We investigated the occurrence and pathogenicity of Beauveria spp. (Hypocreales: Cordycipitaceae) in forest soils in Poland, in outbreak areas of cockchafers (Coleoptera: Scarabaeidae): Melolonthamelolontha L. and M. hippocastani F. We also examined the occurrence of Beauveria in relation to soil pH. Beauveria spp. isolates were characterised at species and genotype levels using ITS and microsatellite markers. Beauveria spp., which were detected at over 80% of sites, were sensitive to pH, preferring neutral or alkaline soils. This suggests that the acidity of forest soils in Poland can affect their efficacy as biological control agents (BCAs). B. brongniartii (Sacc.) Petch as a pathogen of cockchafers occurred at 41% of sites, but often at densities below the threshold values for infection, and it infected only 1.3% of cockchafer grubs. Our results suggest that B. brongniartii genotype isolated from cockchafers in forest soils can potentially expand the pool of BCAs in this environment.
Notes
Handling Editor: Helen Roy
Introduction
Cockchafers (Melolontha spp.) are the most damaging root pests in forest ecosystems in many European countries, including Poland (Blaisinger 1988; Dolci et al. 2006; Fodor et al. 2005; Keller 1988; Malinowski et al. 1996; Niemczyk 2015; Niemczyk et al. 2017; Strasser and Schinner 1996; Švestka 2006, 2010; Wagenhoff et al. 2014). Due to the lack of insecticides registered against Melolontha spp. (Directive 2009/128/EC of the European Parliament and of the Council), biological methods are needed.
During the last several decades, biological control agents (BCAs) have been identified as feasible alternatives to chemical pest treatments (Canfora et al. 2016; Mazid et al. 2011). Although numerous studies have identified and evaluated beneficial bacteria and fungi strains that are pathogenic to insects, the application of BCAs in forestry is still limited by several factors. First, the inoculants are mainly isolated from agricultural soils, which can affect their viability and persistence in different habitats, such as natural forest soil environments. Many studies have shown that the persistence and efficacy of entomopathogenic hyphomycetous fungi in soil depends on complex interactions of intrinsic, edaphic, biotic, and climatic factors (Goble et al. 2012; Kessler et al. 2003; Scheepmaker and Butt 2010). The use of inundative, inoculative, conservative, or classical approaches for fungal BCAs requires an understanding of the biology and ecology of the fungi and different biotic and abiotic factors present (Jackson et al. 2010; Lacey et al. 2015; Meyling and Eilenberg 2007). Soil pH is an abiotic factor that can affect the survival, ecological distribution, and virulence of entomopathogenic fungi (Galani 1988; Inglis et al. 2001; Padmavathi et al. 2003; Sanzhimitupova 1980; Sharma et al. 1992). Due to the influence of soil pH, the actual effects of BCAs may differ from the predicted results. Assessing natural infection rates and the occurrence of entomopathogenic fungi in forest environments in areas where there are mass outbreaks of pests provides a behavioural baseline for these organisms and is thus a key task for improving BCA strain selection and efficacy.
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One of the most important entomopathogenic fungal genera distributed worldwide is Beauveria (Bals.) Vuill. (Ascomycota: Hypocreales) (Imoulan et al. 2017; Li et al. 2001). In Europe, the most prevalent natural pathogen of Melolontha spp. is Beauveria brongniartii (Saccardo) Petch, which infects all developmental stages of these pests (Trzebitzky 1996). Because of the ability of B. brongniartii to specifically infect and kill insects, several strains have been tested and used commercially as BCAs against cockchafer grubs in various European countries (Enkerli et al. 2001, 2004; Keller et al. 1997; Mayerhofer et al. 2015; Sierpińska 2008; Strasser and Enkerli 2001; Strasser et al. 2000). These BCAs have been tested in agricultural and forest environments, but in the latter no satisfactory results have been achieved (Sierpińska et al. 2015). The identification of edaphic factors in natural forest habitats (soil types, pH ranges, etc.) that influence the occurrence and distribution of Beauveria spp. in the soil will help to improve the efficacy of biological control in forests. Simultaneously, the identification of indigenous entomopathogenic fungi from the forest soil environment can provide insight into naturally occurring fungal biodiversity and can expand the pool of potential BCAs for pest control purposes. The aims of the present study were therefore to: (1) investigate the natural occurrence and density of Beauveria spp. in forest soils in areas of cockchafer outbreaks in Poland, (2) characterise Beauveria species richness and variability, (3) investigate the effects of soil pH ranges and edaphic factors on the occurrence of Beauveria spp., and (4) determine the rate of natural infection of cockchafer grubs caused by B. brongniartii.
Materials and methods
Study sites
Research plots were selected in areas in Poland that experience outbreaks of cockchafers, and where these insect pests cause the most serious economic losses in forestry. The sites were in three forest districts in central and southeastern Poland: Ostrowiec Świętokrzyski (50°56′00″N 21°24′00″E), Lubaczów (50°09′33″N 23°07′19″E), and Narol (50°21′01″N 23°19′38″E). The mean annual temperature ranged from 7.2 °C in Lubaczów to 8.3 °C in Ostrowiec Św. The annual rainfall exceeded 700 mm at all research sites, and the growing season lasted for approximately 200 days. Detailed information on research sites is given in Table 1.
Table 1
Basic characteristic of forest research sites in Poland. Each site was characterised (in accordance with Instrukcja ochrony lasu (2012) as a forested area that was homogeneous in terms of habitat conditions and forest stand elements (dominant tree species, age, spatial structure, site index, forest site type, etc.)
Forest district
Stand
Latitude N (Wgs84)
Longitude E (Wgs84)
Main species
Area covered by main species at site (%)
Age of stand (years)
Forest site type
Soil type
Ostrowiec Św.
1
51,00299
21,48032
Pinus sylvestris L.
70
3
Mixed Broad-leaved Forest
Brunic Arenosol (Dystric)
Ostrowiec Św.
2
51,00053
21,46876
Pinus sylvestris L.
60
36
Mixed Broad-leaved Forest
Brunic Arenosol (Dystric)
Ostrowiec Św.
3
50,78952
21,5333
Quercus robur L.
40
26
Broad-leaved Forest
Haplic Cambisol (Eutric)
Ostrowiec Św.
4
50,94104
21,52045
Quercus robur L.
60
7
Broad-leaved Forest
Haplic Cambisol (Eutric)
Ostrowiec Św.
5
51,00591
21,50341
Pinus sylvestris L.
50
17
Mixed Broad-leaved Forest
Brunic Arenosol (Dystric)
Ostrowiec Św.
6
51,00824
21,48193
Quercus robur L.
20
21
Broad-leaved Forest
Haplic Phaeozem
Lubaczów
7
50,23186
23,38211
Pinus sylvestris L.
60
89
Broad-leaved Forest
Haplic Regosol (Calcaric)
Narol
8
50,3356
23,26993
Pinus sylvestris L.
70
57
Mixed Broad-leaved Forest
Rendzic Leptosol
Lubaczów
9
50,22318
23,36223
Pinus sylvestris L.
80
124
Mixed Broad-leaved Forest
Haplic Cambisol (Dystric)
Narol
10
50,33357
23,2669
Pinus sylvestris L.
70
60
Mixed Broad-leaved Forest
Rendzic Leptosol
Ostrowiec Św.
11
51,01408
21,49013
Quercus robur L.
40
44
Broad-leaved Forest
Haplic Phaeozem
Ostrowiec Św.
12
51,00086
21,4665
Pinus sylvestris L.
50
27
Mixed broad-leaved forest
Brunic Arenosol (Dystric)
The study was carried out in the two most representative forest site types for the selected districts: fresh broad-leaved (six sites) and fresh mixed broad-leaved forests (six sites)
Study design
Research was carried out at 12 stands (sites) from 2013 to 2014. Sites were chosen in the two most representative (i.e., most common) forest site types for the selected forest districts: fresh broad-leaved forest (six sites) and fresh mixed broad-leaved forest (six sites). Forest site types were classified according to geographical climatic conditions, spatial structure, species composition, site index, physiographical climatic factors, and undergrowth vegetation (Kliczkowska et al. 2003). Preliminary identification of cockchafer grubs was carried out in 2013. At each site, 25 sampling pits measuring 0.5 m2 (1 × 0.5 m at a depth at least of 0.5 m) were excavated in an overall area of 120 × 200 m to assess grub occurrence. The pits were placed according to a grid superimposed over the sample area, and each pit was permanently marked, both physically and with its GPS position. In 2014, six of the 25 sampling pits were re-excavated at each of the 12 sites. All grubs were collected and identified to genus level (Melolontha spp.) in a laboratory using the key presented by Sierpiński (1975). Instars were determined by measuring the width of the head capsule (L1: 2.6–2.7 mm, L2: 4.2–4.5 mm, L3: 6.5–6.9 mm) (Śliwa 1993).
The white grubs collected in 2014 were reared separately for six weeks in 120-ml laboratory vials containing sterilised sand and were fed carrot slices. Each vial was inspected twice a week for insect mortality. All dead grubs were sterilised in 0.01% HgCl2 in 70% ethanol for 1 min. and washed three times in distilled, sterile water. The dead larvae were then incubated at 23 °C for two weeks in sterile glass Petri dishes, on microscopy glass on wet filter paper. When grub mortality was caused by mycosis, the fungi species responsible were isolated and identified to the genus level using a taxonomy key (Humber 2012) on the basis of morphological characteristics that were determined with a stereoscope (Zeiss, Stemi 2000, Germany). Mortality caused by diseases other than mycosis was not evaluated.
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Soil analysis
General information about the soil characteristics for each stand was taken from soil habitat surveys in the particular forest districts. Soil types and texture were classified in accordance with The Polish Soil Classification (SgP 2011), taking into account the World Reference Base for Soil Resources (FAO 2006).
In addition, in 2014 soil samples were taken from each sampling pit, using a cylindrical soil corer (inner diameter: 55 mm), from a depth of 50–150 mm. The samples were placed separately in two sterile 120 ml vials. One vial was used to measure soil pH, and the other to quantify the occurrence of Beauveria spp. Prior to the pH analyses, all visible plant materials (roots, stems, and leaves) were removed, and the soil samples were air dried and then ground with a rolling pin. The material was then passed through a 2 mm sieve. In accordance with ISO 10390 (ISO 10390 2015), representative 10 ml samples of the air-dried soil (fraction < 2 mm) were potentiometrically measured using a glass electrode in a 1:5 (volume fraction) suspension of soil in water (to measure pH in H2O), and in 0.01 mol l−1 calcium chloride solution (to measure pH in CaCl2).
The quantification of Beauveria spp. in soil was carried out as described by Laengle et al. (2005), with modifications. Prior to the analyses, soil samples were subjected to the same protocols as mentioned above, except that they were passed through a 2.5 mm sieve. Soil samples from each pit were mixed thoroughly and 10 g of soil was added to 90 ml 0.01% (w/v) Tween 80 and shaken at 150 rpm for 60 min. Three Beauveria-selective agar plates (Strasser et al. 1996) were inoculated with 100 µl of undiluted soil suspension and incubated at 23 °C for 14 days. Fungal colonies were identified as Beauveria spp. if they demonstrated the following two characteristics (Rehner et al. 2011): (1) white, yellowish white, or pale-yellow colour of colonies on Sabouraud agar and (2) conidia aggregated as < 0.1 mm spherical clusters, white in colour, as determined with a stereoscope. Colonies that were identified as Beauveria spp. were transferred to Sabouraud agar with the use of a sterile inoculating needle to obtain pure cultures. The number of colonies of Beauveria spp. was determined as the number of colony-forming units (CFUs) per gram of dry weight of soil.
DNA isolates and sequencing
We used sequence comparison of the Bloc Intergenic region to determine Beauveria species affiliation (Rehner et al. 2006), and we used six variable simple sequence repeat (SSR) markers for genotype identification (Mayerhofer et al. 2015) of Beauveria spp. isolates, obtained from the soil samples and from infected cockchafer grubs. DNA of Beauveria spp. isolates was extracted using a Syngen Tissue DNA Mini Kit. Using polymerase chain reaction (PCR) analysis, we amplified the internal transcribed spacer (ITS) region marker with the primers B5.1F/B3.1R (Rehner et al. 2006). The PCR thermal profile was as follows: 95 °C for 3 min; 40 cycles at 94 °C for 30 s, 57 °C for 30 s, and 72 °C for 2 min; and a final extension at 72 °C for 15 min. Amplifications were carried out in 50 µl with 3 µl of DNA, 25 RedTag Ready Mix (Sigma-Aldrich), 1 µl of each primer (10 µM), and 20 µl of PCR water. After visualization of PCR products on agarose gel and purification with a clean-up kit (A&A Biotechnology), nucleotide sequencing was performed with BigDye Terminator Cycle Sequencing Kit using an ABI 3500 Genetic Analyser (Applied Biosystems; Thermo Fisher Scientific, Inc.) and analysed with Data Collection software ver. 2 (Thermo Fisher Scientific, Inc). Sequences were aligned in BioEdit ver. 7.2.5 (Hall 1999) with reference sequences of two Beauveria species haplotypes.
SSR markers were amplified in two multiplex PCRs: (Bb1F4, Bb2A3, Bb2F8) and (Bb4H9, Bb5F4, Bb8D6) in a total reaction volume of 10 µl. The reaction volume contained 1 µl of DNA, 5 µl Multiplex PCR Kit (Qiagen, Germany), 0.2 µl of each primer (forward and reverse) (10 µM), and 2.8 µl of PCR water. The PCR thermal profile was as follows: 95 °C for 15 min; 35 cycles at 94 °C for 30 s, 58 °C for 90 s, and 72 °C for 90 s; and a final extension at 60 °C for 30 min. Genotyping was performed using an ABI 3500 Genetic Analyser (Applied Biosystems) and allele lengths were scored using GeneMapper® ver. 5 (Thermo Fisher Scientific, Inc.).
Data analysis
Analysis of variance (ANOVA) was performed to test for significant differences between the densities of grubs in 2013 and 2014. Means and SE of Beauveria spp. CFU g−1 dry weight soil were calculated for each sampling site. Medians were determined for Beauveria spp. CFU g−1 dry weight soil per soil sample. We used Spearman’s rank correlation test to evaluate the relationship between the density of Beauveria spp. and soil pH range. Logistic regression was used to assess the effects of soil properties on the occurrence of Beauveria spp. and to identify significant variables as predictors of occurrence for given site characteristics. The dependent variable was the absence or presence (0 or 1, respectively) of Beauveria spp. For the independent factors, soil pH and density of white grubs were chosen as quantitative variables, while forest site type, soil type, main pedogenic factor, and similar direction of development of the soil were chosen as qualitative variables. A binomial distribution and logit link function were used. The choice of the optimal model (the best subset) was based on the AIC criterion. The derivation of explanatory variables was based on Wald’s statistics and their associated probability values. When evaluating model parameters, the odds ratio (OR) was calculated as a measure of the relationship between the variables. The statistical analyses were performed using the statistical package Statistica 10.0 (2011).
Results
Density of Melolontha spp. in forest soils
In 2013, the first year of the observation period, the second instar cockchafer larvae (L2) were the most common stage in the mass outbreak areas. In 2014, the numbers of cockchafers were similar, and ANOVA showed no statistically significant differences between the two years (F1,350 = 0.0002, p = 0.9883). In 2014, 91% of cockchafers were third instar larvae (L3). Melolontha spp. densities varied among the sites from 0 to 16 L3 per 0.5 m2. There were only two sites (sites 5 and 6) at which cockchafer grubs were not found. At 50% of the sites, the population density of Melolontha spp. was higher than the threshold level for economic losses defined in Instrukcja ochrony lasu (2012) (i.e., 3 L2 or L3 per 0.5 m2 for forest site types where the research was performed) (Fig. 1).
Fig. 1
Number of cockchafer white grubs (mean ± SE) (Melolontha spp.) per sampling pit (1 × 0.5 m at a depth of at least 0.5 m) at forest sites in two consecutive study years. Means were determined from 25 pits excavated in 2013 and six pits re-excavated in 2014 per site. Pits were placed using a grid superimposed over the sample area. Site numbers correspond to site numbers in Table 1
×
In 2014, the grubs were reared in sterile sand and observed in the laboratory. After six weeks, 76 out of 232 cockchafers had died. Entomopathogenic fungi caused the death of only four of these individuals: three grubs were infected with Beauveria spp., and one with Metarhizium spp.
Soil analyses
Soil samples revealed the presence of Beauveria spp. at ten of the 12 sites. At sites 4 and 8, Beauveria spp. were not detected (Fig. 2). At the other sites, Beauveria densities reached up to 2.7 × 104 CFU g−1 dry weight soil. However, at each site, there were individual samples in which Beauveria colonies were not detected. Only 33.3% of all soil samples contained Beauveria spp.
Fig. 2
Density of Beauveria spp. (CFU g−1 dry weight soil) in forest stands (mean ± SE). Beauveria spp. densities were determined at six sampling pits per site and for three replicates per soil sample. Y-axis values are shown in a logarithmic scale. Site numbers correspond to site numbers in Table 1
×
According to the classification of soil pH ranges (United States Department of Agriculture Natural Resources Conservation Service), the soil pH ranged from extremely acidic (4.3) to moderately alkaline (7.4). In general, very strongly acidic soils predominated (pH of 4.5 to 5.0) (Table 2). We found a positive correlation between pH ranges both in H2O and in CaCl2 and Beauveria densities from the same sampling pits, with α = 0.05 level of significance. The Spearman’s rank correlation coefficients were as follows: rs = 0.1908 (p = 0.0049) and rs = 0.2291 (p = 0.0007).
Table 2
Soil pH values, numbers of cockchafer grubs (Melolontha spp.), and density of Beauveria spp. at forest sites, as determined at six sampling pits per site. Values of Beauveria spp. colony-forming units (CFUs) were determined as three replicates per soil sample
Sitea
pH in H2O
pH in CaCl2
Number of grubs per 0.5 m2 sampled soil
Density of Beauveria spp. (CFU g−1 dry wt soil)
Min
Median
Max
Min
Median
Max
Min
Median
Max
Min
Median
Max
1
4.8
5.1
5.4
3.9
4
4.2
0
7
12
0
50
2,300
2
4.7
4.75
5.3
3.6
3.8
4.3
0
4.5
7
0
0
1,500
3
5
5.3
5.9
4.1
4.4
4.6
1
5
15
0
0
10,300
4
4.6
4.7
5
3.7
3.85
4.1
0
0
1
0
0
0
5
4.5
4.7
4.9
3.7
3.85
4.1
0
0
0
0
0
200
6
4.4
4.9
5.1
3.4
4
4.3
0
0
0
0
0
200
7
4.4
4.5
4.8
3.7
3.75
3.9
2
8
15
0
0
100
8
4.4
4.95
6.1
3.6
3.95
5.4
0
1
3
0
0
0
9
4.3
4.55
4.8
3.6
3.8
4.1
0
3
9
0
0
200
10
6.8
8
8.1
6.3
7.3
7.4
0
4.5
8
0
300
27,000
11
4.4
4.7
4.9
3.7
3.85
4.1
0
1
8
0
100
800
12
4.4
4.7
4.9
3.7
3.85
4
0
1
3
0
100
600
aSite numbers correspond to site numbers in Table 1
Logistic regression
On the basis of Akaike criteria, the best subset among six candidate predictors were soil type and soil pH (Table 3). The results of Hosmer–Lemeshow goodness of fit test of the final model, choosing nine groups (g = 9), were as follows: χ2 = 8.4217 (df = 7, p = 0.2968 for the model with pH in H2O as an explanatory variable) and χ2 = 7.5499 (df = 7, p = 0.3739 for the model with pH in CaCl2 as an explanatory variable), which indicates that there is no evidence of poor fit (there are no differences between the observed and predicted values of the dependent variable). The model correctly predicts the presence of Beauveria spp. in 81% of cases and their absence in 69% of cases for the model with pH in H2O, and 78% and 71% respectively, for the model with pH in CaCl2.
Table 3
Optimal model of logistic regression predicting the occurrence of Beauveria spp. as a function of soil characteristics at study sites (two independent analyses for pH in H2O and pH in CaCl2)
Independent variable
df
Wald’s stat.
p
Level
Coef.
SE
Wald’s stat.
p
OR
− 95% CI
+95% CI
Intercept
1
7.660
0.006
–
− 13.692
4.947
7.660
0.006
–
–
–
soil type
5
12.103
0.033
Brunic Arenosol (Dystric)
1.479
0.577
6.567
0.010
4.389
1.416
13.599
Haplic Cambisol (Eutric)
− 1.783
0.808
4.865
0.027
0.168
0.034
0.819
Albic Luvisol
1.690
0.712
5.628
0.018
5.418
1.342
21.873
Haplic Regosol (Calcaric)
0.785
0.938
0.700
0.403
2.191
0.348
13.775
Rendzic Leptosol
− 3.628
1.767
4.219
0.040
0.027
0.001
0.862
pH H2O
1
7.556
0.006
–
2.679
0.975
7.556
0.006
14.571
2.156
98.498
Intercept
1
6.523
0.011
–
− 17.010
6.660
6.523
0.011
–
–
–
soil type
5
11.388
0.044
Brunic Arenosol (Dystric)
2.266
0.836
7.348
0.007
9.643
1.873
49.64
Haplic Cambisol (Eutric)
− 1.455
0.817
3.169
0.075
0.233
0.047
1.156
Albic Luvisol
2.235
0.912
6.003
0.014
9.346
1.564
55.839
Haplic Regosol (Calcaric)
1.305
1.121
1.356
0.244
3.689
0.410
33.199
Rendzic Leptosol
− 6.241
3.048
4.193
0.041
0.002
< 0.001
0.786
pH CaCl2
1
6.491
0.011
–
3.982
1.563
6.491
0.011
53.612
2.505
1147.399
The output provides the coefficients for explanatory variables and their levels. The Haplic Cambisol (Dystric) was chosen as a reference level for soil types (SE = standard error, CI = confidence intervals, boldface text indicates statistical significance at α = 0.05, OR = odds ratio)
The evaluation of the model parameters showed that the presence of Beauveria spp. in soil is most affected by soil pH (Table 3), and that an increase in pH of one unit was associated with an increased chance of Beauveria spp. occurrence by 14.6 (OR) (for the model with pH in H2O). The logistic regression model also showed that Beauveria spp. occurrence varied with soil type. The probability of Beauveria occurrence was highest in Albic Luvisol soil and lowest in Rendzic Leptosols and Haplic Cambisols (Eutric) (Fig. 3).
Fig. 3
Predicted and observed probability [± confidence intervals (CI)] of Beauveria spp. occurrence depending on forest soil types, using the optimal model of logistic regression
×
DNA sequence alignment
The number of Beauveria spp. isolates collected from different sites varied, ranging from 0 (at sites 4, 7, and 8) to six isolates per site (at site 10). The sequence of ITS markers of Beauveria spp. was 1385 bp long. From 30 samples of Beauveria spp., we obtained three haplotypes: two from B. pseudobassiana (Bals.) Vuill. and one from B. brongniartii. We detected length differences between haplotypes from these two species (three INDELs in total). Two haplotypes of internal transcribed spacer B locus were identical to sequences deposited in the GeneBank database, from Rehner et al. (2011): HQ880728 (B. pseudobassiana) and HQ880713 (B. brongniartii). One of the sequences for B. pseudobassiana was recorded for the first time in the present study (MG029116) and differed by two substitutions from HQ880728. The nucleotide differences on the analysed fragment between these two species were 117 point mutations, giving a genetic distance of 8.6%, discounting deletions.
Among mass outbreak areas, only one site (site 10) contained all three haplotypes of Beauveria spp. Both B. pseudobassiana and B. brongniartii were represented in five other sites. At the rest of the sites, only one of the two above-mentioned haplotypes was identified (Table 4). Isolates obtained from infected cockchafer grubs came from the 2nd (two isolates) and 10th (one isolate) sites and were identified as B. brongniartii (HQ880713).
Table 4
Characteristics of Beauveria spp. genotypes for six simple sequence repeat (SSR) markers (allele size), species affiliation, and GenBank (ITS) accession number identification with references for isolates obtained at study sites
dThe capital letters are used for the identification of genotypes obtained from isolates in our study. The same capital letters obtained from different isolates indicate identical genotypes
SSR analysis showed the presence of 21 Beauveria genotypes out of 30 isolates. PCR amplification of the SSR markers Bb4H9, Bb5F4, and Bb8D6 yielded products from all the isolates. The SSR marker Bb8D6 was monomorphic for B. brongniartii (166 bp), and the markers Bb1F4, Bb4H9, and Bb5F4 were monomorphic for B. pseudobassiana (190, 198, and 148 bp respectively). The markers Bb1F4, Bb2A3, and Bb2F8 were partially amplified for B. brongniartii, and the latter was also partially amplified for B. pseudobassiana. There were 18 genotypes that were represented as single isolates only, and three genotypes were represented by a larger number (two to five). The highest number of different genotypes (five genotypes) was found at site 10. The most common genotypes, C and I, were found at two (sites 1 and 12) and three (sites 3, 11, and 12) different sites, respectively (Table 4).
Discussion
Numerous studies have demonstrated the close relationship between B. brongniartii and Melolontha spp. (Keller et al. 2003; Kessler et al. 2004). B. brongniartii has been reported to be a highly host-specific fungus that exclusively infects Melolontha spp. under natural conditions in Central Europe (Kessler et al. 2004; Neuvéglise et al. 1994). In Switzerland, Keller et al. (2003) demonstrated the natural occurrence of B. brongniartii and M. melolontha together in meadow soils. During a forest cockchafer outbreak in southwest Germany, Trzebitzky (1996) found that more than 50% of the natural infections of M. hippocastani grubs in forest soils were caused by B. brongniartii. In contrast, during an outbreak of the common cockchafer (with a grub population density of 0 to 72 per m2) in Valle D’Aosta, Italy, only two larvae were affected by mycosis in one year (Cravanzola et al. 1996). Similarly, in the present study, only 1.3% of cockchafer grubs were infected by B. brongniartii. The low levels of infection of cockchafer grubs and maintenance of stable populations of larval cockchafers in the present study can be partly explained by the high resistance of older (L2 or L3) instar larvae, which were dominant during the study period. Sukovata et al. (2015) tested the efficacy of a biocide product against Melolontha grubs and observed a higher resistance rate and lower mortality rate among L3 grubs compared with the L1 and L2 instar larvae at the same biocide concentration. Kessler (2004) found that the age and origin of Melolontha larvae influence the efficacy of BCA as much as does the virulence of the spore types.
The low level of infection caused by B. brongniartii in forest soils can be further explained by the pH conditions, which were suboptimal for the growth of Beauveria spp. According to Enkerli et al. (2001), sustainable cockchafer control can be achieved when fungal density reaches 1 × 103–1 × 104 CFU g−1. Densities at this level were found at sites 1, 2, 3, and 10, but the fungus was parasitic only at sites 2 and 10. Notably, site 10 was characterised by the highest soil pH ranges. The importance of pH in Beauveria development and pathogenicity in the present study was reflected by the positive relationship between the pH ranges and fungal densities (Spearman’s rank correlation). Logistic regression analyses confirmed that pH ranges, supported by soil type, were significant predictive variables for the occurrence of Beauveria spp. Strong soil acidity was responsible for the absence of these hyphomycetous fungi. An increase in the pH by one unit resulted in a 14.6-fold higher probability of Beauveria occurrence within the studied pH ranges.
Our findings confirm the results of previous studies. According to Padmavathi et al. (2003), a pH of 3 was toxic to all tested isolates of B. bassiana (closely related to B. brongniartii). Conidia germinated at this pH, but growth was completely inhibited. Qazi (2008) noted that differences in the germination capability of B. bassiana conidia under differing substrate pH conditions were explained by the specific optimal pH values required for the expression of proteases produced by the fungus. Overly acidic (or overly alkaline) reactions can adversely affect conidia germination in B. brongniartii, which may explain our observation of hindered mycosis in cockchafer grub populations.
Considering the isolation of entomopathogenic fungi (including Beauveria) from natural and cultivated areas, Quesada-Moraga et al. (2007) detected a narrow pH that was optimum for B. bassiana, with 52.9% of samples falling within 8.0–8.5. Karthikeyan et al. (2008) confirmed that the optimal soil pH for Beauveria spp. development ranges from 6 to 8. Moreover, these fungi typically occur in lowland soils with neutral or alkaline pH (Medo and Cagáň 2011) and are detected more frequently in natural forest soils than in cultivated ones (Shin et al. 2013). Taking into account that our soil samples were representative of most Polish forest pH ranges (acidic and very acidic soils make up 50% of Poland’s area), our results demonstrate that strong soil acidity in forests provides a suboptimal environment for the development of Beauveria spp.
Our study area encompassed several types of soil, including extremely gravelly and/or stony Leptosols, sandy Arenosols, soils of increasing clay content such as Cambisols, and high-activity clays throughout the argic horizon in the Luvisols. The probability of Beauveria occurrence was highest in the latter types of soil. Many previous studies (Mietkiewski et al. 1997; Milner 1989; Quesada-Moraga et al. 2007) reported that the occurrence of entomopathogenic fungi is associated with soils with high clay content. This may be because leaching of the inoculum is correlated with the water infiltration value of soils, which is higher in sandy soils than in finer-textured soils (Storey and Gardner 1988). Some studies also suggested that a high clay content in soil enhances the abundance and persistence of many insect pathogenic fungi because conidia are adsorbed onto clay particles (Inglis et al. 2001; Studdert et al. 1990). Therefore, the soil type is another source of information (in addition to pH ranges) that indicates the potential occurrence and persistence of entomopathogenic fungi in the forest environment.
Beauveria spp. isolates were detected at more than 80% of sites and in 33.3% of soil samples, comparable to recovery rates from other countries with cold/humid temperate climates. According to Vänninen (1996), Beauveria were detected in 19.8% of soil samples from Finish soils. Typical recovery rates were 18% in the Pacific Northwest (Bruck 2004). Based on sequence alignment in the present study, three different haplotypes of Beauveria spp. were identified in forest sites: two haplotypes belonging to B. pseudobassiana species and one to B. brongniartii. B. brongniartii, an important species of entomopathogenic fungus that is indigenous to the study area, was present in 11 of 30 isolates, and ten different genotypes were detected in the samples. By comparison, 41 different B. brongniartii genotypes were detected among 63 isolates from two sites in Switzerland (Enkerli et al. 2001) and 13 B. brongniartii genotypes were detected from 92 isolates from the Tyrol region (Mayerhofer et al. 2015). In the present study, B. brongniartii was identified in soils in 41% of forest sites (five sites) and was also isolated from cockchafer grubs in two sites.
In summary, only two species of Beauveria were found in the forest soils we sampled: B. pseudobassiana and B. brongniartii. B. brongniartii, an important natural pathogen of cockchafers, did not occur frequently and its density was often below the threshold value for the effective infection of cockchafer grubs. We determined that Beauveria genotypes are sensitive to soil pH and soil types in forest environments. Our results suggest that the B. brongniartii genotype isolated from cockchafers from forest soils can expand the pool of potential BCAs in the forest environment. However, additional studies are needed to explore the genotypes of virulence and optimal pH conditions for Beauveria spp. for use as BCAs.
Acknowledgements
This research was financially supported by the Forest Research Institute (Project No. 240226). We thank our colleague from the Forest Research Institute, Szymon Krajewski for assistance during field data collection.
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Conflicts of interest
The authors declare no conflict of interest.
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