dakota, South Dakota, and Nebraska) regions (Figure 1). High temperature and prolong drought were the most important variable predictor for high WNV outbreak. Conclusion Vector control methods focusing on prevention must be implemented to avoid epidemics of WNV if high temperature is leading to an unusual drought especially at the risk areas, such as Texas and California. However, high temperature with moist spell anomalies in the south central region showed a negative influence on WNV outbreak. Disclosures All authors: No reported disclosures.

Multivariate Analyses of Burkholderia Species in Soil: Effect of Crop and Land Use History

ABSTRACT The assessment of Burkholderia diversity in agricultural areas is important considering the potential use of this genus for agronomic and environmental applications. Therefore, the aim of this work was to ascertain how plant species and land use management drive the diversity of the genus Burkholderia. In a greenhouse experiment, different crops, i.e., maize, oat, barley, and grass, were planted in pots containing soils with different land use histories, i.e., maize monoculture, crop rotation, and permanent grassland, for three consecutive growth cycles. The diversity of Burkholderia spp. in the rhizosphere soil was assessed by genus-specific PCR-denaturing gradient gel electrophoresis (DGGE) and analyzed by canonical correspondence analysis (CCA). CCA ordination plots showed that previous land use was the main factor affecting the composition of the Burkholderia community. Although most variation in the Burkholderia community structure was observed between the permanent grassland and agricultural areas, differences between the crop rotation and maize monoculture groups were also observed. Plant species affected Burkholderia community structure to a lesser extent than did land use history. Similarities were observed between Burkholderia populations associated with maize and grass, on the one hand, and between those associated with barley and oat, on the other hand. Additionally, CCA ordination plots demonstrated that these two groups (maize/grass versus barley/oat) had a negative correlation. The identification of bands from the DGGE patterns demonstrated that the species correlated with the environmental variables were mainly affiliated with Burkholderia species that are commonly isolated from soil, in particular Burkholderia glathei, B. caledonica, B. hospita, and B. caribiensis.

MURAWY PIASKOWE ZE ZWIĄZKU SILENO CONICAE-CERASTION SEMIDECANDRI KORNECK 1974 NA TERENIE KAMPUSU UAM MORASKO W POZNANIU

Field studies on anthropogenic psammophilous swards of the Sileno conicae-Cerastion semidecandri alliance were carried out in the years 2015–2016 in the area of the Adam Mickiewicz University Morasko campus in Poznań, Poland. As a result of a classical table analysis of phytosociological data, five associations were distinguished. The relevés have been arranged with the use of PCA and CCA ordination methods to study the overall variability of the examined communities and their position along some environmental gradients. In the paper, issues concerning successional dynamics and the synsystematical position of swards of the studied type, are also raised.