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Science of the Total Environment 665 (2019) 513-520 



ELSEVIER 


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Science of the Total Environment 


journal homepage: www.elsevier.com/locate/scitotenv 



Diversity and abundance of airborne fungal spores in a rural cold dry ® 

Check for 

desert environment in Argentinean Patagonia 

Carolina Virginia Temperini a '*, Maria Luisa Franchi a,c , Martha Elizabeth Benavides Rozo a , Mariana Greco b,c , 
Alejandro Guillermo Pardo b,c , Graciela Noemi Pose a,c 

a Escuela de Production, Tecnologia y Medio Ambiente, Universidad National de Rio Negro, Mitre 331, 8336 Villa Regina, Provincia de Rio Negro, Argentina 

b Laboratorio de Micologia Molecular, Departamento de Ciencia y Tecnologia, Universidad National de Quilmes, Roque Saenz Pena 352, B1876BXD Bernal, Provincia de Buenos Aires, Argentina 
c Consejo National de Investigaciones Cientificas y Tecnicas, Argentina 


HIGHLIGHTS 


GRAPHICAL ABSTRACT 


This is the first longitudinal study of 
three consecutive years in rural envi¬ 
ronments with Koppen Bwk. 

The annual mean fungal counts were 
found in the order of E+03 CFU/m 3 
of air. 

The aerial mycoflora revealed at least 
28 genera and 50 species in the sam¬ 
pling period. 

Cladosporium was the most abundant 
genus followed by Alternaria and 
Epicoccum. 

C. cladosporioides, C. limoniforme and 
A. tenuissima predominated. 



ARTICLE INFO 


ABSTRACT 


Article history: 

Received 9 October 2018 

Received in revised form 4 February 2019 

Accepted 7 February 2019 

Available online 8 February 2019 

Editor: Frederic Coulon 


Keywords: 

Fungal 
Airborne 
Biodiversity 
Rural environments 
Cold dry desert 


This work describes a longitudinal study of three consecutive years carried out in the air of agricultural environ¬ 
ments located in Northern Patagonia with cold dry desert climate (Koppen: Bwk). This study area comprises a 
rural valley with unique geographical and climatological conditions. Therefore, the aim of this work is to quantify 
and determine its fungal diversity, so this knowledge will contribute to detect potential pathogenic and toxic 
fungi that has been adapted to this type of environment and may overcome the incipient climate change. 
Samplings were conducted in two geographical zones of the study area and a microflow air sampler was used 
to isolate fungal taxa. The annual mean fungal counts were found in the order of E+03 CFU/m 3 of air. The aerial 
mycoflora revealed a wide biodiversity of at least 28 genera and 50 fungal species. Cladosporium was the most 
abundant genus (76.97%), followed by Alternaria (12.48%), Epicoccum (4.41%) and Botrytis (1.81%). The rest of 
the genera were found in relative densities lower than 1%. In terms of species, C. cladosporioides (34.82%), 
C. limoniforme (21.72%), A. tenuissima (10.94%) and C. asperulatum predominated (9.01%). This is the first report 
of the air mycoflora of rural environments with cold diy desert climate which provides useful information to take 
preventive measures to avoid biological damage. 

© 2019 Elsevier B.V. All rights reserved. 


* Corresponding author. 

E-mail address: ctemperini@unm.edu.ar (C.V. Temperini). 


https://doi.Org/l 0.1016/j.scitotenv.2019.02.115 
0048-9697/© 2019 Elsevier B.V. All rights reserved. 

























514 


C.V. Temperini et al. / Science of the Total Environment 665 (2019) 513-520 


1. Introduction 

The atmosphere does not contain an autochthonous microbiota, 
but it is a means for the dispersion of many types of microorganisms 
(De la Rosa et al., 2002). Aeromycological studies have been carried 
out in many parts of the world and they provide extremely important 
information due to the effects that fungal spores can cause on human, 
animal and plant health (Abdel Hameed et al., 2009; Reyes et al., 
2009; Oliveira et al., 2010). Therefore, to systematically evaluate the 
relationship between environmental fungi and their adverse effects, 
the types of fungi and their concentration need to be known. 

Some of these studies were conducted in urban areas and describe 
and quantify the fungal biodiversity of indoor (Cabral, 2010; Newbound 
et al., 2010; Khan and Karuppayil, 2012; Mentese et al„ 2012; Sharpe 
et al., 2015) and outdoor environments (Curtis et al., 2006; Frohlich- 
Nowoisky et al., 2009; Lang-Yona et al., 2016). Other studies have been 
performed in rural areas mainly for agricultural and phytopathological 
purposes (Pepeljnjak and Segvic, 2003; Oliveira et al., 2009; Lanier 
et al., 2010; Temperini et al., 2018). Annually, crop losses due to fungal 
diseases reach large sums, causing big quantitative and/or qualitative 
economic losses to the productive sectors (Kasprzyk, 2008; Kakde, 
2012). Therefore, the atmosphere is the most common means for the 
dispersion of fungal spores, the determination of the aerial mycobiota 
of a region, particularly its phytopathogenic fraction, provides useful 
information to take preventive measures contributing to improve pro¬ 
ductivity and health of the crops (Isard et al., 2007). 

Studies conducted in the field of environmental mycology suggest 
that the fungal composition of air, in terms of concentration and compo¬ 
sition of genera and species, varies with respect to geographical areas 
and is influenced by seasonality and other environmental factors 
(Reyes et al., 2009; Frohlich-Nowoisky et al., 2012). Furthermore, the 
ongoing climate change can profoundly affect the production and 
dispersal of fungal spores, with the consequent modification of their 
air concentration and associated impact. Therefore, the monitoring of 
fungal spores in the air can be useful to detect spatial and temporal 
profiles of a changing climate (Beggs, 2010; Boccacci et al., 2017). 

The High Valley of Rfo Negro is a rural valley located in the northern 
sector of the Argentinean Patagonia whose main activity is the growth 
and exportation of pip fruits. This agricultural environment presents a 
cold dry desert climate and it is classified in the BWk type of Koppen, 
cold dry desert climate with warm summers (Koppen, 1931). This 
study area presents unique geographical and climatological conditions 
(see Section 2.1 sampling area) which, in the context of the global 
climate change, may turn it into an extreme environment. Therefore, 
the aim of this work is to determine the annual concentration, density 
and distribution of the mycological diversity of this particular type of 
agricultural environment in order to detect potential pathogenic and 
toxic fungi that has been already adapted to these conditions and 
may overcome the incipient climate change. Results will allow to take 
preventive measures in order to avoid biological damage. 

2. Material and methods 

2.1. Sampling area 

The High Valley of Rfo Negro is a rural valley located in the northern 
sector of the Argentinean Patagonia, between 38° 40' and 39° 20' S and 
65° 50' and 68° 20' W in the confluence of three rivers. This “Y” shaped 
valley is arranged latitudinally and reaches a 100 km extension approx¬ 
imately. Its main economic activity is related to pip fruit production and 
it is the most important producing and exporting region of the nation. It 
is included in the climatic classification of Koppen in the BWk type 
(Koppen, 1931). The zone is characterized by a mean annual tempera¬ 
ture of 15 °C and a marked thermal amplitude. The mean annual relative 
humidity is 65% and the mean annual rainfall is 243.7 mm. However, 
large variations are observed from year to year, characteristic of arid 


climates. The wind is a factor of importance in the area due to its con¬ 
stancy and intensity, reaching gusts up to 74 km/h and its presence 
makes it necessary to install protective curtains, mainly malls. Due to 
its geographical location, the region has high values of global radiation 
with a mean annual value of 391.4 cal• gr/cm 2 • day (Rodriguez and 
Munoz, 2006). 

Due to its latitudinal extension, this valley could be divided into 
three equidistant zones: west, central and east. Even though weather 
characteristics are homogeneous along the valley, the eastern zone 
has presented, more often, outbreaks of apple and pear scabs which 
are related to high conditions of humidity (National Institute of Agricul¬ 
tural Technology -INTA-, personal communication). In terms of vegeta¬ 
tion, the central zone has a higher density of rural settings (National 
Service for Agri-Food Health and Quality -SENASA-, SENASA, 2017). 
Therefore, eight rural settings were selected, four of them located in 
the eastern zone and the other four in the central zone separated by a 
distance of 50 km approximately (Fig. 1). All of them presented the 
spalier production system for pip fruits, currently the most widespread 
in the region. Regarding population, the eastern zone contains five 
towns and 50,191 habitants and the central zone contains 4 towns 
with 126,873 according to the last census (National Institute ofStatistics 
and Censuses of the Argentine Republic-INDEC, 2010). 

2.2. Sampling method 

As we have not found previous information and/or reference of sam¬ 
pling methods to follow in large rural external environments, before 
starting this work, we carried out a sampling test in one of the selected 
rural settings in order to set methodology and all the parameters that 
would make the sampling campaigns reproducible such as volume 
and speed of sampling and culture medium. 

Samplings were conducted using a microflow air sampler 
(Microflow a 90 Aquaria version 3.0.0 cod. G.1015) containing 90 mm 
disposable Petri dishes that allows to sample volumes of air up to a max¬ 
imum of 2.000 L at five different speeds: 30 L/min, 60 L/min, 90 L/min, 
100 L/min and 120 L/min. The sampler was placed at a height of 
1.50 m above the ground level, on a photographic tripod and the head 
of the equipment was cleaned with isopropanol between each 
sampling. It was not used a positive hole conversion table to obtain 
data with this microflow air sampler. 

First, we tried the medium Dichloran Rose Bengal Chloramphenicol 
Agar (DRBC) (Shelton et al., 2002), using air volumes of 40 L, 100 L 
and 180 L in order to include two extremes and one intermediate 
point of the sampler's range of volumes. Each volume was taken at 
three different speeds: 30 L/min, 90 L/min and 120 L/min. 

The same test was then carried out with Potato Dextrose Agar (PDA) 
medium supplemented with chloramphenicol (0.1 g/L) to inhibit 
bacterial growth (Muhsin and Adlan, 2012). Due to the fact that in 
some of these plates invading fungi were observed, dichloran 
(2 mg/L) was added to the medium in the same concentration used in 
the DRBC medium (Pitt and Hocking, 2009) and the assay was repeated. 

We observed that DRBC is more efficient to control the diameters of 
the colonies but it makes it difficult to observe some of the genera and 
their pigments when performing differential counting. Therefore, the 
culture medium of choice was PDA supplemented with chlorampheni¬ 
col (0.1 g/L) and dichloran (2 mg/L). 

With respect to the selection of sample volume and speed, the 
results yielded excessively high counts using volumes of 100 L or 
more at speeds >30 L/min. In addition, a high variability of counts was 
obtained at different geographical points of the rural setting in the 
same sampling day using the same volume and speed, so instead of 
taking replicas it was decided to take samples at each geographical 
point using two different volumes; 50 L and 100 L at the lowest speed 
of the sampler, 30 L/min. The selection of the plate per geographical 
point was made by the best distribution of the colonies for counting, 
as the first criterion, and then, by the highest biodiversity obtained. 


C.V. Temperini et al. / Science of the Total Environment 665 (2019) 513-520 


515 


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Fig. 1. Map of the agricultural region under study detailing the geographical zones and the eight productive establishments in which the samplings were carried out. 


Occasionally, particularly during the summer season, we obtained 
plates that exceeded 100 CFU/m 3 . In these cases the count was carried 
out by quadrants and, therefore, the fungal counts obtained in this 
study belong to an estimated number. 

The sampling frequency was once for each season of the year 
for three consecutive years between 2014 and 2017. Since the 
Argentinean Patagonia is characterized by the presence of constant 
and intense winds, samplings were carried out in days with 
low wind speed, previously consulting the reference local forecast 


provided by the Agrometeorological Observatory belonging to 
INTA. Samplings of rural settings from each zone were conducted 
in the same day during the morning hours. Within each rural setting, 
samples were taken at three geographical points using a GPS to 
register the coordinates of latitude and longitude of each sampling 
site. Details of sampling times and weather conditions are described 
in Table 1. 

A total of 288 counting plates were obtained from the samplings 
conducted in the eight rural settings during the four seasons of each 


Table 1 

Details of sampling times and weather conditions. 


Season 

Zone 

Date 

Weather conditions 




Temperature (°C) 

Wind (Km/h) 

Humidity (%) 

Global radiation (w/m 2 ) 

AUTUMN 2014 

East 

26/05 

6.8 

1.1 

75 

580 


Central 

29/05 

10 

2.6 

68 

316 

WINTER 2014 

East 

13/08 

1.7 

2.1 

51 

554 


Central 

21/08 

9.1 

2.4 

66 

401 

SPRING 2014 

East 

12/11 

16.6 

2.3 

43 

1022 


Central 

05/11 

14.8 

0.5 

60 

823 

SUMMER 2015 

East 

23/02 

22.1 

0.6 

63 

918 


Central 

20/02 

19.8 

2.3 

46 

597 

AUTUMN 2015 

East 

04/05 

11.9 

3.3 

56 

491 


Central 

12/05 

9.9 

0.8 

87 

214 

WINTER 2015 

East 

13/08 

6.7 

1 

36 

583 


Central 

19/08 

11.2 

4.7 

59 

386 

SPRING 2015 

East 

10/11 

18.6 

2.6 

ND a 

ND a 


Central 

17/11 

16.9 

10.3 

27 

676 

SUMMER 2016 

East 

10/02 

21.8 

0.6 

72 

954 


Central 

11/02 

23.2 

2.6 

74 

626 

AUTUMN 2016 

East 

17/05 

8.1 

0.3 

83 

509 


Central 

23/05 

8.4 

2.3 

87 

403 

WINTER 2016 

East 

19/08 

8.6 

3.7 

65 

628 


Central 

18/08 

10.2 

16.6 

47 

397 

SPRING 2016 

East 

23/11 

22.7 

0.5 

60 

988 


Central 

21/11 

16.4 

1.1 

54 

670 

SUMMER 2017 

East 

07/02 

22.1 

1.1 

65 

917 


Central 

08/02 

20.9 

1.8 

59 

665 


Not determined. 








516 


C.V. Temperini et al. / Science of the Total Environment 665 (2019) 513-520 


Table 2 

Diversity of the fungal genera and species found in each geographical zone. 


Eastern zone 


Central zone 


Genera 

Species 

Genera 

Species 

Acremonium 

spp. 

Acremonium 

spp. 

Alternaria 

A. alternata 

Alternaria 

A alternata 


A. arborescens 


A. arborescens 


A. tenuissima 


A. tenuissima 


A. sp. 


A sp. 

Arthrinium 

spp. 

Arthrinium 

spp. 

Aspergillus 

A. niger 

Aspergillus 

A chevalieri 

A. niger 

A. sydowii 


A. versicolor 


A. wentii 

A. sp. 

Aureobasidium 

spp. 

Aureobasidium 

spp. 

Bipolaris 

spp. 

Bipolaris 

spp. 

Botrytis 

spp. 

Botrytis 

spp. 

Chrysonilia 

spp. 



Cladosporium 

C. aggregatocicatricatum 

Cladosporium 

C. aggregatocicatricatum 

C. allicinum 


C. asperulatum 


C. asperulatum 


C. cladosporioides 


C. cladosporioides 


C. limoniforme 


C. limoniforme 


C. macrocarpum 


C. macrocarpum 


C. pseudocladosporioides 


C. pseudocladosporioides 


C. ramotenellum 


C. ramotenellum 


C. subtilissimum 


C. subtilissimum 


C. tenellum 


C. tenellum 


C. sp. 


C. sp. 

Curvularia 

spp. 

Curvularia 

spp. 

Drechslera 

spp. 

Drechslera 

spp. 

Emericella 

spp. 



Epicoccum 

E. nigrum 

Epicoccum 

E. nigrum 

Fusarium 

F. acuminatum 

Fusarium 

F. acuminatum 

F. chlamydosporum 


F. compactum 

F. crookwellense 

F. dlamini 


F. dlamini 


F. oxysporum 

F. poae 


F. oxysporum 


F. polyphialidicum 

F. proliferatum 


F. proliferatum 

F. reticulatum 


F. sambucinum 

F. semitectum 


F. semitectum 

F. solani 


F. sporotrichioides 


F. sporotrichioides 

F. verticillioides 


F. sp. 

Geotrichum 

Moniliella 

Neosartorya 

F. sp. 

Nigrospora 

spp. 

Nigrospora 

spp. 

Penicillium 

P. brevicompactum 

Penicillium 

P. brevicompactum 


P. canescens 


P. canescens 

P. chrysogenum 


P. expansum 


P. expansum 

P. funiculosum 

P. janckzewsky 


P. paneum 


P. paneum 


P. raistrickii 


P. raistrickii 

P. restrictum 


P. solitum 


P. solitum 

P. verrucosum 

P. sp. 

Phoma 

P. aliena 

Phoma 

P. aliena 

P. betae 


P. glomerata 


P. glomerata 

P. medicaginis 


P. sp. 


P. sp. 

Scopulariopsis 

spp. 

Scopulariopsis 

Sordaria 

Stachybotrys 

spp. 

Stemphylium 

S. vesicarium 

Stemphylium 

S. vesicarium 

Trichoderma 

spp. 

Trichoderma 

spp. 



Trichothecium 

spp. 


Table 2 ( continued) 


Eastern zone 


Central zone 


Genera 

Species 

Genera 

Species 

Ulocladium 

spp. 

Ulocladium 

spp. 



Wallemia 

spp. 


Non sporulating fungi. 
Non identified fungi. 


year of study. This value comes from a total of 24 plates per season 
(3 plates, one for each geographical point of each setting, multiplied 
by the 8 selected settings) that at the end of each year contributed to 
a total of 96 plates, which comprised the total of 288 plates obtained 
during the three years. 

2.3. Isolation and morphological identification 

Plates obtained after sampling were cultured at 25 °C for 5 days and 
concentrations were calculated as colony forming units per cubic meter 
of air (CFU/m 3 ). Differential count of fungal genera was carried out after 
microscopic observation and macroscopic analysis of colonies in PDA, 
Malt Extract Agar (MEA) and Water Agar (WA) according to Samson 
et al. (2000) and Pitt and Hocking (2009). Pure cultures of genera 
identified from the first two years of sampling were isolated for further 
identification at species level, while isolates from the third year were 
limited only to genus identification. 302 isolates of Cladosporium were 
randomly selected and grouped according to the morphological charac¬ 
teristics of the colonies based on mycelia color and diameter, texture, 
furcation and other general observations of the colony appearance. 
Furthermore, they were observed microscopically in order to classify 
them between the three major complexes of this genus based on 
Bensch et al. (2012, 2015). 59 isolates of Cladosporium representative 
of all the morphological groups obtained were proportionally and ran¬ 
domly chosen for performing molecular analysis (Temperini et al., 
2018). 415 monosporic isolates of Alternaria were identified based on 
the determination of group-species according to the sporulation pattern 
and morphological characteristics of the conidia (Simmons, 2007). 
83 monosporic isolates of Fusarium were identified using the keys 
proposed by Nelson et al. (1983) and Leslie and Summerell (2006). 
60 isolates of Penicillium, 11 isolates of Aspergillus and 5 of Eurotium 
were identified according to Pitt and Hocking (2009). 11 isolates 
of Stemphylium were identified according to Kurose et al. (2015) and 
20 isolates of Phoma according to Boerema (2004). Species of these 
two last genera were also confirmed by molecular tests. 

2.4. Data analysis 

The relative density (frequency of appearance) for each genus and 
species found was calculated according to Smith (1980) by the follow¬ 
ing equation (Borrego et al., 2011): 

Relative Density (RD) = Number of isolates from a genus or species x 100 
Total Number of isolates of all genera or species 


Using this equation, data were organized by annual periods (first, 
second and third year of sampling) and an Analysis of Variance 
(ANOVA) followed by Tukey comparison test (p < 0.05) were performed 
in order to determine statistical differences using the InfoStat program 
(Di Rienzo et al., 2018). 

Complete meteorological data from the period 2011-2017 were ob¬ 
tained from the INTA agrometeorological observatory. Temperature, 
wind speed, humidity and global radiation were the parameters chosen 













C.V. Temperini et aL / Science of the Total Environment 665 (2019) 513-520 


517 


Table 3 

Annual mean counts (CFU/m 3 ) and relative densities (%) of the fungal genera found in each year of sampling in each geographical zone. 


Genera 

Eastern zone 





Central zone 





Annual average 
of both zones 

1st YEAR 

2nd YEAR 

3rd YEAR 

Average of the 
three years 

1st YEAR 

2nd YEAR 

3rd YEAR 

Average of the 
three years 

AMC a 

AMC a 

AMC a 

AMC a 

RD b 

AMC a 

AMC a 

AMC a 

AMC a 

RD b 

AMC a 

RD b 

Cladosporium 

4901 

3303 

2496 

3567 

76.076 

3631 

2922 

3767 

3440 

77.925 

3503 

76.973 

Alternaria 

795 

865 

423 

694 

14.807 

404 

489 

433 

442 

10.009 

568 

12.480 

Epicoccum 

105 

271 

285 

221 

4.704 

137 

141 

265 

181 

4.101 

201 

4.412 

Botrytis 

5 

12 

8 

8 

0.176 

89 

200 

181 

157 

3.551 

83 

1.813 

Aureobasidium 

62 

28 

30 

40 

0.847 

56 

36 

58 

50 

1.133 

45 

0.986 

Non identified fungi 

55 

23 

26 

34 

0.731 

44 

90 

19 

51 

1.156 

43 

0.937 

Fusarium 

47 

47 

13 

36 

0.760 

12 

29 

9 

17 

0.375 

26 

0.573 

Arthrinium 

15 

11 

23 

16 

0.347 

5 

8 

14 

9 

0.206 

13 

0.279 

Drechslera 

9 

14 

14 

12 

0.260 

6 

16 

16 

12 

0.278 

12 

0.269 

Phoma 

7 

4 

35 

15 

0.324 

5 

8 

10 

8 

0.170 

11 

0.249 

Penicillium 

8 

25 

3 

12 

0.249 

14 

10 

6 

10 

0.227 

11 

0.238 

Non sporulating fungi 

7 

7 

14 

9 

0.201 

21 

5 

6 

10 

0.237 

10 

0.218 

Ulocladium 

4 

7 

5 

5 

0.114 

15 

9 

4 

9 

0.206 

7 

0.159 

Bipolaris 

4 

14 

1 

6 

0.133 

2 

5 

6 

4 

0.099 

5 

0.117 

Nigrospora 

2 

8 

1 

4 

0.076 

1 

9 

ND C 

4 

0.080 

4 

0.078 

Aspergillus 

1 

1 

1 

1 

0.022 

11 

2 

1 

5 

0.104 

3 

0.062 

Stemphylium 

3 

4 

1 

3 

0.053 

3 

1 

2 

2 

0.038 

2 

0.046 

Curvularia 

1 

2 

1 

1 

0.031 

2 

0 

1 

1 

0.027 

1 

0.029 

Acremonium 

2 

2 

ND C 

1 

0.025 

1 

1 

1 

1 

0.017 

1 

0.021 

Trichoderma 

ND C 

1 

1 

ND C 

0.009 

ND C 

2 

1 

1 

0.019 

1 

0.014 

Mucor 

1 

1 

ND C 

1 

0.018 

1 

ND C 

ND C 

ND C 

0.005 

1 

0.011 

Scopulariopsis 

1 

ND C 

ND C 

ND C 

0.004 

3 

ND C 

ND C 

1 

0.019 

1 

0.011 

Geotrichum 

ND C 

ND C 

ND C 

ND C 

ND C 

1 

1 

ND C 

1 

0.014 

o d 

0.007 

Chrysonilia 

ND C 

1 

ND C 

ND C 

0.009 

ND C 

ND C 

ND C 

ND C 

ND C 

o d 

0.005 

Trichothecium 

ND C 

ND C 

ND C 

ND C 

ND C 

1 

ND C 

ND C 

ND C 

0.009 

o d 

0.005 

Emericella 

ND C 

1 

ND C 

ND C 

0.004 

ND C 

ND C 

ND C 

ND C 

ND C 

o d 

0.002 

Moniliella 

ND C 

ND C 

ND C 

ND C 

ND C 

1 

ND C 

ND C 

ND C 

0.005 

o d 

0.002 

Neosartorya 

ND C 

ND C 

ND C 

ND C 

ND C 

ND C 

1 

ND C 

ND C 

0.005 

o d 

0.002 

Sordaria 

ND C 

ND C 

ND C 

ND C 

ND C 

ND C 

1 

ND C 

ND C 

0.005 

o d 

0.002 

Stachybotrys 

ND C 

ND C 

ND C 

ND C 

ND C 

1 

ND C 

ND C 

ND C 

0.005 

o d 

0.002 

Wallemia 

ND C 

ND C 

ND C 

ND C 

ND C 

1 

ND C 

ND C 

ND C 

0.005 

o d 

0.002 


a Annual mean count. 
b Relative densities. 
c Not determined. 

d These final counts result from the average of the partial counts and their rounding to whole numbers. 


to perform the statistical analysis. These data were correlated with 
fungal mean counts using Pearson correlation coefficient. 

2.5. Storage of isolates 

Pure isolates of each species identified were maintained on solid 
MEA and PDA at 4 °C and stored in glycerol 18% v/v at —80 °C for 
long-term storage. 

3. Results 

3.1. Diversity of fungal taxa and annual distribution of genera and species 

A great fungal diversity was found in the rural environments of the 
region in each year of sampling. At least 28 genera and 50 species 
were determined. In the central zone it was registered a wider diversity 
of fungal genera and species (Table 2). 

In both geographical zones, the predominant genus was Cladosporium 
followed by Alternaria and Epicoccum (Table 3) and the most frequent 
species was C. cladosporioides followed by C. limoniforme (Table 4). 

Anova test followed by Tukey comparison test (p < 0.05) showed 
statistical differences in Cladosporium and Alternaria gebera. 
C. cladosporioides and C. limoniforme did not show statistical differences 
between them, but both were statistically different to the other species 
of the genus. In the same way, Alternaria tenuissima showed statistical 
differences with the other Alternaria species. No statistical differences 


were found for Fusarium spp., Penicillium spp., Aspergillus spp., and 
Phoma spp. 

3.2. Annual fungal counts 

The annual mean count (AMC) of viable fungal spores remained in 
the order of E+04 in the three years of sampling in each geographical 
zone (Table 5). Anova test followed by Tukey comparison test 
(p < 0.05) were used to compare the AMC between both geographical 
zones. There were no significant differences (p > 0.05) between the 
AMC of each geographical zone. There were no significant differences 
(p > 0.05) between the average AMC of both geographical zones. 

3.3. Seasonal fungal counts 

Anova test followed by Tukey comparison test (p < 0.05) were used 
to compare seasons within each year of sampling (Table 6). Significant 
differences were found only between the mean summer count with re¬ 
spect to the other seasons in the first and third year. In the second year, 
significant differences (p < 0.05) were determined only between the 
mean counts of the autumnal season and the spring season, which pre¬ 
sented the highest and the lowest fungal count respectively. 

Statistical analysis was also used to find yearly differences re¬ 
stricted to a specific season. No significant differences were found 
for mean counts in autumn, spring and summer seasons, during the 
three-year sampling period. Nevertheless, significant differences 
















518 


C.V. Temperini et al. / Science of the Total Environment 665 (2019) 513-520 


Table 4 

Annual mean counts (CFU/m 3 ) and relative densities (%) of the fungal species found in each year of sampling in each geographical zone. 


Species 

Eastern zone 




Central zone 




Annual average 
of both zones 

1st YEAR 

2nd YEAR 

Average of the 
two years 

1st YEAR 

2nd YEAR 

Average of the 
two years 

AMC a 

AMC a 

AMC a 

RD b 

AMC a 

AMC a 

AMC a 

RD b 

AMC a 

RD b 

Cladosporium cladosporioides 

1934 

1649 

1791 

33.522 

1877 

1201 

1539 

36.467 

1665 

34.822 

Cladosporium limoniforme 

2022 

551 

1286 

24.075 

1084 

498 

791 

18.745 

1039 

21.723 

Altemaria tenuissima 

649 

656 

653 

12.212 

353 

434 

394 

9.328 

523 

10.939 

Cladosporium asperulatum 

449 

469 

459 

8.587 

278 

529 

403 

9.552 

431 

9.013 

Other species 

169 

130 

150 

2.799 

248 

382 

315 

7.468 

232 

4.859 

Cladosporium pseudocladosporioides 

10 

261 

136 

2.539 

4 

412 

208 

4.921 

172 

3.590 

Epicoccum nigrum 

105 

271 

188 

3.524 

137 

141 

139 

3.294 

164 

3.423 

Cladosporium subtilissimum 

308 

37 

173 

3.231 

96 

13 

55 

1.294 

114 

2.376 

Altemaria altemata 

93 

173 

133 

2.480 

24 

37 

30 

0.718 

81 

1.702 

Cladosporium aggregatocicatricatum 

21 

111 

66 

1.241 

21 

171 

96 

2.269 

81 

1.694 

Cladosporium tenellum 

126 

180 

153 

2.867 

ND C 

17 

9 

0.202 

81 

1.691 

Cladosporium allicinum 

ND C 

ND C 

ND C 

0.000 

203 

ND C 

101 

2.403 

51 

1.060 

Cladosporium sp. 

31 

35 

33 

0.614 

47 

40 

43 

1.029 

38 

0.797 

Altemaria sp. 

54 

12 

33 

0.614 

25 

18 

21 

0.506 

27 

0.567 

Fusarium sporotrichioides 

21 

31 

26 

0.480 

ND C 

9 

5 

0.111 

15 

0.317 

Cladosporium ramotenellum 

ND C 

8 

4 

0.075 

11 

32 

22 

0.510 

13 

0.267 

Fusarium semitectum 

9 

2 

6 

0.105 

6 

14 

10 

0.244 

8 

0.167 

Altemaria arborescens 

ND C 

24 

12 

0.228 

2 

ND C 

1 

0.018 

6 

0.135 

Cladosporium macrocarpum 

ND C 

2 

1 

0.022 

10 

9 

9 

0.224 

5 

0.111 

Penicillium brevicompactum 

ND C 

10 

5 

0.094 

6 

3 

5 

0.111 

5 

0.101 

Penicillium canescens 

3 

13 

8 

0.146 

1 

ND C 

ND C 

0.007 

4 

0.085 

Stemphylium vesicarium 

3 

4 

3 

0.064 

3 

1 

2 

0.037 

3 

0.052 

Phoma aliena 

4 

2 

3 

0.053 

3 

1 

2 

0.044 

2 

0.049 

Fusarium acuminatum 

ND C 

8 

4 

0.070 

ND C 

1 

1 

0.019 

2 

0.048 

Penicillium expansum 

3 

ND C 

1 

0.023 

5 

1 

3 

0.067 

2 

0.042 

Phoma glomerata 

ND C 

1 

ND C 

0.006 

1 

5 

3 

0.074 

2 

0.036 

Fusarium proliferatum 

4 

2 

3 

0.050 

1 

ND C 

1 

0.012 

2 

0.033 

Phoma sp. 

3 

1 

2 

0.041 

1 

1 

1 

0.015 

1 

0.029 

Fusarium crookwellense 

5 

ND C 

3 

0.047 

ND C 

ND C 

ND C 

0.000 

1 

0.026 

Fusarium sp. 

3 

ND C 

1 

0.023 

2 

1 

1 

0.030 

1 

0.026 

Fusarium compactum 

4 

ND C 

2 

0.041 

ND C 

ND C 

ND C 

0.000 

1 

0.023 

Aspergillus chevalieri 

ND C 

ND C 

ND C 

0.000 

4 

ND C 

2 

0.044 

1 

0.020 

Fusarium polyphialidicum 

ND C 

3 

2 

0.029 

ND C 

ND C 

ND C 

0.000 

1 

0.016 

Penicillium paneum 

ND C 

2 

1 

0.018 

ND C 

1 

1 

0.015 

1 

0.016 

Aspergillus niger 

ND C 

1 

ND C 

0.006 

1 

1 

1 

0.024 

1 

0.014 

Penicillium funiculosum 

ND C 

ND C 

ND C 

0.000 

ND C 

3 

1 

0.030 

1 

0.013 

Penicillium raistrickii 

1 

1 

1 

0.018 

ND C 

1 

ND C 

0.007 

1 

0.013 

Aspergillus versicolor 

1 

1 

1 

0.018 

ND C 

ND C 

ND C 

0.000 

0 d 

0.010 

Aspergillus wentii 

ND C 

ND C 

ND C 

0.000 

1 

ND C 

1 

0.015 

0 d 

0.007 

Fusarium oxyspomm 

ND C 

1 

ND C 

0.006 

ND C 

1 

ND C 

0.007 

0 d 

0.007 

Fusarium poae 

1 

ND C 

1 

0.012 

ND C 

ND C 

ND C 

0.000 

0 d 

0.007 

Fusarium reticulatum 

ND C 

ND C 

ND C 

0.000 

ND C 

1 

1 

0.015 

0 d 

0.007 

Fusarium sambucinum 

ND C 

1 

1 

0.012 

ND C 

ND C 

ND C 

0.000 

0 d 

0.007 

Fusarium solani 

ND C 

ND C 

ND C 

0.000 

1 

ND C 

1 

0.015 

0 d 

0.007 

Penicillium restrictum 

ND C 

ND C 

ND C 

0.000 

1 

ND C 

1 

0.015 

0 d 

0.007 

Penicillium solitum 

1 

ND C 

ND C 

0.006 

ND C 

1 

ND C 

0.007 

0 d 

0.007 

Fusarium dlamini 

ND C 

ND C 

ND C 

0.004 

1 

ND C 

ND C 

0.009 

o d 

0.006 

Aspergillus sp. 

ND C 

ND C 

ND C 

0.000 

ND C 

1 

1 

0.012 

0 d 

0.005 

Aspergillus sydowii 

ND C 

ND C 

ND C 

0.000 

1 

ND C 

ND C 

0.007 

o d 

0.003 

Fusarium chlamydospomm 

ND C 

ND C 

ND C 

0.000 

ND C 

1 

ND C 

0.007 

o d 

0.003 

Fusarium verticillioides 

ND C 

ND C 

ND C 

0.000 

ND C 

1 

ND C 

0.007 

o d 

0.003 

Penicillium chrysogenum 

ND C 

ND C 

ND C 

0.000 

1 

ND C 

ND C 

0.007 

o d 

0.003 

Penicillium janckzewsky 

ND C 

ND C 

ND C 

0.000 

1 

ND C 

ND C 

0.007 

o d 

0.003 

Penicillium verrucosum 

ND C 

ND C 

ND C 

0.000 

ND C 

1 

ND C 

0.007 

o d 

0.003 

Penicillium sp. 

ND C 

ND C 

ND C 

0.000 

ND C 

1 

ND C 

0.007 

o d 

0.003 

Phoma betae 

ND C 

ND C 

ND C 

0.000 

ND C 

1 

ND C 

0.007 

o d 

0.003 

Phoma medicaginis 

ND C 

ND C 

ND C 

0.000 

ND C 

1 

ND C 

0.007 

o d 

0.003 


a Annual mean count. 
b Relative densities. 
c Not determined. 

d These final counts result from the average of the partial counts and their rounding to whole numbers. 


were found for the 3 winter seasons: the second year was signifi¬ 
cantly different to the third year (with p-value = 0.0011). Meteoro¬ 
logical data were also analyzed in order to detect any change on 
weather factors during the three sampled winters that could explain 
this difference. As a result, there were statistical differences in the 
mean humidity percentage between winter of 2015 and winter of 


2016 (p-value = 0.027). The winter of 2016 had a mean humidity 
percentage of 66.2%, while 2015 was 50.0% (as a reference 2014 win¬ 
ter was 59.8%). In addition, the same weather factors were analyzed 
for the extended period of 2011-2017, and again the only statistical 
difference found was mean humidity percentage between the winter 
season of 2015 and winter season of 2016. 














C.V. Temperini et at/ Science of the Total Environment 665 (2019) 513-520 


519 


Table 5 

Annual counts (CFU/m 3 ) determined in both geographical zones. 



1st YEAR 



2 nd YEAR 



3 rtl YEAR 



Eastern zone 

Central zone 

Average 

Eastern zone 

Central zone 

Average 

Eastern zone 

Central zone 

Average 

Mean 

6.04E+03 

4.46E+03 

5.25E+03 

4.65E+03 

3.98E+03 

4.32E+03 

3.38E+03 

4.80E+03 

4.09E+03 

Minimum 

1.30E+03 

1.96E+03 

1.63E+03 

2.35E+03 

2.50E+03 

2.42E+03 

2.26E+03 

3.18E+03 

2.72E+03 

Maximum 

9.82E+03 

1.00E+04 

9.92E+03 

8.13E+03 

6.52E+03 

7.32E+03 

3.97E+03 

6.03E+03 

5.00E+03 

Standard deviation 

3.93 E+03 

3.74E+03 

3.84E+03 

2.49E+03 

1.75E+03 

2.12E+03 

7.82E+02 

1.19E+03 

9.84E+02 


3.4. Correlation between meteorological data and annual mean counts 

When Pearson correlation coefficient was applied to correlate mean 
fungal counts and weather factors (average values for the season, and 
specifically for the day of sampling) no correlation was found. 

4. Discussion 

The information obtained from the present work provides knowl¬ 
edge of the fungal genera and species present in the air of agricultural 
environments of a cold dry desert climate. This is the first study in our 
country and the first one in a rural area with this type of climate and 
land use whose results make a valuable contribution. 

A wide diversity of fungi was found with >28 genera and 50 species 
determined through the whole sampling period. Among them, species 
of Cladosporium and Alternaria were the most abundant as other re¬ 
searchers previously reported in rural environments (Pepeljnjak and 
Segvic, 2003; Kasprzyk and Worek, 2006; Oliveira et al., 2010). Further¬ 
more, C. cladosporioides, C. limoniforme and A. tenuissima showed signifi¬ 
cantly higher abundances than the rest of the species of each respective 
genus. Initially, these species could be considered as a bio-indicator of en¬ 
vironmental stress and change, however, we consider that the informa¬ 
tion found so far is not enough to propose any of them as a such. As 
species from the same genus can have different requirements for 
sporulation and growth, additional studies of ecophysiology would be 
needed to determine if it is possible. Moreover, it would be necessary to 
increase the number of sampling in each season and the time of the 
study might also need to be increased (sampling during a longer period 
than three years). Since the study area is dedicated to fruit growth and 
has a considerable population, these genera become important due to 
their health and agricultural implications (Abdel Hameed et al., 2009; 
Reyes et al., 2009; Oliveira et al., 2010). In fact, among the phytopatho- 
genic fungi isolated from air capable of causing disease in pip fruits of 
this study area, Alternaria diseases (black spot on leaves, rot of fruits 
and Moldy heart) were present during the three summers, which is 
consistent with the maximum counts obtained in the summer season of 
the three years. When weather conditions are favorable, these pathogens 
can increase the severity of pathologies. In the Middle Valley of Rio Negro, 
a nearby region of the study area, changes in climatological conditions 
(raise of rainfalls and humidity) during 2013-2014 increased the severity 
of walnut apical necrosis caused by microbial complexes including 
Alternaria (Temperini et al., 2017). Regarding Cladosporium, the rot in 
pears caused by species of the C. herbarum complex in cold long-term 
storage acquired relevance during 2015, which could be related to the 


maximum count found for this genus at the summer season of this 
year. The rest of the genera remained in lower concentrations even at 
relative densities lower than 1%. However, it is important to report their 
presence, since their ability to tolerate the unique conditions of this 
study area may reveal their ability to also overcome and adapt to climate 
change effects and develop or enhance their pathogenic capacity. In fact, 
S. vesicarium is another field pathogen of interest in this area, especially 
during the summer season of 2017, where weather conditions favored 
its development and pathogenic capacity (Llorente et al., 2012) affecting 
mainly leaves and fruits of pear cultivars. These results reveal that 
weather parameters may affect the aeromycoflora behavior and its phy- 
topathological implications. Indeed, the concentration and type of fungal 
taxa present in the atmosphere are influenced by the geographical and 
climatic characteristics of a region which determine the predominant 
type of vegetation, decomposing organic matter and agricultural activity 
(Awad, 2005; Sanchez Espinosa and Almaguer Chavez, 2014). Therefore, 
in this context, the concept of climate change becomes relevant. Climate 
predictions indicate an increase in drought events in summer providing 
the environmental diyness needed for conidia release into the atmo¬ 
sphere, thus airborne fungal concentrations are expected to be increased 
in future years (Maya-Manzano et al., 2016; Sindt et al., 2016). 

Even though no significant differences were found between the AMC 
of each geographical zone, the composition of fungal taxa and their 
abundance vaiy discreetly, with the exception of the three predominant 
genera ( Cladosporium, Alternaria and Epicoccum ) and four predominant 
species (C cladosporioides, C. limoniforme, A. tenuissima, C. asperulatum ). 
These results could be related to the similarities of geographical charac¬ 
teristics and the short distance that separates both zones. On the other 
hand, while no statistical differences were determined between the 
average AMC of both geographical zones, it was revealed a different 
seasonality of fungal counts between the first and third year compared 
to the second one. Moreover, statistical differences were found between 
winter of 2015 and 2016. However, no correlation was found between 
weather parameters and mean counts. In this context, the number of 
environmental factors that affect the daily and seasonal rhythms of 
the occurrence of airborne fungal spores are known to but it remains 
difficult to estimate the relative importance of each factor (Kasprzyk 
and Worek, 2006). Therefore these differences may arise from a combi¬ 
nation of factors including organic matter, changes in vegetation, 
agricultural and/or human activities which dynamically change in this 
rural valley due to economic situation that the productive sector has 
been going through the past years which has led to the replacement 
of fruit fields by other types of farmlands or, even, different kind of 
economical entrepreneurships. 


Table 6 

Seasonal counts (CFU/m 3 ) determined in each year of sampling. 



1st YEAR 




2nd YEAR 




3rd YEAR 




Autumn 

Winter 

Spring 

Summer 

Autumn 

Winter 

Spring 

Summer 

Autumn 

Winter 

Spring 

Summer 

Mean 

3.02E+03 

2.24E+03 

3.06E+03 

1.27E+04 

7.30E+03 

3.42E+03 

1.07E+03 

5.49E+03 

3.44E+03 

7.48E+02 

2.56E+03 

9.61 E+03 

Minimum 

9.30E+02 

7.80E+02 

5.50E+02 

1.21E+03 

2.36E+03 

2.12E+03 

4.50E+02 

1.69E+03 

9.70E+02 

6.80E+02 

4.80E+02 

4.64E+03 

Maximum 

9.67E+03 

4.52E+03 

6.60E+03 

2.91E+04 

2.50E+04 

5.77E+03 

2.84E+03 

1.70E+04 

6.91 E+03 

9.00E+02 

5.94E+03 

1.69E+04 

Standard deviation 

2.83E+03 

1.71E+03 

2.23E+03 

1.15E+04 

7.37E+03 

1.50E+03 

7.94E+02 

4.87E+03 

2.21E+03 

7.01E+01 

2.02E+03 

4.30E+03 














520 


C.V. Temperini et al. / Science of the Total Environment 665 (2019) 513-520 


5. Conclusions 

The study area comprises a region with unique geographical and 
climatic characteristics due to its aridity, dryness, constant intense 
winds and coexistence and proximity of population nuclei to agricul¬ 
tural settings, and its wide fungal diversity has been able to adapt to 
these environmental conditions. Moreover, the fungal flora and its con¬ 
centration have been consistent through the three-year period. Even 
though these results make up the first report of the area and we cannot 
compare them to previous data, they raise the possibility of fungal taxa 
to adapt to climate change and remain present in the atmosphere with 
their potential consequent effects on human, animal and plant health. 
Moreover, due to the particular characteristics of this study area, climate 
change may eventually turn it into an environment which could be 
optimum for the development and prevalence of extremophile fungi. 

In this way, the knowledge generated will allow the use of adequate 
and efficient methods to reduce the biological and economic damage of 
fungal taxa, evaluate the potential mycotoxicological risk and, in the 
context of climate change, predict fungal flora fluctuations and emerg¬ 
ing diseases caused by potential pathogenic microorganisms that have 
not been reported to cause damage at the present. 

Acknowledgements 

Tojavier Alonso (Universidad Nacional de Rio Negro) and to Consejo 
Nacional de Investigaciones Cientificas y Tecnicas (CON1CET). 

Financial support 

This work was supported by the Universidad Nacional de Rio Negro 
(UNRN) and Universidad Nacional de Quilmes (UNQ). 

Conflict of interest 

None. 

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