A C Barac

S Bostanaru

N Brito

M A Çerikçioğlu

Ç Efstratiou

M C Ergin

M C Esposto

Frenkel

Massimo Cogliati
email: massimo.cogliati@unimi.it

S Arikan-Akdagli

A C Bostanaru

S Brito

N Çerikçioğlu

M A Efstratiou

Ç Ergin

M Frenkel

J P Gangneux

A Gitto

C I Gonçalves

H Guegan

N Gunde- Cimerman

M Güran

E Jonikaitė

M Kataržytė

L Klingspor

M Mares

W G Meijer

W J G Melchers

J Meletiadis

V Nastasa

M Novak Babič

D Ogunc

B Ozhak

A Prigitano

S Ranque

L Romanò

R O Rusu

R Sabino

A Sampaio

S Silva

J H Stephens

M Tehupeiory- Kooreman

A Velegraki

C Veríssimo

E Segal

J Brandão

ENVIRONMENTAL AND BIOCLIMATIC FACTORS INFLUENCING YEASTS AND MOLDS DISTRIBUTION ALONG EUROPEAN SHORES

Keywords: Molds, yeasts, spatial distribution modelling, environmental factors, sand, water

Environmental and bioclimatic factors influencing yeasts and molds distribution along European shores

 Little is known about fungal communities in sand and coastal waters  Analysis using a spatial distribution modelling approach  Molds and yeasts are differently distributed along coasts ACCEPTED MANUSCRIPT / CLEAN COPY

Introduction

Fungi have colonized all niches on Earth including the most extreme ones [START_REF]Fungi in extreme environments[END_REF]. They represent the third largest group of living beings by biomass after plants and bacteria, and play multiple roles in the ecosystems equilibrium (Yinon et al., 2018). Some fungi are important for human productive processes but others represent a serious menace to human and animal health causing both superficial and systemic mycoses [START_REF] Kainz | Fungal infections in humans: the silent crisis[END_REF]. The main source of contamination is the environment through contact with soil, water, organic debris, and air contaminated with spores (de S [START_REF] De S Araújo | The hidden pathogenic potential of environmental fungi[END_REF]. Although the fungal communities in the above substrates have been largely studied, little is known about fungal presence in the sand. This substrate is typical of shores along sea coasts, lakes, and rivers, which are the main recreational zones during summer in temperate geographical areas such as Europe. It is therefore important to first characterize and identify the fungal communities in the sand to assess and estimate the relative risk of fungal infections during recreational activities on the beach. This issue was the object of few local studies in worldwide, and only recently the Mycosands network carried out a collaborative study able to collect a great number of sand and water samples from the shores of several European Union countries, Turkey, and Israel [START_REF] Dunn | Filamentous fungal populations of Hawaiian beaches[END_REF][START_REF] Migahed | Distribution of fungi in the sandy soil of Egyptian beaches[END_REF][START_REF] Figueira | Marine fungi from two sandy beaches in Portugal[END_REF][START_REF] Abdel-Aziz | Marine fungi from two sandy Mediterranean beaches on the Egyptian north coast[END_REF]Gonҁalves de Oliveira et al., 2011;[START_REF] Stevens | Human beach use affects abundance and identity of fungi present in sand[END_REF][START_REF] Salleh | Microfungal community in sandy beaches located in Kedah, Pahang and Sabah, Malaysia[END_REF][START_REF] Echevarría | Molecular identification of filamentous fungi diversity in north coast beaches sands of Puerto Rico[END_REF]Brandao et al., 2021;[START_REF] Walker | Fungal diversity and community structure from coastal and barrier island beaches in the United States Gulf of Mexico[END_REF][START_REF] Babič | Occurrence, diversity and anti-fungal resistance of fungi in sand of an urban beach in Slovenia -environmental monitoring with possible health risk implications[END_REF]. The study confirmed the presence on beaches of numerous potentially pathogenic fungi such as Aspergillus, Candida, Fusarium, dematiaceous fungi, and dermatophytes (Brandao et al., 2021).

In the present study we added a further step to the Mycosands survey correlating the fungal diversity observed on beaches with a set of environmental factors using a niche modelling approach, with the aim to identify the most suitable geographical areas for the survival of fungi on the beaches and the main variables contributing to the models.

Materials and methods

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Occurrence data

Data for the present study were collected during the Mycosands survey carried out in European shores and waters from 2018-2019(Brandao et al., 2021)). Mycosands database included data from 88 localities distributed along the Mediterranean basin, Western Atlantic coasts, Northern European coasts, Baltic sea, Black sea, and seven fresh water shores (Supplementary Table S1). In addition, it included also data from Australia that were not considered in our analysis due to the difficulty to compare a geographical area so distant from Europe with different bioclimatic conditions.

Occurrence points were grouped by species or by category of fungi separating sand from water findings (Supplementary Table S1). Each group of data was then edited to obtain a file in the format required by analysis software. Groups of data from sand samples considered for the analysis were the following: Aspergillus flavus, A. fumigatus, A. niger, Aspergillus spp., Fusarium spp., dematiaceous fungi, dermatophytes, all molds, Candida spp., Rhodotorula spp., and all yeasts.

Groups of data from water samples considered for the analysis were the following: A. fumigatus, A. niger, Aspergillus spp., dematiaceous fungi, molds, Candida spp., and yeasts. Groups that included less than eight occurrence points were not considered for the analysis (Table 1).

Environmental factor layers

In order to investigate the influence of environmental factors on fungal distribution, climatic datasets, soil datasets, and water datasets were searched in the main geographic information system (GIS) databases available in the web. Climatic datasets included mean of minimum temperature, maximum temperature, average temperature, and precipitation monthly recorded each year in Europe from 2010 to 2018, as well as horizontal solar radiation. Soil datasets included data of chemical and physical properties of soil whereas water datasets included monthly data of superficial water temperature, sea salinity, and chlorophyll-a concentration. A total of 350 layers were downloaded. A detailed list of the datasets considered for the analysis and the web sources are reported in Table 2. Datasets concerning soil properties included only data recorded in the ACCEPTED MANUSCRIPT / CLEAN COPY European Union countries and therefore occurrence points from Israel, Turkey, Cyprus, and Serbia were not considered.

Each layer containing the dataset values was visualized and edited by QGIS software (https://www.qgis.org)) before being used for the analysis. Editing consisted to homogenize all layers to obtain files with the same geographical area (-10°W, 28°E, 42°S, 71°N), the same grid size (6240 x 5160 cells), and the same file format (ASC format). For each group of data occurrence points observed in sand samples were compared to four sets of datasets independently. The first analysis included all climatic datasets (temperature, precipitation, and solar radiation), the second one included soil chemical properties, the third one included soil physical properties, and finally the forth one included heavy metals concentration in soil.

Distribution model analysis

Analysis

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Group of data containing eight or more occurrence points observed in water were further compared to water datasets (water temperature, salinity, and chlorophyll-a concentration).

The resulting distribution maps from each analysis were imported in QGIS software to optimize the final rendering. First, distribution surface was restricted to coastal areas considering only areas with an elevation ≤ 10 m, this allow to exclude areas not covered by our survey. Then visualization was emphasized on areas with the highest probability of presence (≥ 0.5) to better identify the differences in distribution among groups of data analyzed. Finally, for groups of data where it was possible, the maps of distribution resulting by the analysis of sand occurrence points and water occurrence points were merged to compare the two distributions.

Results

A total of 28 MaxEnt analyses were performed. All analyses produced a valid model with an AUC value ranging from 0.658 to 0.981 (Supplementary Table S2).

Species distribution modelling of Aspergillus species

The distribution map inferred comparing Aspergillus spp. occurrence data on shores (45 locations)

and climatic datasets (49 layers) showed that the geographical areas with the highest probability of presence were Eastern Mediterranean and South Central Mediterranean coasts (Figure 1A). The two most relevant variable contributing to the model were minimum temperature in the months of December and January (41% contribution). At minimum winter temperatures above 5°C the probability of presence quickly increases beyond 0.5 (Figure 1D). Lagoon in Lithuania (Figure 1C). Fresh internal waters with the highest probability of presence are all located in the Iberian Peninsula. Va riable that most contributed to the water model was water temperatures during Spring, in particular in May. In the geographical areas where water temperature in May is above 18°C the probability of presence is clearly higher than in other areas. Chlorophyll-a concentration is also a determinant variable that does not contribute a lot to the model if taken alone but it contributes to extend the predicted distribution area when introduced in the model (Figure 1E).

MaxEnt analysis comparing

Figure 2 merges shore and water distribution models for Aspergillus spp. In the Eastern Mediterranean coasts probability of presence is high in both shores and waters. On the contrary, in Southern Central Mediterranean coasts the suitability is high in the shores but low in the waters, whereas in the other marked areas suitability is high in the waters but low in the shores.

When the analysis comparing climatic features (49 layers) was repeated using only occurrence data in shores for A. fumigatus (20 locations), A. niger (29 locations) and A. flavus (9 locations), results

showed maps very similar to that obtained using Aspergillus spp. data (Supplementary Figure S1).

Only A. fumigatus map showed a slighter diffusion towards northern shores compared to A. flavus and A. niger. In addition, for A. niger it was possible to analyze also occurrence data in water (11 locations) vs. water features (14 layers). The results confirmed a similar distribution as predicted for

Aspergillus spp. (Supplementary Figure S2).

Maxent analysis comparing Aspergillus spp. occurrence points on shores (24 locations) and chemical properties of soil (10 layers) did not show a specific geographical area with high probability of presence, on the contrary distribution was predicted in almost all the coasts.

Distribution of Aspergillus spp. is highly influenced by CaCO 3 concentration and soil pH which contributed for 51% and 11% to the model, respectively. Concentration of CaCO 3 was tolerated up to 200 g/kg and alkaline soil up to 8.5 pH (Figure 3A, C, and D). Response curve to nitrogen concentration showed a peak at 1 g/kg above which probability of presence quickly decreased to 0

ACCEPTED MANUSCRIPT / CLEAN COPY (Figure 3F). Phosphorus concentration response curve displayed a different profile showing a null probability of presence of the fungus around 30 mg/kg (Figure 3G).

On the basis of physical properties of soil, the model predicted a high association of Aspergillus spp. with soils presenting medium texture, characterized by low percentages of clay and medium percentages of sand (Figure 3H).

Comparing Aspergillus spp. occurrence points in the shores (24 locations) and heavy metal concentrations (9 layers), the model showed a high suitability in some specific locations, that mainly corresponded to the mouths of great European rivers or lagoons, probably where these metals are accumulated in river sediments. Although all variables give a contribution to the model, nickel concentration seems to give the highest contribution (30%) (Figure 3B,E).

Species distribution modelling considering all molds

The same analyses performed above were performed considering all mold occurrence points recorded during Micosands survey (51 in sand and 31 in water samples). Distribution maps obtained comparing climatic, soil, and water datasets were identical or very similar to those obtained for Aspergillus spp. (Figure 4). Also variables contribution and response curves confirmed the trends observed by Aspergillus spp. analysis. The only slight difference concerned comparison with soil physical properties which showed a correlation with both medium and fine soil texture (Supplementary Figure S3).

Analysis comparing climatic datasets were repeated using separately the occurrence points for Fusarium spp, dematiaceous fungi, and dermatophytes (20, 27, and 10 in sand samples, respectively), and results were very similar except for dermatophytes which presented a ubiquitous distribution in all geographical areas of Europe (Supplementary Figure S4). Comparison with water datasets was possible only for dematiaceous fungi (11 occurrence points) and confirmed the results obtained by "all molds" model.

Species distribution modelling of Candida species

ACCEPTED MANUSCRIPT / CLEAN COPY temperatures in winters are below zero the suitability of yeasts is higher than molds, whereas where minimum temperatures are above zero molds has a higher suitability (Figure 9).

In both models chlorophyll-a concentration is important to predict distribution in waters, whereas water temperatures (especially during spring) are important only for molds and does not affect distribution of yeasts.

As concerns comparison to heavy metal concentration in soil, distribution models are very similar for both molds and yeasts with high suitability areas distributed in correspondence to the mouths of great European rivers or lagoons. Cadmium concentration is relevant for yeasts model whereas nickel concentration is important for molds distribution model.

Both distributions of molds and yeasts are influenced by soil pH. CaCO 3 concentration contributes significantly to the molds model but not to the yeasts model. In both models nitrogen concentration and suitability are inversely correlated. Soils with nitrogen concentrations higher than 1 g/kg decreases the suitability of molds and yeasts. Phosphorus concentration variable does not influence distribution model of yeasts, whereas its response curve in molds model shows a range of high suitability at values <20 mg/kg and >40 mg/kg.

Finally, medium and fine soils showed the highest suitability in molds model whereas no differences in the contribution of each type of soil was observed in yeasts model.

Discussion and conclusions

The present study is the first attempt to understand spatial distribution of fungi along the European coasts based on their correlation with a number of environmental factors. The main distribution differences were observed considering molds and yeasts as distinct groups. On the contrary, no differences were observed when single species or group of molds/yeasts were compared. This suggests that morphological, physiological, and biochemical divergences between molds and yeasts are determinant for their distribution in the environment. Temperature of both air and water was one of the main discriminant variable. Results showed that geographical areas with an average winter ACCEPTED MANUSCRIPT / CLEAN COPY minimum temperatures below 0°C were more suitable for yeasts survival than for molds. This could be explained by differences in morphology and modality of growth characteristic of yeasts and molds. Molds grow producing hyphae that form a mycelium that can cover a wide surface exposed to the environment. Many studies comparing hyphal growth rate and temperatures confirm that most of molds have a very low growth at temperatures below 5°C and absent below 0°C [START_REF] Fedorik | HAM and mould growth analysis of a wooden wall[END_REF]Mannaa & Ki Deok, 2018). Low temperatures also affect germination of spores that are the main propagules responsible for mold dissemination [START_REF] Damare | Spore germination of fungi belonging to Aspergillus species under deep-sea conditions[END_REF]). On the contrary, yeasts usually have an oval or ovoid shape that reduces at minimum the cell surface in contact with the environment and therefore can better protect the cell content from low temperatures. Furthermore, yeasts reproduce primarily by budding that form a colony of cells where the inner cells are less exposed to the environment. This divergence was more evident when fungal presence was surveyed in water where yeasts suitability was higher than molds along the cold coasts of Northern Europe whereas molds suitability was higher along the warm Southern Mediterranean coasts. This because water is able to induce a loss of heat higher and quicker than air and therefore yeast shape could be favorite respect hyphae. In addition, hyphal growth can be inhibited by water movements that can destroy mycelium integrity. On the other hand, yeasts can survive underwater as they are usually anaerobic organisms whereas molds can float only on the water surface since they are aerobic. Our results also showed that fungal distribution (both yeasts and molds) in water can be influenced by chlorophyll-a concentration that correlates with the eutrophication grade of a specific area. Water eutrophication is a consequence of high concentration of organic material and nutrients that favor plant growth but also the growth of fungi which are able to assimilate a high number of organic compounds (Pawlowska et al. 2019). Some studies showed how a high concentration of fungi in water can be an index to evaluate the quality of the water and its grade of eutrophication [START_REF] Bai | Fungal community as a bioindicator to reflect anthropogenic activities in a river ecosystem[END_REF]).

Distribution of fungi on beaches was correlated to some chemical characteristics of soil such as pH, and concentration of CaCO 3 , nitrogen, and phosphorus. Our analyses confirm that fungi can tolerate ACCEPTED MANUSCRIPT / CLEAN COPY alkaline soils up to pH 8.5 as reported by several other studies [START_REF] Nagai | Studies on the distribution of alkalophilic and alkali-tolerant soil fungi I[END_REF][START_REF] Nagai | Studies on the distribution of alkalophilic and alkali-tolerant soil fungi II: fungal flora in two limestone caves in Japan[END_REF][START_REF] Grum-Grzhimaylo | On the diversity of fungi from soda soils[END_REF]. Furthermore, distribution of molds was correlated to soil with a high CaCO 3 concentration probably due to the important role played by most of filamentous fungi in the cycle of this compound. It was showed that they are able to precipitate CaCO 3 contributing to accumulation of this compound in soil and therefore increasing pH [START_REF] Li | CaCO 3 and SrCO 3 bioprecipitation by fungi isolated from calcareous soil[END_REF][START_REF] Zhao | Fungal-induced CaCO3 and SrCO3 precipitation: a potential strategy for bioprotection of concrete[END_REF]. Alkalinization of the environment by fungi was also shown to play an important role to activate biochemical pathways leading to a pathogenic transition of some fungi [START_REF] Fernandes | How alkalinization drives fungal pathogenicity[END_REF]. Therefore, the presence of potential pathogenic fungi in alkaline environments could increase the risk of fungal infections. Finally, calcium uptake is a crucial step for mold since it is required for hyphal growth and thus soils rich in CaCO 3 represent suitable environments [START_REF] Jackson | Roles of calcium ions in hyphal tip growth[END_REF].

Presence of optimal nitrogen and phosphorus concentrations in soil is also essential for both plants and fungal growth. Interestingly our study showed that fungi seem to occupy soil niches with values of nitrogen and phosphorus concentration higher or lower respect the optimal range required by most of plants suggesting that, within those ranges of concentration, fungi are probably more competitive than plants. Association of fungi with plants forming mycorrhiza are well known and confirms the crucial role played by fungi in nitrogen and other mineral uptake in poor soils [START_REF] Makarov | The role of mycorrhiza in transformation of nitrogen compounds in soil and nitrogen nutrition of plants: A review[END_REF].

Differences between yeasts and molds were observed when presence data were compared with physical features of soil. Molds seem prefer substrates composed by fine or medium size granules where probably functionality of hyphae as water and nutrient collector is favored by this type of soil which is able to trap water between its small-size particles. This is not the case of yeasts that reproduce by budding and therefore did not correlate with any specific type of soil.

Concentration of heavy metals in the soil can also determine a different distribution of yeasts and molds. In particular, yeast seems to have an affinity with soil rich in cadmium whereas molds to those rich in nickel. These findings are supported by some studies that confirmed that some species ACCEPTED MANUSCRIPT / CLEAN COPY of molds and yeasts were tolerant to high concentrations of nickel and cadmium, respectively [START_REF] Magyarosy | Nickel accumulation and nickel oxalate precipitation by Aspergillus niger[END_REF][START_REF] Rehman | Cadmium uptake by yeast, Candida tropicalis, isolated from industrial effluents and its potential use in wastewater clean-up operations[END_REF]. In Europe the soils heavily contaminated with these metals are focused around the mouths of the greatest rivers where most of iron and steel plants are located, and where our models predicted the highest suitability for both yeasts and molds.

In conclusion, the spatial distribution modelling applied in the present study represents a valid method to compare occurrence data of fungi with a multitude of data from the environment, and the resulting distribution maps as well as analysis of contributing variables are useful tools to understand the complex relationships of fungi with their environment. These tools may also help to understand how climate changes are impacting on the structure and distribution of fungal communities posing the risk for the emergence of thermotolerant species potentially dangerous for human health [START_REF] Casadevall | Environmental Candida auris and the global warming emergence hypothesis[END_REF][START_REF] Cogliati | Global warming impact on the expansion of fundamental niche of Cryptococcus gattii VGI in Europe[END_REF][START_REF] Brandão | Climate change impacts on microbiota in beach sand and water: looking ahead[END_REF].

Although the results here reported are a first attempt to investigate these relationships, improvement of techniques for fungal isolation and identification, and development of new model algorithms could lead to new insights in future studies. The final goal is to monitor diversity, distribution and evolution of fungal communities to predict potential risks for humans, animals and ecosystems equilibrium. Red numbers indicate that the number of occurrence points was not sufficient to perform MaxEnt analysis. ACCEPTED MANUSCRIPT / CLEAN COPY ACCEPTED MANUSCRIPT / CLEAN COPY

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  Aspergillus spp. occurrence points in sea and fresh waters (27 locations) and water features (14 layers) are shown in Figure 1B-C. Considering water temperature and salinity, geographical areas with high probability of presence (>0.5) are located along the coasts of Eastern Mediterranean Sea, the gulf of Lion, Southern coasts of Spain, and Southern and Central Atlantic coast of Portugal (Figure 1B). When the eutrophication variable (chlorophyll-a concentration) was introduced, five new areas were identified: Southern ACCEPTED MANUSCRIPT / CLEAN COPY Atlantic coast of France, Netherlands coasts, mouth of Danube, coasts of the Ireland sea, Kuri

FIG. 1

 1 FIG. 1. (A) Distribution map of Aspergillus spp. on the shores inferred by Maxent analysis

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Table 1 .

 1 Number of locations where filamentous fungi and yeast-like fungi were isolated from sand or water samples.

	Filamentous fungi		Yeast-like fungi
	Fungal species/category	Sand Water Fungal species/category	Sand Water
	Aspergillus flavus	8	1	Candida albicans	5	2
	Aspergillus fumigatus	20	8	Candida dubliniensis	3	2
	Aspergillus niger	29	11	Candida. glabrata	3	3
	Aspergillus spp.	45	27	Candida parapsilosis s.l.	4	1
	Fusarium spp.	20	4	Candida tropicalis	4	2
	Dematiaceous	27	11	Candida spp.	21	8
	Dermatophytes	10	2	Cryptococcus spp.	7	0
	Molds	51	31	Rhodotorula spp.	21	7
				Yeasts	32	14

Distribution map of Candida spp. occurrence points on shores (23 locations) vs. climatic features (temperature, precipitation and solar radiation, 49 layers) showed geographical areas with high probability of presence (>0.5) located along the coasts of Eastern Mediterranean Sea, Southern Central Mediterranean Sea, Northern Adriatic Sea coasts, Kuri Lagoon, and Northern European coasts from France to Denmark (Figure 5A). The two most relevant variables contributing to the model were minimum temperature in the months of December and January (32% contribution). At minimum winter temperatures from -10°C to 0°C the probability of presence quickly increased beyond 0.4, then slowly from 0°C to 5°C, and again quickly beyond 5°C (Figure 5D-E)).

Comparing Candida spp. occurrence points in sea and fresh waters (8 locations) to water temperature and salinity, the models did not identify a specific geographical area with high probability of presence (>0.5) but only a slight increase along the coasts of Northern Black Sea, Northern Adriatic Sea, coasts of Ireland Sea, and the Northern European coasts from France to Denmark (Figure 5B). By introducing also the eutrophication variable (chlorophyll-a concentration) the model significantly increased the probability of presence of Candida spp. in five geographical areas: Northern Black Sea, Northern Adriatic Sea, Western Atlantic coast of France, Northern European coasts from Netherlands to western Denmark, and Baltic Sea coasts. Area with high suitability were not identified for internal fresh waters (Figure 5C). Variable that most contributed to the water model was chlorophyll-a concentration in sea water contributing for more than 90% to the model whereas water temperatures and salinity gave a little contribute to the model (Figure 5F).

When shore and water distribution models for Candida spp. were compared, results showed that along Eastern and Southern Central Mediterranean coasts the probability of presence was high in shores but low in waters, in Northern Black Sea, Britain coasts and Baltic coasts the suitability was high in waters but low in shores, whereas in the other marked areas suitability was high in both waters and shores (Figure 6).

Due to the scarce number of occurrence points it was not possible to perform the analysis of Candida spp. data vs. chemical soil properties but it was possible to explore the correlation with ACCEPTED MANUSCRIPT / CLEAN COPY physical soil properties as well as with heavy metal concentrations. On the basis of physical properties of soil, the model did not identify neither a specific geographical area nor a specific type of soil associated to Candida spp (Supplementary Figure S5). On the contrary, most of soil textures seem to be suitable for Candida spp. survival. Furthermore, considering heavy metal concentrations in soil, the analysis of contributing variables showed a correlation with soils containing high cadmium concentrations which are spotted on the distribution map in some specific locations (Supplementary Figure S6).

Species distribution modelling considering all yeasts

A complete analysis including all environmental layers was possible when all yeasts occurrence data (32 and 14 from shores and water, respectively) were considered. Results were similar to those obtained by Candida spp. model confirming the same areas of distribution and the same contribution of variables (Figure 7). In addition, comparison with soil chemical properties showed that variables that most influenced the model were soil pH (tolerance up to 8.5), and nitrogen concentration which, above 1 g/kg, determined a rapid decrease of the suitability of yeasts (Supplementary Figure S7). MaxEnt analysis performed comparing Rhodotorula spp. data to climatic datasets again confirmed the results obtained above.

Comparison of molds and yeasts distribution models

Predicted distribution of molds and yeasts in shores is very similar with high suitability in Eastern Mediterranean coasts and Southern Central Mediterranean coasts. In contrast, differences were observed comparing their distributions in waters. The high suitability for molds observed in Eastern Mediterranean waters, Gulf of Lion, Gibraltar, and Atlantic waters along Spain and Portugal coasts, was not observed in yeasts model which showed a high suitability in Ireland sea, Baltic sea, Northern Black sea, and along all coasts of Northern Europe (Figure 8).

In both yeasts and molds model the variable that mainly contributes to the shores model are minimum temperatures during winter season. Comparing the response curves of yeasts and molds for these variables a difference can be observed. In the geographical areas where minimum ACCEPTED MANUSCRIPT / CLEAN COPY

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Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: