Potential Impact of Climate Change on Fungal Distributions: Analysis of 2 Years of Contrasting Weather in the UK

The impact of climate change on fungal growth and spore production is less well documented than for allergenic pollen grains, although similar implications for respiratory tract diseases in humans occur. Fungal spores are commonly described as either “dry” or “wet” according to the type of weather associated with their occurrence in the air. This study examined the distribution of selected fungal spores (Alternaria spp., Cladosporium spp., Didymella spp., Epicoccum spp., Leptosphaeria spp. and rusts) occurring in the West Midlands of UK during 2 years of contrasting weather. Spore specimens were collected using a 7-day volumetric air sampler and then analysed with the aid of light microscopy. Distributions of spores were then studied using normality tests and Mann–Whitney U test, while relationships with meteorological parameters were investigated using Spearman’s rank test and angular-linear correlation for wind direction analysis. Our results showed that so-called wet spores were more sensitive to the weather changes showing statistically significant differences between the 2 years of study, in contrast to “dry” spores. We predict that in following years we will observe accelerated levels in allergenic fungal spore production as well as changes in species diversity. This study could be a starting point to revise the grouping system of fungal spores as either “dry” or “wet” types and their response to climate change

An Analysis of Local Wind and Air Mass Directions and Their Impact on Cladosporium Distribution Using HYSPLIT and Circular Statistics

Ecological studies that examine species-environment relationships are often limited to several meteorological parameters, i.e. mean air temperature, relative humidity, precipitation, vapour pressure deficit and solar radiation. The impact of local wind, its speed and direction are less commonly investigated in aerobiological surveys mainly due to difficulties related to the employment of specific analytical tools and interpretation of their outputs. Identification of inoculum sources of economically important plant pathogens, as well as highly allergenic bioaerosols like Cladosporium species, has not been yet explored with remote sensing data and atmospheric models such as Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). We, therefore, performed an analysis of 24 h intra-diurnal cycle of Cladosporium spp. spores from an urban site in connection with both the local wind direction and overall air mass direction computed by HYSPLIT. The observational method was a volumetric air sampler of the Hirst design with 1 h time resolution and corresponding optical detection of fungal spores with light microscopy. The atmospheric modelling was done using the on-line data set from GDAS with 1° resolution and circular statistical methods. Our results showed stronger, statistically significant correlation (p ≤ 0.05) between high Cladosporium spp. spore concentration and air mass direction compared to the local wind direction. This suggested that a large fraction of the investigated fungal spores had a regional origin and must be located more than a few kilometers away from the sampling point.

Investigating and Predicting Atmospheric Concentrations of Allergenic Fungal Spores (Alternaria, Cladosporium, Didymella and Ganoderma)

Air quality is an increasing concern of the European Union, local authorities, scientists and most of all inhabitants that become more aware of the quality of the surrounding environment. Bioaerosols may be consisted of various elements, and the most important are pollen grains, fungal spores, bacteria, viruses. More than 100 genera of fungal spores have been identified as potential allergens that cause immunological response in susceptible individuals. Alternaria and Cladosporium have been recognised as the most important fungal species responsible for respiratory tract diseases, such as asthma, eczema, rhinitis and chronic sinusitis. While a lot of attention has been given to these fungal species, a limited number of studies can be found on Didymella and Ganoderma, although their allergenic properties were proved clinically. Monitoring of allergenic fungal spore concentration in the air is therefore very important, and in particular at densely populated areas like Worcester, UK. In this thesis a five year spore data set was presented, which was collected using a 7-day volumetric spore trap, analysed with the aid of light microscopy, statistical tests and geographic information system techniques. Although Kruskal-Wallis test detected statistically significant differences between annual concentrations of all examined fungal spore types, specific patterns in their distribution were also found. Alternaria spores were present in the air between mid-May/mid-June until September-October with peak occurring in August. Cladosporium sporulated between mid-May and October, with maximum concentration recorded in July. Didymella spores were seen from June/July up to September, while peaks were found in August. Ganoderma produced spores for 6 months (May-October), and maximum concentration could be found in September. With respect to diurnal fluctuations, Alternaria peaked between 22:00h and 23:00h, Cladosporium 13:00-15:00h, Didymella 04:00-05:00h and 22:00h-23:00h and Ganoderma from 03:00h to 06:00h. Spatial analysis showed that sources of all fungal species were located in England, and there was no evidence for a long distance transport from the continent. The maximum concentration of spores was found several hours delayed in comparison to the approximate time of the spore release from the crops. This was in agreement with diurnal profiles of the spore concentration recorded in Worcester, UK. Spores of Alternaria, Didymella and Ganoderma revealed a regional origin, in contrast to Cladosporium, which sources were situated locally. Hence, the weather conditions registered locally did not exhibit strong statistically significant correlations with fungal spore concentrations. This has had also an impact on the performance of the forecasting models. The best model was obtained for Cladosporium with 66% of the accuracy.